BR112020002555A2 - methods and materials to assess and treat cancer - Google Patents

Abstract

The present invention relates to methods and materials for detecting and / or treating an individual (e.g., a human) having cancer. In some modalities, methods and materials for identifying an individual as having cancer (for example, localized cancer) are provided in which the presence of the member (s) of two or more classes of biomarkers is detected. In some modalities, methods and materials to identify an individual as having cancer (for example, localized cancer) are provided in which the presence of the member (s) of at least one class of markers and the presence of aneuploidy are detected . In some embodiments, the methods described here provide high sensitivity and / or specificity in detecting cancer in an individual (for example, a human).

Description

Descriptive Report of the Invention Patent for “METHODS AND MATERIALS TO EVALUATE AND TREAT CANCER”. Federal funding statement

[1] This invention was made with the Support of the United States Government under: Subsidies Nos. CA062924 and HG007804 of the National Institutes of Health. The United States Government has certain rights in the invention. String Listing

[2] This application includes a String Listing in electronic format sent to the United States Patent and Trademark Office through the electronic filing system and is incorporated by reference in its entirety. This sequence listing, created on August 6, 2018, is named 448070306WO1SL.txt and is 208,305 bytes in size. Electronically deposited tables

[3] This application includes tables in electronic format sent to the United States Patent and Trademark Office through the electronic filing system. The ASCII text files, each of which is incorporated here by reference in its entirety, include a text file called Table1.txt, created on August 7, 2018, with a size of 152,000 bytes; a text file called Table2.txt, created on August 7, 2018, with a size of 351,000 bytes; a text file called Table3.txt, created on August 7, 2018, with a size of 438,000 bytes; a text file called Table4.txt, created on August 7, 2018, with a size of

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1. Technical Field

[4] Methods and materials for detecting and / or treating individuals (eg, humans) with cancer are provided here. In some modes, methods and materials for identifying an individual as having cancer (for example, localized cancer) are provided in which the presence of two or more members of two or more classes of biomarkers is detected. In some modalities, methods and materials to identify an individual as having cancer (for example, a localized cancer) are provided in which the presence of two or more members of at least one class of biomarkers and the presence of aneuploidy are detected. In some embodiments, the methods described here provide greater sensitivity and / or specificity for detecting cancer in an individual (for example, a human).

2. Basic information

[5] Cancer will kill 592,000 Americans this year and, according to the Center for Disease Control, it will soon be the leading cause of death in this country. How can this terrible situation be avoided? Currently, the vast majority of research on translational cancer is focused on prolonging survival in patients with advanced disease. Our research perspective is different: in the long run, prevention is always better than cure. Examples of the value of this perspective are abundant, ranging from infectious diseases to cardiovascular diseases. Cardiovascular diseases are particularly relevant because the combination of primary and secondary prevention measures for this disease has reduced deaths by 75% over the past 60 years. In contrast, cancer deaths in general have barely changed over the same period.

[6] Early detection through the application of blood tests for cancer can be seen as a form of secondary prevention. The last three letters of the word "previous" are particularly important. For all cancers studied, the probability of cure is much higher in diseases located early than in advanced diseases. The earlier the stage, the more likely the tumor will be cured by surgery alone. In addition, cancers do not need to be detected when they are in their early stages to be cured. Theoretically, responses to therapy are dictated by the total number of cancer cells prior to therapy and the mutation rates in human cells. The more cancer cells, the greater the likelihood that at least one of them will contain or develop a mutation (s) that confers resistance to any form of therapy, be it conventional chemotherapy, radiation therapy, targeted therapy or immunotherapy. Clinically, a large number of studies have shown that drugs can be curative in the adjuvant setting, but not in patients with advanced disease. For example, almost half of patients with Stage III colorectal cancer who would die from their disease can be cured by adjuvant therapy, but virtually no patients with Stage IV colorectal cancer can be cured with the same regimens.

[7] There is a strong correlation between tumor stage and prognosis in many cancers (Ansari D, et al. (2017) Relationship between tumor size and outcome in pancreatic ductal adenocarcinoma. Br J Surg 104 (5): 600-607 ). Very few patients with cancer of the lung, colon, esophagus or stomach who have distant metastases at the time of diagnosis survive for more than five years (Hladlad N, et al. (2016) SEER Cancer Statistics Review, 1975-2013 , National Cancer Institute, Bethesda, MD, http: //seer.cancer.gov/csr/1975_2013/, based on November 2015 SEER data submission, posted to the SEER web site, April 2016). The size of cancers is also important in a general sense, as smaller tumors have less metastasis than larger tumors at the time of diagnosis and, therefore, are more likely to be curable only by surgery. Even when cancer is metastasized to distant sites, a lower burden of disease is managed much more easily than bulky lesions (Bozic I, et al. (2013) Evolutionary dynamics of cancer in response to targeted combination therapy. Elife 2: e00747 ). Thus, adjuvant chemotherapeutic agents administered to patients with micro-metastases due to colorectal cancer can be curative in almost 50% of cases (Semrad TJ, Fahrni AR, Gong IY, & Khatri VP (2015) Integrating Chemotherapy into the Management of Oligometastatic Colorectal Cancer: Evidence-Based Approach Using Clinical Trial Findings. Ann Surg Oncol 22 Suppl 3: S855-862; Moertel CG, et al. (1995) Fluorouracil plus levamisole as effective adjuvant therapy after resection of stage III colon carcinoma: the final report Ann Intern Med 122 (5): 321-326; Andre T, et al. (2009) Improved overall survival with oxaliplatin, fluorouracil, and leucovorin as adjuvant treatment in stage II or III colon cancer in the MOSAIC trial, J Clin Oncol 27 (19): 3109-3116). The same chemotherapeutic agents delivered to patients with radiologically visible metastatic lesions produce virtually no cure (Dy GK, et al. (2009) Long-term survivors of metastatic colorectal cancer treated with systemic chemotherapy alone: a North Central Cancer Treatment Group review of 3811 patients, No. 144. Clin Colorectal Cancer 8 (2): 88-93).

[8] Therefore, it is clear that early detection of cancers is a key to reducing deaths from these diseases, including pancreatic cancer. In addition to offering the possibility of surgical resection, the new adjuvant chemotherapy and emergent chemotherapy regimens will undoubtedly be more effective in patients with minimal disease in addition to that surgically curable (Huang AC, et al. (2017) T-cell invigoration to tumor burden ratio associated with anti-PD-1 answer Nature 545 (7652): 60-65). Circulating biomarkers provide one of the best ways, in principle, to detect cancers at an earlier stage. Historically, the type of biomarkers used to monitor cancer were proteins (Liotta LA & Petricoin EF, 3rd (2003) The promise of proteomics. Clin Adv Hematol Oncol 1 (8): 460-

462), and included carcinoembryonic antigen (CEA), carbohydrate antigen 19-9 (CA19-9), and cancer antigen 125 (CA125). These biomarkers have proven to be useful for monitoring patients with known disease, but none have been approved for screening purposes, in part because of their low sensitivity or specificity (Lennon AM & Goggins M (2010) Diagnostic and Therapeutic Response Markers.

Pan-creatic Cancer, (Springer New York, New York, NY), pp 675-701; Clarke- Pearson DL (2009) Clinical practice.

Screening for ovarian cancer.

N Engl J Med 361 (2): 170-177; Locker GY, et al. (2006) ASCO 2006 update of recommendations for the use of tumor markers in gastrointestinal cancer.

J Clin Oncol 24 (33): 5313-5327). More recently, the mutant DNA has been explored as a biomarker.

The concept behind this approach, often called "liquid biopsies", is that cancer cells, like normal self-renewing cells, often reverse.

DNA released from dying cells can escape into body fluids, such as urine, faeces and plasma (Haber DA & Velculescu VE (2014) Blood-based analyzes of cancer: circulating tumor cells and circulating tumor DNA.

Cancer Discov 4 (6): 650-661; Dawson SJ, et al. (2013) Analysis of circulating tumor DNA to monitor metastatic breast cancer.

N Engl J Med 368 (13): 1199-1209; Bettegowda C, et al. (2014) Detection of circulating tumor DNA in early- and late-stage human malignancies.

Science translational medicine 6 (224): 224ra224; Kinde I, et al. (2013) Evaluation of DNA from the Papanicolaou test to detect ovarian and endometrial cancers.

Science translational medicine 5 (167): 167ra164; Wang Y, et al. (2015) Detection of somatic mutations and HPV in the saliva and plasma of patients with head and neck squamous cell carcinomas.

Science translational medicine 7 (293): 293ra104; Wang Y, et al. (2015) Detection of tumor-derived DNA in cerebrospinal fluid of patients with primary tumors of the brain and spinal cord.

Proc Natl Acad Sci U S A 112 (31): 9704-9709; Wang Y, et al. (2016) Diagnostic potential of tumor DNA from ovarian cyst fluid. Elife 5; Springer S, et al. (2015) A Combination of Molecular Markers and Clinical Features Im proves the Classification of Pancreatic Cysts. Gastroenterology 149 (6): 1501-1510; Forshew T, et al. (2012) Noninvasive identification and monitoring of cancer mutations by targeted deep sequencing of plasma DNA. Science translational medicine 4 (136): 136ra168; Vogelstein B & Kinzler KW (1999) Digital PCR. Proc Natl Acad Sci U S A 96 (16): 9236-9241; Dressman D, Yan H, Traverso G, Kinzler KW, & Vo- gelstein B (2003) Transforming single DNA molecules into fluorescent magnetic particles for detection and enumeration of genetic variations. Proc Natl Acad Sci U S A 100 (15): 8817-8822). An advantage of using mutant DNA in the circulation as a biomarker is its exquisite specificity. All cancer cells have a major set of somatic mutations in the conducting genes responsible for their clonal growth (Vogelstein B, et al. (2013) Cancer genome landscaper. Science 339 (6127): 1546-1558). On the other hand, normal cells do not expand clonally during adulthood and the fraction of normal cells that show any specific somatic mutation is extremely low.

[9] Most studies on circulating tumor DNA (ctDNA) have focused on following cancer patients, rather than evaluating their use in screening environments. Available data indicate that ctDNA is elevated in> 85% of patients with advanced forms of many types of cancer (Bettegowda C, et al. (2014) Detection of circulating tumor DNA in early- and late-stage human malignancies. Science translational medicine 6 (224): 224ra224; Wang Y, et al. (2015) Detection of somatic mutations and HPV in the saliva and plasma of patients with head and neck squamous cell carcinomas. medicine 7 (293): 293ra104). However, a considerably smaller fraction of patients with early stages of cancer have detectable levels of plasma ctDNA (Bettegowda C, et al. (2014) Detection of circulating tumor DNA in early- and late-stage human malig- Science translational medicine 6 (224): 224ra224; Wang Y, et al. (2015) Detection of somatic mutations and HPV in the saliva and plasma of patients with head and neck squamous cell carcinomas. Science translational medicine 7 (293): 293ra104).

[10] Most localized cancers can be cured with surgery only, without any systemic therapy (Siegel et al., 2017 CA Cancer J Clin 67: 7-30). Once distant metastases occur, however, surgical excision is rarely curative. An important goal in cancer research is, therefore, the detection of cancers before they metastasize to distant sites. Depending on the type of cancer, 20 to 30 years seem to be necessary for typical cancer in adults to progress from incipient neoplastic lesions to late stage cancers (Vogelstein et al., 2013 Science 339: 1546-1558; Jones et al., 2008 Proc Natl Acad Sci USA 105: 4283-4288; and Yachida et al., 2012 Clin Cancer Res 18: 6339-6347). Only in the last years of this long process, neoplastic cells appear to sow successfully and give rise to metastatic lesions (Vogelstein et al., 2013 Science 339: 1546-1558; Jones et al., 2008 Proc Natl Acad Sci USA 105: 4283- 4288; Yachida et al., 2012 Clin Cancer Res 18: 6339-6347; and Vogelstein et al., 2015 N Engl J Med 373: 1895-1898). Thus, there is a wide window of opportunity to detect cancers before the onset of metastases. However, once large and metastatic tumors are formed, current therapies are not effective (Bozic et al., 2013 Elife 2: e00747; Semrad et al., 2015 Ann Surg Oncol 22 (Suppl 3): S855-862; Moertel et al., 1995 Ann Intern Med 122: 321-326; Huang et al., 2017 Nature 545: 60-65).

[11] Pancreatic ductal adenocarcinoma (hereinafter "pancreatic cancer") is the third leading cause of cancer death and is predicted to become the second most common cause in the United States by 2030 (Rahib L, et al. ( 2014) Projecting cancer incidence and deaths to 2030: the unexpected burden of thyroid, liver, and pancreas cancers in the United States.

Cancer Res 74 (11): 2913-2921). Pancreatic cancer is notoriously lethal, with less than 9% of patients surviving five years after diagnosis (Siegel RL, Miller KD, & Jemal A (2016) Cancer statistics, 2016. CA Cancer J Clin 66 (1 ): 7-30). The poor prognosis of patients with pancreatic cancer is partly due to the fact that 80% to 85% of patients are diagnosed in advanced stages, when tumor invasions in the main surrounding vessels or distant metastases are evident in radiological studies (Ryan DP, Hong TS, & Bardeesy N (2014) Pancreatic adenocarcinoma.

N Engl J Med 371 (22): 2140-2141). At this late point in the disease, pancreatic cancer is not amenable to surgical resection and the 3-year survival rate is <5%. On the other hand, a five-year survival rate of almost 60% is reported for very small localized tumors; among resectable cancers, the smaller the tumor, the better the prognosis (Ansari D, et al. (2017) Relationship between tumor size and outcome in pancreatic ductal adenocarcinoma.

Br J Surg 104 (5): 600-607; Jung KW, et al. (2007) Clinicopathological aspects of 542 cases of pancreatic cancer: a special emphasis on small pancreatic cancer.

J Korean Med Sci 22 Suppl: S79-85; Egawa S, et al. (2004) Clinical nicopathological aspects of small pancreatic cancer.

Pancreas 28 (3): 235-240; Ishikawa O, et al. (1999) Minute carcinoma of the pancreas measuring 1 cm or less in diameter - collective review of Japanese case reports.

Hepatogastroenterology 46 (25): 8-15; Tsuchiya R, et al. (1986) Collective review of small carcinomas of the pancreas.

Ann Surg 203 (1): 77-81).

[12] Pancreatic cancer is no different from other types of cancer with regard to its strong correlation between tumor stage and prognosis (Ansari D, et al. (2017) Relationship between tumor size and outcome in pancreatic ductal adenocarcinoma.

Br J Surg 104 (5): 600-607). Very few patients with lung, colon, esophagus or stomach cancer who have distant metastases at the time of diagnosis survive for more than five years (Howlader N, et al. (2016) SEER Cancer Statistics Review, 1975-2013, National Cancer Institute.

Bethesda, MD, http://seer.cancer.gov/csr/1975_2013/, based on November 2015 SEER data submission, posted to the SEER web site, April 2016). The size of cancers is also important in a general sense, as smaller tumors have less metastasis than larger tumors at the time of diagnosis and, therefore, are more likely to be curable only by surgery.

Even when cancer is metastasized to distant sites, a lower burden of disease is managed much more easily than bulky lesions (Bozic I, et al. (2013) Evolutionary dynamics of cancer in response to targeted combination therapy.

Elife 2: e00747). Thus, adjuvant chemotherapeutic agents administered to patients with micro-metastases due to colorectal cancer can be curative in almost 50% of cases (Semrad TJ, Fahrni AR, Gong IY, & Khatri VP (2015) Integrating Chemotherapy into the Management of Oligometastatic Colorectal Canc: Evidence-Based Approach Using Clinical Trial Findings.

Ann Surg Oncol 22 Suppl 3: S855-862; Moertel CG, et al. (1995) Fluorouracil plus levamisole as effective adjuvant therapy after resection of stage III colon carcinoma: a final report.

Ann Intern Med 122 (5): 321-326; Andre T, et al. (2009) Improved overall survival with oxaliplatin, fluorouracil, and leucovirus as adjuvant treatment in stage II or III colon cancer in the MOSAIC trial.

J Clin Oncol 27 (19): 3109-3116). The same chemotherapeutic agents delivered to patients with radiologically visible metastatic lesions produce virtually no cure (Dy GK, et al. (2009) Long-term survivors of metastatic colorectal cancer treated with systemic cheerapy alone: a North Central Cancer Treatment Group review of 3811 patients, No. 144. Clin Colorectal Cancer 8 (2): 88-93).

[13] Therefore, it is clear that early detection of cancers is a key to reducing deaths from these diseases, including pancreatic cancer. In addition to offering the possibility of surgical resection, the new adjuvant chemotherapy and emergent chemotherapy regimens will undoubtedly be more effective in patients with minimal disease in addition to that surgically curable (Huang AC, et al. (2017) T-cell invigoration to tumor burden ratio associated with anti-PD-1 answer Nature 545 (7652): 60-65). Circulating biomarkers provide one of the best ways, in principle, to detect cancers at an earlier stage. Historically, the type of biomarkers used to monitor cancer were proteins (Liotta LA & Petricoin EF, 3rd (2003) The promise of proteomics. Clin Adv Hematol Oncol 1 (8): 460- 462), and included carcinoembryonic antigen (CEA) , carbohydrate antigen 19-9 (CA19-9), and cancer antigen 125 (CA125). These biomarkers have proven to be useful for monitoring patients with known disease, but none have been approved for screening purposes, in part because of their low sensitivity or specificity (Lennon AM & Goggins M (2010) Diagnostic and Therapeutic Response Markers. Pan - creatic Cancer, (Springer New York, New York, NY), pp 675-701; Clarke- Pearson DL (2009) Clinical practice. Screening for ovarian cancer. N Engl J Med 361 (2): 170-177; Locker GY , et al. (2006) ASCO 2006 update of recommendations for the use of tumor markers in gastrointestinal cancer J Clin Oncol 24 (33): 5313-5327). More recently, the mutant DNA has been explored as a biomarker. The concept behind this approach, often called "liquid biopsies", is that cancer cells, like normal self-renewing cells, often reverse.

DNA released from dying cells can escape into body fluids, such as urine, feces and plasma (Haber DA & Velculescu VE (2014) Blood-based analyzes of cancer: circulating tumor cells and circulating tumor DNA.

Cancer Discov 4 (6): 650-661; Dawson SJ, et al. (2013) Analysis of circulating tumor DNA to monitor metastatic breast cancer.

N Engl J Med 368 (13): 1199-1209; Bettegowda C, et al. (2014) Detection of circulating tumor DNA in early- and late-stage human malignancies.

Science translational medicine 6 (224): 224ra224; Kinde I, et al. (2013) Evaluation of DNA from the Papanicolaou test to detect ovarian and endometrial cancers.

Science translational medicine 5 (167): 167ra164; Wang Y, et al. (2015) Detection of somatic mutations and HPV in the saliva and plasma of patients with head and neck squamous cell carcinomas.

Science translational medicine 7 (293): 293ra104; Wang Y, et al. (2015) Detection of tumor-derived DNA in cerebrospinal fluid of patients with primary tumors of the brain and spinal cord.

Proc Natl Acad Sci U S A 112 (31): 9704-9709; Wang Y, et al. (2016) Diagnostic potential of tumor DNA from ovarian cyst fluid.

Elife 5; Springer S, et al. (2015) A Combination of Molecular Markers and Clinical Features Im proves the Classification of Pancreatic Cysts.

Gastroenterology 149 (6): 1501-1510; Forshew T, et al. (2012) Noninvasive identification and monitoring of cancer mutations by targeted deep sequencing of plasma DNA.

Science translational medicine 4 (136): 136ra168; Vogelstein B & Kinzler KW (1999) Digital PCR.

Proc Natl Acad Sci U S A 96 (16): 9236-9241; Dressman D, Yan H, Traverso G, Kinzler KW, & Vo- gelstein B (2003) Transforming single DNA molecules into fluorescent magnetic particles for detection and enumeration of genetic variations.

Proc Natl Acad Sci U S A 100 (15): 8817-882). An advantage of using mutant DNA in the circulation as a biomarker is its exquisite specificity.

All cancer cells have a major set of somatic mutations in the conducting genes responsible for their clonal growth (Vogelstein B, et al. (2013) Cancer genome landscapes. Science 339 (6127): 1546-1558). On the other hand, normal cells do not expand clonally during adulthood and the fraction of normal cells that show any specific somatic mutation is extremely low.

[14] Most studies on circulating tumor DNA (ctDNA) have focused on following cancer patients, rather than evaluating their use in screening environments. Available data indicate that ctDNA is elevated in> 85% of patients with advanced forms of many types of cancer (Bettegowda C, et al. (2014) Detection of circulating tumor DNA in early- and late-stage human malignancies. Science translational medicine 6 (224): 224ra224; Wang Y, et al. (2015) Detection of somatic mutations and HPV in the saliva and plasma of patients with head and neck squamous cell carcinomas. medicine 7 (293): 293ra104). However, a considerably smaller fraction of patients with early stages of cancer have detectable levels of plasma ctDNA (Bettegowda C, et al. (2014) Detection of circulating tumor DNA in early- and late-stage human malig- Science translational medicine 6 (224): 224ra224; Wang Y, et al. (2015) Detection of somatic mutations and HPV in the saliva and plasma of patients with head and neck squamous cell carcinomas. Science translational medicine 7 (293): 293ra104).

[15] There is a continuing need in the art to increase the sensitivity of detecting resectable or treatable cancers in other ways under conditions that preserve high specificity.

[16] The Papanicolaou (Pap) test dramatically decreased the incidence and mortality of cervical cancer in the screened population. Unfortunately, the Papanicolaou test is generally unable to detect endometrial or ovarian cancers ((L. Geldenhuys, M. L. Murray,

Sensitivity and specificity of the Pap smear for glandular lesions of the cervix and endometrium. Acta cytologica 51, 47-50 (2007); A. B. Ng, J. W. Reagan, S. Hawliczek, B. W. Wentz, Significance of endometrial cells in the detection of endometrial carcinoma and its precursors. Acta cyto-logic 18, 356-361 (1974); P. F. Schnatz, M. Guile, D. M. O'Sullivan, J. I. Sorosky, Clinical significance of atypical glandular cells on cervical cytology. Obstetrics and gynecology 107, 701-708 (2006); C. Zhao, A. Florea, A. Onisko, R. M. Austin, Histologic follow-up results in 662 patients with Pap test findings of atypical glandular cells: results from a large academic womens hospital laboratory employing sensitive screening methods. Gynecologic oncology 114, 383-389 (2009)). In light of the successful Pap smear test in the early detection of safe cervical cancers, ovarian and endometrial cancer are currently the most lethal and most common gynecological neoplasms, respectively, in countries where Pap smears are routinely performed (N. Ho - wladeret al., SEER Cancer Statistics Review, 1975-2014, National Cancer Institute. (2017)). Together, endometrial and ovarian cancers are responsible for approximately 25,000 deaths each year and are the third leading cause of cancer mortality in women in the United States (N. Howlader et al., SEER Cancer Statistics Review, 1975-2014 , National Cancer Institute. (2017)). Most of these deaths are caused by subtypes of high-grade tumors, which tend to undergo metastasis before the onset of symptoms (RJ Kurman, M. Shih Ie, The origin and pathogenesis of epithelial ovarian cancer: a proposed unifying theory The American journal of surgical pathology 34, 433-443 (2010); KN Moore, AN Fader, Uterine papillary serous carcinoma. Clin Obstet Gynecol 54, 278-291 (2011)).

[17] Endometrial cancer is the most common gynecological cancer, with 61,380 new cases estimated in 2017 in the United States (N. Howlader et al., SEER Cancer Statistics Review, 1975-2014,

National Cancer Institute. (2017)). The incidence of endometrial cancer has increased with increasing obesity and increased life expectancy (M. Arnoldet al., Global burden of cancer attributable to high body-mass index in 2012: a population-based study. The Lancet. Oncology 16, 36-46 (2015)). At the same time, relative survival has not improved in recent decades (N. Howlader et al., SEER Cancer Statistics Review, 1975-2014, National Cancer Institute. (2017); L. Rahib et al., Projecting cancer incidence and deaths to 2030: the unexpected burden of thyroid, liver, and pancreas cancers in the United States.Cancer research 74, 2913-2921 (2014)). Much effort was directed towards the development of a screening test for this type of cancer. The most common diagnostic test is transvaginal ultrasound (TVUS), which measures the thickness of the endometrium. TVUS's potential as a screening test is undermined by its inability to reliably distinguish between benign and malignant lesions, subjecting women without cancer to unnecessary invasive procedures and their associated complications. Their high rate of false positives is demonstrated by the fact that only one in 50 women who tested positive by TVUS proved to have endometrial cancer after undergoing additional diagnostic procedures (Jacobs et al., Sensitivity of transvaginal ultrasound screening for endometrial cancer in postmenopausal women: a case-control study within the UKCTOCS cohort. The Lancet. Oncology 12, 38-48 (2011)).

[18] Ovarian cancer is the second most common gynecological cancer in the USA and Europe. It is usually diagnosed at a late stage, when the 5-year survival is less than 30% (N. Howlader et al., SEER Cancer Statistics Review, 1975-2014, National Cancer Institute. (2017)). High mortality has made developing an effective screening test a high priority. Large randomized studies have evaluated the use of CA-125 and TVUS as possible screening tests for ovarian cancer (Buys et al., Effect of screening on ovarian cancer mortality: the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Randomized Controlled Trial. JAMA 305, 2295-2303 (2011); Kobayashi et al., A randomized study of screening for ovarian cancer: a multicenter study in Japan. Int J Gynecol Cancer 18, 414-420 (2008); Jacobs et al ., Ovarian cancer screening and mortality in the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS): a randomized controlled trial.Lancet 387, 945-956 (2016); Menon et al., Risk Algorithm Using Serial Biomarker Measurements Doubles the Number of Screen-Detected Cancers Compared With a Single- Threshold Rule in the United Kingdom Collaborative Trial of Ovarian Cancer Screening.J Clin Oncol 33, 2062-2071 (2015)). However, screening with current diagnostic approaches is not recommended for the general population, as it leads to "major damage, including major surgical interventions in women who do not have cancer" (VA Moyer, USPST Force, Screening for ovarian cancer: US Preventive Services Task Force reaffirmation recommendation statement, Annals of internal medicine 157, 900-904 (2012)). Thus, new diagnostic approaches are urgently needed.

[19] Among ovarian cancers, high-grade serous carcinomas (HGSC) represent 90% of all deaths from ovarian cancer. Increasing evidence suggests that most HGSC arises in the fallopian tube and subsequently implant on the ovarian surface (16-21R. J. Kurman, M. Shih Ie, Molecular pathogenesis and extraovarian origin of epithelial ovarian cancer - shifting the paradigm Human pathology 42, 918-931 (2011); Lee et al., A candidate precursor to serous carcinoma that originates in the distal fallopian tube. The Journal of pathology 211, 26-35 (2007) A candidate precursor to serous carcinoma that originates in the distal fallopian tube.The Journal of pathology 211, 26-35 (2007); Eckert et al., Genomics of Ovarian Cancer

Progression Reveals Diverse Metastatic Trajectories Including Intraepithelial Metastasis to the Fallopian Tube. Cancer Discov 6, 1342-1351 (2016); A. M. Karst, K. Levanon, R. Drapkin, Modeling high-grade serous ovarian carcinogenesis from the fallopian tube. Proc Natl Acad Sci U S A 108, 7547-7552 (2011); Zhai et al., High-grade serous carcinomas arise in the mouse oviduct via defects linked to the human disease. The Journal of pathology 243, 16-25 (2017); R. J. Kurman, M. Shih Ie, The Dualistic Model of Ovarian Carcinogenesis: Revisited, Revised, and Expanded. Am J Pathol 186, 733-747 (2016)). A recent prospective study of symptomatic women reported that most early diagnosed HGSCs have extra-ovarian origins (Gilbert et al. Assessment of symptomatic women for early diagnosis of ovarian cancer: results from the prospective DOvE pilot project. The Lancet. Oncology 13, 285-291 (2012)). This may explain the low sensitivity of TVUS to early disease, when ovarian abnormalities are not detected. Multimodal screening with serum levels of CA-125 improves sensitivity, however, CA-125 lacks specificity and is elevated in a variety of common benign conditions (H. Meden, A. Fattahi-Meibodi, CA 125 in benign gynecological conditions, Int J Biol Markers 13, 231-237 (1998)).

[20] In contrast to the markers associated with neoplasia, changes in the cancer-causing gene are agents that cause neoplasia and are absent in non-neoplastic conditions. It has been shown that tumor DNA can be detected in the vaginal tract of women with ovarian cancer (Erickson et al., Detection of somatic TP53 mutations in tampons of patients with high-grade serous ovarian cancer. Obstetrics and gynecology 124, 881-885 (2014)). In addition, a recent proof-of-principle study showed that endometrial and ovarian cancers release cells that accumulate in the cervix, allowing detectable levels of tumor DNA to be found in fluids obtained during routine screening tests. Papanicolaou (Kinde et al., Evaluation of DNA from the Papanicolaou test to detect ovarian and endometrial cancers. Sci Transl Med 5, 167ra164 (2013)). These cells are sampled with a brush (a "Papanicolaou brush") that is inserted into the endocervical canal. The brush is then dipped in the preservative liquid. For the detection of cervical cancers, the fluid cells are applied to a slide for cytological examination (the classic Pap smear). In addition, DNA is often purified from the fluid to look for HPV sequences.

[21] Bladder cancer (BC) is the most common malignancy of the urinary tract. According to the American Cancer Society, an estimated 79,030 new cases of bladder cancer and 18,540 deaths occur in the United States alone in 2017 [Siegel RL, Miller KD, Jemal A (2017) Cancer Statistics, 2017. CA Cancer J Clin 67: 7-30]. Predominantly in urothelial histology, invasive BC arises from pillar precursors or noninvasive plans. Many patients with BC experience multiple relapses before progression, providing ample waiting time for early detection and treatment before metastases [Netto GJ (2013) Clinical applications of recent molecular advances in urologic malignancies: no longer chasing a "mirage "?. Adv Anat Pathol 20: 175-203]. Cytology and cystoscopy in urine with transurethral biopsy (TURB) are currently the gold standard for the diagnosis and monitoring of bladder cancer. Although urine cytology has value for detecting high-grade neoplasms, it is unable to detect the vast majority of low-grade tumors [Netto GJ, Tafe LJ (2016) Emerging Bladder Cancer Biomarkers and Targets of Therapy. Urol Clin North Am 43: 63-76; Lotan Y, Roehrborn CG (2003) Sensitivity and specificity of com- monly available bladder tumor markers versus cytology: results of a comprehensive literature review and meta-analyzes. Urology 61: 109-18;

discussion 118; Zhang ML, Rosenthal DL, VandenBussche CJ (2016) The cytomorphological features of low-grade urothelial neoplasms vary by specimen type.

Cancer Cytopathol 124: 552-564]. This fact, together with the high cost and invasive nature of repeated cystoscopic and TURB procedures, has led to many attempts to develop new non-invasive strategies.

This includes urine or serum-based genetic and protein assays for screening and surveillance [Kawauchi et al., (2009) 9p21 Index as Estimated by Dual-Color Fluorescence in Situ Hybridization is Useful to Predict Urothelial Carcinoma Recurrence in Bladder Washing Cytology.

Hum Pathol 40: 1783-1789; Kruger S, Mess F, Bohle A, Feller AC (2003) Numerical aberrations of chromo- some 17 and the 9p21 locus are independent predictors of tumor recurrence in non-invasive transitional cell carcinoma of the urinary bladder.

Int J Oncol 23: 41-48; Skacel et al., (2003) Multitarget fluorescence in situ hybridization assay detects transitional cell carcinoma in the majority of patients with bladder cancer and atypical or negative urine cytology.

J Urol 169: 2101-2105; Sarosdy et al., (2006) Use of a multitarget fluorescence in situ hybridization assay to diagnose bladder cancer in patients with hematuria.

J Urol 176: 44-47; Moonen et al., (2007) UroVysion compared with cytology and quantitative cytology in the surveillance of non- muscle-invasive bladder cancer.

Eur Urol 51: 1275-80; discussion 1280; Fradet Y, Lockhard C (1997) Performance characteristics of a new monoclonal antibody test for bladder cancer: ImmunoCyt trade mark.

Can J Urol 4: 400-405; Yafi et al., (2015) Prospective analysis of sensitivity and specificity of urinary cytology and other urinary biomarkers for cancer bladder.

Urol Oncol 33: 66.e25-66.e31; Serizawa et al., (2010) Integrated genetic and epigenetic analysis of bladder cancer reveals an additive diagnostic value of FGFR3 mutations and hypermethylation events.

Int J Cancer; Kinde et al., (2013) TERT promoter mutations occur early in urothelial neoplasia and are biomarkers of early disease and disease recurrence in urine.

Cancer Res 73: 7162-7167; Hurst CD, Platt FM, Knowles MA (2014) Comprehensive mutation analysis of the TERT promoter in bladder cancer and detection of mutations in voided urine.

Eur Urol 65: 367-369; Wang et al., (2014) TERT promoter mutations are associated with distant metastases in upper tract urothelial carcinomas and serve as urinary biomarkers detected by a sensitive castPCR.

Oncotarget get 5: 12428-12439; Ralla et al., (2014) Nucleic acid-based biomarkers in body fluids of patients with urologic malignancies.

Crit Rev Clin Lab Sci 51: 200-231; Ellinger J, Muller SC, Dietrich D (2015) Epigenetic bio-markers in the blood of patients with urological malignancies.

Expert Rev Mol Diagn 15: 505-516; Bansal N, Gupta A, Sankhwar SN, Mahdi AA (2014) Low- and high-grade bladder cancer appraisal via serum-based proteomics approach.

Clin Chim Acta 436: 97-103; Goodison S, Chang M, Dai Y, Urquidi V, Rosser CJ (2012) A multi-analyte assay for the non- invasive detection of bladder cancer.

PLoS One 7: e47469; Allory et al., (2014) Telomerase reverse transcriptase promoter mutations in bladder cancer: high frequency across stages, detection in urine, and lack of association with outcome.

Eur Urol 65: 360-366]. The trials currently approved by the US Food and Drug Administration (FDA) include the ImmunoCyt test (Scimedx Corp), nuclear matrix protein immunoassay 22 (NMP22) test (Matritech) and multi-objective FISH test (UroVysion) [Kawauchi et al. , (2009) 9p21 Index as Estimated by Dual-Color Fluorescence in Situ Hybridization is Useful to Predict Urothelial Carcinoma Recurrence in Bladder Washing Cytology.

Hum Pathol 40: 1783-1789; Kruger S, Mess F, Bohle A, Feller AC (2003) Numerical aberrations of chromosome 17 and the 9p21 locus are independent predictors of tumor recurrence in non-invasive transitional cell carcinoma of the urinary bladder.

Int J Oncol 23: 41-48; Skacel et al., (2003) Multitarget fluorescence in situ hybridization assay detects transitional cell carcinoma in the majority of patients with bladder cancer and atypical or negative urine cytology. J Urol 169: 2101-2105; Sarosdy et al., (2006) Use of a multitarget fluorescence in situ hybridization assay to diagnose bladder cancer in patients with hematuria. J Urol 176: 44-47; Moonen et al., (2007) UroVysion compared with cytology and quantitative cytology in the surveillance of non-muscle-invasive bladder cancer. Eur Urol 51: 1275-80; discussion 1280; Fradet Y, Lockhard C (1997) Performance characteristics of a new monoclonal antibody test for bladder cancer: ImmunoCyt trade mark. Can J Urol 4: 400-405; Yafi et al., (2015) Prospective analysis of sensitivity and specificity of urinary cytology and other urinary biomarkers for bladder cancer. Urol Oncol 33: 66.e25-66.e31]. Sensitivities between 62% and 69% and specificities between 79% and 89% have been reported for some of these tests. However, due to inconsistencies in the performance of the assays, costs or necessary technical knowledge, the integration of these assays in routine clinical practice has not yet occurred.

[22] Bladder cancer generally divides into three types that begin in the cells of the bladder lining. In some modalities, bladder cancers are named for the type of cells that become malignant (cancerous), including transition cell carcinoma, squamous cell carcinoma and adenocarcinoma. Transitional cell carcinomas begin in the cells of the innermost layer of the bladder. Transition cell carcinomas can be low or high grade. Low-grade transition cell carcinomas may recur after treatment, but they rarely spread to the muscle layer of the bladder or other parts of the body. High-grade transitional cell carcinomas may recur after treatment and usually spread in the muscular layer of the bladder, in other parts of the body and in the lymph nodes. Almost all bladder cancer deaths are due to high-grade illnesses. Squamous cell carcinomas begin in squamous cells, which are thin, flat cells that can form in the bladder after infection or prolonged irritation. Adeno-carcinomas begin in the glandular (secretory) cells found in the lining of the bladder and are a very rare type of bladder cancer.

[23] High rates of activating mutations in the upstream promoter of the TERT gene are found in most BC and other cancers [Huang FW, Hodis E, Xu MJ, Kryukov GV, Chin L, Garraway LA (2013) Highly recurrent TERT promoter mutations in human melanoma. Science 339: 957-959; Killela et al., (2013) TERT promoter mutations occure frequently in gliomas and a subset of tumors derived from cells with low rates of self-renewal. Proc Natl Acad Sci U S A 110: 6021-6026; Scott GA, Laughlin TS, Rothberg PG (2014) Mutations of the TERT promoter are common in basal cell carcinoma and squamous cell carcinoma. Mod Pathol 27: 516-523]. Mutations in the TERT promoter predominantly affect two access points, g.1295228 C> T and g.1295250 C> T. They lead to the generation of CCGGAA / T or GGAA / T motifs that alter the binding site for the factors transcription of ETS and subsequently increase the activity of the TERT promoter [Huang FW, Hodis E, Xu MJ, Kryukov GV, Chin L, Garraway LA (2013) Highly recurrent TERT promoter mutations in human melanoma. Science 339: 957-959; Horn et al., (2013) TERT promoter mutations in familial and sporadic melanoma. Science 339: 959-961]. TERT promoter mutations occur in up to 80% of invasive urothelial carcinomas of the bladder and upper urinary tract, as well as in several of their histological variants [Kinde et al., (2013) TERT promoter mutations occur early in urothelial neoplasia and are biomarkers of early disease and disease recurrence in urine. Cancer Res 73: 7162-7167; Killela et al., (2013) TERT promoter mutations occur frequently in gliomas and a subset of tumors derived from cells with low rates of self-renewal. Proc Natl Acad Sci U S A 110: 6021-6026; Allory et al., (2014) Telomerase reverse transcriptase promoter mutations in bladder cancer: high frequency across stages, detection in urine, and lack of association with outcome.

Eur Urol 65: 360-366; Cowan et al., (2016) Detection of TERT promoter mutations in primary adenocarcinoma of the urinary bladder.

Hum Pathol 53: 8-13; Nguyen et al., (2016) High prevalence of TERT promoter mutations in micropapillary urothelial carcinoma.

Virchows Arch 469: 427-434]. In addition, TERT promoter mutations occur in 60-80% of BC precursors, including Malignant Low Potential Papillary Urothelial Neoplasms [Rodriguez et al., (2017) Spectrum of genetic mutations in de novo PUN- LMP of the urinary bladder.

Virchows Arch], Non-invasive Low-Grade Papillary Urothelial Carcinoma, Non-invasive High-Grade Papillary Urothelial Carcinoma and "Flat" Carcinoma in Situ (CIS), as well as in the urinary cells of a subset of these patients [Kinde et al., (2013 ) TERT promoter mutations occur early in urothelial neoplasia and are biomarkers of early disease and disease recurrence in urine.

Cancer Res 73: 7162-7167]. The TERT promoter mutations were thus established as the most common genetic alteration in BC [Kinde et al., (2013) TERT promoter mutations occur early in urothelial neoplasia and are biomarkers of early disease and disease recurrence in urine.

Cancer Res 73: 7162-7167; Cheng L, Montironi R, Lopez-Beltran A (2017) TERT Promoter Mutations Occur Frequently in Urothelial Papilloma and Papillary Urothelial Neoplasm of Low Malignant Potential.

Eur Urol 71: 497-498]. Other oncogene-activating mutations include those of FGFR3, RAS and PIK3CA, which have been shown to occur in a high fraction of invasive non-muscular bladder cancers [International Agency for Research on Cancer. (2016) WHO Classification of Tumors of the Urinary System and Male Genital Organs.

World Health Organization; 4 edition; Netto GJ (2011) Molecular biomarkers in urothelial carcinoma of the bladder: are we there yet ?. Nat Rev Urol 9: 41-51]. In muscle-invading bladder cancers, TP53, CDKN2A, MLL and

ERBB2 are also frequently found [Netto GJ (2011) Molecular biomarkers in urothelial carcinoma of the bladder: are we there yet ?. Nat Rev Urol 9: 41-51; Mo et al., (2007) Hyperactivation of Ha-ras oncogene, but not Ink4a / Arf deficiency, triggers bladder tumorigenesis. J Clin Invest 117: 314-325; Sarkis et al., (1993) Nuclear overexpression of p53 protein in transitional cell bladder carcinoma: a marker for disease progression. J Natl Cancer Inst 85: 53-59; Lin et al., (2010) Increase sitivity in detecting superficial, low grade bladder cancer by combination analysis of hypermethylation of E-cadherin, p16, p14, RASSF1A genes in urine. Urol Oncol 28: 597-602; Sarkis et al., (1994) Association of P53 nuclear overexpression and tumor progression in carcinoma in situ of the bladder. J Urol 152: 388-392; Wu XR (2005) Urothelial tumorigenesis: a tale of divergent pathways. Nat Rev Cancer 5: 713-725; Cancer Ge- name Atlas Research Network (2014) Comprehensive molecular characterization of urothelial bladder carcinoma. Nature 507: 315-322].

[24] As urine cytology is relatively insensitive to detecting recurrence, cystoscopies are performed every three months in these patients in the USA. In fact, the cost of managing these patients is higher than the cost of managing any other type of cancer and is equivalent to 3 billion dollars a year [Netto GJ, Epstein JI (2010) Theranostic and prognostic biomarkers: genomic applications in urological malignancies. Pathology 42: 384-394]. A noninvasive test that could predict which of these patients was more likely to develop recurrent BC could, therefore, be clinically and economically important.

[25] More than 400,000 new cases of urological transitional cell carcinoma are diagnosed worldwide each year (Antoni, S., Ferlay, J., Soerjomataram, I., Znaor, A., Jemal, A. , & Bray, F. (2017). Bladder Cancer Incidence and Mortality: A Global Overview and Recent Trends. Eur Urol, 71 (1), 96-108. Doi: 10.1016 / j.eururo.2016.06.010). Although the majority of these urothelial carcinomas arise in the bladder in the lower urinary tract, 5-10% originate in the upper urinary tract in the renal pelvis and / or ureter (Roupret, M., Babjuk, M., Comperat, E., Zigeuner, R ., Sylvester, RJ, Burger, M., Cowan, NC, Bohle, A., Van Rhijn, BW, Kaasinen, E., Palou, J., & Shariat, SF (2015). European Association of Urology Guidelines on Upper Urinary Tract Urothelial Cell Carcinoma: 2015 Up-date. Eur Urol, 68 (5), 868-879.doi: 10.1016 / j.eururo.2015.06.044; Soria, F., Shariat, SF, Lerner, SP, Fritsche, HM, Rink, M., Kassouf, W., Spiess, PE, Lotan, Y., Ye, D., Fernandez, MI, Kikuchi, E., Chad, DC, Babjuk, M., Grollman, AP, & Thalmann , GN (2017). Epidemiology, diagnosis, preoperative evaluation and prognostic assessment of uppertract urothelial carcinoma (UTUC). World J Urol, 35 (3), 379-387. Doi:

10.1007 / s00345-016-1928-x). The annual incidence of these upper-tract urothelial carcinomas (UTUCs) in western countries is 1-2 cases per 100,000, but occurs at a much higher rate in populations exposed to aristolochic acid (AA) (Chen, CH, Dickman, KG , Mo- riya, M., Zavadil, J., Sidorenko, VS, Edwards, KL, Gnatenko, DV, Wu, L., Turesky, RJ, Wu, XR, Pu, YS, & Grollman, AP (2012). Aristolochic acid-associated urothelial cancer in Taiwan. Proc Natl Acad Sci USA, 109 (21), 8241-8246. Doi: 10.1073 / pnas.1119920109; Grollman, AP (2013). Aristolochic acid nephropathy: Harbinger of a global iatrogenic disease. Environ Mol Mutagen, 54 (1), 1-7.

10,1002 / em.21756; Lai, M. N., Wang, S. M., Chen, P. C., Chen, Y. Y., & Wang, J. D. (2010). Population-based case-control study of Chinese herbal products containing aristolochic acid and urinary tract cancer risk. J Natl Cancer Inst, 102 (3), 179-186. doi: 10.1093 / jnci / djp467; Taiwan Cancer Registry. (2017). Bureau of Health Promotion, Dept. of Health, Taiwan. The incidence of renal pelvic and ureteral tumor in Taiwan. Taiwan cancer registry. Retrieved on August 14, 2017, from the URL cris.bhp.doh.gov.tw/pagepub/Home.aspx?itemNo=cr.q.10). AA is a carcinogenic and nephrotoxic nitrophenanthrocarboxylic acid produced by Aristolochia plants (Hsieh, SC, Lin, IH, Tseng, WL, Lee, CH, & Wang, JD (2008). products: an analysis of National Health Insurance data in Taiwan between 1997 and 2003, Chin Med, 3, 13. doi:

10.1186 / 1749-8546-3-13; National Toxicology Program. (2011). Aristolo-chic acids. Rep Carcinog, 12, 45-49). An etiological link between exposure to AA and UTUC has been established in two distinct populations. The first resides in the Balkan countries, where Aristolochia plants grow naturally in wheat fields (Jelakovic, B., Karanovic, S., Vukovic-Lela, I., Miller, F., Edwards, KL, Nikolic, J. , Tomic, K., Slade, N., Brdar, B., Turesky, RJ, Stipancic, Z., Dittrich, D., Grollman, AP, & Dickman, KG (2012). Aristolactam-DNA adducts are a biomarker of environmental exposure to aristolochic acid. Kidney Int, 81 (6), 559-567. doi: 10.1038 / ki.2011.371). The second population is in Asia, where Aristolochia herbs are widely used in the practice of Traditional Chinese Medicine (Grollman, 2013; National Toxicology Program, 2011). The public health threat posed by the medical use of Aristolochia herbs is exemplified by Taiwan, which has the highest incidence of UTUC in the world (Chen, CH, Dickman, KG, Moriya, M., Zavadil ,, J., Sidorenko, VS, Edwards, KL, Gnatenko, DV, Wu, L., Turesky, RJ, Wu, XR, Pu, YS, & Grollman, AP (2012). Aristolochic acid-associated urothelial cancer in Taiwan. Proc Natl Acad Sci USA, 109 (21), 8241-8246.doi: 10.1073 / pnas.1119920109; Yang, MH, Chen, KK, Yen, CC, Wang, WS, Chang, YH, Huang, WJ, Fan, FS, Chiou, TJ , Liu, JH, & Chen, PM (2002). Unusually high incidence of upper urinary tract urothelial carcinoma in Taiwan. Urology, 59 (5), 681-687). More than a third of Taiwan's adult population received herbal remedies containing AA (Hsieh, SC, Lin, IH, Tseng, WL, Lee, CH, & Wang, JD (2008). Prescription profile of potentially aristolochic acid containing Chinese herbal products: an analysis of National Health Insurance data in Taiwan between 1997 and 2003. Chin Med, 3, 13. doi: 10.1186 / 1749-8546-3-13), resulting in an extraordinarily high proportion (37%) cases of UTUC in relation to all urothelial cancers (Taiwan Cancer Registry. (2017). Bureau of Health Promotion, Dept. of Health, Taiwan. The incidence of renal pelvic and ureteral tumor in Taiwan. Taiwan cancer registry. Retrieved in 14 August 2017, from the URL cris.bhp.doh.gov.tw/page- pub / Home.aspx? itemNo = cr.q.10).

[26] Nephroureterectomy can be curative for UTUC patients when detected at an early stage (Li, CC, Chang, TH, Wu, WJ, Ke, HL, Huang, SP, Tsai, PC, Chang, SJ, Shen, JT, Chou, YH, & Huang, CH (2008). Significant predictive factors for prognosis of primary upper urinary tract cancer after radical nephroureterectomy in Taiwanese patients. Eur Urol, 54 (5), 1127-1134. Doi: 10.1016 / j.eu- ruro.2008.01.054). However, these cancers are largely silent until the appearance of evident clinical symptoms, usually hematuria, and as a result, most patients are diagnosed only at an advanced stage (Roupret, M., Babjuk, M., Comperat, E ., Zigeuner, R., Sylvester, RJ, Burger, M., Cowan, NC, Bohle, A., Van Rhijn, BW, Kaasinen, E., Palou, J., & Shariat, SF (2015). European Association of Urology Guidelines on Upper Urinary Tract Urothelial Cell Carcinoma: 2015 Update. Eur Urol, 68 (5), 868-879. doi: 10.1016 / j.eu- ruro.2015.06.044). Diagnostic tests for the detection of UTUC at an early stage are not currently available. There is, therefore, a need for clinical tools that can be used to identify early UTUCs in populations at risk of developing this type of malignancy. Relapse after surgery is also a concern, as UTUC may resort to the contralateral upper urinary tract and / or the bladder (Roupret, M., Babjuk, M., Comperat, E., Zigeuner,

R., Sylvester, R. J., Burger, M., Cowan, N. C., Bohle, A., Van Rhijn, B. W., Kaasinen, E., Palou, J., & Shariat, S. F. (2015). European Association of Urology Guidelines on Upper Urinary Tract Urothelial Cell Carcinoma: 2015 Update. Eur Urol, 68 (5), 868-879. doi: 10.1016 / j.eu-uro.2015.06.044; Soria, F., Shariat, SF, Lerner, SP, Fritsche, HM, Rink, M., Kassouf, W., Spiess, PE, Lotan, Y., Ye, D., Fernandez, MI, Kikuchi, E., Chad, DC, Babjuk, M., Grollman, AP, & Thalmann, GN (2017). Epidemiology, diagnosis, preoperative evaluation and prognostic assessment of upper-tract urothelial carcinoma (UTUC). World J Urol, 35 (3), 379-387. doi: 10.1007 / s00345-016-1928-x). Careful surveillance of signs of malignancy is therefore an essential part of follow-up in patients with UTUC, and non-invasive tests for recurrent diseases can substantially improve post-surgical treatment, mainly because urine cytology cannot detect most UTUCs (Baard, J., de Bruin, DM, Zondervan, PJ, Kamphuis, G., de la Rosette, J., & Laguna, MP (2017). Di-agnostic dilemmas in patients with upper tract urothelial carcinoma.Nat Rev Urol, 14 (3), 181-191.doi: 10.1038 / nrurol.2016.252). summary

[27] In general, methods and materials for identifying the presence of cancer in an individual with increased sensitivity and specificity compared to conventional methods of identifying the presence of cancer in an individual are provided here. In some modes, the methods provided here to identify the presence of cancer in an individual with increased sensitivity and specificity are performed on a liquid sample obtained from the individual (for example, blood, plasma or serum), while conventional methods identification of the presence of cancer in an individual does not reach the level of sensitivity, the level of specificity or both when performed on a liquid sample obtained from the individual. In some modalities,

the methods provided here to identify the presence of cancer in an individual with increased sensitivity and specificity are performed before determining that the individual already suffers from cancer, before determining that the individual is harboring a cancer cell and / or before the individual exhibits symptoms associated with cancer. In some ways, the methods provided here to identify the presence of cancer in an individual with increased sensitivity and specificity are used as a first-line detection method, and not simply as a confirmation (for example, a "called") another method of detecting that the individual has cancer.

[28] In some embodiments, methods are provided here to identify the presence of pancreatic cancer in an individual which includes: detecting in a first biological sample obtained from the individual the presence of one or more genetic biomarkers in one or more of the se - following genes: KRAS, TP53, CDKN2A or SMAD4; detect a level of one or more of the following protein biomarkers in a second biological sample obtained from the individual: carbohydrate antigen 19-9 (CA19-9), carcinoembryonic antigen (CEA), hepatocyte growth factor (HGF ) or osteopontin (OPN); comparing the detected levels of one or more protein biomarkers with one or more reference levels of protein biomarkers; and to identify the presence of pancreatic cancer in the individual when the presence of one or more genetic biomarkers is detected, the detected levels of one or more protein biomarkers are higher than the reference levels of one or more biomarkers of protein, or both. In some of the methods for identifying the presence of pancreatic cancer in an individual, the first biological sample, the second biological sample, or both include plasma. In some of the methods for identifying the presence of pancreatic cancer in an individual, the first and second biological samples are the same. In some of the methods to identify the presence of pancreatic cancer in an individual, the presence of one or more genetic biomarkers is detected in each of the following methods: KRAS, TP53, CDKN2A and SMAD4. In some of the methods to identify the presence of pancreatic cancer in an individual, the level of each of the carbohydrate antigens 19-9 (CA19-9), carcinoembryonic antigen (CEA), hepatocyte growth factor (HGF) and osteopontin (OPN) is detected. In some of the methods to identify the presence of pancreatic cancer in an individual, the presence of one or more genetic biomarkers in one or more KRAS, TP53, CDKN2A or SMAD4 is detected using a multiplex PCR-based sequencing assay that includes The. assign a unique identifier (UID) to each of a plurality of model molecules present in the sample; B. amplify each uniquely labeled model molecule to create UID families; and C. redundant sequencing of the amplification products. In some of the methods to identify the presence of pancreatic cancer in an individual, the detection of the presence of one or more genetic biomarkers, the detection of the level of one or more biomarkers of proteins or both is performed when the individual is not known to harbor a cancer cell. In some of the methods to identify the presence of pancreatic cancer in an individual, the individual is administered with one or more therapeutic interventions (for example, surgery, adjuvant chemotherapy, neoadjuvant chemotherapy, radiotherapy, immunotherapy, targeted therapy and / or immune checkpoint inhibitor).

[29] In some embodiments, methods are provided here to identify the presence of cancer in an individual which includes: detecting in a first biological sample obtained from the individual the presence of one or more genetic biomarkers in one or more of the following genes: NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A or GNAS; detecting a level of one or more of the following protein biomarkers in a second biological sample obtained from the individual: CA19-9, CEA, HGF, OPN, CA125, prolactin, TIMP-1 or MPO; compare the detected levels of one or more protein biomarkers with one or more reference levels of protein biomarkers; and identifying the presence of cancer in the individual when the presence of one or more genetic biomarkers is detected, the detected levels of one or more protein biomarkers are higher than the reference levels of one or more protein biomarkers, or both .

In some of the methods for identifying the presence of pancreatic cancer in an individual, the first biological sample, the second biological sample, or both include plasma.

In some types of methods to identify the presence of pancreatic cancer in an individual, the first and second biological samples are the same.

In some modalities of methods to identify the presence of cancer in an individual, the presence of one or more genetic biomarkers in each of: NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A and GNAS is detected.

In some modalities of methods to identify the presence of cancer in an individual, the level of each of CA19-9, CEA, HGF, OPN, CA125, prolactin, TIMP-1 and MPO is detected.

In some ways to identify the presence of cancer in an individual, the presence of one or more genetic biomarkers in one or more NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS , KRAS, AKT1, TP53, PPP2R1A, or GNAS is detected using a multiplex PCR-based sequencing assay that includes. assigning a unique identifier (UID) to each of a plurality of model molecules present in the sample; B. amplify each uniquely labeled model molecule to create UID families; and C. redundant sequencing of the amplification products.

In some modalities of methods to identify the presence of cancer in an individual, the cancer is liver cancer, ovarian cancer, esophageal cancer, stomach cancer, pancreatic cancer, colorectal cancer, lung cancer, breast cancer or cancer prostate cancer. In some modalities to identify the presence of cancer in an individual, the presence of one or more genetic biomarkers, the detection of the level of one or more protein biomarkers or both is performed when the individual is not known to harbor a cancer cell . In some modalities to identify the presence of cancer in an individual, the individual is administered with one or more therapeutic interventions (for example, surgery, adjuvant chemotherapy, neoadjuvant chemotherapy, radiation therapy, immunotherapy, targeted therapy and / or immune verification).

[30] In some embodiments, methods are provided here to identify the presence of cancer in an individual which includes: detecting in a first biological sample obtained from the individual the presence of one or more genetic biomarkers in one or more of the following genes: NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A or GNAS; detect a level of one or more of the following protein biomarkers in a second biological sample obtained from the individual: CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1, follistatin, G-CSF or CA15 -3; comparing the detected levels of one or more protein biomarkers with one or more reference levels of protein biomarkers; and identifying the presence of cancer in the individual when the presence of one or more genetic biomarkers is detected, the detected levels of one or more protein biomarkers are higher than the reference levels of one or more protein biomarkers, or both. In some of the methods for identifying the presence of pancreatic cancer in an individual, the first biological sample, the second biological sample, or both include plasma.

In some modalities of methods to identify the presence of pancreatic cancer in an individual, the first and second biological samples are the same.

In some types of methods to identify the presence of cancer in an individual, the presence of one or more genetic biomarkers in each of: NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A and GNAS is detected.

In some modalities of methods to identify the presence of cancer in an individual, the level of each of CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1, phallistatin, G-CSF is detected and CA15-3. In some ways to identify the presence of cancer in an individual, the presence of one or more genetic biomarkers in one or more NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS , AKT1, TP53, PPP2R1A, or GNAS is detected using a multiplex PCR-based sequencing assay that includes. assigning a unique identifier (UID) to each of a plurality of model molecules present in the sample; B. amplify each uniquely labeled model molecule to create UID families; and C. redundant sequencing of the amplification products.

In some modalities of methods to identify the presence of cancer in an individual, the cancer is liver cancer, ovarian cancer, esophageal cancer, stomach cancer, pancreatic cancer, colorectal cancer, lung cancer, breast cancer or cancer prostate cancer.

In some modalities to identify the presence of cancer in an individual, the presence of one or more genetic biomarkers, the detection of the level of one or more protein biomarkers or both is performed when the individual is not known to harbor a cancer cell .

In some modalities to identify the presence of cancer in an individual, the individual is administered with one or more therapeutic interventions (for example, surgery, adjuvant chemotherapy, neoadjuvant chemotherapy, radiation therapy, immunotherapy, targeted therapy and / or immune verification).

[31] In some embodiments, methods are provided here to identify the presence of cancer in an individual which includes: detecting in a first biological sample obtained from the individual the presence of one or more genetic biomarkers in one or more of the following genes: NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A or GNAS; detecting a level of one or more of the following protein biomarkers in a second biological sample obtained from the individual: CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1 or CA15-3; comparing the detected levels of one or more protein biomarkers with one or more reference levels of protein biomarkers; and identifying the presence of cancer in the individual when the presence of one or more genetic biomarkers is detected, the detected levels of one or more protein biomarkers are higher than the reference levels of one or more protein biomarkers, or both. In some of the methods for identifying the presence of pancreatic cancer in an individual, the first biological sample, the second biological sample, or both include plasma. In some modalities of methods to identify the presence of pancreatic cancer in an individual, the first and second biological samples are the same. In some modalities of methods to identify the presence of cancer in an individual, the presence of one or more genetic biomarkers in each of: NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A and GNAS is detected. In some modalities of methods to identify the presence of cancer in an individual, the level of each of CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1 and

CA15-3. In some modalities to identify the presence of cancer in an individual, the presence of one or more genetic biomarkers in one or more NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1 , TP53, PPP2R1A, or GNAS is detected using a multiplex PCR-based sequencing assay that includes a. assign a unique identifier (UID) to each of a plurality of model molecules present in the sample; B. amplify each uniquely labeled model molecule to create UID families; and C. sequencing redundantly the amplification products. In some modalities of methods to identify the presence of cancer in an individual, the cancer is liver cancer, ovarian cancer, esophageal cancer, stomach cancer, pancreatic cancer, colorectal cancer, lung cancer, cancer breast cancer or prostate cancer. In some modalities to identify the presence of cancer in an individual, the presence of one or more genetic biomarkers, the detection of the level of one or more protein biomarkers or both is performed when the individual is not known to harbor a cancer cell. In some modalities to identify the presence of cancer in an individual, the individual is administered with one or more therapeutic interventions (for example, surgery, adjuvant chemotherapy, neoadjuvant chemotherapy, radiotherapy, immunotherapy, targeted therapy and / or immune checkpoint inhibitor).

[32] In some embodiments, methods are provided here to identify the presence of bladder cancer or urothelial carcinoma of the upper tract in an individual that includes: detecting in a first biological sample obtained from the individual the presence of one or more genetic biomarkers in one or more of the following genes: TP53, PIK3CA, FGFR3, KRAS, ERBB2, CDKN2A, MLL, HRAS, MET or VHL; detect the presence of at least one mutation in a promoter

TERT in a second biological sample obtained from the individual; and detecting the presence of aneuploidy in a third biological sample obtained from the individual; and to identify the presence of bladder cancer or urothelial carcinoma of the upper tract in the individual when the presence of one or more genetic biomarkers is detected, the presence of at least one mutation in the TERT promoter, the presence of aneuploidy or combinations thereof.

In some modalities of methods to identify the presence of bladder cancer or urothelial carcinoma of the upper tract in an individual, the first biological sample and the second biological sample are the same; the first biological sample and the third biological sample are the same; the second biological sample and the third biological sample are the same; or the first biological sample, the second biological sample and the third biological sample are the same.

In some modalities of methods to identify the presence of bladder cancer or urothelial carcinoma of the upper tract in an individual, the first biological sample, the second biological sample or the third biological sample is a urine sample.

In some modalities of methods to identify the presence of bladder cancer or urothelial carcinoma of the upper tract in an individual, the presence of aneuploidy is detected in one or more of the chromosomal arms 5q, 8q or 9p.

In some modalities of methods to identify the presence of bladder cancer or urothelial carcinoma of the upper tract in an individual, the presence of one or more genetic biomarkers in each of the following: TP53, PIK3CA, FGFR3, KRAS, ERBB2, CDKN2A , MLL, HRAS, MET and VHL is detected.

In some modalities of methods to identify the presence of bladder cancer or an urothelial carcinoma of the upper tract in an individual, the presence of one or more genetic biomarkers in one or more of TP53, PIK3CA, FGFR3, KRAS, ERBB2, CDKN2A , MLL, HRAS, MET or VHL is detected using a multiplex PCR-based sequencing assay that comprises: a. assigning a unique identifier (UID) to each of a plurality of model molecules present in the sample; B. amplify each model molecule uniquely marked to create UID families; and C. redundant sequencing of amplification products. In some modalities of methods to identify the presence of bladder cancer or urothelial carcinoma of the upper tract in an individual, the detection of the presence of one or more genetic biomarkers, the detection of the presence of at least one mutation in the TERT promoter or the detection of the presence of aneuploidy is performed when the individual is not known to harbor a cancer cell. In some modalities of methods to identify the presence of bladder cancer or urothelial carcinoma of the upper tract in an individual, the individual is administered with one or more therapeutic interventions (for example, surgery, adjuvant chemotherapy, neoadjuvant chemotherapy, radiotherapy , immunotherapy, targeted therapy and / or immune checkpoint inhibitor).

[33] In some modalities, methods are provided here to identify the presence of ovarian or endometrial cancer in an individual which includes: detecting in a first biological sample obtained from the individual the presence of one or more genetic biomarkers in one or more of the following genes: NRAS, PTEN, FGFR2, KRAS, POLE, AKT1, TP53, RNF43, PPP2R1A, MAPK1, CTNNB1, PIK3CA, FBXW7, PIK3R1, APC, EGFR, BRAF or CDKN2A; detect the presence of aneuploidy in a second biological sample obtained from the individual; and to identify the presence of ovarian or endometrial cancer in the individual when the presence of one or more genetic biomarkers is detected, the presence of aneuploidy is detected or both. In some modalities of methods to identify the presence of ovarian or endometrial cancer in an individual, the first biological sample and the second biological sample are the same. In some modalities of methods to identify the presence of ovarian or endometrial cancer in an individual, the first biological sample or the second biological sample is a cervical sample or an endometrial sample.

In some modalities of methods to identify the presence of ovarian or endometrial cancer in an individual, the presence of aneuploidy is detected in one or more of the 4p, 7q, 8q or 9q chromosomal arms.

In some modalities of methods to identify the presence of ovarian or endometrial cancer in an individual, the presence of one or more genetic biomarkers in each of the following: NRAS, PTEN, FGFR2, KRAS, POLE, AKT1, TP53, RNF43, PPP2R1A , MAPK1, CTNNB1, PIK3CA, FBXW7, PIK3R1, APC, EGFR, BRAF and CDKN2A is detected.

In some modalities to identify the presence of cancer in an individual, a In some modalities of methods to identify the presence of ovarian or endometrial cancer in an individual, the presence of one or more genetic biomarkers in one or more of : NRAS, PTEN, FGFR2, KRAS, POLE, AKT1, TP53, RNF43, PPP2R1A, MAPK1, CTNNB1, PIK3CA, FBXW7, PIK3R1, APC, EGFR, BRAF or CDKN2A is detected using a multiplex PCR-based sequencing assay that comprises: The. assigning a unique identifier (UID) to each of a plurality of model molecules present in the sample; B. amplify each uniquely labeled model molecule to create UID families; and C. redundant sequencing of the amplification products.

In some modalities of methods to identify the presence of ovarian or endometrial cancer in an individual, the methods also include detecting in a sample of circulating tumor DNA (ctDNA) obtained from the individual the presence of at least one genetic biomarker in one or more of the following genes: AKT1, APC, BRAF, CDKN2A, CTNNB1, EGFR, FBXW7, FGFR2, GNAS, HRAS, KRAS, NRAS, PIK3CA, PPP2R1A, PTEN or TP53. In some modalities of methods to identify the presence of ovarian or endometrial cancer in an individual, the presence of one or more genetic biomarkers or detection of the presence of aneuploidy is performed when the individual is not known to harbor a cancer cell . In some modalities to identify the presence of ovarian or endometrial cancer in an individual, the individual is administered with one or more therapeutic interventions (for example, surgery, adjuvant chemotherapy, neoadjuvant chemotherapy, radiotherapy, immunotherapy, targeted therapy and / or immune checkpoint inhibitor).

[34] Unless otherwise defined, all technical and scientific terms used in this document have the same meaning as commonly understood by one versed in the technique to which this invention belongs. Methods and materials are described herein for use in the present invention; other suitable methods and materials known in the art can also be used. The materials, methods and examples are illustrative only and are not intended to be limiting. All publications, patent applications, patents, sequences, database entries and other references mentioned in this document are incorporated by reference in their entirety. In the event of a conflict, this descriptive report, including definitions, will serve as a basis for control. Headings used in various sections of this document should not be interpreted as limiting the disclosure of that section to the topic of the header, nor as limiting the disclosure of other sections to topics other than the header. These headings are exemplary and are simply included for easy reading. These headings are not yet intended to restrict the applicability or generality of this section to other parts of this disclosure.

[35] Other features and advantages of the invention will be evident from the following detailed description and figures, and from the claims. Description of Drawings

[36] Figure 1 contains a schematic overview showing a CancerSEEK test for the detection and localization of cancers.

[37] Figure 2 contains graphs showing the development of a PCR-based assay to identify tumor-specific mutations in plasma samples. Colored curves indicate the proportion of cancers of the eight types evaluated in this study that can be detected with an increasing number of short amplicons (<40 bp). Detection sensitivity increases with the number of amplicons but plateaus by ~ 60 amplicons. Colored dots indicate the fraction of cancer detected using the 61 amplicon panel used in 805 types of cancer evaluated in our study, which averaged 82% (see main text). The publicly available sequencing data were obtained from the Somatic Mutations in Cancer catalog (COSMIC) repository.

[38] Figure 3 contains a graph showing the distribution of the number of detectable mutations in the 805 primary tumors evaluated.

[39] Figure 4 contains graphs showing the performance of CancerSEEK. (A) A ROC curve (characteristic of the receiver operator) for CancerSEEK. The red dot on the curve indicates the average performance of the test (61%) with specificity at> 99%. The error bars represent 95% confidence intervals for sensitivity and specificity at this specific point. The median performance among the 8 types of cancer evaluated was 70%, as noted in the main text. (B) CancerSEEK sensitivity by stage. The error bars represent standard errors of the median. (C) Cancer sensitivity by type of tumor. Error bars represent 95% confidence intervals.

[40] Figure 5 contains cascading graphs of ctDNA and eight protein characteristics used in CancerSEEK illustrate the separation between

between healthy donors and healthy patients. Values are classified from high (left) to low (right). Each column represents a sample of an individual patient (red, cancer patient; blue, healthy control).

[41] Figure 6 contains a graph showing the analysis of the main components of ctDNA and the eight protein characteristics used in CancerSEEK. Each point represents an individual patient sample (red, cancer patient; blue, healthy control).

[42] Figure 7 contains graphs showing the effect of individual CancerSEEK features on sensitivity. (A) Cancer sensitivity by type of tumor, as in Fig. 4C. (B-J) Each panel displays the sensitivity achieved when a particular CancerSEEK resource is excluded from logistic regression. The difference in sensitivity compared to that achieved by CancerSEEK reflects the relative contribution of each biomarker to the performance of the CancerSEEK test.

[43] Figure 8 contains a graph showing the identification of the type of cancer by supervised machine learning for patients classified by CancerSEEK as positive. The percentages correspond to the proportion of patients correctly classified by one of the two most likely types (sum of the light and dark blue bars) or by the most likely type (light blue bar). The predictions for all patients for all types of cancer are given in Table 6. The error bars represent 95% confidence intervals.

[44] Figure 9 contains graphs that combine mutations of KRAS ctDNA with protein biomarkers, increasing sensitivity for early detection of PDAC. (A) Sensitivity of KRAS ctDNA mutations alone, KRAS ctDNA mutations plus CA19-9 and K19 ctDNA mutations with CA19-9 and other proteins (combination assay) in relation to the AJCC stage. (B) Sensitivity of KRAS ctDNA mutations alone, KRAS ctDNA mutations plus CA19-9 and KRAS ctDNA mutations with CA19-9 and other proteins (combination assay) in relation to tumor size. The error bars represent 95% confidence intervals.

[45] Figure 10 contains a diagram showing that the combination of ctDNA and protein markers increases sensitivity because a large proportion of patients are detected by just one marker. Number of patients detected by mutations in the KRAS ctDNA (red circle), CA19-9 (green circle) and the other three protein biomarkers (blue circle) and combinations of them (overlapping regions). Eighty patients (36% of the total) were not detected by any of the three markers.

[46] Figure 11 contains a graph showing that the frequencies of mutant alleles (MAFs) of the KRAS and TP53 mutations are strongly correlated (Pearson's r = 0.885) in the plasma of the 12 patients whose plasma contained detectable amounts both mutations, providing validation of the reliability of the ctDNA assay and its quantitative nature. The shaded region represents the 95% confidence interval.

[47] Figure 12 contains a Kaplan-Meier survival chart of the 221 PDAC patients included in this study stratified by the AJCC stage (stage IA or IB: blue curve, stage IIA or IIB: red curve).

[48] Figure 13 contains graphs showing correlations between triplex assay markers and tumor size. (A) KRAS mutations were found more frequently in larger tumors than in smaller tumors, but the frequency of the mutant allele did not correlate with tumor size (Pearson's r = 0.039). (B) In patients with elevated CA19-9, the plasma concentration of CA19-9 correlates weakly with tumor size (Pearson's r =

0.287). (C-E) Plasma levels of CEA, HGF and OPN were less dependent on tumor size than KRAS or CA19-9 mutations (CEA Pearson's r = 0.153; HGF Pearson's r = 0.037; OPN Pearson's r = 0.018). Shaded regions represent 95% confidence intervals.

[49] Figure 14 contains graphs showing that levels of prolactin (G) and midkine (E) were significantly elevated in samples that were collected after administration of anesthesia, but before surgical excision. On the other hand, no difference was observed in the proportion of samples with mutant KRAS ctDNA (A), plasma concentration and CA19-9 (B), plasma concentration of CEA (C), plasma concentration of HGF (D) and plasma concentration of OPN between samples that were collected before or after the administration of anesthesia. Non-significant N.S., P> 0.05 (Exact permutation t test).

[50] Figure 15 contains a graph showing the doubled change in the levels of protein biomarkers from 29 pairs of plasma samples collected before and immediately after the administration of anesthesia. Of the six markers evaluated, it was found that only prolactin and midkine were elevated by anesthesia, perfectly in accordance with the correlation between the collection site and protein levels.

[51] Figure 16 contains Kaplan-Meier survival graphs stratified by independent predictors of overall survival identified by multivariate analysis: (A) combination assay status (HR = 1.76, 95% CI, 1.10 -2.84, p = 0.018); (B) degree of differentiation (poorly differentiated, HR = 1.72, 95% CI 1.11–2.66, p = 0.015); (C) lymphovascular invasion (present, HR = 1.81, 95% CI 1.06 - 3.09, p = 0.028); (D) nodal disease (present, HR = 2.35, 95% CI 1.20 - 4.61, p = 0.013); (E) margin status (HR = 1.59, 95% CI 1.01 - 2.55, p = 0.050)

[52] Figure 17 contains ROC curves (characteristic of the receiving operator) for mutations (A) KRAS, (B) CA19-9, (C) CEA, (D) HGF, (E)

OPN and (F) combination test. The ROC curves (A-E) demonstrate the performance of each combination assay biomarker individually. The red dots on the curves indicate the performance of the marker at the thresholds used in the combination test. The error bars represent 95% confidence intervals for sensitivity and specificity at the specific threshold (red font). (D) ROC curves showing the performance of the combination test when the KRAS threshold was varied and the CA19-9, CEA, HGF and OPN thresholds were set at the levels used in the combination test (black curve), the threshold of CA19-9 was varied and the thresholds of KRAS, CEA, HGF and OPN were fixed at the levels used in the combination assay (red curve), the threshold of CEA was varied and the thresholds of KRAS, CA19-9, HGF and OPN were fixed at the levels used in the combination test (blue curve), the HGF threshold was varied and the thresholds of KRAS, CA19-9, CEA and OPN were fixed at the levels used in the combination test (green curve) , and the OPN threshold was varied and the thresholds of KRAS, CA19-9, CEA and HGF were fixed at the levels used in the combination test (orange curve). The intersection of these three curves means the overall performance of the triplex assay (sensitivity of 64%, sensitivity of 99.5%).

[53] Figure 18 shows the performance of the marker panel to identify cancer in 8 types of cancer. (A) numerical data. (B) graphic data.

[54] Figure 19 contains a schematic of an exemplary PapSEEK test for the detection of tumor DNA in the Pap brush, Tao brush and plasma samples from patients with endometrial or ovarian cancer. Tumor cells released by endometrial or ovarian cancers are transported to the uterine cavity, where they can be collected by the Tao brush. Tumor cells that pass into the endocervical canal can be captured using the Pap smear used in the routine Pap test. These brushes are dipped in a liquid fixative, from which the DNA is isolated and sequenced. The sequences are analyzed for somatic mutations and aneuploidy. In addition, tumor DNA spilled into the bloodstream can be detected by ctDNA analysis.

[55] Figure 20 contains graphs showing the detection of somatic and aneuploidy changes (PapSEEK) in samples of the Pap smear brush (A) and Tao brush (B) from healthy controls and patients with endometrial and ovarian cancer. The error bars represent 95% confidence intervals.

[56] Figure 21 contains Venn diagrams showing that the combined somatic mutation and aneuploidy test increased the sensitivity to endometrial and ovarian cancer in the Pap (A) and Tao (B) brush samples. For ovarian cancer, the combined Pap brush and plasma test also increased sensitivity compared to testing any type of sample alone (C).

[57] Figure 22 contains graphs showing the detection of endometrial (A) or ovarian (B) cancers in Pap or Tao brush samples with PapSEEK, by stage. The error bars represent 95% confidence intervals.

[58] Figure 23 contains a graph showing the detection of ovarian cancer in Pap and plasma samples. The error bars represent 95% confidence intervals.

[59] Figure 24 contains a graph showing the detection of endometrial and ovarian cancer with PapSEEK in the Pap brush, Tao brush and plasma samples. Error bars represent 95% confidence intervals.

[60] Figure 25 contains a schematic drawing of an exemplary approach used to evaluate urinary cells in this study.

[61] Figure 26 contains a flow diagram indicating the number of patients in the Early Detection Cohort and in the Surveillance Cohort with summaries of the data. Cytology was performed on a subset of patients.

[62] Figure 27 contains graphs showing the fraction of mutations found in the panel of ten genes in 231 urinary cell samples assessed in the Early Detection Cohort (A) and 132 urinary cell samples assessed in the Surveillance Cohort (B ).

[63] Figure 28 contains Venn diagrams of the sample distribution that were positive for each of the three assays for the Early Detection Cohort (A) and the Surveillance Cohort (B). URO = panel of ten genes, TERT = TERT promoter region, ANEU = Aneuploidy test.

[64] Figure 29 contains bar graphs of the waiting time between a positive UroSEEK test and disease detection at the clinical level in the Early Detection Cohort (A) and in the Surveillance Cohort (B).

[65] Figure 30 contains bar graphs showing cytology performance compared to UroSEEK in the diagnosis of low and high grade urothelial neoplasms in the Early Detection Cohort and the Surveillance Cohort.

[66] Figure 31 contains a schematic diagram of an exemplary noninvasive detection of upper tract urothelial cancer (UTUC) through genetic analysis of urinary cell DNA. Tumors of the upper urinary tract appear in the renal pelvis and / or ureter and are in direct contact with urine. Urine contains a mixture of normal cells that are eliminated constitutively from various sites throughout the urinary system, together with malignant cells when present (blue). The UroSEEK assay is based on mutational analyzes of genes often mutated in urinary cancers, along with a determination of chromosomal gains and losses.

[67] Figure 32 contains a Venn diagram showing the distribution of positive results for each of the three Uro-SEEK assays.

[68] Figure 33 contains a graph showing a comparison of the variations in the number of copies in DNA samples of corresponding urinary and tumor cells from the UTUC cohort. The primary tumor is shown at the top of each section and the DNA of the urinary cell at the bottom. Chromosomal gains are in blue, while losses are in red. Significance levels for gains and losses were established at Z scores> 3 and <-3, respectively. The X axis is the Chromosome Arm. The Y axis is the Z score.

[69] Figure 34 contains a graph showing the fraction of the total mutations for each gene in the 10-gene panel used to analyze urinary cell DNA from UTUC patients.

[70] Figure 35 contains graphs that show comparisons of variations in the number of copies in DNA samples of corresponding urinary and tumor cells from four individual patients with UTUC (Figs. 35A-D). Z scores> 3 or <-3 were considered significant for chromosomal gains or losses, respectively. N.S. indicates not significant. Data for all 56 patients are provided in Table 28.

[71] Figure 36 contains a schematic showing an overview of an exemplary WALDO approach. (A) A single pair of primers amplifies ~ 38,000 long interspersed nucleotide elements (LINEs). (B) One test sample corresponds to seven euploid samples with genomic DNA of similar size. (C) The genome is divided into 4361 intervals, each 500 kb in size. (D) Readings within these 500 kb genomic ranges in euploid samples are grouped into 4361 clusters. All 500 kb genomic intervals in clusters have similar reading depths. (E) The readings for each of the 500 kb genomic intervals in the test sample are placed in the predefined groupings. (F) Statistical tests, including a Support Vector Machine (SVM) based algorithm, are used to determine whether the total readings for all 500 kb genomic intervals on each chromosome arm are distributed as expected, whether the sample for euploid. The statistical tests are based on the observed distribution of readings in the sample clusters, and not in comparison with the readings in euploid samples. (G) Germline sequence variants at sites of common polymorphisms known in LINEs provide information on allelic imbalance at arm level that can also be used to assess aneuploidy of individual chromosomal arms. These same polymorphisms can be used to determine whether any two samples are derived from the same individual. (H) When there is a corresponding normal sample from the same individual available, WALDO can detect the number and nature of substitutions and insertions and exclusions from a single base in the LINEs.

[72] Figure 37 contains a graph showing individual gains and losses in the chromosome arm that have been identified in nine types of cancer. The average fraction of tumors with gain or loss in each chromosomal arm is shown in the figure. The same nine types of tumor were analyzed in both cohorts, but there was no overlap between the samples evaluated by WALDO (red) or GISTIC (blue). WALDO used the LINE sequencing data for the tumors reported here, while GISTIC used the Affymetrix SNP6.0 data provided by TCGA.

[73] Figure 38 shows aneuploidy detected in plasma samples from cancer patients. The operating characteristics of the receiver

(ROC) and the area under the curve (AUC) are shown for three ranges of fractions of neoplastic cells. The true positives were defined as samples of cancer patients with positive scores, while the false positives were defined as those of normal individuals with positive scores. The fraction of neoplastic cells in each plasma sample was estimated from the sequencing data of the conducting gene, as described in the text. (A) Samples with neoplastic cell fractions <0.5%. (B) Samples with fractions of neoplastic cells ranging from 0.5 to 1%. (C) Samples with neoplastic cell fractions> 1%.

[74] Figure 39 contains graphs showing comparisons of aneuploid correlation of cancers detected using FAST-SeqS and WALDO compared to the Cancer Genome Atlas (TCGA) using Affymetrix SNP6.0 and GISTIC in 9 different types of cancer. (A) Correlation of the fraction of gains of the chromosome arm. (B) Correlation of the loss fraction of the chromosomal arm.

[75] Figure 40 contains graphs showing aneuploidy comparisons of individual types of cancer that were detected using the WALDO structure compared to the Cancer Genome Atlas (TCGA). For each type of cancer, the fraction of each gained and lost chromosomal arm was compared. The correlation of these gains and losses in WALDO with TCGA was compared. Each sub-figure represents a different type of cancer. (A) Invasive breast carcinoma (BRCA). (B) Colon adenocarcinoma and rectal adenocarcinoma (COAD; COADREAD). (C) Esophageal carcinoma (ESCA). (D) Head and neck squamous cell carcinoma (HNSC). (E) Hepatocellular carcinoma of the liver (LIHC). (F) Pancreatic adenocarcinoma (PAAD). (G) Serous ovarian cystadenocarcinoma (OV). (H) stomach adenocarcinoma (STAD). (I) Endometrial carcinoma of the uterine body (UCEC).

[76] Figure 41 contains graphs showing the performance of the Trisomy 21 as a function of reading depth. DNA samples from individuals with trisomies were physically mixed at a rate of 2 ng normal DNA and ~ 0.2 ng DNA from Trisomy 21 and normal peripheral white blood cell (WBC) samples. The mixtures were created to replicate typical fetal fractions in non-invasive prenatal tests (approximately 10%). Using LINE-amplicons polymorphisms, it is estimated that the sample's trisomy mixture varies from 7.7% to 10.4%). Using a z threshold of 2.5, sensitivities (A) and specificities (B) were calculated for a range of reading depths.

[77] Figure 42 contains a graph showing a comparison of the total number of single base somatic substitutions (SBS) that were detected in Exome Sequence vs WALDO.

[78] Figure 43 contains a graph showing a comparison of the percentages of single base substitution mutations that are A: T> T: A mutations that were detected by Exome vs WALDO sequencing.

[79] Figure 44 contains graphs showing the spectra of single base substitution (SBS) mutations. (A) SBS identified by WALDO. (B) SBS identified by exome sequencing.

[80] Figure 45 contains a graph showing a distribution of the number of genomic intervals included in a cluster for a representative sample of normal WBC.

[81] Figure 46 contains graphs showing distributions of readings to scale. (A) Distribution of readings at scale illustrating that readings in the FAST-SeqS amplicon sequencing were not randomly distributed. (B) Representative cluster for a normal WBC sample that illustrates the normality of scale readings in a cluster. (C) The representative cluster for a primary aneuploid tumor sample that illustrates the normality of scale readings in a cluster.

[82] Figure 47 contains graphs showing an example of the statistical procedure for identifying a gain or loss in the chromosome arm.

[83] Figure 48 contains a graph showing an empirical estimate of the frequency variation of the B allele for heterozygous SNPs as a function of reading depth. Increasing the depth of the UID improved the frequency estimate of the B allele for heterozygous SNPs.

[84] Figure 49 shows an example pseudocode for generating synthetics with alteration of an arm.

[85] Figure 50 shows an example pseudocode for generating synthetics with multiple changes in the arm.

[86] Figure 51 contains a graph showing a distribution of aneuploidy scores across the genome (SVM scores) as a function of reading depth. The smaller reading depth was more likely to produce higher scores and, by not correcting the UID depth, can produce false positives.

[87] Figure 52 contains a schematic showing an exemplary overview of a bottleneck sequencing methodology. Each color at the top of the figure represents double-stranded DNA from a cell genome within a population. Random, non-clonal point mutations (red) are deprived for individual cells. On the other hand, changes in clonal reference (A in black) are present in all genomes in the cell population. (step 1) Random stranding generates DNA molecules of varying size. (step 2) The non-complementary single strand regions of the Y Illumina adapters (P5 in gray and P7 in black) are represented as bifurcated structures connected to both ends of each DNA molecule. (step 3) Dilution randomly decreases the number of DNA molecules (five are shown) from the original population.

The ends of the DNA molecules align exclusively with the reference genome. The mapping coordinates are used as the molecule's unique “barcodes” during data processing. (step 4) The PCR primer (black arrowhead) anneals and the primer extends (dotted lines) the Watson and Crick model of the original DNA molecule independently. The red asterisk represents an error generated during the library's PCR. (step 5) The Watson and Crick models generate two families of PCR duplicates. The orientation of adapters containing P5 (gray) and P7 (black) for the DNA molecule (insertion) distinguishes the two families. Sequences P5 and P7 determine which end will be sequenced at reading 1 versus reading 2, respectively, in the Illumina flow cell. Red asterisks represent the PCR error propagated in Watson, but not in the members of the Crick family. Unlike artifacts, real mutations (C: G mutation in red) will be present in the Watson and Crick family members. (step 6) The BotSeqS pipeline identifies and quantifies the number of exclusive DNA molecules and point mutations (C: G in red) in the sequencing data, eliminating artifacts and clonal changes (A: T in black).

[88] Figure 53 contains graphs showing the increase in changes in the nuclear spot in normal tissues of individuals with defects in DNA repair or with exposure to environmental carcinogens compared to controls. (A) Comparison of the prevalence of point mutations in the nuclear (Left) and mitochondrial (Right) genomes in the age-matched normal colon epithelium (full circle) with different DNA incompatibility repair genotypes (PMS2 + / + or PMS2- / -) or normal renal cortex compatible with age (filled square) without (none) or with exposure to carcinogen (aristolochic acid or smoke). Red lines represent mean. * P <0.05, t test; ** P <0.001 and *** P <0.0001, one-way ANOVA with Bonferroni's multiple comparison post-test; ns, not significant, indicates P> 0.05. (B) Stacked columns representing the substitution frequencies (eixoy) for each substitution among the six possible types (see legend). Cohort markers are indicated at A directly above each column. The number of substitutions (N) that generate each mutational spectrum is indicated on the x axis. and not determined due to an insufficient number of mutations (N = 7) for analysis of the mutational spectrum. * P = 0.04, Fisher's exact test; ** P = 2.6 × 10−8 and *** P = 1.5 × 10−16, Fisher's exact test with Bonferroni's multiple comparison correction; ns, not significant, indicates P> 0.05. All statistical tests in this figure were two-tailed.

[89] Figure 54 contains graphs showing that normal human tissues accumulate point mutations throughout life with genome-specific and tissue-specific mutational patterns. Prevalence of point mutations in the nuclear (Upper) and mythondrial (Lower) genomes measured in four normal types of tissue (cerebral frontal cortex of 9 individuals, renal cortex of 5 individuals, colonic epithelium of 11 individuals and duodenum of 1 individual). Twenty-six individuals were evaluated in total, with each individual contributing to a normal tissue type. The pie chart inserts show the prevalence of each substitution among the six possible substitution types (see the legend of the pie chart, right side). Each pie chart was compiled from the individuals represented in their respective scatter charts, with the exception that the duodenum was omitted. The number of substitutions that generate pie charts for the nuclear genome was n = 31 for the brain, n = 73 for the kidney and n = 94 for the colon, and for the mitochondrial genome it was n = 181 for the brain, n = 299 for the kidney, and n = 116 for the colon.

[90] Figure 55 contains an assessment of double counts

with pre-running the MiSeq ™. Histograms showing the distribution of family members (PCR duplicates of individual model molecules, shown on the x-axis). Two or three serial dilutions (103, 104, 105 or 106) were evaluated on the MiSeq ™ for six samples (COL373, SA_117, KID038, BRA01, BRA04, BRA07) to generate ~ 5 M of readings properly matched by library. The counts of family members were determined here using Picard's Estimate Library Complexity program. The libraries generated from the 105 dilution (blue) were subsequently used for the final execution of the HiSeq ™ reported in this study. Note that the HiSeq ™ distribution should shift to the right compared to the MiSeq ™ distribution due to the increase in clusters sequenced by the library (clusters of ~ 5 M sized for clusters of ~ 70 M). For example, dilution 106 BotSeqS libraries (red) were not used because members per family would be very high in a HiSeq ™ run, limiting the number of different families that could be evaluated with a certain amount of sequencing.

[91] Figure 56 contains a graph showing the count of family members from 44 BotSeqS libraries reported in this study. Horizontal box and chip graphs for 44 BotSeqS libraries (y-axis) and number of members per family (duplicate count, x-axis). The white boxes represent the first to the third quartiles, with the dotted mark indicating the median. The chips represent 1.5 * IQR (interquartile range) and the data points outside the chips are shown as points outside the curve. An average of 3.97 M (range 0.38 to 10.91 M) from unfiltered families per library was evaluated. Families were identified through the BotSeqS pipeline using genomic mapping co-ordinates as unique molecule identifiers. Blue names indicate replicated technical samples.

Note that Bot01-Bot06 and Bot23-28 were performed on the same samples with a 100-fold difference in dilution (see Table 43).

[92] Figure 57 contains graphs that show that the consideration of both members of the Watson and Crick family decreases the artifacts, specifically the G> T transversions. (A) Nuclear point mutation frequencies (y-axis) considering mutations observed in the “Watson AND / OR Crick” (black circle) or “Watson AND Crick” (black square) families in normal tissues derived from the cerebral frontal cortex bral (left side), renal cortex (medium, shaded) or colon epithelium (right side). Specifically, “OR” mutations represent ≥ 90% of the mutation fraction in the Watson family with a minimum of two Watson readings or ≥ 90% of the Crick mutation fraction with a minimum of two Crick readings. Note that “OR” mutations have only the Watson or Crick families represented in the data, but not both. “AND” mutations represent ≥ 90% of the mutation fraction in the Watson family with a minimum of two Watson readings and ≥ 90% of the Crick mutation fraction with a minimum of two Crick readings. "AND" mutations are an internal subset of the "AND / OR" data set, which is a modified version of the BotSeqS pipeline. Twenty-five individuals are organized by increasing the age within each tissue. (B) Pie charts of the frequencies of each nuclear substitution among the six possible substitution types (see legend) of (a) considering Watson AND / OR Crick (top pies) or Watson AND Crick (bottom pies) in each type of normal tissue. The number of nuclear mutations that generate mutational spectra for Watson AND / OR Crick was n = 616 for the brain, n =

1,257 for the kidney, n = 2,542 for the colon and for Watson AND Crick it was n = 33 for the brain, n = 74 for the kidney, n = 99 for the colon.

[93] Figure 58 contains graphs showing rare point mutations that accumulate in normal colon tissues rather than in the brain. Frequency of point mutation (y-axis) in the nuclear genome (upper graph) and mitochondrial (lower graph) in the normal frontal cortex of the brain (left side) and normal colon epithelium (right side) grouped by age (baby / young child) in green, young adult in purple, elderly adult in blue). The averages for each age cohort are shown with error bars that represent standard deviations. Two-way ANOVA with Bonferroni's multiple comparison post-test was performed using the GraphPad Prism ™ 5.0f software with the P values reported above the bars. n.s. (not significant) indicates P> 0.05. For the brain, the number of individuals and the average age of the group are as follows - baby / child: n = 3, 3.5 years of age (a / i) (BRA01, BRA02, BRA03); young adult: n = 3 individuals, 22 years old (BRA04, BRA05, BRA06); and elderly adult: n = 3, 93 years (BRA07, BRA08, BRA09). For the colon, baby / child: n = 2, 5.5 years (COL229, COL231); young adult: n = 6, 28 (COL235, COL236, COL237, COL373, COL374, COL375); elderly adult: n = 3, 96 years (COL232, COL233, COL234).

[94] Figure 59 contains a graph showing the frequencies of mutation at the Mitochondrial and nuclear point in normal tissues of the same individual. The data points represent the ratio between the mitochondrial and nuclear (y-axis) mutation frequencies in the normal tissue of the same individual. Subjects were grouped into four cohorts (x-axis) with n = 24 subjects for Control (see Table 51), n = 2 subjects (COL238, COL239) for DNA repair defects PMS2 - / -, n = 3 subjects (AA_105, AA_124, AA_126) for exposure to aristolochic acid and n = 3 individuals (SA_117, SA_118, SA_119) for exposure to smoke. A control cohort ratio (COL229) was zero and omitted from this analysis. The average proportion (red line) for each cohort is 24.5 for the Control, 0.5 for the DNA repair defect

PMS2 - / -, 1.1 for exposure to aristolochic acid and 2.0 for exposure to smoke. * P <0.05, ** P <0.01, unidirectional ANOVA with Bonferroni multiple comparison post-test.

[95] Figure 60 contains graphs showing that normal tissues and tumors derived from the same type of tissue have similar mutation spectra. (A) Pie charts of the nuclear and mitochondrial frequencies of each substitution among the six types of possible substitution (see legend) comparing normal (left side) and tumors (right side) derived from the colon (upper) and kidney (lower) ). "Normal" represents the rare spectral mutational data derived from normal tissues shown in Figure 54. "Nuclear tumor mutations" represent clonal mutation data for colorectal carcinomas (COAD / READ) or renal clear cell carcinoma (KIRC) of the TCGA data set #! Synapse: syn1729383 found on synapse.org. Colon and kidney "mtDNA tumor mutations" were acquired from "colorectal" and "renal" tumor types in supplementary file 2 of Ju et al. (2014 eLife 3). For normal tissues, the number of substitutions evaluated was as follows: nuclear colon n = 94 from 13 individuals, colon mtDNA n = 116 from 12 individuals, nuclear kidney n = 73 from 7 individuals and mtDNA kidney n = 299 from five individuals. For tumor tissue, the number of substitutions evaluated was as follows: nuclear colorectal carcinoma n = 18,538 of 193 individuals, colorectal carcinoma mtDNA n = 64 of 76 individuals, renal clear cell carcinoma of nuclear cells n = 24,559 of 417 individuals and mtDNA renal carcinoma n = 16 of 23 individuals. (B) Principal component analysis (PCA) of mutational spectra from the cohorts indicated in (A). PCA performed and plotted using the R software.

[96] Figure 61 contains a schematic showing elements of Safe-SeqS. In the first stage, each fragment to be analyzed receives a unique identification sequence (UID) (dotted metal or dotted bars). In the second stage, the fragments marked exclusively are amplified, producing UID families, each member with the same UID. A supermutant is defined as a UID family in which ≥95% of family members have the same mutation.

[97] Figure 62 contains a schematic showing an exemplary Safe-SeqS with endogenous UIDs plus capture. The end sequences of each fragment produced by random shear (bars shaded in various ways) serve as unique identifiers (UIDs). These fragments are connected to adapters (bars dotted in globe and dotted in cross) so that they can be further amplified by PCR. A uniquely identifiable fragment is produced from each tape in the double tape model; only one tape is shown. The fragments of interest are captured in a solid phase containing oligonucleotides complementary to the sequences of interest. After PCR amplification to produce UID families with primers containing 5 'graft' sequences (adhesive-filled bars and light dotted bars), sequencing is performed and supermutants are defined as in Fig. 61.

[98] Figure 63 contains a schematic showing an exemplary Safe-SeqS with exogenous UIDs. DNA (cut or not) is amplified with a set of specific gene primers. One of the initiators has a random DNA sequence (for example, a set of 14 N's) that forms the unique identifier (UID; bars shaded in various ways), located 5 'from its specific gene sequence , and both have sequences that allow universal amplification in the next stage (bars dotted in globe and dotted in cross). Two UID assignment cycles produce two fragments - each with a different UID - from each double-stranded model molecule, as shown. Subsequent PCR with universal primers, which also contain "graft" sequences (adhesive-filled bars and light dotted bars), produce UID families that are directly sequenced. Supermutants are defined as in the legend of Fig. 61.

[99] Figure 64 contains graphs showing single-base substitutions identified by conventional and Safe-SeqS analysis. The exogenous UID strategy represented in Figure 63 was used to produce PCR fragments from the CTNNB1 gene from three normal, unrelated individuals. Each position represents one of the 87 possible single base substitutions (3 possible substitutions / base x 29 bases analyzed). These fragments were sequenced on an Illumina GA IIx instrument and analyzed in the conventional manner (A) or with Safe-SeqS (B). Safe-SeqS results are displayed on the same scale as conventional analysis for direct comparison; the insertion is an enlarged view. Note that most of the variants identified by the conventional analysis probably represent sequencing errors, as indicated by their high frequency in relation to Safe-SeqS and their consistency between unrelated samples.

[100] Figure 65 contains a schematic showing an exemplary Safe-SeqS with endogenous UIDs plus inverse PCR. The sequence of the ends of each fragment produced by random shear serves as unique identifiers (UIDs; bars shaded in various ways). These fragments are attached to adapters (globe-dotted and cross-dotted bars) as in a standard preparation from the Illumina library. A uniquely marked fragment is produced from each tape in the double tape model; only one tape is shown. After circularization with a ligase, reverse PCR is performed with specific gene primers that also contain 5 'graft' sequences (bars filled with adhesive and lightly dotted bars). This PCR produces UID families that are directly sequenced. Supermutants are defined as in Fig. 61.

[101] Figure 66 contains graphs showing the position of single base substitution versus the frequency of error in oligonucleotides synthesized with phosphoramidites and Phusion. A representative portion of the same 31 base DNA fragment synthesized with phosphoramidites (A) or Phusion polymerase (B) was analyzed by Safe-SeqS. The means and standard deviations for seven independent experiments of each type are represented. There was an average of 1,721 ± 383 and 196 ± 143 SBS supermutants identified in the fragments synthesized with phosphosidite and generated by Phusion, respectively. The y-axis indicates the fraction of the total errors at the indicated position. Note that the errors in the DNA fragment synthesized with phosphoramidite were consistent across the seven replications, as would be expected if the errors were systematically introduced during the synthesis itself. On the other hand, the errors in the fragments generated by Phusion appeared to be heterogeneous among the samples, as expected from a stochastic process (Lu- ria and Delbruck, 1943 Genetics 28: 491-511).

[102] Figure 67 contains a graph showing the distribution of UID family members. The exogenous UID strategy shown in Figure 63 was used to produce PCR fragments from a CTNNB1 region from three normal, unrelated individuals (Table 53); a representative example of UID families with ≤ 300 members (99% of the total UID families) generated from an individual is shown. The y-axis indicates the number of different UID families that contained the number of family members shown on the x-axis.

[103] Figure 68 contains an exemplary random forest model tree for classification of tumor location. (A) shows the complete tree and (B) - (K) contains enlarged images of sections of the complete tree, as indicated in (A).

[104] Figure 69 contains exemplary rules for the recognition of tissues extracted from the random forest model. The randomForest function of the randomForest package (v4.6-14) was applied to the protein data of the CancerSEEK project. The protein data has 33 proteins and the 626 tumor samples that were correctly predicted as cancer by CancerSEEK. The values of each protein were adjusted to zero if they were less than the 25th quantile of values in normal samples. To obtain specific decision rules (Table 58), the inTrees package (v1.2) was applied to extract all rules of length less than or equal to 6 of all 500 trees created by randomForest. From this set of rules, using the functions (selectRuleRRF, buildLearner, applyLearner) of the inTrees package, relevant and non-redundant rules were selected, a classifier was created and applied to the data and the final list of rules was extracted. This final list of rules performs similarly to the complete random forest and represents a good approximation of the complete forest.

DETAILED DESCRIPTION Definitions

[105] As used in this document, the word "one / one" before a noun represents one or more of the particular nouns. For example, the phrase "a genetic change" encompasses "one or more genetic changes".

[106] As used in this document, the term "about" means approximately, in the region of, approximately, or around. When used in conjunction with a numerical range, the term "about" modifies that range by extending the links above or below the established numerical values. In general, the term "about" is used here to modify a numerical value above and below the value declared by a 10% variation.

[107] As used in this document, the term "aneuploidy" refers to the condition that it has less than or more than the natural diploid chromosome number, or any deviation from euploidy.

[108] As used in this document in the context of circulating tumor DNA or cellless DNA, the phrase "derived from a gene" means that circulating tumor DNA is eliminated from tumor cells (for example, tumor cells that have lysed or died). For example, the circulating tumor DNA "derived from a KRAS gene" means that the circulating tumor DNA was originally present in a tumor cell. When detecting a mutation present in the circulating tumor DNA derived from a gene, it is not necessary to first identify the mutation in the tumor cell itself.

[109] As used in this document, the phrases "genetic biomarker" and "genetic marker" refer to a nucleic acid that is characteristic, alone in combination with other genetic or other biomarkers, of cancer in an individual . A genetic biomarker can include a modification (for example, a mutation) in a gene. Examples of modifications include, without limitation, single base substitutions, insertions, deletions, indels, translocations and variations in the number of copies. In some embodiments, a genetic biomarker includes a modification (for example, an inactivating modification) in a tumor suppressor gene. In some modalities, a genetic biomarker includes a modification (for example, an activating modification) in an oncogene. Several genetic biomarkers and panels of genetic biomarkers are described in more detail here.

[110] As used in this document, the terms "mutation", "genetic modification" and "genetic alteration" are used interchangeably to indicate a change in a wild-type nucleic acid sequence. For example, in some modalities, methods for detecting a mutation in DNA without cells (eg, ctDNA) are described here. It should be understood that such methods can be described interchangeably as detecting mutations, genetic modifications or genetic alterations.

[111] As used in this document, the phrases "protein biomarker", "protein marker", "peptide biomarker" and "peptide marker" refer to a protein that is characteristic, alone in combination with another protein or other biomarkers, of cancer in an individual. In some embodiments, a protein biomarker includes a high level of protein in an individual (for example, an individual with cancer, regardless of whether the individual has cancer) compared to a reference individual who does not have cancer . In some embodiments, a protein biomarker includes a decreased level of protein in an individual (for example, an individual with cancer, regardless of whether the individual has cancer) compared to a reference individual who does not have cancer . As used in this document, the phrase "detection of a protein biomarker" can refer to the detection of a level (for example, an increased level or a decreased level) of the protein biomarker. Several protein biomarkers and panels of protein biomarkers are described in more detail here. In some embodiments, peptides that are distinct from a protein biomarker are used in the methods provided here.

[112] As used in this document with reference to protein biomarkers, the phrase "high level" refers to a level of the protein biomarker that is greater than a reference level of the protein biomarker normally seen in a sample ( for example, a reference sample) from a healthy individual (for example, an individual who does not exhibit a specific disease or condition). In some modalities, a reference sample may be a sample obtained from an individual (for example, a different or reference individual) who does not have cancer. For example, for a protein biomarker associated with colorectal cancer, a reference sample can be a sample obtained from a different individual or reference who does not have colorectal cancer. In some modalities, a reference sample can be a sample obtained from the same individual in which the high level of a protein biomarker is observed, where the reference sample was obtained before the onset of cancer. In some modalities, a reference sample obtained from the same individual is frozen or preserved for future use as a reference sample. In some embodiments, when the reference samples have undetectable levels of a protein biomarker, a high level can be any detectable level of the protein biomarker. It will be appreciated that comparable sample levels can be used when determining whether a specific level is a high level or not.

[113] As used in this document with reference to protein biomarkers, the phrase "reference level" refers to the level of the protein biomarker that is normally present in a healthy individual (for example, an individual who does not exhibits a specific disease or condition). A reference level for a protein biomarker may be present in a reference individual who does not exhibit a disease or condition (for example, cancer). For example, for a protein biomarker associated with colorectal cancer, a reference sample can be a sample taken from an individual who does not have colorectal cancer. As another example, a reference level for a protein biomarker may be a level present in an individual before the onset of the disease or condition (for example, cancer) in that individual. In some modalities, a disease or condition can be identified in an individual when the measured or detected level of one or more protein biomarkers is higher than the reference level (s) of one or more protein biomarkers .

[114] As used in this document, the term "sensitivity" refers to the ability of a method to correctly identify or diagnose the presence of a disease in an individual (for example, the sensitivity of a method can be described as the ability of the method to identify the true positive rate or probability of detecting a condition in an individual). For example, when used in reference to any of the various methods described here that can detect the presence of cancer in an individual, a high sensitivity means that the method correctly identifies the presence of cancer in the individual in a large percentage of the time. For example, a method described here that correctly detects the presence of cancer in an individual 95% of the time that the method is performed has a sensitivity of 95%. In some embodiments, a method described here that can detect the presence of cancer in an individual provides a sensitivity of at least 80% (for example, at least 80%, 85%, 90%, 91%, 92%, 93 %, 94%, 95%, 96%, 97%, 98%, 99%, 99.5% or higher). In some embodiments, the methods provided here that include detecting the presence of one or more members of two or more classes of biomarkers (for example, genetic biomarkers and / or protein biomarkers) provide a higher sensitivity than the methods which include detecting the presence of one or more members of only one class of biomarkers.

[115] As used in this document, the term "specificity" refers to the ability of a method to correctly reject the presence of a disease in an individual (for example, the specificity of a method can be described as the ability of the method for identifying the true negative rate or probability of correctly determining that a condition does not exist in an individual, for example, when used in reference to any of the various methods described here that can detect the presence of cancer in an individual , a high specificity means that the method correctly identifies the absence of cancer in the individual in a large percentage of the time (for example, the method does not incorrectly identify the presence of cancer in the individual in a large percentage of the time). For example, a method described here that correctly detects the absence of cancer in an individual 95% of the time that the method is performed has a specificity of 95%. a method described here that can detect the absence of cancer in an individual provides a specificity of at least 80% (for example, at least 80%, 85%, 90%, 91%, 92%, 93%, 94% , 95%, 96%, 97%, 98%, 99%, 99.5% or higher). In some embodiments, the methods provided here that include detecting the presence of one or more members of two or more classes of biomarkers (for example, genetic biomarkers and / or protein biomarkers) provide a higher specificity than the methods that include detecting the presence of one or more members of only one class of biomarkers.

[116] As used in this document, the term "individual" is used interchangeably with the term "patient" and means a vertebrate, including any member of the mammalian class, including humans, domestic and farm and zoo animals, farm animals. sports or pets, such as mice, rabbits, pigs, sheep, goats, cattle, horses (eg racehorses) and superior primates. In some modalities, the individual is a human. In some modalities, the individual has a disease. In some modalities, the individual has cancer. In some embodiments, the individual is a human harboring a cancer cell. In some modalities, the individual is a human harboring a cancer cell, but is not known to harbor the cancer cell. In some modalities, the individual has a viral disease. In some embodiments, the individual has a bacterial disease. In some modalities, the individual has a fungal disease. In some modalities, the individual has a parasitic disease. In some modalities, the individual has asthma. In some modalities, the individual has an autoimmune disease. In some modalities, the individual has graft versus host disease.

[117] As used in this document, the term "treatment" is used interchangeably with the phrase "therapeutic intervention".

[118] Methods of testing DNA isolated or obtained from white blood cells (for example, white blood cell clones that arise during age-associated clonal hematopoiesis (for example, clonal hematopoiesis expansion, also known as clonal hematopoiesis) - undetermined potential or CHIP) or myelodysplasia) regarding the presence or absence of a genetic mutation associated with cancer to determine whether this genetic alteration originates from a cancer cell in the individual is generically described here as "checking for genetic alteration against white blood cells "," check for genetic alteration against white blood cell DNA "," white blood cell check "and similar phrases.

[119] In general, methods and materials for detecting or identifying the presence of cancer in an individual with high sensitivity and specificity compared to conventional methods of identifying the presence of cancer in an individual are provided here. In some modes, the methods provided here to identify the presence of cancer in an individual with high sensitivity and specificity are performed on a liquid sample (s) obtained from the individual (for example, blood, plasma or serum), while conventional methods of identifying the presence of cancer in an individual do not reach the level of sensitivity, the level of specificity or both when performed on a liquid sample obtained from the individual. In some embodiments, the methods provided here to identify the presence of cancer in an individual with high sensitivity and specificity are performed before determining that the individual is already suffering from cancer, before determining that the individual is harboring a cancer cell and / or before the individual exhibits symptoms associated with cancer. Therefore, in some modalities, the methods provided here to identify the presence of cancer in an individual with high sensitivity and specificity are used as a first-line detection method, and not simply as a confirmation (for example, a "call" ") another method of detecting that the individual has cancer.

[120] In some modalities, the methods and materials provided here provide high sensitivity in the detection or diagnosis of cancer (for example, a high frequency or incidence of correct identification of an individual as having cancer). In some embodiments, the methods and materials provided here provide a sensitivity of at least about 10%, at least about 15%, at least about 20%, at least about 25%, at least about 30%, at least at least about 35%, at least about 40%, at least about 45%, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99% or greater. In some modalities, the methods and materials provided here provide high sensitivity in detecting a single type of cancer. In some modalities, the methods and materials provided here provide high sensitivity in detecting two or more types of cancer. Any of the various types of cancer can be detected using the methods and materials provided here (see, for example, the section entitled "Cancers"). In some modalities, cancers that can be detected using methods and materials provided here include pancreatic cancer. In some embodiments, cancers that can be detected using methods and materials provided herein include liver cancer, ovarian cancer, esophageal cancer, stomach cancer, pancreatic cancer, colorectal cancer, lung cancer, or lung cancer . In some embodiments, cancers that can be detected using methods and materials provided here include cancers of the female reproductive tract (for example, cervical cancer, endometrial cancer, ovarian cancer or fallopian tube cancer). In some embodiments, cancers that can be detected using the methods and materials provided here include bladder cancer or urothelial carcinomas of the upper tract.

[121] In some modalities, the methods and materials provided here provide high specificity in the detection or diagnosis of cancer (for example, a low frequency or incidence of incorrect identification of an individual as having cancer when that individual does not have cancer ). In some embodiments, the methods and materials provided here provide a specificity of at least about 10%, at least about 15%, at least about 20%, at least about 25%, at least about 30%, at least at least about 35%, at least about 40%, at least about 45%, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99% or greater. As will be understood by those skilled in the art, a specificity of 99% means that only 1% of individuals who do not have cancer are incorrectly identified as having cancer. In some modalities, the methods and materials provided here provide high specificity in detecting a single cancer (for example, there is a low probability of incorrectly identifying this individual as having this single type of cancer). In some modalities, the methods and materials provided here provide high specificity in detecting two or more cancers (for example, there is a low probability of incorrectly identifying this individual as having these two or more types of cancer).

[122] As will be appreciated by those skilled in the art, an appropriate sensitivity or specificity in the detection or diagnosis of cancer can be chosen based on a variety of factors. As a non-limiting example, a method designed to provide a lower specificity in the detection or diagnosis of cancer can be designed to have an increased sensitivity. As another non-limiting example, a method designed to provide increased specificity in the detection or diagnosis of cancer can be designed to have a lower sensitivity. In some modalities, even a low sensitivity can be advantageous (for example, in screening a population that is not normally screened). In some modalities, in populations where cancer (for example, a particular type of cancer) is prevalent, a method for detecting or diagnosing the presence of cancer can be designed to have a relatively high sensitivity, even when cost of reduced specificity. In some modalities, the sensitivity and specificity of the various detection methods provided here are determined based on the prevalence of the disease in a specific population of patients. For example, screening tests for a general population of patients who are not known to have cancer can be chosen with high specificity (to eliminate false positive diagnoses and other unnecessary additional diagnostic tests and / or monitoring). As another example, screening tests for high-risk cancer populations (for example, populations where the risk of having or developing cancer is greater than the general population, for example, due to the fact that the population gets involved or has involved in risky behaviors, having family history of risk, experiencing or experiencing risky environments and the like) can be chosen to have high sensitivity (in order to increase the certainty of detecting a cancer present, even at the expense of other tests and / or additional diagnostic monitoring that may not be appropriate for the general population). As a non-limiting example, a test with 90% sensitivity and 95% specificity will have a positive predictive value (PPV) of 15% and a negative predictive value (NPV) of> 99% in a population with a prevalence of 0.01 %, while both predictive values can be greater than 99% if the prevalence is 40% (high-risk population). PPV can be calculated as follows: # true positives (TP) / (# true positives + # false positives). PPV can also be calculated as follows: (prevalence of sensitivity X) / (prevalence of sensitivity X) + ((specificity 1) x (prevalence 1)). The VPN can be calculated as follows: # true negatives / # negative calls. PPV can also be calculated as follows: specificity X (prevalence 1) / ((sensitivity 1) x prevalence) + (specificity X (prevalence 1)). See, for example, Lalkhen and McCluskey, Clinical tests: sensitivity and specifi- city, Continuing Education in Anesthesia, Critical Care & Pain, Volume 8, 2008, incorporated herein by reference in its entirety. Detection methods

[123] Methods and materials are provided here to detect the presence of one or more members (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more members) of one or more classes of biomarkers and / or the presence of aneuploidy in a sample obtained from an individual. In some embodiments, the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy are tested simultaneously (for example, in a test procedure, including modalities in which the test procedure itself may include several test methods other than systems). In some embodiments, the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy are tested sequentially (for example, in two or more different test procedures conducted at two or more different time points, including modalities in which the test procedure itself may include various test methods other than systems). In some modalities of simultaneous and sequential testing for the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy, the test can be performed on a single sample or on two or more different samples (for example, two or more different samples obtained from the same individual).

[124] Any of the various detection methods described here (see, for example, the sections entitled "Detection of genetic biomarkers", "Detection of protein biomarkers" and "Detection of aneuploidy") can be used to detect the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy in a sample obtained from an individual. In some embodiments, the one or more members of one or more classes of biomarkers and / or one or more classes of biomarkers are associated with a disease in an individual. In some modalities, aneuploidy is associated with a disease in an individual. In some modalities, the disease is cancer (for example, any of the various types of cancer described here). In some modalities, one or more members are members of a class of genetic biomarkers. In some embodiments, the one or more members are members of a class of protein biomarkers. In some modes, methods that include detecting the presence of one or more members of one or more classes of biomarkers in a sample obtained from the individual include also detecting the presence of aneuploidy in a sample obtained from the individual. For example, methods that include detecting the presence of one or more members of a class of genetic biomarkers in a sample obtained from an individual may also include detecting the presence of aneuploidy in a sample obtained from the individual (for example, the same sample or two different samples from the individual). As another example, methods that include detecting the presence of one or more members of a class of protein biomarkers in a sample obtained from an individual may also include detecting the presence of aneuploidy in a sample obtained from the individual ( for example, the same sample or two different samples from the individual). In some embodiments, methods that include detecting the presence of one or more members of a class of genetic biomarkers and detecting the presence of one or more members of a class of protein biomarkers in a sample obtained from an individual may include also the detection of the presence of aneuploidy in a sample obtained from the individual (for example, the same sample or two or more different samples from the individual).

[125] In some embodiments, the methods provided here include detecting the presence of one or more members of a single class of biomarkers in one or more samples obtained from an individual (for example, genetic biomarkers or protein biomarkers). In some embodiments, the methods provided here include detecting the presence of aneuploidy in one or more samples obtained from an individual (for example, genetic biomarkers or protein biomarkers).

In some embodiments, the methods provided here include detecting the presence of one or more members of a single class of biomarkers in one or more samples obtained from an individual (for example, genetic biomarkers or protein biomarkers) and detecting the presence of aneuploidy in one or more samples obtained from the individual. In some embodiments, the methods provided here include detecting the presence of one or more members of two or more classes of biomarkers in one or more samples obtained from an individual (for example, genetic biomarkers and protein biomarkers). In some embodiments, the methods provided here include detecting the presence of one or more members of two or more classes of biomarkers in one or more samples obtained from an individual (for example, genetic biomarkers and protein biomarkers) and detecting the presence of aneuploidy in one or more samples obtained from the individual.

[126] In some embodiments, a single sample obtained from an individual can be tested for the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy. Alternatively, two or more samples can be obtained from an individual and each of the two or more samples can be tested individually for the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy. As a non-limiting example, a first sample obtained from an individual can be tested for the presence of one or more members of a first class of biomarkers (for example, genetic biomarkers) and a second sample obtained from the individual can be tested to detect the presence of one or more members of a second class of biomarkers (for example, protein biomarkers). As another non-limiting example, a first sample obtained from an individual can be tested for the presence of one or more members of a class of biomarkers (for example, genetic biomarkers or protein biomarkers) and a second sample obtained from the individual it can be tested to detect the presence of aneuploidy. As another non-limiting example, a first sample obtained from an individual can be tested to detect the presence of one or more members of a first class of biomarkers (for example, genetic biomarkers) to detect the presence of one or more members of a second class of biomarkers (for example, protein biomarkers), while a second sample obtained from the individual can be tested to detect the presence of aneuploidy. As another non-limiting example, a first sample obtained from an individual can be tested to detect the presence of one or more members of a first class of biomarkers (for example, genetic or protein biomarkers) and to detect presence of aneuploidy, while a second sample obtained from the individual can be tested to detect the presence of one or more members of a second class of biomarkers (for example, a class of biomarkers different from the first class tested in the first sample).

[127] In some modalities, the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy (for example, detected by any of the various forms of methods disclosed here) in a sample obtained from an individual is associated with a disease and indicates that the individual suffers from that disease. In some modalities, an individual is diagnosed with a disease when the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy (which biomarkers and / or aneuploidy are associated with a disease) in a sample obtained of an individual is detected. In some modalities, the disease is cancer (for example, any of the variety of cancers described here). In some modalities, an individual is not known to have a disease (for example, cancer) before detecting the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy. In some modalities, an individual is not known to harbor a cancer cell before detecting the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy. In some modalities, an individual does not exhibit symptoms associated with cancer before detecting the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy. Diagnostic methods

[128] Methods and materials for diagnosing or identifying the presence of a disease in an individual (for example, identifying the individual as having cancer) are also provided in this document by detecting one or more members (for example, 1, 2 , 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more members) of one or more classes of biomarkers and / or presence of aneuploidy in a sample obtained from the individual. In some modalities to diagnose or identify the presence of a disease in an individual (for example, identifying the individual as having cancer), the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy they are tested simultaneously (for example, in a test procedure, including modalities in which the test procedure itself may include several test methods other than systems). In some modalities to diagnose or identify the presence of a disease in an individual (for example, identifying the individual as having cancer), the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy are tested sequentially (for example, on two or more different test procedures conducted at two or more different points in time, including modalities in which the test procedure itself may include several different systems test methods). In some modalities to diagnose or identify the presence of a disease in an individual (for example, identifying the individual as having cancer) that include simultaneous or sequential tests (or both) for the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy, the test can be performed on a single sample or on two or more different samples (for example, two or more different samples obtained from the same individual).

[129] Any of the various detection methods described here (see, for example, the sections entitled "Detection of genetic biomarkers", "Detection of protein biomarkers" and "Detection of aneuploidy") can be used to detect the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy in a sample obtained from an individual. In some embodiments, the one or more members of one or more classes of biomarkers and / or one or more classes of biomarkers are associated with a disease in an individual. In some modalities, aneuploidy is associated with a disease in an individual. In some modalities, the disease is cancer (for example, any of the various types of cancer described here). In some modalities, one or more members are members of a class of genetic biomarkers. In some embodiments, the one or more members are members of a class of protein biomarkers. In some modes, methods that include diagnosing the presence of cancer in an individual (for example, identifying the individual as having cancer) by detecting the presence of one or more members of one or more classes of biomarkers in a sample obtained of the individual also include detecting the presence of aneuploidy in a sample obtained from the individual. For example, methods that include diagnosing or identifying the presence of a disease in an individual (for example, identifying the individual as having cancer) by detecting the presence of one or more members of a class of genetic biomarkers in a sample obtained from the individual may include still detecting the presence of aneuploidy in a sample obtained from the individual (for example, the same sample or two different samples from the individual). As another example, methods that include diagnosing or identifying the presence of a disease in an individual (for example, identifying the individual as having cancer) by detecting the presence of one or more members of a class of protein biomarkers in a sample obtained of the individual may also include detecting the presence of aneuploidy in a sample obtained from the individual (for example, the same sample or two different samples from the individual). In some modalities, methods that include diagnosing the presence of cancer in an individual (for example, identifying the individual as having cancer) by detecting the presence of one or more members of a class of genetic biomarkers and detecting the presence of a or more members of a class of protein biomarkers in a sample obtained from an individual may further include detecting the presence of aneuploidy in a sample obtained from the individual (for example, the same sample or two or more different samples from the individual).

[130] In some embodiments, the methods provided here include diagnosing the presence of cancer in an individual (for example, identifying the individual as having cancer) by detecting the presence of one or more members of a single class of biomarkers in one or more more samples taken from the individual (for example, genetic biomarkers or protein biomarkers). In some embodiments, the methods provided here include diagnosing the presence of cancer in an individual (for example, identifying the individual as having cancer) by detecting the presence of aneuploidy in one or more samples obtained from the individual (for example, genetic biomarkers or biomarkers of proteins). In some modalities, the methods provided here include diagnosing the presence of cancer in an individual (for example, identifying the individual as having cancer) by detecting the presence of one or more members of a single class of biomarkers in one or more samples obtained from the individual (for example, genetic biomarkers or protein biomarkers) and detect the presence of aneuploidy in one or more samples obtained from the individual. In some embodiments, the methods provided here include diagnosing the presence of cancer in an individual (for example, identifying the individual as having cancer) by detecting the presence of one or more members of two or more classes of biomarkers in one or more more samples taken from the individual (for example, genetic biomarkers and protein biomarkers). In some embodiments, the methods provided here include diagnosing the presence of cancer in an individual (for example, identifying the individual as having cancer) by detecting the presence of one or more members of two or more classes of biomarkers in one or more more samples obtained from the individual (for example, genetic biomarkers and protein biomarkers) and detect the presence of aneuploidy in one or more samples obtained from the individual.

[131] In some embodiments, a single sample obtained from an individual can be tested for the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy, and the individual can be diagnosed as having cancer (for example, it is identified as having cancer) when the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy is detected. As an alternative, two or more samples can be obtained from an individual and each of the two or more samples can be individually tested for the presence of one or more members of one or more classes of biomarkers and / or the presence aneuploidy, and the individual can be diagnosed as having cancer (for example, he is identified as having cancer) when the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy is detected.

As a non-limiting example, a first sample obtained from an individual can be tested for the presence of one or more members of a first class of biomarkers (for example, genetic biomarkers) and a second sample obtained from individual can be tested for the presence of one or more members of a second class of biomarkers (for example, protein biomarkers), in which the individual is diagnosed as having cancer (for example, is identified as having cancer) when the presence of one or more members of the first class of biomarkers is detected and / or the presence of one or more members of the second class of biomarkers (for example, when the presence of one or more members of the class of biomarkers is detected and the presence of one or more members of the second class of biomarkers is detected). As another non-limiting example, a first sample obtained from an individual can be tested for the presence of one or more members of a class of biomarkers (for example, genetic biomarkers or protein biomarkers) and a second sample obtained from the individual can be tested for the presence of aneuploidy, in which the individual is diagnosed as having cancer (for example, is identified as having cancer) when the presence of one or more members of the class of biomarkers is detected and / or the presence of aneuploidy is detected (for example, when the presence of one or more members of the class of biomarkers is detected and the presence of aneuploidy is detected). As another non-limiting example, a first sample obtained from an individual can be tested to detect the presence of one or more members of a first class of biomarkers (for example, genetic biomarkers) and to detect the presence of one or more members second class of biomarkers (for example, protein biomarkers), while a second sample obtained from the individual can be tested for the presence of aneuploidy, in which the individual is diagnosed as having cancer (for example, it is identified as having cancer) when the presence of one or more members of the first class of biomarkers is detected, the presence of one or more members of the second class of biomarkers is detected and / or the presence of aneuploidy is detected (for example, when presence of one or more members of the first class of biomarkers is detected, the presence of one or more members of the second class of biomarkers is detected and the presence of aneuploidy is detected gives). As another non-limiting example, a first sample obtained from an individual can be tested to detect the presence of one or more members of a first class of biomarkers (for example, genetic or protein biomarkers) and to detect the presence of aneuploidy, while a second sample obtained from the individual can be tested for the presence of one or more members of a second class of biomarkers (for example, a class of biomarkers different from the first class tested in the first sample), in that the individual is diagnosed as having cancer (for example, is identified as having cancer) when the presence of one or more members of the first class of biomarkers is detected, the presence of one or more members of the second class of biomarkers is detected , and / or the presence of aneuploidy is detected (for example, when the presence of one or more members of the first class of biomarkers is detected, the presence of one or more members of the second of the class of biomarkers is detected and the presence of aneuploidy is detected).

[132] In some modalities to diagnose or identify the presence of a disease (for example, cancer) in an individual (for example, using any of the variety of methods described here), the individual is also identified as a candidate for further testing of diagnosis. In some modalities to diagnose or identify the presence of a disease (for example, cancer) in an individual (for example, using any of the various ways described here), the individual is also identified as a candidate for increased monitoring . In some modalities to diagnose or identify the presence of a disease (for example, cancer) in an individual (for example, using any of the various ways described here), the individual is also identified as a candidate who will respond or likely will respond to a treatment (for example, any of the various therapeutic interventions described here). In some ways to diagnose or identify the presence of a disease (for example, cancer) in an individual (for example, using any of the various forms described here), the individual is also administered with a treatment (for example , any of the various therapeutic interventions described here). Methods of identifying an individual as being at risk of having or developing a disease

[133] Methods and materials are provided here to identify an individual as being at risk (for example, increased risk) of having or developing a disease (for example, cancer) by detecting the presence of one or more members (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more members) of one or more classes of biomarkers and / or the presence of aneuploidy in a sample obtained from the individual. In some modalities to identify an individual as being at risk (for example, increased risk) of having or developing a disease (for example, cancer), the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy is tested simultaneously (for example, in a test procedure, including modalities in which the test procedure itself may include several test methods other than systems). In some ways to identify an individual as being at risk (for example, increased risk) of having or developing a disease (for example, cancer), the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy is tested sequentially (for example, in two or more different test procedures conducted at two or more different points in time, including modalities in which the test procedure itself may include several different systems test methods) . In some ways to identify an individual as being at risk (for example, increased risk) of having or developing a disease (for example, cancer) that include simultaneous or sequential tests (or both) for the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy, the test can be performed on a single sample or on two or more different samples (for example, two or more different samples obtained from the same individual).

[134] Any of the various detection methods described here (see, for example, the sections entitled "Detection of genetic biomarkers", "Detection of protein biomarkers" and "Detection of aneuploidy") can be used to detect the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy in a sample obtained from an individual. In some embodiments, the one or more members of one or more classes of biomarkers and / or one or more classes of biomarkers are associated with a disease in an individual. In some modalities, aneuploidy is associated with a disease in an individual. In some embodiments, the disease is cancer (for example, any of the various types of cancer described here). In some modalities, one or more members are members of a class of genetic biomarkers.

In some embodiments, the one or more members are members of a class of protein biomarkers.

In some modalities, methods that include identifying an individual as being at risk (for example, increased risk) of having or developing a disease (for example, cancer) by detecting the presence of one or more members of one or more classes of biomarkers in a sample obtained from the individual they also include detecting the presence of aneuploidy in a sample obtained from the individual.

For example, methods that include identifying an individual as being at risk (for example, increased risk) of having or developing a disease (for example, cancer) by detecting the presence of one or more members of a class of genetic biomarkers in a sample obtained from the individual it may also include the detection of the presence of aneuploidy in a sample obtained from the individual (for example, the same sample or two different samples from the individual). As another example, methods that include identifying an individual as being at risk (for example, increased risk) of having or developing a disease (for example, cancer) by detecting the presence of one or more members of a class of biomar - protein scavengers in a sample obtained from the individual may also include detecting the presence of aneuploidy in a sample obtained from the individual (for example, the same sample or two different samples from the individual). In some modalities, methods that include identifying an individual as being at risk (for example, increased risk) of having or developing a disease (for example, cancer) by detecting the presence of one or more members of a class of genetic biomarkers and detecting the presence of one or more members of a class of protein biomarkers in a sample obtained from the individual may further include detecting the presence of aneuploidy in a sample obtained from the individual (for example, the same sample or two or more different samples from the individual).

[135] In some modalities, the methods provided here to identify an individual as being at risk (for example, increased risk) of having or developing a disease (for example, cancer) include detecting the presence of one or more members of a single class of biomarkers in one or more samples obtained from the individual (for example, genetic biomarkers or biomarkers of proteins). In some modalities, the methods provided here to identify an individual as being at risk (for example, increased risk) of having or developing a disease (for example, cancer) include detecting the presence of aneuploidy in one or more samples obtained from the individual (for example, genetic biomarkers or protein biomarkers). In some modalities, the methods provided here to identify an individual as being at risk (for example, increased risk) of having or developing a disease (for example, cancer) include detecting the presence of one or more members of a single class of biomarkers in one or more samples obtained from the individual (for example, genetic biomarkers or protein biomarkers) and detect the presence of aneuploidy in one or more samples obtained from the individual. In some embodiments, the methods provided here to identify an individual as being at risk (for example, increased risk) of having or developing a disease (for example, cancer) include detecting the presence of one or more members of two or more classes of biomarkers in one or more samples obtained from the individual (for example, genetic biomarkers and protein biomarkers). In some modalities, the methods provided here to identify an individual as being at risk (for example, increased risk) of having or developing a disease (for example,

include detecting the presence of one or more members of two or more classes of biomarkers in one or more samples obtained from the individual (for example, genetic biomarkers and protein biomarkers) and detecting the presence of aneuploidy in one or more samples obtained from individual.

[136] In some embodiments, a single sample obtained from an individual can be tested for the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy, and the individual identified as being at risk ( for example, increased risk) of having or developing a disease (for example, cancer) when the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy is detected. Alternatively, two or more samples can be obtained from an individual and each of the two or more samples can be individually tested for the presence of one or more members of one or more classes of biomarkers and / or the presence of aneu - ploidy, and the individual can be identified as being at risk (for example, increased risk) of having or developing a disease (for example, cancer) when the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy is detected. As a non-limiting example, a first sample taken from an individual can be tested for the presence of one or more members of a first class of biomarkers (for example, genetic biomarkers) and a second sample obtained from the individual. duo can be tested for the presence of one or more members of a second class of biomarkers (for example, protein biomarkers), in which the individual identified as being at risk (for example, increased risk) of having or developing a disease (for example, cancer) when the presence of one or more members of the first class of biomarkers is detected and / or the presence of one or more members of the second class of biomarkers (for example, when the presence of one or more members of the class of biomarkers is detected and the presence of one or more members of the second class of biomarkers is detected). As another non-limiting example, a first sample obtained from an individual can be tested for the presence of one or more members of a class of biomarkers (for example, genetic biomarkers or protein biomarkers) and a second sample obtained of the individual can be tested for the presence of aneuploidy, in which the individual identified as being at risk (for example, increased risk) of having or developing a disease (for example, cancer) when the presence of one or more members of the class of biomarkers is detected and / or the presence of aneuploidy is detected (for example, when the presence of one or more members of the class of biomarkers is detected and the presence of aneuploidy is detected). As another non-limiting example, a first sample obtained from an individual can be tested to detect the presence of one or more members of a first class of biomarkers (for example, genetic biomarkers) and to detect the presence of one or more members second class of biomarkers (eg protein biomarkers), while a second sample obtained from the individual can be tested for the presence of aneuploidy, in which the individual is identified as being at risk (for example, example, increased risk) of having or developing a disease (eg cancer) when the presence of one or more members of the first class of biomarkers is detected, the presence of one or more members of the second class of biomarkers is detected and / or the presence of aneuploidy is detected (for example, when the presence of one or more members of the first class of biomarkers is detected, the presence of one or more members of the second class of biomarkers is detected and the presence of aneuploidy is detected).

As another non-limiting example, a first sample obtained from an individual can be tested to detect the presence of one or more members of a first class of biomarkers (for example, genetic or protein biomarkers) and to detect the presence of aneuploidy, while a second sample obtained from the individual can be tested for the presence of one or more members of a second class of biomarkers (for example, a class of biomarkers different from the first class tested in the first sample), where the individual is identified as being at risk (eg increased risk) of having or developing a disease (eg cancer) when the presence of one or more members of the first class of biomarkers is detected, the presence of one or more members of the second class of biomarkers is detected, and / or the presence of aneuploidy is detected (for example, when the presence of one or more members of the first class of biomarkers is detected, the presence of one or more members of the second class of biomarkers is detected and the presence of aneuploidy is detected).

[137] In some modalities to identify an individual as being at risk (for example, increased risk) of having or developing a disease (for example, using any of the variety of methods described here), the individual is also identified as a candidate for more diagnostic tests. In some modalities to identify an individual as being at risk (for example, increased risk) of having or developing a disease (for example, using any of the various ways described here), the individual is also identified as a candidate for increased monitoring. In some modalities to identify an individual as being at risk (for example, increased risk) of having or developing a disease (for example, using any of the various ways described here), the individual is also identified as a candidate who will or you are likely to respond to a treatment (for example, any of the various therapeutic interventions described here, including, without limitation, a chemopreventive). In some modalities to identify an individual as being at risk (for example, increased risk) of having or developing a disease (for example, using any of the various ways described here), the individual also receives treatment (for example, example, any of the variety of therapeutic interventions described here, including, without limitation, a chemopreventive). Treatment methods

[138] Also provided here are methods and materials for treating an individual who has been diagnosed or identified as having a disease (eg cancer) or who has been identified as being at risk (eg increased risk) of having or develop a disease (for example, cancer) by detecting one or more members (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , 15, 16, 17, 18, 19, 20 or more members) of one or more classes of biomarkers and / or the presence of aneuploidy in a sample obtained from the individual. In some treatment modalities of an individual who has been diagnosed or identified as having a disease (eg cancer) or who has been identified as being at risk (eg increased risk) of having or developing a disease (eg cancer), the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy are tested simultaneously (for example, in a test procedure, including modalities in which the The test procedure itself may include several different test methods for systems). In some treatment modalities of an individual who has been diagnosed or identified as having a disease (eg, cancer) or who has been identified as being at risk (eg, increased risk) of having or developing a disease (eg, cancer), the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy are tested sequentially (for example, in two or more different test procedures conducted at two or more different points in time , including modalities in which the test procedure itself may include several test methods other than systems). In some treatment modalities of an individual who has been diagnosed or identified as having a disease (for example, cancer) or who has been identified as being at risk (for example, increased risk) of having or developing a disease (for example, cancer) cer) that include simultaneous or sequential tests (or both) for the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy, the test can be performed on a single sample or on two or more different samples (for example, two or more different samples obtained from the same individual).

[139] Any of the various detection methods described here (see, for example, the sections entitled "Detection of genetic biomarkers", "Detection of protein biomarkers" and "Aneuploidy detection") can be used to detect the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy in a sample obtained from an individual. In some embodiments, the one or more members of one or more classes of biomarkers and / or one or more classes of biomarkers are associated with a disease in an individual. In some modalities, aneuploidy is associated with a disease in an individual. In some modalities, the disease is cancer (for example, any of the various types of cancer described here). In some modalities, one or more members are members of a class of genetic biomarkers. In some embodiments, the one or more members are members of a class of protein biomarkers. In some modalities, methods that include treating an individual who has been diagnosed or identified as having a disease (for example, cancer) or who has been identified as being at risk (for example, increased risk) of having or developing a disease (for example, cancer) detecting the presence of one or more members of one or more classes of biomarkers in a sample obtained from the individual also include detecting the presence of aneuploidy in a sample obtained from the individual.

In some embodiments, methods that include treating an individual who has been diagnosed or identified as having a disease (for example, cancer) or who has been identified as being at risk (for example, increased risk) of having or developing a disease (for example, example, cancer) detecting the presence of one or more members of a class of genetic biomarkers in a sample obtained from the individual may further include detecting the presence of aneuploidy in a sample obtained from the individual (for example, the same sample or two samples different from the individual). In some modalities, methods that include treating an individual who has been diagnosed or identified as having a disease (for example, cancer) or who has been identified as being at risk (for example, increased risk) of having or developing a disease (eg cancer) detecting the presence of one or more members of a class of protein biomarkers in a sample obtained from the individual may further include detecting the presence of aneuploidy in a sample obtained from the individual ( for example, the same sample or two different samples from the individual). In some modalities, methods that include treating an individual who has been diagnosed or identified as having a disease (for example, cancer) or who has been identified as being at risk (for example, increased risk) of having or developing a disease (for example, cancer) detecting the presence of one or more members of a class of genetic biomarkers and detecting the presence of one or more members of a class of protein biomarkers in a sample obtained from the individual may include further detecting the presence of aneuploidy in a sample obtained from the individual (for example, the same sample or two or more different samples from the individual).

[140] In some embodiments, the methods provided here for treating an individual who has been diagnosed or identified as having a disease (for example, cancer) or who has been identified as being at risk (for example, increased risk) of having or Developing a disease (for example, cancer) includes detecting the presence of one or more members of a single class of biomarkers in one or more samples obtained from an individual (for example, genetic biomarkers or protein biomarkers). In some embodiments, the methods provided here for treating an individual who has been diagnosed or identified as having a disease (for example, cancer) or who has been identified as being at risk (for example, increased risk) of having or developing a disease (eg, cancer) include detecting the presence of aneuploidy in one or more samples obtained from an individual (for example, genetic biomarkers or protein biomarkers). In some embodiments, the methods provided here for treating an individual who has been diagnosed or identified as having a disease (for example, cancer) or who has been identified as being at risk (for example, increased risk) of having or developing a disease (eg cancer) includes detecting the presence of one or more members of a single class of biomarkers in one or more samples obtained from an individual (eg, genetic biomarkers or protein biomarkers) and detecting the presence of aneuploidy in one or more samples obtained from the individual. In some embodiments, the methods provided here for treating an individual who has been diagnosed or identified as having a disease (for example, cancer) or who has been identified as being at risk (for example, increased risk) of having or developing a disease (eg cancer) includes the detection of the presence of one or more members of two or more classes of biomarkers in one or more samples obtained from an individual (for example, genetic biomarkers and protein biomarkers). In some embodiments, the methods provided here for treating an individual who has been diagnosed or identified as having a disease (eg, cancer) or who has been identified as being at risk (eg, increased risk) of having or developing a disease (for example, cancer) includes detecting the presence of one or more members of two or more classes of biomarkers in one or more samples obtained from an individual (for example, genetic biomarkers and protein biomarkers) and detect the presence of aneuploidy in one or more samples obtained from the individual.

[141] In some embodiments, a single sample obtained from an individual can be tested for the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy, and the individual can be diagnosed or identified as having a disease (for example, cancer) or as being at risk (for example, increased risk) of having or developing a disease (for example, cancer) and / or the individual can be treated when the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy is detected. Alternatively, two or more samples can be obtained from an individual and each of the two or more samples can be tested individually to detect the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy , and the individual can be diagnosed or identified as being at risk (for example, increased risk) of having or developing a disease (for example, cancer) and / or the individual can be treated when the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy is detected.

As a non-limiting example, a first sample obtained from an individual can be tested for the presence of one or more members of a first class of biomarkers (for example, genetic biomarkers) and a second sample obtained from individual can be tested for the presence of one or more members of a second class of biomarkers in which the individual is diagnosed or identified as having a disease (eg cancer) or being at risk (eg increased risk) ) of having or developing a disease (eg cancer) and / or the individual can be treated when the presence of one or more members of the first class of biomarkers is detected and / or the presence of one or more members of the second class of biomarkers biomarkers (for example, when the presence of one or more members of the class of biomarkers is detected and the presence of one or more members of the second class of biomarkers is detected). As another non-limiting example, a first sample obtained from an individual can be tested for the presence of one or more members of a class of biomarkers (for example, genetic biomarkers or protein biomarkers) and a second sample obtained of the individual can be tested for the presence of aneuploidy, in which the individual is diagnosed or identified as having a disease (for example, cancer) or as being at risk (for example, increased risk) of having or developing a disease (eg cancer) and / or the individual is treated when the presence of one or more members of the class of biomarkers is detected and / or the presence of aneuploidy is detected (for example, when the presence of one or more members of the class of biomarkers is detected and the presence of aneuploidy is detected). As another non-limiting example, a first sample obtained from an individual can be tested to detect the presence of one or more members of a first class of biomarkers (for example, genetic biomarkers) and to detect the presence of one or more members a second class of biomarkers (for example, protein biomarkers), while a second sample obtained from the individual can be tested for the presence of aneuploidy, in which the individual is diagnosed or identified as having a disease (for example, cancer) or as being at risk (eg increased risk) of having or developing a disease (eg cancer) and / or the individual is treated when the presence of one or more members of the first class of biomarkers is detected, the presence of one or more members of the second class of biomarkers is detected and / or the presence of aneuploidy is detected (for example, when the presence of one or more members of the first class of biomarkers is detected, the presence of one or more members of the second class of biomarkers is detected and the presence of aneuploidy is detected). As another non-limiting example, a first sample obtained from an individual can be tested to detect the presence of one or more members of a first class of biomarkers (for example, genetic or protein biomarkers) and to detect the presence of aneuploidy, while a second sample obtained from the individual can be tested for the presence of one or more members of a second class of biomarkers (for example, a class of biomarkers different from the first class tested in the first sample), in which the individual is diagnosed or identified as having a disease (for example, cancer) or as being at risk (for example, increased risk) of having or developing a disease (for example, cancer) and / or the individual is treated when the presence of one or more members of the first class of biomarkers is detected, the presence of one or more members of the second class of biomarkers is detected, and / or the presence of aneuploidy is detected (for example, when the presence of one or more members of the first class of biomarkers is detected, the presence of one or more members of the second class of biomarkers is detected and the presence of aneuploidy is detected).

[142] In some treatment modalities of an individual who has been diagnosed or identified as having a disease or who has been identified as being at risk (for example, increased risk) of having or developing a disease by detecting one or more members (for example, example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more members) of one or more classes biomarkers and / or the presence of aneuploidy in a sample obtained from the individual, the treatment is any of the various therapeutic interventions disclosed here, including, without limitation, chemotherapy, neoadjuvant chemotherapy, radiotherapy, hormonal therapy, cytotoxic therapy, immunotherapy therapy, adoptive T-cell therapy (for example, chimeric antigen receptors and / or T-cells with wild-type or modified T-cell receptors), targeted therapy, such as administration of kinase inhibitors (eg, kinase inhibitors that target a specific genetic lesion, with translocation or mutation), (for example, a kinase inhibitor, an antibody, a bispecific antibody), signal transduction inhibitors, bispecific antibodies or antibody fragments (for example, BiTEs), monoclonal antibodies, spot inhibitors immune check, surgery (for example, surgical resection) or any combination of the above. In some modalities in which the disease is cancer, a therapeutic intervention reduces the severity of the cancer, reduces a symptom of the cancer and / or reduces the number of cancer cells present in the individual.

[143] In some treatment modalities of an individual who has been diagnosed or identified as having a disease or who has been identified as being at risk (for example, increased risk) of having or developing a disease (for example, by any of the various methods described here), the individual is also identified as an individual who will or will likely respond to that treatment. In some treatment modalities of an individual who has been diagnosed or identified as having a disease or who has been identified as being at risk (for example, increased risk) of having or developing a disease (for example, by any of the various methods described here), the individual is also identified as a candidate for additional diagnostic tests (for example, before treatment is administered and / or after treatment is administered to determine the effect of that treatment and / or whether the individual is a candidate for other administrations of the same or different treatment). In some treatment modalities of an individual who has been diagnosed or identified as having a disease or who has been identified as being at risk (for example, increased risk) of having or developing a disease (for example, by any of the various forms described here), the individual is also identified as a candidate for increased monitoring (for example, before treatment administration and / or after treatment administration to determine the effect of that treatment and / or whether the individual is a candidate additional administrations of the same treatment or one of a different treatment). Method of identifying a treatment

[144] Methods and materials are also provided here to identify a treatment for an individual who has been diagnosed or identified as having a disease (for example, cancer) or who has been identified as being at risk (for example, increased risk) of having or developing a disease (for example, cancer) by detecting one or more members (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more members) of one or more classes of biomarkers and / or the presence of aneuploidy in a sample obtained from the individual. In some modalities to identify a treatment for an individual who has been diagnosed or identified as having a disease (for example, cancer) or who has been identified as being at risk (for example, increased risk) of having or developing a disease (for example, cancer), the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy are tested simultaneously (for example, in a test procedure, including modalities in which the test procedure itself may include several different systems test methods). In some ways to identify a treatment for an individual who has been diagnosed or identified as having a disease (eg, cancer) or who has been identified as being at risk (eg, increased risk) of having or developing a disease (eg cancer), the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy are tested sequentially (for example, in two or more different test procedures conducted at two or more different points in time, including modalities in which the test procedure itself may include several different systems test methods). In some modalities to identify a treatment for an individual who has been diagnosed or identified as having a disease (for example, cancer) or who has been identified as being at risk (for example, increased risk) of having or developing a disease (for example, cancer) that include simultaneous or sequential tests (or both) for the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy, the test can be performed on a single sample or on two or more different samples (for example, two or more different samples obtained from the same individual).

[145] Any of the various detection methods described here (see, for example, the sections entitled "Biomark detection

genetic pain "," Detection of protein biomarkers "and" Detection of aneuploidy ") can be used to detect the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy in a sample obtained from an individual.

In some embodiments, the one or more members of one or more classes of biomarkers and / or one or more classes of biomarkers are associated with a disease in an individual.

In some modalities, aneuploidy is associated with a disease in an individual.

In some modalities, the disease is cancer (for example, any of the various types of cancer described here). In some modalities, one or more members are members of a class of genetic biomarkers.

In some embodiments, the one or more members are members of a class of protein biomarkers.

In some modalities, methods that include identifying a treatment for an individual who has been diagnosed or identified as having a disease (for example, cancer) or who has been identified as being at risk (for example, increased risk) of having or Developing a disease (for example, cancer) by detecting the presence of one or more members of one or more classes of biomarkers in a sample obtained from the individual also includes detecting the presence of aneuploidy in a sample obtained from the individual.

In some modalities, methods that include identifying a treatment for an individual who has been diagnosed or identified as having a disease (eg, cancer) or who has been identified as being at risk (eg, increased risk) of having or developing a disease (eg, cancer) detecting the presence of one or more members of a class of genetic biomarkers in a sample obtained from the individual may further include detecting the presence of aneuploidy in a sample obtained from the individual (for example, the same sample or two different samples from the individual). In some modalities, methods that include identifying a treatment for an individual who has been diagnosed or identified as having a disease (for example, cancer) or who has been identified as being at risk (for example, increased risk) of having or developing a disease (eg, cancer) by detecting the presence of one or more members of a class of protein biomarkers in a sample obtained from the individual may further include detecting the presence of aneuploidy in a sample obtained from the individual (eg, the same sample or two different samples from the individual). In some modalities, methods that include identifying a treatment for an individual who has been diagnosed or identified as having a disease (for example, cancer) or who has been identified as being at risk (for example, increased risk) of having or developing a disease (for example, cancer) detecting the presence of one or more members of a class of genetic biomarkers and detecting the presence of one or more members of a class of protein biomarkers in a sample obtained from the individual can further include detecting the presence of aneuploidy in a sample obtained from the individual (for example, the same sample or two or more different samples from the individual).

[146] In some embodiments, the methods provided here to identify a treatment for an individual who has been diagnosed or identified as having a disease (eg, cancer) or who has been identified as being at risk (eg, increased risk) of having or developing a disease (eg, cancer) includes detecting the presence of one or more members of a single class of biomarkers in one or more samples obtained from an individual (eg, genetic biomarkers or protein biomarkers) . In some embodiments, the methods provided here to identify a treatment for an individual who has been diagnosed or identified as having a disease (eg, cancer) or who has been identified as being at risk (eg, increased risk) of having or developing a disease (eg cancer) includes detecting the presence of aneuploidy in one or more samples obtained from an individual (eg, genetic biomarkers or protein biomarkers). In some embodiments, the methods provided here to identify a treatment for an individual who has been diagnosed or identified as having a disease (eg, cancer) or who has been identified as being at risk (eg, increased risk) of having or developing a disease (eg cancer) include detecting the presence of one or more members of a single class of biomarkers in one or more samples obtained from an individual (eg, genetic biomarkers or protein biomarkers) and detecting the presence of aneuploidy in one or more samples obtained from the individual. In some embodiments, the methods provided here to identify a treatment for an individual who has been diagnosed or identified as having a disease (eg, cancer) or who has been identified as being at risk (eg, increased risk) of having or developing a disease (for example, cancer) includes detecting the presence of one or more members of two or more classes of biomarkers in one or more samples obtained from an individual (for example, genetic biomarkers and protein biomarkers) . In some modalities, the methods provided here to identify a treatment for an individual who has been diagnosed or identified as having a disease (eg, cancer) or who has been identified as being at risk (eg, increased risk) of having or developing a disease (for example, cancer) includes detecting the presence of one or more members of two or more classes of biomarkers in one or more samples obtained from an individual (for example, genetic biomarkers and protein biomarkers) and detecting the presence of aneuploidy in one or more samples obtained from the individual.

[147] In some embodiments, a single sample obtained from an individual can be tested for the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy, and the individual can be diagnosed or identified as having a disease (for example, cancer) or as being at risk (for example, increased risk) of having or developing a disease (for example, cancer) and / or a treatment for the individual can be identified when the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy is detected.

Alternatively, two or more samples can be obtained from an individual and each of the two or more samples can be individually tested for the presence of one or more members of one or more classes of biomarkers and / or the presence of aneu - ploidy, and the individual can be diagnosed or identified as being at risk (for example, increased risk) of having or developing a disease (for example, cancer) and / or a treatment for the individual can be identified when the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy is detected.

As a non-limiting example, a first sample obtained from an individual can be tested for the presence of one or more members of a first class of biomarkers (for example, genetic biomarkers) and a second sample obtained from the individual can be tested to detect the presence of one or more members of a second class of biomarkers in which the individual is diagnosed or identified as having a disease (eg cancer) or being at risk (eg increased risk) of having or developing a disease ( eg cancer) and / or a treatment for the individual can be identified when the presence of one or more members of the first class of biomarkers is detected and / or the presence of one or more members of the second class of biomarkers (for example, when the presence of one or more members of the class of biomarkers is detected and the presence of one or more members of the second class of biomarkers is detected). As another non-limiting example, a first sample obtained from an individual can be tested for the presence of one or more members of a class of biomarkers (for example, genetic biomarkers or protein biomarkers) and a second sample obtained from the individual can be tested for the presence of aneuploidy, in which the individual is diagnosed or identified as having a disease (for example, cancer) or as being at risk (for example, increased risk) of having or developing a disease (for example, cancer) and / or a treatment for the individual can be identified when the presence of one or more members of the class of biomarkers is detected and / or the presence of aneuploidy is detected (for example, when the presence of one or more members of the class of biomarkers is detected and the presence of aneuploidy is detected). As another non-limiting example, a first sample obtained from an individual can be tested to detect the presence of one or more members of a first class of biomarkers (for example, genetic biomarkers) and to detect the presence of one or more more members of a second class of biomarkers (for example, protein biomarkers), while a second sample obtained from the individual can be tested for the presence of aneuploidy, in which the individual is diagnosed or identified as having a disease (for example, cancer) or as being at risk (for example, increased risk) of having or developing a disease (for example, cancer) and / or treatment for the individual is identified when the presence of one or more members of the first class of biomarkers are detected, the presence of one or more members of the second class of biomarkers is detected and / or the presence of aneuploidy is detected (for example, when the presence of one or more members of the first the class of biomarkers is detected, the presence of one or more members of the second class of biomarkers is detected and the presence of aneuploidy is detected). As another non-limiting example, a first sample obtained from an individual can be tested to detect the presence of one or more members of a first class of biomarkers (for example, genetic or protein biomarkers) and to detect the presence of aneuploidy, while a second sample obtained from the individual can be tested for the presence of one or more members of a second class of biomarkers (for example, a class of biomarkers different from the first class tested in the first sample), in that the individual is diagnosed or identified as having a disease (for example, cancer) or as being at risk (for example, increased risk) of having or developing a disease (for example, cancer) and / or treatment for the individual can be identified when the presence of one or more members of the first class of biomarkers is detected, the presence of one or more members of the second class of biomarkers is detected, and / or the presence of ane uploidy is detected (for example, when the presence of one or more members of the first class of biomarkers is detected, the presence of one or more members of the second class of biomarkers is detected and the presence of aneuploidy is detected).

[148] In some modalities to identify a treatment for an individual who has been diagnosed or identified as having a disease or who has been identified as being at risk (for example, increased risk) of having or developing a disease by detecting one or more members (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more) from one or more classes of biomarkers and / or the presence of aneuploidy in a sample obtained from the individual, the treatment identified is any of the various therapeutic interventions disclosed here, including, without limitation, chemotherapy, neoadjuvant chemotherapy, radiotherapy, hormonal therapy, cytotoxic therapy , immunotherapy, adoptive T cell therapy (eg chimeric antigen receptors and / or T cells with wild-type or modified T cell receptors), targeted therapy, such as administration of kinase inhibitors (eg, kinase inhibitors that target a specific genetic damage, such as translocation or mutation), (for example, a kinase inhibitor, an antibody, a bispecific antibody), signal transduction inhibitors, bispecific antibodies or antibody fragments (for example, BiTEs) , monoclonal antibodies, immune checkpoint inhibitors, surgery (for example, surgical resection) or any combination of the above. In some modalities in which the disease is cancer, an identified therapeutic intervention reduces the severity of the cancer, reduces a symptom of the cancer and / or reduces the number of cancer cells present in the individual.

[149] In some modalities to identify a treatment for an individual who has been diagnosed or identified as having a disease or who has been identified as being at risk (for example, increased risk) of having or developing a disease (for example, by anyone) of the various methods described here), the individual is also identified as an individual who will respond or likely respond to this treatment. In some modalities to identify a treatment for an individual who has been diagnosed or identified as having a disease or who has been identified as being at risk (for example, increased risk) of having or developing a disease (for example, by any variety of methods here described), the individual is also identified as a candidate for additional diagnostic tests. In some modalities to identify a treatment for an individual who has been diagnosed or identified as having a disease or who has been identified as being at risk (for example, increased risk) of having or developing a disease (for example, by anyone) one of the several methods described here), the individual is also identified as a candidate for increased monitoring. In some modalities to identify a treatment for an individual who has been diagnosed or identified as having a disease or who has been identified as being at risk (for example, increased risk) of having or developing a disease (for example, for any variety of illnesses). methods described here), the individual is also administered with a treatment (for example, any of the various therapeutic interventions described here). Identifying an individual who will or will likely respond to treatment

[150] Methods and materials are also provided here to identify an individual who will respond or is likely to respond to a treatment by detecting one or more members (eg 1, 2, 3, 4, 5, 6, 7, 8, 9 , 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more members) of one or more classes of biomarkers and / or presence of aneuploidy in a sample obtained from the individual. In some modalities to identify an individual who will or will likely respond to a treatment, the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy are tested simultaneously (for example, in a test procedure, including modalities in which the test procedure itself may include several test methods other than systems). In some modalities to identify an individual who will or will likely respond to a treatment, the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy are tested sequentially (for example, in two or more different test procedures conducted at two or more different points in time, including modalities in which the test procedure itself may include several different system test methods). In some modalities to identify an individual who will or probably will respond to a treatment that includes simultaneous or sequential tests (or both) for the presence of one or more members of one or more classes of biomarkers and / or presence of aneuploidy, the test can be performed on a single sample or on two or more different samples (for example, two or more different samples obtained from the same individual).

[151] Any of the various detection methods described here (see, for example, the sections entitled "Detection of genetic biomarkers", "Detection of protein biomarkers" and "Aneuploidy detection") can be used to detect the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy in a sample obtained from an individual. In some embodiments, the one or more members of one or more classes of biomarkers and / or one or more classes of biomarkers are associated with a disease in an individual. In some modalities, aneuploidy is associated with a disease in an individual. In some modalities, the disease is cancer (for example, any of the various types of cancer described here). In some modalities, one or more members are members of a class of genetic biomarkers. In some embodiments, the one or more members are members of a class of protein biomarkers. In some modes, methods that include identifying an individual who will respond or likely respond to a treatment by detecting the presence of one or more members of one or more classes of biomarkers in a sample obtained from the individual include detecting the presence of aneuploidy in a sample obtained from the individual. For example, methods that include

Identifying an individual who will or will likely respond to a treatment by detecting the presence of one or more members of a class of genetic biomarkers in a sample obtained from the individual may also include detecting the presence of aneuploidy in a sample obtained from the individual ( for example, the same sample or two different samples from the individual). As another example, methods that include identifying an individual who will or will likely respond to a treatment by detecting the presence of one or more members of a class of protein biomarkers in a sample obtained from the individual may further include detecting the presence of aneuploidy in a sample obtained from the individual (for example, the same sample or two different samples from the individual). In some modalities, methods that include identifying an individual who will or will likely respond to a treatment by detecting the presence of one or more members of a class of genetic biomarkers and detecting the presence of one or more members of a class of protein biomarkers in a sample obtained from the individual may further include the detection of the presence of aneuploidy in a sample obtained from the individual (for example, the same sample or two or more different samples from the individual).

[152] In some embodiments, the methods provided here to identify an individual who will or will likely respond to a treatment include detecting the presence of one or more members of a single class of biomarkers in one or more samples obtained from an individual (for example , genetic biomarkers or protein biomarkers). In some embodiments, the methods provided here to identify an individual who will or will likely respond to a treatment include detecting the presence of aneuploidy in one or more samples obtained from an individual (for example, genetic biomarkers or protein biomarkers) . In some embodiments, the methods provided here to identify an individual who will respond or likely respond to a treatment include detecting the presence of one or more members of a single class of biomarkers in one or more samples obtained from an individual (for example, genetic biomarkers or protein biomarkers) and detect the presence of aneuploidy in one or more samples obtained from the individual. In some modalities, the methods provided here to identify an individual who will or will likely respond to a treatment include detecting the presence of one or more members of two or more classes of biomarkers in one or more samples obtained from a individual (for example, genetic biomarkers and protein biomarkers). In some embodiments, the methods provided here to identify an individual who will or will likely respond to a treatment include detecting the presence of one or more members of two or more classes of biomarkers in one or more samples obtained from an individual (for example , genetic biomarkers and protein biomarkers) and detect the presence of aneuploidy in one or more samples obtained from the individual.

[153] In some embodiments, a single sample obtained from an individual can be tested for the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy, and the individual can be diagnosed or identified as having a disease (for example, cancer) or as being at risk (for example, increased risk) of having or developing a disease (for example, cancer) and / or the individual can be identified as an individual who will or will likely respond to a treatment when the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy is detected. Alternatively, two or more samples can be obtained from an individual and each of the two or more samples can be tested individually to detect the presence of one or more members of one or more classes of biomarkers and / or the presence aneuploidy, and the individual can be diagnosed or identified as being at risk (for example, increased risk) of having or developing a disease (for example, cancer) and / or the individual can be identified as an individual who will respond or likely to respond to treatment when the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy is detected.

As a non-limiting example, a first sample obtained from an individual can be tested for the presence of one or more members of a first class of biomarkers (for example, genetic biomarkers) and a second sample obtained from the individual can be tested to detect the presence of one or more members of a second class of biomarkers in which the individual is diagnosed or identified as having a disease (eg, cancer) or as being at risk (eg, increased risk) of having or developing a disease (for example, cancer) and / or the individual is identified as an individual who will or will likely respond to a treatment when the presence of one or more members of the first class of biomarkers is detected and / or the presence of one or more more members of the second class of biomarkers (for example, when the presence of one or more members of the class of biomarkers is detected and the presence of one or more members of the second class of biomarkers adores is detected). As another non-limiting example, a first sample obtained from an individual can be tested for the presence of one or more members of a class of biomarkers (for example, genetic biomarkers or protein biomarkers) and a second sample obtained from the individual can be tested for the presence of aneuploidy, in which the individual is diagnosed or identified as having a disease (for example, cancer) or as being at risk (for example, increased risk) of having or developing a disease (for example, cancer) and / or individual is identified as an individual who will or will likely respond to a treatment when the presence of one or more members of the class of biomarkers is detected and / or the presence of aneuploidy is detected (for example, when the presence of one or more members of the class of biomarkers is detected and the presence of aneuploidy is detected). As another non-limiting example, a first sample obtained from an individual can be tested to detect the presence of one or more members of a first class of biomarkers (for example, genetic biomarkers) and to detect the presence of one or more members of a second class of biomarkers (for example, protein biomarkers), while a second sample obtained from the individual can be tested for the presence of aneuploidy, in which the individual is diagnosed or identified as having a disease (for example, cancer) or as being at risk (for example, increased risk) of having or developing a disease (for example, cancer) and / or the individual is identified as an individual who will respond or is likely to respond to treatment when the presence of one or more members of the first class of biomarkers is detected, the presence of one or more members of the second class of biomarkers is detected and / or the presence of aneuploidy is detected (for example o, when the presence of one or more members of the first class of biomarkers is detected, the presence of the one or more members of the second class of biomarkers is detected and the presence of aneuploidy is detected). As another non-limiting example, a first sample obtained from an individual can be tested to detect the presence of one or more members of a first class of biomarkers (for example, genetic or protein biomarkers) and to detect the presence of aneuploidy, while a second sample obtained from the individual can be tested for the presence of one or more members of a second class of biomarkers (for example, a class of biomarkers different from the first class tested in the first sample), where the individual is diagnosed or identified as having a disease (for example, cancer) or as being at risk (for example, increased risk) of having or developing a disease (for example, cancer) and / or the individual is identified as an individual duo that will respond or likely respond to treatment when the presence of one or more members of the first class of biomarkers is detected, the presence of one or more members of the second class of biomarkers biomarkers is detected, and / or the presence of aneuploidy is detected (for example, when the presence of one or more members of the first class of biomarkers is detected, the presence of one or more members of the second class of biomarkers is detected and the presence of aneuploidy is detected).

[154] In some modalities to identify an individual who will respond or is likely to respond to treatment by detecting one or more members (eg, increased risk) of having or developing a disease by detecting one or more members (eg, 1, 2 , 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more members) of one or more classes of biomarkers and / or the presence of aneuploidy in a sample obtained from the individual, the individual is identified as an individual who will or probably will respond to a treatment that is one of the various therapeutic interventions disclosed here, including, without limitation, chemotherapy, neoadjuvant chemotherapy, radiotherapy, hormone therapy, cytotoxic therapy, immunotherapy, adoptive T cell therapy (for example, chimeric antigen receptors and / or T cells with wild-type or modified T cell receptors), targeted therapy, such as administration of kinase inhibitors (for example, kinase inhibitors that target a specific genetic lesion, such as translocation or mutation), (for example, a kinase inhibitor, an antibody, a bispecific antibody),

signal transduction inhibitors, bispecific antibodies or antibody fragments (eg, BiTEs), monoclonal antibodies, immune checkpoint inhibitors, surgery (eg, surgical resection) or any combination of the above. In some modalities where the disease is cancer, an individual who is identified as an individual who will respond or is likely to respond to an identified therapeutic intervention is identified as an individual in whom the therapeutic intervention will reduce or likely reduce the severity of the cancer, reduce a symptom cancer and / or will reduce the number of cancer cells present in the individual.

[155] In some modalities, an individual identified as an individual who will respond or is likely to respond to a treatment (for example, using any of the various methods described here) is also identified for additional diagnostic tests. In some modalities, an individual identified as an individual who will respond or is likely to respond to a treatment (for example, using any of the various methods described here) is also identified for increased monitoring. In addition or alternatively, an individual identified as an individual who will respond or is likely to respond to a treatment (for example, using any of the various methods described here) is also administered with a treatment (for example, any of the various therapeutic interventions). described here). Methods of identifying an individual as a candidate for additional diagnostic tests

[156] Methods and materials are also provided here to identify an individual as a candidate for additional diagnostic tests by detecting one or more members (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more members) of one or more classes of biomarkers and / or presence of aneuploidy in a sample obtained from the individual. In some modalities to identify an individual as a candidate for additional diagnostic tests, the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy are tested simultaneously (for example, in a testing, including modalities in which the test procedure itself may include several test methods other than systems). In some modalities to identify an individual as a candidate for additional diagnostic tests, the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy are tested sequentially (for example, in two or more different test procedures conducted at two or more different points in time, including modalities in which the test procedure itself may include several different system test methods). In some modalities to identify an individual as a candidate for additional diagnostic tests that include simultaneous or sequential tests (or both) for the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy, the test can be performed on a single sample or on two or more different samples (for example, two or more different samples obtained from the same individual).

[157] Any of the various detection methods described here (see, for example, the sections entitled "Detection of genetic biomarkers", "Detection of protein biomarkers" and "Detection of aneuploidy") can be used to detect the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy in a sample obtained from an individual. In some modalities, the one or more members of one or more classes of biomarkers and / or one or more classes of biomarkers are associated

to a disease in an individual.

In some modalities, aneuploidy is associated with a disease in an individual.

In some modalities, the disease is cancer (for example, any of the several types of cancer described here). In some embodiments, the one or more members are members of a class of genetic biomarkers.

In some modalities, the one or more members are members of a class of protein biomarkers.

In some modalities, methods that include identifying an individual as a candidate for additional diagnostic tests by detecting the presence of one or more members of one or more classes of biomarkers in a sample obtained from the individual include also detecting the presence of aneuploidy in a sample obtained from the individual.

For example, methods that include identifying an individual as a candidate for additional diagnostic tests by detecting the presence of one or more members of a class of genetic biomarkers in a sample obtained from the individual may further include detecting the presence of aneuploidy in a sample taken from the individual (for example, the same sample or two different samples from the individual). As another example, methods that include identifying an individual as a candidate for additional diagnostic tests by detecting the presence of one or more members of a class of protein biomarkers in a sample obtained from the individual may further include detecting the presence of aneuploidy in a sample obtained from the individual (for example, the same sample or two different samples from the individual). In some modalities, methods that include identifying an individual as a candidate for additional diagnostic tests by detecting the presence of one or more members of a class of genetic biomarkers and detecting the presence of one or more members of a class Protein biomarkers in a sample obtained from the individual may also include the detection of the presence of aneuploidy in a sample obtained from the individual (for example, the same sample or two or more different samples from the individual).

[158] In some embodiments, the methods provided here to identify an individual as a candidate for further diagnostic tests include detecting the presence of one or more members of a single class of biomarkers in one or more samples obtained from an individual ( for example, genetic biomarkers or protein biomarkers). In some embodiments, the methods provided here to identify an individual as a candidate for additional diagnostic tests include detecting the presence of aneuploidy in one or more samples obtained from an individual (for example, genetic biomarkers or protein biomarkers). In some embodiments, the methods provided here to identify an individual as a candidate for additional diagnostic tests include detecting the presence of one or more members of a single class of biomarkers in one or more samples obtained from an individual (for example, genetic biomarkers or protein biomarkers) and detect the presence of aneuploidy in one or more samples obtained from the individual. In some embodiments, the methods provided here to identify an individual as a candidate for additional diagnostic tests include detecting the presence of one or more members of two or more classes of biomarkers in one or more samples obtained from an individual ( for example, genetic biomarkers and protein biomarkers). In some embodiments, the methods provided here to identify an individual as a candidate for additional diagnostic tests include detecting the presence of one or more members of two or more classes of biomarkers in one or more samples obtained from an individual ( for example, genetic biomarkers and protein biomarkers) and to detect the presence of aneuploidy in one or more samples obtained from the individual.

[159] In some embodiments, a single sample obtained from an individual can be tested for the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy, and the individual can be diagnosed or identified as having a disease (for example, cancer) or as being at risk (for example, increased risk) of having or developing a disease (for example, cancer) and / or the individual can be identified as an individual who is a candidate for HIV testing additional diagnoses when the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy is detected.

Alternatively, two or more samples can be obtained from an individual and each of the two or more samples can be tested individually to detect the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy , and the individual can be diagnosed or identified as being at risk (for example, increased risk) of having or developing a disease (for example, cancer) and / or the individual can be identified as an individual who is a candidate to additional diagnostic tests when the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy is detected.

As a non-limiting example, a first sample obtained from an individual can be tested to detect the presence of one or more members of a first class of biomarkers (for example, genetic biomarkers) and a second sample obtained from the individual can be tested to detect the presence of one or more members of a second class of biomarkers (for example, protein biomarkers), in which the individual is diagnosed or identified as having a disease (for example, cancer) or as being at risk ( for example, increased risk) of having or developing a disease (for example, cancer) and / or the individual is identified as an individual who is a candidate for additional diagnostic tests when the presence of one or more members of the first class of biomarkers is detected and / or the presence of one or more members of the second class of biomarkers (for example, when the presence of one or more members of the class of biomarkers is detected and the presence of a or more members of the second class of biomarkers is detected). As another non-limiting example, a first sample obtained from an individual can be tested for the presence of one or more members of a class of biomarkers (for example, genetic biomarkers or protein biomarkers) and a second sample obtained from the individual can be tested to detect the presence of aneuploidy, in which the individual is diagnosed or identified as having a disease (eg, cancer) or being at risk (eg, increased risk) of having or developing a disease (eg, cancer) and / or individual is identified as an individual who is a candidate for additional diagnostic tests when the presence of one or more members of the class of biomarkers is detected and / or the presence of aneuploidy is detected (for example, when the presence of one or more members of the class of biomarkers is detected and the presence of aneuploidy is detected). As another non-limiting example, a first sample obtained from an individual can be tested to detect the presence of one or more members of a first class of biomarkers (for example, genetic biomarkers) and to detect the presence of one or more members of a second class of biomarkers (for example, protein biomarkers), while a second sample obtained from the individual can be tested for the presence of aneuploidy, in which the individual is diagnosed or identified as having a disease (for example, cancer) or as being at risk (for example, increased risk) of having or developing a disease (for example, cancer) and / or the individual is identified as a candidate for further diagnostic tests when the presence of one or more members of the first class of biomarkers is detected, the presence of one or more members of the second class of biomarkers is detected and / or the presence of aneuploidy is detected (for example, when the presence that of one or more members of the first class of biomarkers is detected, the presence of one or more members of the second class of biomarkers is detected and the presence of aneuploidy is detected). As another non-limiting example, a first sample obtained from an individual can be tested to detect the presence of one or more members of a first class of biomarkers (for example, genetic or protein biomarkers) and to detect the presence of aneuploidy , while a second sample obtained from the individual can be tested for the presence of one or more members of a second class of biomarkers (for example, a class of biomarkers different from the first class tested in the first sample), where the individual is diagnosed or identified as having a disease (for example, cancer) or as being at risk (for example, increased risk) of having or developing a disease (for example, cancer) and / or the individual is identified as an individual candidate for additional diagnostic tests when the presence of one or more members of the first class of biomarkers is detected, the presence of one or more members of the second class of biomarkers es is detected and / or the presence of aneuploidy (for example, when the presence of one or more members of the first class of biomarkers is detected, the presence of one or more members of the second class of biomarkers is detected and the presence of aneuploidy is detected detected).

[160] In some modalities to identify an individual as a candidate for further diagnostic tests, detecting one or more members (for example, increased risk) of having or developing a disease by detecting one or more members (for example, 1, 2, 3 , 4, 5, 6,

7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more members) of one or more classes of biomarkers and / or the presence of aneuploidy in a sample obtained from individual, the individual is for any one of several types of additional diagnostic tests disclosed herein, including, without limitation, a scan (for example, a computed tomography (CT) scan, a CT angiotomography (CTA), an esophagogram (a swallowing of Barium), a Barium enema, magnetic resonance imaging (MRI), a PET scan, a positron emission tomography and a computed tomography scan (PET-CT), an ultrasound (for example, an endobronchial ultrasound, a endoscopic ultrasound), an X-ray or a DEXA scan) or a physical exam (for example, an anoscopy, a bronchoscopy (for example, an autofluorescence bronchoscopy, a white light bronchoscopy, navigation bronchoscopy), a colonoscopy, a digital breast tomosynthesis, end cholangiopancreatography retrograde oscopic examination (ERCP), an ensophagogastroduodenoscopy, a mammogram, a Pap test or a pelvic exam).

[161] In some modalities, an individual identified as a candidate for additional diagnostic tests (for example, using any of the several methods described here) is also identified as a candidate for increased monitoring. In addition or alternatively, an individual identified as a candidate for additional diagnostic tests (for example, using any of the various methods described herein) is also identified as an individual who will or will likely respond to treatment. Additionally or alternatively, an individual identified as a candidate for additional diagnostic tests (for example, using any of the various methods described here) is also administered with a treatment (for example, any of the various therapeutic interventions described here) ).

Methods of identifying an individual as a candidate for increased monitoring

[162] Methods and materials are also provided here to identify an individual as a candidate for increased monitoring by detecting one or more members (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more members) of one or more classes of biomarkers and / or presence of aneuploidy in a sample obtained from the individual. In some modalities to identify an individual as a candidate for increased monitoring, the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy are tested simultaneously (for example, in a test procedure, including modalities in which the test procedure itself may include several different system test methods). In some ways to identify an individual as a candidate for increased monitoring, the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy are tested sequentially (for example, in two or more different test procedures conducted at two or more different points in time, including modalities in which the test procedure itself may include several different systems test methods). In some ways to identify an individual as a candidate for increased monitoring that include simultaneous or sequential testing (or both) for the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy, the test can be performed on a single sample or on two or more different samples (for example, two or more different samples obtained from the same individual).

[163] Any of the various detection methods described here (see, for example, the sections entitled "Biomark detection

genetic pain "," Detection of protein biomarkers "and" Detection of aneuploidy ") can be used to detect the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy in a sample obtained from an individual.

In some embodiments, the one or more members of one or more classes of biomarkers and / or one or more classes of biomarkers are associated with a disease in an individual.

In some modalities, aneuploidy is associated with a disease in an individual.

In some modalities, the disease is cancer (for example, any of the various types of cancer described here). In some modalities, one or more members are members of a class of genetic biomarkers.

In some embodiments, the one or more members are members of a class of protein biomarkers.

In some modes, methods that include identifying an individual as a candidate for increased monitoring by detecting the presence of one or more members of one or more classes of biomarkers in a sample obtained from the individual also include detecting the presence of aneuploidy in a sample obtained from the individual.

For example, methods that include identifying an individual as a candidate for increased monitoring by detecting the presence of one or more members of a class of genetic biomarkers in a sample obtained from the individual may further include detecting the presence of aneuploidy in a sample obtained from the individual (for example, the same sample or two different samples from the individual). As another example, methods that include identifying an individual as a candidate for increased monitoring by detecting the presence of one or more members of a class of protein biomarkers in a sample obtained from the individual may further include detecting the presence of aneuploidy in a sample obtained from the individual (for example, the same sample or two different samples

individual). In some modalities, methods that include identifying an individual as a candidate for increased monitoring by detecting the presence of one or more members of a class of genetic biomarkers and detecting the presence of one or more members of a class of biomarkers of proteins in a sample obtained from the individual may further include detecting the presence of aneuploidy in a sample obtained from the individual (for example, the same sample or two or more different samples from the individual).

[164] In some modalities, the methods provided here to identify an individual as a candidate for increased monitoring include detecting the presence of one or more members of a single class of biomarkers in one or more samples obtained from an individual ( for example, genetic biomarkers or protein biomarkers). In some modalities, the methods provided here to identify an individual as a candidate for increased monitoring include detecting the presence of aneuploidy in one or more samples obtained from an individual (for example, genetic biomarkers or protein biomarkers). In some embodiments, the methods provided here to identify an individual as a candidate for increased monitoring include detecting the presence of one or more members of a single class of biomarkers in one or more samples obtained from an individual (for example, genetic biomarkers). - cos or biomarkers of proteins) and detect the presence of aneuploidy in one or more samples obtained from the individual. In some ways, the methods provided here to identify an individual as a candidate for increased monitoring include detecting the presence of one or more members of two or more classes of biomarkers in one or more samples obtained from an individual (for example, biomarking - genetic markers and protein biomarkers). In some modalities, the methods provided here to identify an individual as a candidate for increased monitoring include detecting the presence of one or more members of two or more classes of biomarkers in one or more samples obtained from an individual (for example, biomarking - genetic markers and protein biomarkers) and detect the presence of aneuploidy in one or more samples obtained from the individual.

[165] In some embodiments, a single sample obtained from an individual can be tested for the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy, and the individual can be diagnosed or identified as having a disease (for example, cancer) or as being at risk (for example, increased risk) of having or developing a disease (for example, cancer) and / or the individual can be identified as an individual who is a candidate for increased monitoring when the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy is detected. Alternatively, two or more samples can be obtained from an individual and each of the two or more samples can be tested individually to detect the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy , and the individual can be diagnosed or identified as being at risk (for example, increased risk) of having or developing a disease (for example, cancer) and / or the individual can be identified as an individual who is a candidate increased monitoring when the presence of one or more members of one or more classes of biomarkers and / or the presence of aneuploidy is detected. As a non-limiting example, a first sample obtained from an individual can be tested for the presence of one or more members of a first class of biomarkers (for example, genetic biomarkers) and a second sample obtained from the individual can be tested to detect the presence of one or more members of a second class of biomarkers (for example, protein biomarkers), in which the individual is diagnosed or identified as having a disease (for example, cancer) or as being at risk (for example , increased risk) of having or developing a disease (for example, cancer) and / or the individual is identified as an individual who is a candidate for increased monitoring when the presence of one or more members of the first class of biomarkers is detected and / or the presence of one or more members of the second class of biomarkers (for example, when the presence of one or more members of the class of biomarkers is detected and the presence of one or more members the second class of biomarkers is detected). As another non-limiting example, a first sample obtained from an individual can be tested for the presence of one or more members of a class of biomarkers (for example, genetic biomarkers or protein biomarkers) and a second sample obtained of the individual can be tested for the presence of aneuploidy, in which the individual is diagnosed or identified as having a disease (for example, cancer) or as being at risk (for example, increased risk) of having or developing a disease (eg, cancer) and / or individual is identified as an individual who is a candidate for increased monitoring when the presence of one or more members of the class of biomarkers is detected and / or the presence of aneuploidy is detected (for example, when the presence of one or more members of the class of biomarkers is detected and the presence of aneuploidy is detected). As another non-limiting example, a first sample obtained from an individual can be tested to detect the presence of one or more members of a first class of biomarkers (for example, genetic biomarkers) and to detect the presence of one or more members a second class of biomarkers (for example, protein biomarkers), while a second sample obtained from the individual can be tested for the presence of aneuploidy, in which the individual is diagnosed or identified as having a disease (for example, cancer) or as being at risk (eg increased risk) of having or developing a disease (eg cancer) and / or the individual is identified as a candidate for increased monitoring when the presence of one or more members of the first class of biomarkers is detected, the presence of one or more members of the second class of biomarkers is detected and / or the presence of aneuploidy is detected (for example, when the presence of one or more members of the first class of biomarkers is detected, the presence of one or more members of the second class of biomarkers is detected and the presence of aneuploidy is detected). As another non-limiting example, a first sample obtained from an individual can be tested to detect the presence of one or more members of a first class of biomarkers (for example, genetic or protein biomarkers) and to detect the presence of aneuploidy , while a second sample obtained from the individual can be tested to detect the presence of one or more members of a second class of biomarkers (for example, a class of biomarkers different from the first class tested in the first sample), in which the individual is diagnosed or identified as having a disease (for example, cancer) or as being at risk (for example, increased risk) of having or developing a disease (for example, cancer) and / or the individual is identified as an individual candidate for increased monitoring when the presence of one or more members of the first class of biomarkers is detected, the presence of one or more members of the second class of biomarkers is d and / or the presence of aneuploidy (for example, when the presence of one or more members of the first class of biomarkers is detected, the presence of one or more members of the second class of biomarkers is detected and the presence of aneuploidy is detected).

[166] In some modalities, an individual identified as a candidate for increased monitoring (for example, using any of the various methods described here) is also identified as a candidate for additional diagnostic tests. In addition or alternatively, an individual identified as a candidate for increased monitoring (for example, using any of the various methods described here) is also identified as an individual who will or will likely respond to treatment. In addition or alternatively, an individual identified as a candidate for increased monitoring (for example, using any of the various methods described here) is also administered with a treatment (for example, any of the various therapeutic interventions described here). Genetic biomarkers in combination with protein biomarkers

[167] In one aspect, methods and materials are provided here to detect the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of protein biomarkers in one or more samples obtained from an individual. In another aspect, methods and materials are provided here to diagnose or identify the presence of a disease in an individual (for example, identifying the individual as having cancer) by detecting the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of protein biomarkers in one or more samples obtained from the individual. In another aspect, methods and materials are provided here to identify an individual as being at risk (for example, increased risk) of having or developing a disease (for example, cancer) by detecting the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of protein biomarkers in one or more samples obtained from the individual.

In another aspect, methods and materials are provided here to treat an individual who has been diagnosed or identified as having a disease (for example, cancer) or who has been identified as being at risk (for example, increased risk) of having or developing a disease (for example, cancer) detecting the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of protein biomarkers in one or more samples obtained from the individual.

In another aspect, methods and materials are provided here to identify a treatment for an individual who has been diagnosed or identified as having a disease (eg cancer) or who has been identified as being at risk (eg increased risk) ) to have or develop a disease (for example, cancer) by detecting the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of protein biomarkers in one or more samples obtained from the individual .

In another aspect, methods and materials are provided here to identify an individual who will or will likely respond to a treatment by detecting the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of bio- protein markers in one or more samples obtained from the individual.

In another aspect, methods and materials are provided here to identify an individual as a candidate for additional diagnostic tests by detecting the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of biomarkers of proteins in one or more samples obtained from the individual.

In another aspect, methods and materials are provided here to identify an individual as a candidate for increased monitoring by detecting the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of biomarkers of proteins in one or more samples obtained from the individual.

[168] In some embodiments, the methods provided here that include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of protein biomarkers in one or more samples obtained of an individual provide high sensitivity in detecting or diagnosing cancer (for example, a high frequency or incidence of correctly identifying an individual as having cancer). In some embodiments, the methods provided here that include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of protein biomarkers in one or more samples obtained from an individual provide a sensitivity in the detection or diagnosis of cancer (for example, a high frequency or incidence of correctly identifying an individual as having cancer) that is superior to the sensitivity provided by the separate detection of the presence of one or more members a panel of genetic biomarkers or the presence of one or more members of a panel of protein biomarkers. In some embodiments, the methods and materials provided here include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of protein biomarkers in one or more samples obtained from an individual provide a sensitivity of at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 91 %, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98% , at least about 99% or higher. In some embodiments, methods and materials provided herein that include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of protein biomarkers in one or more samples obtained from an individual provide high sensitivity in detecting a single type of cancer.

In some modalities, methods and materials provided here that include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of protein biomarkers in one or more samples obtained from a provide high sensitivity in detecting two or more types of cancer.

Any of the various types of cancer can be detected using the methods and materials provided here (see, for example, the section entitled "Cancers"). In some modalities, cancers that can be detected using methods and materials that include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of biomarkers from proteins in one or more samples taken from an individual include pancreatic cancer.

In some modalities, cancers that can be detected using methods and materials that include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of protein biomarkers in one or more samples taken from an individual include liver cancer, ovarian cancer, esophageal cancer, stomach cancer, pancreatic cancer, colorectal cancer, lung cancer or breast cancer.

In some modalities, cancers that can be detected using methods and materials that include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of protein biomarkers in one or more samples obtained from an individual include cancers of the female reproductive system (for example, cancer of the cervix, cancer of the endometrium, ovarian cancer or cancer of the fallopian tubes). In some embodiments, cancers that can be detected using methods and materials that include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of protein biomarkers in one or more samples obtained from an individual include bladder cancer or urothelial carcinomas of the upper tract.

[169] In some embodiments, the methods provided here include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of protein biomarkers in one or more samples obtained of an individual provide high specificity in the detection or diagnosis of cancer (for example, a low frequency or incidence of incorrectly identifying an individual as having cancer when that individual does not have cancer). In some embodiments, the methods provided here that include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of protein biomarkers in one or more samples obtained from an individual provide a specificity in the detection or diagnosis of cancer (for example, a high frequency or incidence of correctly identifying an individual as having cancer) which is superior to the specificity provided by the separate detection of the presence of one or more members of a cancer panel. genetic biomarkers or the presence of one or more members of a panel of protein biomarkers. In some embodiments, the methods and materials provided herein that include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of protein biomarkers in one or more samples obtained from an individual provide a specificity of at least about 70%, at least about 75%,

at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99% or greater. As will be understood by those skilled in the art, a specificity of 99% means that only 1% of individuals who do not have cancer are incorrectly identified as having cancer. In some embodiments, the methods and materials provided herein that include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of protein biomarkers in one or more samples obtained from an individual they provide high specificity in detecting a single cancer (for example, there is a low probability of incorrectly identifying that individual as having this type of cancer). In some embodiments, the methods and materials provided here include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of protein biomarkers in one or more samples obtained from an individual provides high specificity in detecting two or more cancers (for example, there is a low probability of incorrectly identifying that individual as having these two or more types of cancer).

[170] In some embodiments, the methods provided here include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of protein biomarkers in one or more samples obtained of an individual include detecting the presence of: 1) one or more genetic biomarkers in one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 or 16) of the following genes: NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN,

FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A and / or GNAS and 2) one or more (for example, 1, 2, 3, 4, 5, 6, 7 or 8) of the following protein biomarkers: CA19-9 , CEA, HGF, OPN, CA125, prolactin, TIMP-1 and / or myeloperoxidase (MPO). In some embodiments, the methods provided here that include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of protein biomarkers in one or more samples obtained from an individual include detecting the presence 1) one or more genetic biomarkers in each of the following genes: NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A and GNAS, and 2 ) one or more (for example, 1, 2, 3, 4, 5, 6, 7 or 8) from the following protein biomarkers: CA19-9, CEA, HGF, OPN, CA125, prolactin, TIMP-1 and / or myeloperoxidase (MPO). In some embodiments, the methods provided here include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of protein biomarkers in one or more samples obtained from an individual include detecting the presence of: 1) one or more genetic biomarkers in one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 or 16) of the following genes: NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A and / or GNAS and 2) each of the following protein biomarkers : CA19 -9, CEA, HGF, OPN, CA125, prolactin, TIMP-1 and myeloperoxidase (MPO). In some embodiments, the methods provided here include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of protein biomarkers in one or more samples obtained from an - individual include detecting the presence of: 1) one or more genetic biomarkers in each of the following genes: NRAS, CTNNB1, PIK3CA,

FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A and / or GNAS and 2) each of the following protein biomarkers: CA19-9, CEA, HGF, OPN, CA125, prolactin, TIMP-1 and / or myeloperoxidase (MPO). In some modalities of methods provided here, which include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of protein biomarkers in one or more samples obtained from an individual , include detecting the presence of: 1) one or more genetic biomarkers in one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 or 16) of the following genes: NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A and / or GNAS and 2) one or more ( for example, 1, 2, 3, 4, 5, 6, 7 or 8) of the following protein biomarkers: CA19-9, CEA, HGF, OPN, CA125, pro-lactin, TIMP-1 and / or myeloperoxidase (MPO), the individual is determined to have (for example, diagnosed as having) or is determined to be (for example, diagnosed as being) at high risk of having or developing one of the following types of cancer: liver cancer , Ovary cancer, esophageal cancer, stomach cancer, pancreatic cancer, colorectal cancer, lung cancer and / or breast cancer.

[171] In some embodiments, the methods provided here include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of protein biomarkers in one or more samples obtained of an individual include detecting the presence of: 1) one or more genetic biomarkers in one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 or 16) of the following genes: NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A and / or GNAS and 2) one or more ( for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or 11) of the following protein biomarkers: CA19-9, CEA, HGF, OPN, CA125, AFP, pro-lactin , TIMP-1, follistatin, G-CSF and / or CA15-3. In some modalities, the methods provided here that include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of protein biomarkers in one or more samples obtained from an individual include detecting the presence 1) one or more genetic biomarkers in each of the following genes: NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A and GNAS, and 2) one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or 11) of the following protein biomarkers: CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1, follistatin, G-CSF and / or CA15-3. In some modalities, the methods provided here that include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of protein biomarkers in one or more samples obtained from an individual include detecting the presence of: 1) one or more genetic biomarkers in one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , 15 or 16) of the following genes: NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A and / or GNAS and 2) each of the following biomarkers of proteins: CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1, follistatin, G-CSF and CA15-3. In some embodiments, the methods provided here include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of protein biomarkers in one or more samples obtained from an - individual include detecting the presence of: 1) one or more genetic biomarkers in one or more: NRAS, CTNNB1, PIK3CA, FBXW7, APC,

EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A and / or GNAS and 2) each of the following protein biomarkers: CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1, follistatin, G-CSF and / or CA15-3. In some modalities of methods provided here, which include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of protein biomarkers in one or more samples obtained from an individual , include detecting the presence of: 1) one or more genetic biomarkers in one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 or 16) of the following genes: NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A and / or GNAS and 2) one or more (for example , 1, 2, 3, 4, 5, 6, 7 or 8) of the following protein biomarkers: CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1, follistatin, G -CSF and / or CA15-3, the individual is determined to be (for example, diagnosed as having) or is determined to be (for example, diagnosed as being) at high risk of having or developing one of the following cancers an of the following types of cancer: cancer liver disease, ovarian cancer, esophageal cancer, stomach cancer, pancreatic cancer, colorectal cancer, lung cancer and / or breast cancer.

[172] In some embodiments, the methods provided here that include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of protein biomarkers in one or more samples obtained of an individual include detecting the presence of: 1) one or more genetic biomarkers in one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 or 16) of the following genes: NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A and / or GNAS and 2) one or more ( for example, 1, 2, 3, 4, 5, 6, 7, 8 or 9) of the following protein biomarkers: CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1 and / or CA15- 3. In some embodiments, the methods provided here that include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of protein biomarkers in one or more samples obtained from an individuals include detecting the presence 1) one or more genetic biomarkers in each of the following genes: NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A and GNAS , and 2) one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8 or 9) of the following protein biomarkers: CA19-9, CEA, HGF, OPN, CA125, AFP , prolactin, TIMP-1 and / or CA15-3). In some embodiments, the methods provided here that include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of protein biomarkers in one or more samples obtained from an individual include detecting the presence of: 1) one or more genetic biomarkers in one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 or 16 ) of the following genes: NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A and / or GNAS and 2) each of the following protein biomarkers: CA19 -9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1, and CA15-3. In some modalities, the methods provided here that include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of protein biomarkers in one or more samples obtained from an individual include detecting the presence of: 1) one or more genetic biomarkers in each of the following genes: NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53,

PPP2R1A and GNAS and 2), each of the following genes: the following protein biomarkers: CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1 and CA15-3. In some modalities of methods provided here, which include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of protein biomarkers in one or more samples obtained from an individual , include detecting the presence of: 1) one or more genetic biomarkers in one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 or 16) of the following genes: NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A and / or GNAS and 2) one or more (for example , 1, 2, 3, 4, 5, 6, 7 or 8) of the following protein biomarkers: CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1 and / or CA15 -3, the individual is determined to have (for example, diagnosed as having) or is determined to be (for example, diagnosed as being) at high risk of having or developing one of the following types of cancer: liver cancer, cancer of ovary, esophageal cancer ago, stomach cancer, pancreatic cancer, colorectal cancer, lung cancer and / or breast cancer.

[173] In some embodiments, the methods provided here that include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of protein biomarkers in one or more samples obtained of an individual include detecting the presence of: 1) one or more genetic biomarkers in one or more (for example, 1, 2, 3 or 4) of the following genes: KRAS (for example, genetic biomarkers in codes 12 and / or 61), TP53, CDKN2A and / or SMAD4 and 2) one or more (for example, 1, 2, 3 or 4) of the following protein biomarkers: CA19-9, CEA, HGF and / or OPN. In some embodiments, the methods provided here that include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of protein biomarkers in one or more samples obtained from an individual include detecting the presence of: 1) one or more genetic biomarkers in each of the following genes: KRAS (for example, genetic biomarkers in codons 12 and / or 61), TP53, CDKN2A and SMAD4 and 2) one or more (for example, 1 , 2, 3 or 4) of the following protein biomarkers: CA19-9, CEA, HGF and / or OPN.

In some embodiments, the methods provided here include the detection of the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of protein biomarkers in one or more samples obtained from a include detecting the presence of: 1) one or more genetic biomarkers in one or more (for example, 1, 2, 3 or 4) of the following genes: KRAS (for example, genetic biomarkers in codons 12 and / or 61) , TP53, CDKN2A and / or SMAD4 and 2) each of the following protein biomarkers: CA19-9, CEA, HGF and OPN.

In some embodiments, the methods provided here include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of protein biomarkers in one or more samples obtained from an - individual include detecting the presence of: 1) one or more genetic biomarkers in each of the following genes: KRAS (for example, genetic biomarkers in codons 12 and / or 61), TP53, CDKN2A and SMAD4 and 2) each of the following protein biomarkers: CA19-9, CEA, HGF and OPN.

In some modalities of the methods provided here that include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of protein biomarkers in one or more samples obtained from an individual include detect the presence of: 1) one or more genetic biomarkers in one or more (for example, 1,

2, 3 or 4) of the following genes: KRAS (for example, genetic biomarkers in codons 12 and / or 61), TP53, CDKN2A and / or SMAD4 and 2) one or more (for example, 1, 2, 3 or 4) of the following protein biomarkers: CA19-9, CEA, HGF and / or OPN, an individual is determined to be (for example, diagnosed as having) or is determined to be (for example, diagnosed as being) at risk elevated from having or developing pancreatic cancer.

[174] A sample obtained from an individual can be any of the variety of samples described here that contain DNA without cells (for example, ctDNA) and / or proteins. In some embodiments, DNA without cells (for example, ctDNA) and / or proteins in a sample taken from the individual are derived from a tumor cell. In some modalities, DNA without cells (for example, ctDNA) in a sample obtained from the individual includes one or more genetic biomarkers. In some embodiments, the proteins in a sample obtained from the individual include one or more protein biomarkers. Non-limiting examples of samples in which genetic biomarkers and / or protein biomarkers can be detected include blood, plasma and serum. In some modalities, the presence of one or more genetic biomarkers and the presence of one or more protein biomarkers is detected in a single sample obtained from the individual. In some modalities, the presence of one or more genetic biomarkers is detected in a first sample obtained from an individual, and the presence of one or more protein biomarkers is detected in a second sample obtained from the individual.

[175] In some embodiments, methods provided here that include detecting the presence of one or more members of a panel of genetic biomarkers (for example, each member of a panel of genetic biomarkers) and the presence of one or more members of a panel of protein biomarkers (for example, each member of a panel of protein biomarkers) in one or more samples obtained from an individual, a high level of one or more members of the protein biomarker panel can be detected. For example, a high level of a protein biomarker may be higher than the reference level. A reference level can be any level of the protein biomarker that is not associated with the presence of cancer. For example, a reference level for a protein biomarker may be present in a reference individual who does not have cancer or does not harbor a cancer cell. A reference level for a protein biomarker can be the average level that is present in a plurality of reference individuals who do not have cancer or do not harbor a cancer cell. A reference level for a protein biomarker in an individual determined to have cancer may be the level that was present in the individual before the onset of cancer. In some embodiments, a panel of protein biomarkers in which one or more panel members is present at a high level includes one or more of (for example, 1, 2, 3, 4, 5, 6, 7 or each one of): CA19-9, CEA, HGF, OPN, CA125, prolactin, TIMP-1 and / or myeloperoxidase (MPO). In some embodiments, a panel of protein biomarkers in which one or more panel members is present at a high level includes one or more of (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9 , 10, or each of): CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1, follistatin, G-CSF and / or CA15-3. In some embodiments, a panel of protein biomarkers in which one or more panel members is present at a high level includes one or more of (for example, 1, 2, 3, 4, 5, 6, 7, 8 or each one of): CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1, and / or CA15-

3. In some embodiments, a panel of protein biomarkers in which one or more panel members is present at a high level includes one or more of (for example, 1, 2, 3 or each of): CA19-

9, CEA, HGF and / or OPN.

[176] In some embodiments, methods provided here that include detecting the presence of one or more members of a panel of genetic biomarkers (for example, each member of a panel of genetic biomarkers) and the presence of one or more members of a panel of protein biomarkers (for example, each member of a panel of protein biomarkers) in one or more samples obtained from an individual, a reduced level of one or more members of the protein biomarkers panel can be detected. For example, a reduced level of a protein biomarker may be lower than the reference level. A reference level can be any level of the protein biomarker that is not associated with the presence of cancer. For example, a reference level for a protein biomarker may be present in a reference individual who does not have cancer or does not harbor a cancer cell. A reference level for a protein biomarker can be the average level that is present in a plurality of reference individuals who do not have cancer or do not harbor a cancer cell. A reference level for a protein biomarker in an individual determined to have cancer may be the level that was present in the individual before the cancer started. In some embodiments, a panel of protein biomarkers in which one or more panel members is present at a high level includes one or more of (for example, 1, 2, 3, 4, 5, 6, 7 or each of): CA19-9, CEA, HGF, OPN, CA125, prolactin, TIMP-1 and / or myeloperoxidase (MPO). In some embodiments, a panel of protein biomarkers in which one or more panel members is present at a reduced level includes one or more of (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9 , 10, or each of): CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1, follistatin, G-CSF and / or CA15-3. In some embodiments, a panel of protein biomarkers in which one or more panel members is present at a reduced level includes one or more of (for example, 1, 2, 3, 4, 5, 6, 7, 8 or each one of): CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1, and / or CA15-

3. In some embodiments, a panel of protein biomarkers in which one or more panel members is present at a reduced level includes one or more of (for example, 1, 2, 3 or each of): CA19- 9, CEA, HGF and / or OPN.

[177] In some embodiments, when an individual is determined to have (for example, diagnosed as having cancer) or determined to be (for example, diagnosed as being) at high risk of having or developing cancer (for example, detec - tando: 1) the presence of one or more genetic biomarkers in one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 or 16) of the following genes: NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A and / or GNAS and 2) the presence of one or more biomarkers of proteins in any of the panels described here as being useful in conjunction with this panel of genetic biomarkers), the individual is selected as a candidate for (for example, selected for) additional diagnostic tests (for example, any of the several other forms of diagnostic methods described here), the individual is selected as a candidate for (for example, selected for) monitor increased awareness (for example, any of the various augmented monitoring methods described here), the individual is identified as an individual who will respond or likely respond to a treatment (for example, any of the various therapeutic interventions described here), the individual is selected as a candidate for (for example, selected for) a treatment, a treatment (for example, any of the varieties of therapeutic interventions described here) is selected for the individual and / or a treatment (for example, any of the various therapeutic interventions described here) is administered to the individual.

For example, when an individual is determined to have (for example, diagnosed as having) cancer or determined to be (for example, diagnosed as being) at high risk of having or developing cancer, the individual may undergo diagnostic tests additional, such additional diagnostic tests can confirm the presence of cancer in the individual.

In addition or alternatively, the individual can be monitored more frequently.

In some modalities of an individual determined to have (for example, diagnosed as having) cancer or determined to be (for example, diagnosed as being) at high risk of having or developing cancer in which the individual is tested for additional diagnosis and / or increased monitoring, the individual can additionally be administered with a therapeutic intervention.

In some modalities, after an individual is administered with a therapeutic intervention, the individual undergoes further additional diagnostic tests (for example, the same type of additional diagnostic test that was performed previously and / or a different type of test additional diagnostics) and / or continuous increased monitoring (for example, increased monitoring at the same frequency or at a different frequency than previously performed). In modalities, after an individual is administered with a therapeutic intervention and the individual undergoes further additional diagnostic tests and / or additional increased monitoring, the individual is administered with another therapeutic intervention (for example, the same therapeutic intervention that was previously administered and / or a different therapeutic intervention). In some modalities, after the individual is administered with a therapeutic intervention, the individual is tested for the presence of one or more genetic biomarkers in one or more (for example, 1, 2, 3, 4, 5 , 6, 7, 8, 9,

10, 11, 12, 13, 14, 15 or 16) of the following genes: NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A, and / or GNAS, and 2) the presence of one or more protein biomarkers in any of the panels described herein as being useful in conjunction with this panel of genetic biomarkers.

[178] In some embodiments, the methods provided here that include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of protein biomarkers in one or more samples obtained of an individual also include detecting the presence of aneuploidy in a sample obtained from the individual (for example, the same sample is used to detect one or both the presence of one or more members of a panel of genetic biomarkers and the presence of a or more members of a panel of protein biomarkers or a different sample). The presence of aneuploidy in any chromosome or portion thereof (for example, an arm of a chromosome) can be detected. In some types of methods that include detecting the presence of genetic biomarkers, protein biomarkers and aneuploidy, the presence of aneuploidy in one or more of the chromosomal arms 5q, 8q and 9p is detected. In some types of methods that include detecting the presence of genetic biomarkers, protein biomarkers and aneuploidy, the presence of aneuploidy in one or more of the chromosomal arms 4p, 7q, 8q, and 9q is detected.

[179] In some modalities of methods provided here, which include detecting in one or more samples obtained from an individual, the presence of: 1) one or more genetic biomarkers in one or more (for example, 1, 2, 3, 4 , 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 or 16) of the following genes: NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR,

BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A and / or GNAS and 2) one or more (for example, 1, 2, 3, 4, 5, 6, 7 or 8) of the following biomarkers of proteins: CA19-9, CEA, HGF, OPN, CA125, prolactin, TIMP-1 and / or myeloperoxidase (MPO), the methods also include detecting the presence of aneuploidy in a sample obtained from the individual (for example, same sample use to detect the presence of one or both the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of protein biomarkers or a different sample). In some modalities of methods provided here, which include detecting in one or more samples obtained from an individual, the presence of: 1) one or more genetic biomarkers in each of the following genes: NRAS, CTNNB1, PIK3CA, FBXW7, APC , EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A and GNAS and 2) one or more (for example, 1, 2, 3, 4, 5, 6, 7 or 8) of the following protein biomarkers: CA19-9, CEA, HGF, OPN, CA125, prolactin, TIMP-1 and / or myeloperoxidase (MPO), the methods also include detecting the presence of aneuploidy in a sample obtained from the individual (for example, the same sample use to detect one or both the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of protein biomarkers or a different sample). In some modalities of methods provided here, which include detecting in one or more samples obtained from an individual, the presence of: 1) one or more genetic biomarkers in one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 or 16) of the following genes: NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A and / or GNAS and 2) each of the following protein biomarkers: CA19-9, CEA, HGF, OPN, CA125, prolactin, TIMP-1 and myeloperoxidase (MPO), the methods also include detecting the presence aneuploidy in a sample obtained from the individual (for example, the same sample use to detect the presence of one or both the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of genetic protein biomarkers or a different sample). In some modalities of methods provided here, which include detecting in one or more samples obtained from an individual, the presence of: 1) one or more genetic biomarkers in each of the following genes: NRAS, CTNNB1, PIK3CA, FBXW7 , APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A and GNAS and 2) each of the following protein biomarkers: CA19-9, CEA, HGF, OPN, CA125, prolactin , TIMP-1 and / or myeloperoxidase (MPO), the methods also include detecting the presence of aneuploidy in a sample obtained from the individual (for example, at the same sample use to detect the presence of one or both the presence of an or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of protein biomarkers or a different sample). In some modalities of methods provided here, which include detecting in one or more samples obtained from an individual the presence of: 1) one or more genetic biomarkers in one or more (for example, 1, 2, 3, 4, 5 , 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 or 16) of the following genes: NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS , AKT1, TP53, PPP2R1A and / or GNAS, 2) one or more (for example, 1, 2, 3, 4, 5, 6, 7 or 8) of the following protein biomarkers: CA19-9, CEA, HGF, OPN, CA125, prolactin, TIMP-1 and / or myeloperoxidase (MPO), and 3) the presence of aneuploidy, the individual is determined to have (for example, diagnosed as having) or is determined to be (for example, diagnosed as being at high risk of having or developing one of the following types of cancer: liver cancer, ovarian cancer, esophageal cancer, stomach cancer, pancreatic cancer, colorectal cancer, lung cancer and / or breast cancer.

[180] In some modalities of methods provided here, which include detecting in one or more samples obtained from an individual, the presence of: 1) one or more genetic biomarkers in one or more (for example, 1, 2, 3, 4 , 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16) of the following genes: NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A and / or GNAS and 2) one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or 11) of the following protein biomarkers : CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1, follistatin, G-CSF, and / or CA15-3, the methods also include detecting the presence of aneuploidy in a sample obtained from the individual ( for example, the same sample use to detect the presence of one or both the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of protein biomarkers or a different sample). In some modalities of methods provided here, which include detecting in one or more samples obtained from an individual, the presence of: 1) one or more genetic biomarkers in each of the following genes: NRAS, CTNNB1, PIK3CA, FBXW7 , APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A and GNAS and 2) one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or 11) of the following protein biomarkers: CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1, follistatin, G-CSF and / or CA15-3, the methods also include detecting the presence of aneuploidy in a sample obtained from the individual (for example, at the same sample use to detect one or both the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of genetic protein biomarkers or a different sample). In some embodiments, the methods provided here include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of protein biomarkers in one or more samples obtained from an individual include detecting the presence of: 1) one or more genetic biomarkers in one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , 15 or 16) of the following genes: NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A and / or GNAS and 2) each of the following biomarkers of proteins: CA19 -9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1, follistatin, G-CSF and CA15-3, the methods also include detecting the presence of aneuploidy in a sample obtained from the individual (for example, example, the same sample use to detect the presence of one or both the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of genetic d and protein biomarkers or a different sample). In some modalities of methods provided here, which detect in one or more samples obtained from an individual, the presence of: 1) one or more genetic biomarkers in each or more of the following genes: NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A and / or GNAS and 2) each of the following protein biomarkers: CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1, follistatin and / or CA15-3, the methods also include detecting the presence of aneuploidy in a sample obtained from the individual (for example, at the same sample use to detect the presence of one or both the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of protein biomarkers or a different sample). In some modalities of methods provided here, which detect in one or more samples obtained from an individual the presence of: 1) one or more genetic biomarkers in one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 or 16) of the following genes: NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A and / or GNAS, 2) one or more (for example, 1, 2, 3, 4, 5, 6, 7 or 8) of the following protein biomarkers: CA19-9, CEA, HGF, OPN , CA125, AFP, prolactin, TIMP-1, follistatin, G-CSF and / or CA15-3 and 3) the presence of aneuploidy, the individual is determined to have (for example, diagnosed as having) or is determined to be ( for example, diagnosed as being) at high risk of having or developing cancer one of the following types of cancer: liver cancer, ovarian cancer, esophageal cancer, stomach cancer, pancreatic cancer, colorectal cancer, cancer lung and / or breast cancer.

[181] In some modalities of methods provided here, which include detecting in one or more samples obtained from an individual, the presence of: 1) one or more genetic biomarkers in one or more (for example, 1, 2, 3, 4 , 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16) of the following genes: NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A and / or GNAS and 2) one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8 or 9) of the following protein biomarkers: CA19-9 , CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1, and / or CA15-3, methods also include detecting the presence of aneuploidy in a sample obtained from the subject (for example, at the same sample use to detect the presence of one or both the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of protein biomarkers or a different sample). In some modalities or methods provided here, which include detecting in one or more samples obtained from an individual, the presence of: 1) one or more genetic biomarkers in each of the following genes: NRAS, CTNNB1, PIK3CA, FBXW7, APC , EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A and GNAS and 2) one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8 or 9) of the following protein biomarkers: CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1, and / or CA15-3, the methods also include detecting the presence of aneuploidy in a sample obtained from the individual (eg example, the same use of a sample to detect one or both the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of protein biomarkers or a different sample). In some modalities of methods provided here, which include detecting in one or more samples obtained from an individual, the presence of: 1) one or more genetic biomarkers in one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 or 16) of the following genes: NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A and / or GNAS and 2) each of the following protein biomarkers: CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1 and CA15-3, the methods also include detecting the presence of aneuploidy in a sample obtained from the individual (for example, at the same sample use to detect the presence of one or both the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of protein biomarkers or a different sample). In some modalities of methods provided here, which include detecting in one or more samples obtained from an individual, the presence of: 1) one or more genetic biomarkers in each or more of the following genes: NRAS, CTNNB1, PIK3CA, FBXW7 , APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A and GNAS and 2) each of the following protein biomarkers: CA19-9, CEA, HGF, OPN, CA125,

AFP, prolactin, TIMP-1 and CA15-3, the methods also include detecting the presence of aneuploidy in a sample obtained from the individual (for example, at the same sample use to detect the presence of one or both the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of protein biomarkers or a different sample). In some modalities of methods provided here, which include detecting in one or more samples obtained from an individual the presence of: 1) one or more genetic biomarkers in one or more (for example, 1, 2, 3, 4, 5, 6 , 7, 8, 9, 10, 11, 12, 13, 14, 15 or 16) of the following genes: NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1 , TP53, PPP2R1A and / or GNAS, 2) one or more (for example, 1, 2, 3, 4, 5, 6, 7 or 8) of the following protein biomarkers: CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1 and / or CA15-3, and 3) the presence of aneuploidy, the individual is determined to have (for example, diagnosed as having) or is determined to be (for example , diagnosed as being) at high risk of having or developing one of the following types of cancer: liver cancer, ovarian cancer, esophageal cancer, stomach cancer, pancreatic cancer, colorectal cancer, lung cancer and / or breast cancer.

[182] In some modalities of methods provided here, which include detecting in one or more samples obtained from an individual, the presence of: 1) one or more genetic biomarkers in one or more (for example, 1, 2, 3 or 4 ) of the following genes: KRAS (for example, genetic biomarkers in codons 12 and / or 61), TP53, CDKN2A and / or SMAD4 and 2) one or more (for example, 1, 2, 3 or 4) of the following protein markers: CA19 -9, CEA, HGF and / or OPN, the methods also include detecting the presence of aneuploidy in a sample obtained from the individual (for example, the same sample use to detect the presence of one or both the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of protein biomarkers or a different sample). In some modalities of methods provided here, which include detecting in one or more samples obtained from an individual, the presence of: 1) one or more genetic biomarkers in each of the following genes: KRAS (for example, genetic biomarkers in the codes) 12 and / or 61), TP53, CDKN2A and SMAD4 and 2) one or more (for example, 1, 2, 3 or 4) of the following protein biomarkers: CA19 -9, CEA, HGF and / or OPN, the methods further include detecting the presence of aneuploidy in a sample obtained from the individual (for example, at the same sample use to detect the presence of one or both the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of protein biomarkers or a different sample). In some modalities of methods provided here, which include detecting in one or more samples obtained from an individual, the presence of: 1) one or more genetic biomarkers in one or more (for example, 1, 2, 3 or 4) the following genes: KRAS (for example, genetic biomarkers in codons 12 and / or 61), TP53, CDKN2A and / or SMAD4 and 2) each of the following protein biomarkers: CA19 -9, CEA, HGF and OPN, the methods further include detecting the presence of aneuploidy in a sample obtained from the individual (for example, using the same sample to detect the presence of one or both the presence of one or more members of a panel of genetic biomarkers and the presence of a or more members of a panel of protein biomarkers or a different sample). In some modalities or methods provided here, which include detecting in one or more samples obtained from an individual, the presence of: 1) one or more genetic biomarkers in each of the following genes: KRAS (for example, genetic biomarkers in codons 12 and / or 61), TP53,

CDKN2A and SMAD4 and 2) each of the following protein biomarkers: CA19 -9, CEA, HGF and OPN, the methods also include detecting the presence of aneuploidy in a sample obtained from the individual (for example, at the same sample use to detect the presence of one or both the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of protein biomarkers or a different sample). In some modalities of the methods provided here that include detecting in one or more samples obtained from an individual the presence of: 1) one or more genetic biomarkers in one or more (for example, 1, 2, 3 or 4) of the following genes: KRAS (for example, genetic biomarkers in codons 12 and / or 61), TP53, CDKN2A and / or SMAD4 and 2) one or more (for example, 1, 2, 3 or 4) of the following protein biomarkers: CA19 - 9, CEA, HGF and / or OPN, an individual is determined to have (for example, diagnosed as having) or is determined to be (for example, diagnosed as being) at high risk of having or developing pancreatic cancer.

[183] In some embodiments, any of the variety of methods provided here that includes detecting the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of protein biomarkers in one or more Samples obtained from an individual further include detecting the presence of one or more members of one or more additional classes of biomarkers. Non-limiting examples of these additional classes of biomarkers include: changes in copy number, changes in DNA methylation, other nucleic acids (eg, mRNAs, miRNAs, lncRNAs, circRNA, mtDNA, telomeric DNA, translocation and genomic rearrangements), peptides and / or metabolites.

[184] In some embodiments, the one or more additional classes of biomarkers include a metabolite biomarker. In some modalities,

an individual is determined to be at high risk of developing or developing cancer if the biological sample contains one or more cancer-inducing metabolites.

In some modalities, an individual is determined to have cancer if the biological sample contains one or more metabolites indicative of cancer.

Non-limiting examples of cancer-indicating metabolites include: 5-methylthioadenosine (MTA), Reduced glutathione (GSH), N-acetylglutamate, Lactose, N-acetylneuraminate, UDP-acetylglucosamine, UDP-Acetylgalactosamine, UDP-glucuronate, Pantoten , Araquidonate (20: 4n6), Choline, Cytidine 5′-diphosphocholine, Dihomino-linolenate (20: 3n3), Docosapentaenoate (DPA 22: 5n3), Eicosapentaenoate (EPA 20: 5n3), Glycerophosphorylcholine (GPC), Docosahexaeenate (DHA 22: 6n3), Linoleate (18: 2n6), Cytidine 5'-monophosphate (5′-CMP), Gamma-glutamylglutamate, X - 14577, X - 11583, Isovalerylcarnitine, Phosphocreatine, 2-aminoadipic acid, Gluconic acid, O-Acetylcarnitine, aspartic acid, Deamido-NAD +, glutamic acid, Isobutyrylcarnitine, Carnitine, Pyridoxal, Citric acid, Adenosine, ATP, valine, XC0061, Isoleucine, γ-Butir lactic, alanine, phenylalanine, Glucono-lactone, leucine, Glutathione (GSSG) _divalent, tyrosine, NAD +, XC0016, UTP, creatine, Theobromine, CTP, GTP, 3 -Methylhistidine, Succinic Acid, Glycerol 3-phosphate, glutamine, 5-Oxoproline, Thiamine, Butyryl-carnitine, 4-acetamidobutanoic acid, UDP-Glucose, UDP-Galactose, threonine, N-Acetylglycine, proline, ADP, Choline, Malic Acid, S-Adenosylmethionine, Pantothenic acid, Cysteine sulfinic acid, 6-Aminohexanoic acid, Homocysteic acid, Hydroxyproline, Methionine sulfoxide, 3-Guanidinopropionic acid, Glucose 6-phosphate, Tryptophanic acid, Tryptic acid, Pyridine acid , N-acetyl-aspartic acid, 4-guanidinobutyric acid, serine, Citrulline, Betaine, N-acetyl-asparagine, 2-hydroxyglutaric acid, arginine, Glutathione (GSH), creatinine, Dihydroxyacetone phosphate, histidine, glycine, Glucose 1-phosphate, N-formylglycine, Keto-

profene, lysine, beta-alanine, N-acetylglutamic acid, 2-Amino-2- (hydroxymethyl) -1,3-propanediol, Ornithine, Phosphorylcholine, Glycerophosphocholine, Terephthalic acid, Glyceraldehyde 3-phosphate, Gli-Asp, Taurine , Fructose 1,6-diphosphate, 3-aminoisobutyric acid, Sperididine, GABA, Triethanolamine, Glycerol, N-acetyl-serine, N-Acetylornithine, Diethanolamine, AMP, Glutathione cysteine disulfide, Streptomycin sulfate + H2O diva- lens, trans-glutaconic acid, nicotinic acid, isobutylamine, betaine aldehyde + H2O, urocanic acid, 1-aminocyclopropane-1-carboxylic acid Homoserinalactone, 5-aminovaleric acid, 3-hydroxybutyric acid, ethanolamine, isovalic acid methylglutamic, Cystathionine, Spermine, Carnosine, 1-Methylnicotinamide, N-Acetylneuraminic acid, Sarcosine, GDP, N-Methylalanine, palmitic acid, 1,2-dioleoyl-sn-glycero-3-phospho-rac-glycerolcholesterol 5α, 6α epoxidelanosterol, lignoceric acid, 1oleoil_rac_GL, cholesterol_epoxide, erucic acid, T-LCA, oleoyl-L-carnitine, oleanolic acid, 3-phosphoglycerate, 5-hydroxynorvaline, 5-methoxytryptamine, adenosine-5-monophosphate, alpha-ketoglutarate, asparagine, benzoic acid, hypoxanthine, maltose, maltotriose, methionine sulfoxide nornicotine, phenol, phosphoethanolamine, pyrophosphate, pyruvic acid, quinine acid, taurine, uric acid, inosine, lactamide, 5-hydroxy-norvaline NIST, cholesterol, deoxypentitol, 2-hydroxyestrone, 2-hydroxy-tradiol, 2-metholestrone, 2-methoxyestradiol, 2-hydroxyestrone-3-methyl ether, 4-hydroxyestrone, 4-methoxestrone, 4-methoxyestradiol, 16alpha-hydroxyesterone, 17-epiestriol, striol, 16-ketoestradiol, 16-epiestriol, acylcarnitine C18: 1, amino acids citrulline and trans-4-hydroxyproline, hydroxyproline, glycerophospholipids PC aa C28: 1, PC aa and C30: 0 and PC aa and C30: 2, and sphingo lipid SM (OH) C14: 1. See, for example, Halama et al., Nesting of colon and ovarian cancer cells in the endothelial niche is associated with changes in glycan and lipid metabolism, Scientific Reports volume 7, Article number: 39999 (2017); Hur et al., Systems approach to characterize the metabolism of liver cancer stem cells expressing CD133, Sci Rep.,

7: 45557, doi: 10.1038 / srep45557, (2017); Eliassen et al., Urinary Estrogen and Estrogen Metabolites and Subsequent Risk of Breast Cancer among Premenopausal Women, Cancer Res; 72 (3); 696–706 (2011); Gangi et al., Metabolomic profile in pancreatic cancer patients: a con- sensus-based approach to identify highly discriminating metabolites, Oncotarget, Feb 2; 7 (5): 5815–5829 (2016); Kumar et al., Serum and Plasma Metabolomic Biomarkers for Lung Cancer, Bioinformation, 13 (6): 202–208, doi: 10.6026 / 97320630013202 (2017); Schmidt et al., Pre-diagnostic metabolite concentrations and prostate cancer risk in 1077 cases and 1077 matched controls in the European Prospective Investigation into Cancer and Nutrition, BMC Med., 15: 122, doi:

10.1186 / s12916-017-0885-6 (2017); each of which is incorporated herein by reference in its entirety.

[185] In some embodiments, the one or more additional classes of biomarkers include a peptide (for example, a peptide that is distinct from the various protein biomarkers described herein as useful in one or more methods). In some modalities, an individual is determined to be at high risk of having or developing cancer if the biological sample contains one or more peptides indicating cancer. In some modalities, an individual is determined to have cancer if the biological sample contains one or more peptides indicative of cancer. In some embodiments, a peptide is derived from a protein (for example, the peptide includes an amino acid sequence present in a protein biomarker or a different protein). Non-limiting examples of cancer-specific peptides include the following peptides and peptides derived from the following proteins: CEACAM, CYFRA21-1, CA125, PKLK, Pro-GRP, NSE, TPA 6, TPA 7, TPA 8, NRG, NRG 100 , CNDP, APOВ100, SCC, VEGF, EGFR, PIK3CA, HER2, BRAF, ROS, RET, NRAS, MET, MEK1, HER2, C4.4A, PSF3, FAM83B, ECD, CTNNB, VIM, S100A4,

S100A7, COX2, MUC1, KLKB1, SAA, HP-β, C9, Pgrmc1, Ciz1, Transferrin, α-1 antitrypsin, apolipo 1 protein, c3a complement, Caveolin-1, Kallikrein 6, Glucose-regulated protein 8 , α defense -1, -2, -3, Serum C peptide, Alpha-2-HS glycol protein, Tryptic KRT 8 peptide, plasma glycol protein, Catenin, Defensin α 6, MMPs, Cyclin D, S100 P, filament protein Lamina A / C, heat shock protein, aldehyde dehydrogenase, Txl-2 (thioredoxin-like protein-2), P53, nm23, u-PA, VEGF, Eph B4, CRABP2, WT-1, Rab-3D, Mesothelin, ERα, ANXA4, PSAT1, SPB5, CEA5, CEA6, A1AT, SLPI, APOA4, VDBP, HE4, IL-1, -6, -7, -8, -10, -11, -12, -16, -18, -21, -23, -28A, -33, LIF, TNFR1–2, HVEM (TNFRSF14), IL1R-a, IL1R-b, IL-2R, M-CSF, MIP-1a, TNF-α, CD40, RANTES , CD40L, MIF, IFN-β, MCP-4 (CCL13), MIG (CXCL9), MIP-1δ (CCL15), MIP3a (CCL20), MIP-4 (CCL18), MPIF-1, SDF-1a + b ( CXCL12), CD137 / 4–1BB, lymphotactin (XCL1), eotaxin-1 (CCL11), eotaxin-2 (CCL24), 6Ckine / CCL21), BLC (CXCL1 3), CTACK (CCL27), BCA-1 (CXCL13), HCC4 (CCL16), CTAP-3 (CXCL7), IGF1, VEGF, VEGFR3, EGFR, ErbB2, CTGF, PDGF AA, BB, PDGFRb, bFGF, TGFbRIII, β-cellulin, IGFBP1–4, 6, BDNF, PEDF, angiopoietin-2, renin, lysophosphatidic acid, β2-microglobulin, sialyl TN, ACE, CA 19–9, CEA, CA 15–3, CA-50, CA 72–4, OVX1, mesothelin, sialyl TN, MMP-2, - 3, -7, -9, VAP-1, TIMP1–2, tenascin C, VCAM-1, osteopontin, KIM-1, NCAM, tetranectin, nidogen-2, cathepsin L, prostasin, matriptase, calcium-2, 6, 10, cystatin C, claudin, spondin2, SLPI, bHCG, urinary gonadotropin peptide, inhibin, leptin, adiponectin, GH, TSH, ACTH, PRL, FSH, LH, cortisol, TTR, osteocalcin, insulin, ghrelin, GIP, GLP-1, amylin, glucagon, YY peptide, follistatin, hepcidin, CRP, Apo A1, CIII, H, transthyretin, SAA, SAP, complement C3,4 , complementary factor H, albumin, ceruloplasmin, haptoglobin, β-hemoglobin, transferrin, ferritin, fibrinogen, thrombin, von Willebrand factor, myoglobin, acid protein immunosuppressant, lipid-associated sialic acid, S100A12 (EN-RAGE), fetuin A, clusterin, α1-antitrypsin, a2-macroglobulin, serpin1 (human plasminogen activator inhibitor-1), Cox-1, Hsp27, Hsp60, Hsp80, Hsp90, oxidized LDL receptor of the type lectin 1, CD14, lipocalin 2, ITIH4, sFasL, Cyfra21-1, TPA, perforin, DcR3, AGRP, creatine kinase-MB, human milk fat globule 1-2, NT-Pro -BNP, neuron-specific enolase, CASA, NB / 70K, AFP, afamine, collagen, prohibitin, keratin-6, PARC, B7-H4, YK-L40, AFP-L3, DCP, GPC3, OPN, GP73, CK19, MDK, A2, 5-HIAA, CA15-3, CA19-9, CA27.29, CA72-4, calcitonin, CGA, BRAF V600E, BAP, BCT-ABL fusion protein, KIT, KRAS, PSA, Lactate dehydrogenase , NMP22, PAI-1, uPA, fibrin D-dimer, S100, TPA, thyroglobulin, CD20, CD24, CD44, RS / DJ-1, p53, alpha-2-HS glycoprotein, lipophilin B, beta-globin, hemopexin, UBE2N, PSMB6, PPP1CB, CPT2, COPA, MSK1 / 2, Pro-NPY, Secernina-1, Vinculina, NAAA, PTK7, TFG, MCCC2, TRAP1, IMPDH2, PTEN, POSTN, EPLIN, eIF4A3, DDAH1, ARG2, PRDX3 & 4, P4HB, YWHAG, Enoyl CoA-hydrase, PHB, TUBB, KRT2, DES, HSP71, ATP5B, CKB, HSPD1, LMNA, EZH2, EZH2 , SLP2, SM22, Bax, Smac / Diablo phosphorylated Bcl2, STAT3 and Smac / Diablo expression, PHB, PAP, AMACR, PSMA, FKBP4, PRDX4, KRT7 / 8/18, GSTP1, NDPK1, MTX2, GDF15, PCa-24 , Caveololine-2, Prothrombin, Antithrombin III, Haptoglobin, serum amyloid protein A-1, ZAG, ORM2, APOC3, CALML5, IGFBP2, MUC5AC, PNLIP, PZP, TIMP1, AMBP, alpha-inhibitor heavy chain trypsin H1, heavy chain of alpha-trypsin inhibitor H2, heavy chain of alpha trypsin inhibitor H3, type V proton ATPase B subunit, renal isoform, hepatocyte growth factor protein, serum amyloid P component , acylglycerol kinase, protein 9 containing leucine-rich repeat, Beta-2-glycoprotein 1, Plasma C1 protease inhibitor, protein 1 containing the homology domain of lipoxygenase, Protoca-dherin alfa-13. See, for example, Kuppusamy et al., Volume 24, Issue

6, September 2017, Pages 1212-1221; Elzek and Rodland, Cancer Metastasis Rev. 2015 Mar; 34 (1): 83–96; Noel and Lokshin, Future Oncol. 2012 Jan; 8 (1): 55–71; Tsuchiya et al., World J Gastroenterol. 2015 Oct 7; 21 (37): 10573-10583; Lou et al., Biomark Cancer. 2017; 9: 1–9; Park et al., Oncotarget. 2017 Jun 27; 8 (26): 42761–42771; Saraswat et al., Cancer Med. 2017 Jul; 6 (7): 1738–1751; Zamay et al., Cancers (Basel). 2017 Nov; 9 (11): 155; Tanase et al., Oncotarget. 2017 Mar 14; 8 (11): 18497–18512, each of which is incorporated herein by reference in its entirety.

[186] In some embodiments, the one or more additional classes of biomarkers include lesions or variations of nucleic acid (for example, an injury or variation of nucleic acid that is distinct from the various genetic biomarkers described herein as being useful in one or more more methods). In some embodiments, an individual is determined to be at high risk of developing or developing cancer if the biological sample contains one or more nucleic acid lesions or variations indicative of cancer. In some embodiments, an individual is determined to have cancer if the biological sample contains one or more nucleic acid lesions or indicative variations in cancer. Non-limiting examples of lesions or variations in nucleic acids include changes in copy number, changes in DNA methylation and / or other nucleic acids (for example, mRNAs, miRNAs, lncRNAs, circRNA, mtDNA, telomeric DNA, translocation and rearrangements genomic). Translocations and genomic rearrangements have been correlated with various types of cancer (eg, prostate, glioma, lung cancer, non-small cell lung cancer, melanoma and thyroid cancer) and used as biomarkers for years (for example , Demeure et al., 2014, World J Surg., 38: 1296-305; Hogenbirk et al., 2016, PNAS USA, 113: E3649-56; Gasi et al., 2011, PLoS One, 6: e16332; Ogiwara et al., 2008, Oncogene, 27: 4788-97; US Patent

9,745,632; and U.S. Patent 6,576,420). In addition, changes in the number of copies were used as biomarkers for various types of cancer, including, without limitation, head and neck squamous cell carcinoma, lymphoma (eg, non-Hodgkin's lymphoma) and colorectal cancer (Kumar et al ., 2017, Tumor Biol, 39: 1010428317740296; Kumar et al., 2017, Tumor Biol., 39: 1010428317736643; Henrique et al., 2014, Ex-pert Rev.

Mol.

Diagn., 14: 419-22; and U.S. Patent 9,816,139). DNA methylation and changes in DNA methylation (eg, hypomethylation, hypermethylation) are also used as biomarkers in cancer.

For example, hypomethylation has been associated with hepatocellular carcinoma (see, for example, Henrique et al., 2014, Expert Rev.

Mol.

Diagn., 14: 419-22), esophageal carcinogenesis (see, for example, Alvarez et al., 2011, PLoS Genet., 7: e1001356) and gastric and liver cancer (see, for example, US Patent 8,728,732) , and hypermethylation has been associated with colorectal cancer (see, for example, US Patent 9,957,570;). In addition to changes in methylation across the genome, specific changes in methylation within specific genes can be indicative of specific cancers (see, for example, U.S. Patent 8,150,626). Li et al. (2012, J.

Epidemiol., 22: 384-94) provides a review of the association between various types of cancer (eg, breast, bladder, gastric, lung, prostate, head and neck squamous cell and nasopharynx) and aberrant methylation.

In addition or alternatively, additional types of nucleic acids or characteristics of nucleic acids have been associated with several types of cancer.

Non-limiting examples of such nucleic acids or characteristics of nucleic acids include the presence or absence of several microRNAs (miRNAs) that have been used in the diagnosis of colon, prostate, colorectal and ovarian cancer (see, for example, D'Souza et al., 2018, PLos One, 13: e0194268; Fukagawa et al., 2017, Cancer Sci., 108: 886-96; Giraldez et al., 2018, Methods Mol.

Biol., 1768: 459-74; U.S. Patent 8,343,718; 9,410,956; and 9,074,206).

For a review of the specific association of miR-22 with cancer, see Wang et al. (2017, Int.

J.

Oncol. 50: 345-55); the abnormal expression of long noncoding RNAs (lncRNAs) has also been used as a biomarker in cancers such as prostate cancer, colorectal cancer, cervical cancer, melanoma, non-small cell lung cancer, gastric cancer, endometrial carcinoma and hepatocellular carcinoma (see, for example, Wang et al., 2017, Oncotarget, 8: 58577086; Wang et al., 2018, Mol.

Cancer, 17: 110; Yu et al., 2018, Eur.

Rev.

Med.

Pharmacol.

Sci., 22: 4812-9; Yu et al., 2018, Eur.

Rev.

Med.

Pharmacol.

Sci., 22: 993-1002; Zhang et al., 2018, Eur.

Rev.

Med.

Pharmacol.

Sci., 22: 4820-7; Zhang et al., 2018, Eur.

Rev.

Med.

Pharmacol.

Sci., 22: 2304-9; Xie et al., 2018, EBioMedicine, 33: 57-67; and U.S. Patent 9,410,206); the presence or absence of circular RNA (circRNA) has been used as a biomarker in lung cancer, breast cancer, gastric cancer, colorectal cancer and liver cancer (for example, Geng et al., 2018, J.

Hematol.

Oncol., 11:98) and melanoma (e.g., Zhang et al., 2018, Oncol.

Lett. 16: 1219-25); changes in telomeric DNA (for example, in length or heterozygosity) or in centromeric DNA (for example, changes in the expression of centromeric genes) were also associated with cancers (for example, prostate, breast, lung, lymphoma and Ewing's sarcoma) (see for example, Baretton et al., 1994, Cancer Res., 54: 4472-80; Liscia et al., 1999, Br.

J.

Cancer, 80: 821-6; Proctor et al., 2009, Biochim.

Biophys.

Acta, 1792: 260-74; and Sun et al., 2016, Int.

J.

Cancer, 139: 899-907); various mutations (eg, deletions), rearrangements and / or changes in the number of copies in mitochondrial DNA (mtDNA) have been used prognostically and diagnostically for various types of cancer (eg, prostate cancer, melanoma, breast cancer, cancer lung and colorectal cancer). See, for example, Maragh et al., 2015, Cancer Biomark., 15: 763-73; Shen et al., 2010, Mitochondrion, 10: 62-68; Hosgood et al., 2010, Carcinogen., 31: 847-9;

Thyagarajan et al., 2012, Cancer Epid. Biomarkers & Prev., 21: 1574-81; and U.S. Patent 9,745,632; and the abnormal presence, absence or quantity of messenger RNAs (mRNAs) have also been correlated with various types of cancer, including, without limitation, breast cancer, Wilms' tumors and cervical cancer (see, for example, Guetschow et al. , 2012, Anal. Bioanaly. Chem., 404: 399-406; Schwienbacher et al., 2000, Cancer Res., 60: 1521-5; and Ngan et al., 1997, Genitourin Med., 73: 54- 8 ). Each of these quotes is incorporated by reference in its entirety here.

[187] This document provides methods and materials for assessing and / or treating mammals (eg, humans) having or suspected of having cancer. In some embodiments, this document provides methods and materials for identifying a mammal as having cancer. For example, a sample (for example, a blood sample) obtained from a mammal can be evaluated to determine whether the mammal has cancer based, at least in part, on the presence or absence of one or more first biomarkers (for example, example, genetic biomarkers) and / or a high level of one or more second biomarkers (for example, peptide biomarkers) in the sample. A panel of biomarkers (for example, a set of one or more biomarkers) described herein may include the presence of two or more (for example, three, five, nine, 10, 25, 100, 250, 500, 1000, 1500, 2000, 2500, or more) biomarkers (for example, biomarkers associated with cancer). In some embodiments, a panel of biomarkers may include about 2,011 biomarkers (for example, about 2,001 genomic biomarkers and about 10 peptide biomarkers). In some embodiments, the methods and materials described here may also include identifying the location (for example, the anatomical site) of a cancer in a mammal. For example, a sample (for example, a blood sample) obtained from a mammal can be evaluated to determine the location of cancer in the mammal based, at least in part, on the presence or absence of one or more first biomarkers (for example, genetic biomarkers) and / or a high level of one or more second biomarkers (for example, peptide biomarkers). In some embodiments, the methods and materials described herein may also include treating a mammal with cancer (for example, administering one or more cancer treatments to treat the mammal). For example, a sample (for example, a blood sample) obtained from a mammal can be evaluated to determine whether the mammal has cancer based, at least in part, on the presence or absence of one or more first biomarkers (for example, genetic biomarkers) and / or a high level of one or more second biomarkers (eg peptide biomarkers) and administer one or more cancer treatments to treat the mammal (eg to reduce the severity of cancer, to reduce a cancer symptom, and / or reduce the number of cancer cells present in the mammal).

[188] The term "high level", as used in this document, in relation to a level of peptide biomarker refers to any level higher than the reference level of the peptide normally observed in a sample (for example, a sample of reference) of one or more healthy mammals. In some embodiments, a reference sample may be a sample obtained from a mammal that does not have cancer. For example, for a peptide biomarker associated with colorectal cancer, a reference sample can be a sample obtained from an individual who does not have colorectal cancer. In some embodiments, a reference sample may be a sample obtained from the same mammal in which the high level of a peptide biomarker is observed, where the reference sample was obtained before the onset of cancer. In some embodiments, a reference sample obtained from the same mammal is frozen or preserved for future use as a reference sample. In some embodiments, when the reference samples have undetectable levels of a peptide biomarker, a high level can be any detectable level of the peptide biomarker. It will be appreciated that comparable sample levels are used when determining whether a specific level is a high level or not.

[189] Any suitable mammal can be assessed and / or treated as described herein. A mammal can be a mammal with cancer. A mammal can be a mammal suspected of having cancer. In some embodiments, humans or other primates, such as monkeys, can be assessed for the presence or absence of one or more first biomarkers (for example, genetic biomarkers) and / or a high level of one or more second biomarkers (for example, biomarkers peptides) as described herein. In some modalities, dogs, cats, horses, cows, pigs, sheep, mice and rats can be assessed for the presence or absence of one or more first biomarkers (for example, genetic biomarkers) and / or a high level of one. or more second biomarkers (e.g., peptide biomarkers) as described herein. For example, a human being can be assessed for the presence or absence of one or more first biomarkers (for example, genetic biomarkers) and / or a high level of one or more second biomarkers (for example, peptide biomarkers), as described here and optionally can be treated with one or more cancer treatments, as described here.

[190] Any appropriate sample of a mammal can be assessed as described here (for example, assessed for the presence or absence of one or more first biomarkers (for example, genetic biomarkers) and / or a high level of a or more second bi-markers (for example, peptide biomarkers)). In some embodiments, a sample may include DNA (for example, genomic DNA). In some embodiments, a sample may include DNA without cells (for example, circulating tumor DNA (ctDNA)). In some embodiments, a sample may include peptides. For example, a sample may include circulating peptides (for example, cancer-related peptides). As used herein, a "circulating peptide" is a peptide that can be detected in any closed system (for example, the circulatory system) within a mammal's body. In some embodiments, a sample may be a fluid sample (for example, a liquid biopsy). Examples of samples that may contain DNA and / or peptides include, without limitation, blood (eg, whole blood, serum or plasma), anion, tissue, urine, cerebrospinal fluid, saliva, sputum, bronchoalveolar lavage , bile, lymphatic fluid, cyst fluid, feces, ascites, Pap tests, breast milk and expired air condensation. For example, a plasma sample can be assessed for the presence or absence of one or more first biomarkers (for example, genetic biomarkers) and / or a high level of one or more second biomarkers (for example, peptide biomarkers), as described here.

[191] In some embodiments, a sample can be processed (for example, to isolate and / or purify DNA and / or peptides from the sample). For example, DNA isolation and / or purification may include cell lysis (for example, using detergents and / or surfactants), protein removal (for example, using a protease) and / or RNA removal (for example, example using an RNase). As another example, isolation and / or purification of peptides can include cell lysis (for example, using detergents and / or surfactants), DNA removal (for example, using a DNase) and / or RNA removal (for example, using an RNase).

[192] Any appropriate biomarkers can be used as described here (for example, to determine whether a mammal has cancer based, at least in part, on the presence or absence of one or more first biomarkers (for example, genetic biomarkers) and / or a high level of one or more second biomarkers (for example, peptide biomarkers) in the sample. Examples of biomarkers include, without limitation, genetic biomarkers, peptide biomarkers, metabolites, mRNA transcripts, miRNAs, patterns of methylation (eg, DNA methylation patterns), proteins (eg, antibodies) and chromatin patterns. In some embodiments, the presence of one or more genetic biomarkers can be used to identify a mammal as having cancer. some modalities, one or more high-level peptide markers may be used to identify a mammal as having cancer In some modalities, the presence of one or more gen biomarkers Ethics and a high level of one or more peptide biomarkers in combination can be used to identify a mammal as having cancer. In some embodiments, detecting the presence of one or more genetic biomarkers and a high level of one or more peptide biomarkers in combination may increase the specificity and / or sensitivity of the detection compared to the detection of genetic biomarkers or peptide biomarkers in isolation.

[193] A genetic biomarker can be any appropriate genetic biomarker. For example, a genetic biomarker can be a genetic biomarker associated with cancer. A genetic biomarker can include a modification in a gene. Examples of modifications include, without limitation, single base substitutions, insertions, deletions, indels, translocations and variations in the number of copies. A genetic biomarker can be in any appropriate gene. In some embodiments, a genetic biomarker may include a modification (for example, an inactivating modification)

in a tumor suppressor gene.

In some embodiments, a genetic biomarker may include a modification (for example, an activating modification) in an oncogene.

Examples of genes that can include a genetic biomarker include, without limitation, NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A, GNAS, JUN, ABCA7, ACVR1B, ACVR2A, AJUBA, AKT1, ALB, ALDOB, ALK, AMBRA1, AMER1, AMOT, ANKRD46, APC, AR, ARHGAP35, ARID1A, ARID1B, ARID2, ARID4B, ARL15, ARMCX1, ASLL1, ASXL1, ASXL1, ASLL1 ATG5, ATM, ATRX, ATXN2, AXIN1, B2M, BAP1, BCL9, BCLAF1, BCOR, BIRC6, BIRC8, BLVRA, BRAF, BRCA1, BRCA2, BRD7, BRE, BRWD3, BTBD7, BTRC, C11orf70, C12orf57, C2 C8orf34, CAMKV, CAPG, CASP8, CBFB, CBX4, CCAR1, CCDC117, CCDC88A, CCM2, CCNC, CCND1, CCR3, CD1D, CD79B, CDC73, CDCP1, CDH1, CDK12, CDK4, CDKN1A, CDKN1B, CDKN1, CEKN1 CENPB, CEP128, CHD2, CHD4, CHD8, CHEK2, CHRDL1, CHUK, CIC, CLEC4C, CMTR2, CNN2, CNOT1, CNOT4, COL11A1, COPS4, COX7B2, CREBBP, CSDE1, CSMD3, CTCF, CTDNEP1, CTCF, CTDNEP1, CYB5B, DACH1, DCHS1, DCUN1D1, DDX3X, DDX5, DHX15, DHX16, DICER1, DIRC2, DIS3, DIXDC1, DKK2, DNAJB5, DNER, D NM1L, DNMT3A, EED, EGFR, EIF1AX, EIF2AK3, EIF2S2, EIF4A1, EIF4A2, ELF3, EMG1, EMR3, EP300, EPB41L4A, EPHA2, EPS8, ERBB2, ERBB3, ER- RFI1, EXO5, F5, FBN2, FBXW7, FCER1G, FGFR1, FGFR2, FGFR3, FLT3, FN1, FOXA1, FUBP1, FUS, GALNTL5, GATA3, GGCT, GIGYF2, GK2, GLIPR2, GNPTAB, GNRHR, GXM1, GOL, GZMA, HDAC1, HERC1, HERC4, HGF, HIST1H2BO, HLA-A, HLA-B, HMCN1, HNRNPA1, HRAS, HSP90AB1, ID3, IDH1, IDH2, IFNGR2, IFT88, IKZF2, INO80, INPP1, INPP1, JAK2, KANSL1, KAT8, KATNAL1, KBTBD7, KCNMB4, KDM5C, KDM6A, KEAP1, KIAA1467, KLF4, KMT2A, KMT2B,

KMT2C, KMT2D, KMT2E, KRAS, KRT15, LAMTOR1, LARP4B, LPAR2, LYN, MAP2K1, MAP2K2, MAP2K4, MAP3K1, MAP4K3, MAPK1, MAX, MB21D2, MBD1, MBD6, MBNL1, MBNL3, MED, MKLN1, MLLT4, MOAP1, MORC4, MS4A1, MSI1, MTOR, MYC, MYCN, MYD88, MYL6, MYO1B, MYO6, NAA15, NAA25, NAP1L2, NAP1L4, NCOA2, NCOR1, NEK9, NF1, NF2, NFE2, NFE2, NFE2 NIT1, NKX3-1, NME4, NOTCH1, NOTCH2, NPM1, NRAS, NSD1, PBRM1, PCBP1, PCOLCE2, PHF6, PIK3CA, PIK3CB, PIK3R1, POLA2, POT1, PPARD, PPM1D, PPP2R1A, PPP2P1, PSIP1, PTEN, PTH2, PTMS, PTN, PTPN11, RAB18, RAC1, RAF1, RANBP3L, RAPGEF6, RASA1, RB1, RBBP6, RBM10, RBM26, RC3H2, REL, RERE, RFC4, RHEB, RHOA, RH1 RNF43, RPL11, RPL5, RQCD1, RRAS2, RUNX1, RXRA, SARM1, SCAF11, SEC22A, SENP3, SENP8, SETD1B, SETD2, SF3A3, SF3B1, SFPQ, SIN3A, SKAP2, SMAD2, SMAD3, SMAD4, SMAD4, SMAD4 SNCB, SOS1, SOX4, SOX9, SP3, SPEN, SPOP, SPSB2, STAG2, STK11, STK31, SUFU, TAF1A, TARDBP, TAS2R30, TBL1XR1, TBX3, TCF12, TCF7L2, TET2, TEX1 1, TFDP2, TGFBR2, THRAP3, TM9SF1, TMCO2, TMED10, TMEM107, TMEM30A, TMPO, TNFRSF9, TNRC6B, TP53, TP53BP1, TRAF3, TRIM8, TRIP12, TSC1, TTK, TTR, TUBA3C, UBR2, U2AF1 UNKL, UPP1, USO1, USP28, USP9X, VHL, VN1R2, VPS33B, WAC, WDR33, WDR47, WT1, WWP1, XPO1, YOD1, ZC3H13, ZDHHC4, ZFHX3, ZFP36L1, ZFP36L2, ZGRF3, ZYM3 ZNF292, ZNF318, ZNF345, ZNF600, ZNF750, and ZNF800. For example, a genetic biomarker can be in one or more NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A and / or GNAS.

In some embodiments, the methods and materials described herein may include detecting one or more genetic biomarkers (for example, one or more modifications to one or more genes). For example, methods and materials described in this document may include detecting mutations in one or more genes that encode any of the proteins set out in Example 1, or in one or more of the genes set out in Table 3 or Table 5. In some embodiments, the methods and materials described herein may include detecting one or more of the modifications set out in Table 3 or Table 5. In some embodiments, methods and materials described herein may include detecting the presence or absence of about

2,001 modifications in about 16 genes. For example, methods and materials described here may include detecting the presence or absence of about 2,001 genetic biomarkers in one or more of NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A and GNAS. In some embodiments, genetic biomarkers can be those described elsewhere (see, for example, Bettegowda et al., 2014 Science translational medicine 6: 224ra224; Haber et al., 2014 Cancer Discov 4: 650-661; Dawson et al., 2013 N Engl J Med 368: 1199-1209; Wang et al., 2015 Science translational medicine 7: 293ra104; Forshew et al., 2012 Science translational medicine 4: 136ra168; Abbosh et al., 2017 Nature 545: 446-451; Beddowes et al., 2017 Breast 34 (Suppl 1): S31-S35; and Phallen et al., 2017 Science translational medicine 9).

[194] Any appropriate method can be used to detect the presence or absence of one or more biomarkers (for example, genetic biomarkers), as described here. In some modalities, one or more genetic biomarkers can be detected independently (for example, through syncopeplex peptide tools). In some modalities, one or more genetic biomarkers can be detected simultaneously (for example, through multiplex DNA tools, such as "chips" or microarrays). Examples of methods for detecting genetic biomarkers include, without limitation, sequencing (eg, PCR-based sequencing, such as multiplex PCR-based sequencing), DNA hybridization methods (eg, Southern blotting), digestion methods with restriction enzymes, PCR-based multiplex methods, digital PCR methods, digital droplet PCR (ddPCR) methods, PCR-based singleplex PCR methods, Sanger sequencing methods, next generation sequencing methods ( for example, real-time single molecule sequencing, nanopore sequencing and Polony sequencing), quantitative PCR methods, binding methods and microarray methods.

In some modalities, methods and materials described here may include sequencing based on multiplex PCR.

For example, methods and materials described in this document may include sequencing based on multiplex PCR, as set out in Example 1. In some method modalities provided here, the presence of one or more mutations present in a sample obtained from an individual is detected using a method that is performed to increase the sensitivity of massively parallel sequencing instruments with an error reduction technique.

For example, these techniques may allow the detection of rare mutant alleles in a range of 1 mutant model between 5,000 and 1,000,000 wild-type models.

In some embodiments, the presence of one or more mutations present in a sample obtained from an individual is detected by amplifying DNA (for example, DNA obtained from cells in a sample or DNA without cells) to form amplicon families in which each member of a family is derived from a single model molecule in cellless DNA, where each member of a family is marked by a common oligonucleotide bar code and where each family is marked by a distinct oligonucleotide bar code.

For example, the presence of one or more mutations present in a sample obtained from an individual can be detected by assigning a unique identifier (UID) to each model molecule, amplifying each model molecule uniquely marked to create UID families and sequencing redundantly the amplification products. In some embodiments, the oligonucleotide bar code is introduced into the model molecule by an amplification step with a population of primers that collectively contain a plurality of oligonucleotide bar codes. In some embodiments, the oligonucleotide bar code is endogenous to the model molecule and an adapter comprising a DNA synthesis initiation site is attached to one end of the model molecule adjacent to the oligonucleotide bar code. See, for example, Kinde et al., 2011 Proc Natl Acad Sci USA 108: 9530-9535.

[195] In some modalities of the methods provided here, the presence of one or more mutations present in a sample obtained from an individual is detected using sequencing technology (for example, next generation sequencing technology). A variety of sequencing technologies are known in the art. For example, methods for detecting and characterizing circulating viral DNA in cellless DNA can be described elsewhere (see, for example, Haber and Velculescu, 2014 Cancer Discov., 4: 650-61). Non-limiting examples of such techniques include SafeSeqs (see, for example, Kinde et al., 2011 Proc Natl Acad Sci USA; 108: 9530-5), OnTarget (see, for example, Forshew et al., 2012 Sci Transl Med; 4: 136ra68,) and TamSeq (see, for example, Thompson et al., 2012 PLoS ONE, 7: e31597). In some modalities, the presence of one or more mutations present in a sample obtained from an individual is detected using digital drop PCR (ddPCR), a method that is known to be highly sensitive to detecting mutations. In some modes, the presence of one or more mutations present in a sample obtained from an individual is detected using other sequencing technologies, including, but not limited to, chain termination techniques, shotgun techniques, synthesis sequencing methods thesis, methods using microfluidics, other capture technologies or any of the other sequencing techniques known in the art that are useful for detecting small amounts of DNA in a sample (eg, ctDNA in a cell-free DNA sample).

[196] In some modalities, the presence of one or more mutations present in a sample obtained from an individual is detected using array-based methods. For example, the step of detecting a genetic change (for example, one or more genetic changes) in DNA without cells is performed using a microarray of DNA. In some embodiments, a DNA microarray can detect more than one of a plurality of mutations in cancer cells. In some modalities, DNA without cells is amplified before the detection of the genetic alteration. Non-limiting examples of array-based methods that can be used in any of the methods described here include: a complementary DNA microarray (cDNA) (see, for example, Kumar et al. 2012 J. Pharm. Bioallied Sci. 4 (1): 21-26; Laere et al. 2009 Methods Mol. Biol. 512: 71-98; Mackay et al. 2003 Oncogene 22: 2680-2688; Alizadeh et al. 1996 Nat. Genet. 14: 457 -460), an oligonucleotide microarray (see, for example, Kim et al. 2006 Carcinogenesis 27 (3): 392-404; Lodes et al. 2009 PLoS One 4 (7): e6229), a chromosome clone chip artificial bacterial (BAC) (see, for example, Chung et al. 2004 Genome Res. 14 (1): 188-196; Thomas et al. 2005 Gen-name Res. 15 (12): 1831-1837), a microarray single nucleotide polymorphism (SNP) (see, for example, Mao et al. 2007 Curr. Genomics 8 (4): 219-228; Jasmine et al. 2012 PLoS One 7 (2): e31968), a comparative genomic hybridization array based on microarrays (CGH arrangement) (see, for example, Beers and Nederlof, 2006 Bre ast Cancer

Res. 8 (3): 210; Pinkel et al. 2005 Nat. Genetics 37: S11-S17; Michels et al. 2007 Genet. Med. 9: 574-584), a molecular inversion probe (MIP) assay (see, for example, Wang et al. 2012 Cancer Genet 205 (7- 8): 341-55; Lin et al. 2010 BMC Genomics 11: 712). In some modalities, the cDNA microarray is an Affymetrix microarray (see, for example, Irizarry 2003 Nucleic Acids Res 31: e15; Dalma-Weiszhausz et al. 2006 Methods Enzymol. 410: 3-28), a NimbleGen microarray ( see, for example, Wei et al. 2008 Nucleic Acids Res 36 (9): 2926-2938; Albert et al. 2007 Nat. Methods 4: 903-905), an Agilent microarray (see, for example, Hughes et al. 2001 Nat. Biotechnol. 19 (4): 342-347), or a BeadArray arrangement (see, for example, Liu et al. 2017 Biosens Bioelectron 92: 596-601). In some embodiments, the oligonucleotide microarray is a DNA placement arrangement (see, for example, Mockler and Ecker, 2005 Genomics 85 (1): 1-15; Bertone et al. 2006 Genome Res 16 (2): 271 -281). Other suitable arrangement-based methods are known in the art.

[197] In some embodiments, multiplex PCR-based sequencing may include a number of amplicons that provide improved sensitivity for the detection of one or more genetic biomarkers. For example, multiplex PCR-based sequencing can include about 60 amplicons (for example, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65 , 66, 67, 68, 69 or 70 amplicons). In some embodiments, multiplex PCR-based sequencing may include 61 amplicons. Amplicons produced using multiplex PCR-based sequencing can include nucleic acids ranging from about 15 bp to about 1000 bp (for example, from about 25 bp to about 1000 bp, from about 35 bp to about 1000 bp, from about 50 bp to about 1000 bp, from about 100 bp to about 1000 bp, from about 250 bp to about 1000 bp, from about 500 bp to about 1000 bp, from about 750 bp to about 1000 bp, from about 15 bp to about 750 bp, from about 15 bp to about 500 bp, from about 15 bp to about 300 bp, from about 15 bp to about 200 bp, from about 15 bp to about 100 bp, from about 15 bp to about 80 bp, from about 15 bp to about 75 bp, from about 15 bp to about 50 bp, from about 15 bp to about 40 bp, from about 15 bp to about 30 bp, from about 15 bp to about 20 bp, from about 20 bp to about 100 bp, from about 25 bp to about 50 bp or from about 30 bp to about 40 bp). For example, amplicons produced using multiplex PCR-based sequencing can include nucleic acids about 33 bp in length.

[198] A peptide biomarker can be any appropriate peptide biomarker. In some modalities, a peptide biomarker can be a cancer-associated peptide biomarker. For example, a peptide biomarker can be a peptide with high levels in a cancer (for example, compared to a reference level of the peptide). Examples of peptide biomarkers include, without limitation, AFP, Angiopoietin-2, AXL, CA125, CA 15-3, CA19-9, CD44, CEA, CYFRA 21-1, DKK1, Endoglina, FGF2, Folistatina, Galectina-3 , G -CSF, GDF15, HE4, HGF, IL-6, IL-8, Kallikrein-6, Lactin, LRG-1, Mesothelin, Midkine, Myeloperoxidase, NSE, OPG, OPN, PAR, Prolactin, sEGFR, sFas , SHBG, sHER2 / sEGFR2 / sErbB2, sPE-CAM-1, TGFa, Thrombospondin-2, TIMP-1, TIMP-2 and Vitronectin. For example, a peptide biomarker can include one or more of OPN, IL-6, CEA, CA125, HGF, Myeloperoxidase, CA19-9, Midkine and / or TIMP-

1. In some embodiments, methods and materials described herein may include one or more peptide biomarkers (for example, one or more peptides have elevated levels in a cancer). For example, methods and materials described herein may include one or more of the peptide biomarkers set forth in Example 1. For example, methods and materials described herein may include elevated levels of one or more of the peptide biomarkers set out in Table 4. In some instances - dalities, methods and materials described here may include detecting levels of about 10 peptides. For example, the methods and materials described herein may include detection of the level of OPN, IL-6, CEA, CA125, HGF, Myeloperoxidase, CA19-9, Midkine and / or TIMP-1. In some embodiments, peptide biomarkers may be as described elsewhere (see, for example Liotta et al., 2003 Clin Adv Hematol Oncol 1: 460-462; Wang et al., 2016 Expert Rev Proteomics 13: 99-114; and Patz, Jr. et al., 2007 J Clin Oncol 25: 5578-5583).

[199] Any appropriate method can be used to detect the level (for example, a high level) of one or more biomarkers (for example, peptide biomarkers), as described here. In some embodiments, the levels of one or more peptide biomarkers can be detected independently (for example, through singleplex peptide tools). In some embodiments, the levels of one or more peptide biomarkers can be detected simultaneously (for example, through multiplexed peptide tools such as chips or microarrays). Examples of methods for detecting peptide levels include, without limitation, spectrometry methods (for example, high performance liquid chromatography (HPLC) and liquid chromatography-mass spectrometry (LC / EM)), antibody-dependent methods (for example, example, enzyme linked immunosorbent assay (ELISA), protein immunoprecipitation, immunelectrophoresis, western blotting and protein immunostaining) and aptamer-dependent methods. In some embodiments, the level of one or more peptide biomarkers can be detected as described in the Examples. For example, the level of one or more peptide biomarkers can be detected by multiplex immunoassay.

[200] Any appropriate cancer can be identified and / or treated as described here. In some modalities, a cancer can be a common cancer. In some modalities, a cancer can be a cancer in which no blood-based test is available. In some modalities, a cancer can be a cancer in which no test for early detection is available. In some modalities, a cancer can be a Stage I cancer. In some modalities, a cancer can be a Stage II cancer. In some modalities, a cancer can be a Stage III cancer. In some modalities, a cancer can be a Stage IV cancer. In some modalities, a cancer can be a surgically resectable cancer. Examples of cancers that are identified as described here (for example, based, at least in part, on the presence or absence of one or more first biomarkers (for example, genetic biomarkers) and / or a high level of a or more second biomarkers (for example, peptide biomarkers)) include, without limitation, liver cancer, ovarian cancer, esophageal cancer, stomach cancer, pancreatic cancer, colorectal cancer, lung cancer, cancer of breast and prostate cancer.

[201] The methods and materials provided here can also identify the location of the cancer (for example, it can determine the location and / or type of cancer) in a mammal. The location of any cancer (for example, the location and / or type of cancer) that has mutations in one or more genetic biomarkers and / or one or more peptide biomarkers, as described here, can be determined. In some modalities, the materials and methods provided here can identify the presence of colorectal cancer. For example, the presence of one or more genetic biomarkers in one or more mutations of the APC, KRAS and / or TP53 genes and a high level of CEA in a sample obtained from a mammal can be used to identify the presence of cancer colorectal in the mammal. In some modalities, the materials and methods provided here can identify the presence of liver cancer. For example, the presence of one or more genetic biomarkers in one or more TP53, CTNNB1 and / or TERT and a high level of AFP in a sample obtained from a mammal can be used to identify the presence of liver cancer in the mammal. In some modalities, the materials and methods provided here can identify the presence of ovarian cancer. For example, the presence of one or more genetic biomarkers in TP53 and a high level of CA125 in a sample obtained from a mammal can be used to identify the presence of ovarian cancer in the mammal. In some embodiments, the materials and methods provided here can identify the presence of pancreatic cancer. For example, the presence of one or more genetic biomarkers in KRAS (for example, codon KRAS 12) and a high level of CA19-9 in a sample obtained from a mammal can be used to identify the presence of pancreatic cancer in the mammal.

[202] In some embodiments, a mammal identified as having cancer as described here (for example, based at least in part on the presence or absence of one or more first biomarkers (for example, genetic biomarkers) and / or a level elevated number of one or more second biomarkers (for example, peptide biomarkers) can have the diagnosis of cancer confirmed using any appropriate method. Examples of methods that can be used to diagnose or confirm the diagnosis of cancer include, without limitation, physical exams (eg, pelvic exam), imaging tests (eg, ultrasound or CT scans), cytology and tests - tissue tests (eg biopsy).

[203] In some embodiments, any of the various methods disclosed here can be performed on individuals who have previously undergone cancer treatments. In some embodiments, the methods provided here can be used to determine the effectiveness of the treatment. For example, an individual with cancer may receive treatment (also referred to here as "therapeutic intervention"), after which the continued presence of cancer or the amount of cancer (or the lack of it) is determined by detecting the presence of one or more more mutations in one or more genes (for example, NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A and / or GNAS) and / or high levels one or more peptide biomarkers (for example, OPN, IL-6, CEA, CA125, HGF, Myeloperoxidase, CA19-9, Midkine and / or TIMP-1).

[204] In some modalities, once an individual has been determined to have cancer, the individual can be additionally monitored or selected for increased monitoring. In some modalities, the methods provided here can be used to select an individual for increased monitoring in a period prior to the period when conventional techniques are able to diagnose the individual with early-stage cancer. For example, the methods provided here for selecting an individual for increased monitoring can be used when an individual has not been diagnosed with cancer by conventional methods and / or when an individual is not known to harbor cancer. In some modalities, an individual selected for increased monitoring can be administered a diagnostic test (for example, any of the diagnostic tests disclosed here) with an increased frequency compared to an individual who has not been taught for increased monitoring. For example, an individual selected for increased monitoring can be administered a diagnostic test twice a day, daily, biweekly, weekly, bi-monthly, monthly, quarterly, semi-annually, or any-

want frequency in it.

In some modalities, an individual selected for increased monitoring can be administered with one or more additional diagnostic tests compared to an individual who was not selected for increased monitoring.

For example, an individual selected for increased monitoring can receive two diagnostic tests, while an individual who was not selected for increased monitoring receives only a single diagnostic test (or no diagnostic test). In some modalities, an individual who was selected for increased monitoring can also be selected for additional diagnostic tests.

Once the presence of a cancer cell is identified (for example, by any of the methods disclosed here), it may be beneficial for the individual to undergo increased monitoring (for example, to assess the progression of the tumor or cancer in the individual and / or assess the development of additional mutations in the cancer cells) and further diagnostic tests (for example, to determine the size and / or the exact location of the tumor that houses the cancer cell). In some modalities, a therapeutic intervention is administered to the individual who is selected for increased monitoring after detecting a mutation in the cancer cells.

Any of the therapeutic interventions disclosed herein or known in the art can be administered.

For example, an individual who was selected for increased monitoring can be monitored later and a therapeutic intervention can be administered if the presence of the cancer cell is maintained throughout the increased monitoring period.

In addition or alternatively, an individual who has been selected for increased monitoring can be administered with a therapeutic intervention and monitored additionally as the therapeutic intervention progresses.

In some modalities, after administering a therapeutic intervention to an individual who has been selected for increased monitoring, the increased monitoring will reveal one or more additional mutations in the cancer cells. In some embodiments, one or more additional cancer cell mutations will provide a cause for administering a different therapeutic intervention (for example, a resistance mutation may arise in a cancer cell during the therapeutic intervention, whose cancer cell harboring the mutation in resistance is resistance to the original therapeutic intervention).

[205] In some modalities, once an individual has been determined to have cancer, the individual can be administered more tests or selected for additional diagnostic tests. In some modalities, the methods provided here can be used to select an individual for increased monitoring in a period prior to the period when conventional techniques are able to diagnose the individual with early-stage cancer. For example, the methods provided here for selecting an individual for additional diagnostic tests can be used when an individual has not been diagnosed with cancer by conventional methods and / or when an individual is not known to harbor cancer. In some embodiments, an individual selected for additional diagnostic tests may be administered a diagnostic test (for example, any of the diagnostic tests disclosed here) at an increased frequency compared to an individual who was not selected for diagnostic tests. additional diagnostics. For example, an individual selected for additional diagnostic tests can be administered with a diagnostic test twice a day, daily, biweekly, weekly, bi-monthly, monthly, quarterly, bi-annually, or any frequency in it. In some embodiments, an individual selected for additional diagnostic tests may be administered with one or more additional diagnostic tests compared to an individual who was not selected for additional diagnostic tests.

For example, an individual selected for additional diagnostic tests may receive two diagnostic tests, while an individual who was not selected for additional diagnostic tests receives only a single diagnostic test (or no diagnostic test). In some modalities, the diagnostic test method can determine the presence of the same type of cancer as the cancer that was originally detected.

In addition or alternatively, the diagnostic test method can determine the presence of a different type of cancer such as the cancer detected originally.

In some modalities, the diagnostic test method is a scan.

In some modalities, the scan is a computed tomography (CT), a computerized tomography angiography (CTA), an esophagram (a barium swallow), a barium enema, an magnetic resonance imaging (MRI) scan, a PET scan (PET), an ultrasound (for example, an endobronchial ultrasound, an endoscopic ultrasound), an X-ray, a DEXA scan.

In some modalities, the diagnostic test method is a physical examination, such as an anoscopy, a bronchoscopy (for example, an autofluorescence bronchoscopy, a white light bronchoscopy, a navigation bronchoscopy), a colonoscopy, a digital tomosynthesis of the breast, a retrograde endo- scopic cholangiopancreatography (ERCP), an ensophagogastroduodenoscopy, a mammogram, a Pap smear, a pelvic exam, a positron emission tomography and computerized tomography scan (PET-CT). In some modalities, an individual who has been selected for further diagnostic tests may also be selected for increased monitoring.

Once the presence of a cancer cell is identified (for example, by any of the methods disclosed here), it may be beneficial for the individual to undergo increased monitoring (for example, to assess the progression of the tumor or cancer in the individual and / or assess the development of additional mutations in cancer cells) and further diagnostic tests (for example, to determine the size and / or exact location of the tumor that houses the cancer cell). In some modalities, a therapeutic intervention is administered to the individual who is selected for additional diagnostic tests after detecting a mutation in the cancer cells. Any of the therapeutic interventions disclosed herein or known in the art can be administered. For example, an individual who has been selected for additional diagnostic tests can be administered with an additional diagnostic test and a therapeutic intervention can be administered if the presence of the cancer cell is confirmed. In addition or alternatively, an individual who has been selected for additional diagnostic tests can be administered with a therapeutic intervention and can be monitored additionally as the therapeutic intervention progresses. In some embodiments, after an individual who has been selected for additional diagnostic testing has been administered with a therapeutic intervention, the additional test will reveal one or more additional mutations in the cancer cells. In some modalities, one or more additional cancer cell mutations will provide a cause for administering a different therapeutic intervention (for example, a resistance mutation may arise in a cancer cell during the therapeutic intervention, whose cancer cell harboring the resistance mutation is a resistance to the original therapeutic intervention).

[206] Once identified as having cancer, as described here (for example, based, at least in part, on the presence or absence of one or more first biomarkers (for example, biomar-

genetic markers) and / or a high level of one or more second biomarkers (for example, peptide biomarkers) and / or the presence of aneuploidy), a mammal can be treated with one or more cancer treatments (also referred to herein as "therapeutic interventions").

[207] In certain respects, state-of-the-art tests are provided here that can detect mutations in cancer cells that are released into the bloodstream. In some modalities, one or more types of cancer can be detected. The assays described here can be used as a cancer screening test with improved sensitivity, while maintaining specificity. For example, these assays can combine the detection of genetic biomarkers (for example, mutations in the circulating tumor DNA (ctDNA)) with the detection of protein threshold markers in plasma. In some embodiments, a genetic biomarker (for example, a mutation in the circulating tumor DNA (ctDNA)) is tested alone. In some modalities, protein biomarkers are tested alone. In some modalities, the combination of the genetic biomarker (for example, a mutation in the circulating tumor DNA (ctDNA)) and protein markers may be superior to any single marker. In an exemplary pilot study of 1,703 patients (1,240 cancer and 463 healthy controls), the panel of ctDNA and protein biomarkers showed a sensitivity of 64% and specificity of 99.35%.

[208] In some embodiments, the tests described here can be applied to apparently healthy individuals. For example, the trials described here can reduce deaths and cancer by detecting pre-symptomatic cancers through a blood test performed during routine visits to doctors. In addition, the trials described here can be applied to patients with localized cancers, particularly those who have been or can be treated or resected. The assays described here can improve management and prognosis through previous detection of recurrence.

[209] In some embodiments, genetic biomarkers (eg, mutations in DNA without cells (eg, ctDNA)) can be tested from a variety of biological samples obtained from an individual (eg, a human individual) including, but not limited to blood, plasma, serum, urine, cerebrospinal fluid, saliva, sputum, bronchoalveolar lavage, bile, lymphatic fluid, cyst fluid, feces, ascites and combinations thereof.

[210] In some embodiments, genetic biomarkers (eg, mutations in ctDNA) in 10 exemplary genes (AKT1, APC, BRAF, CDKN2A, CTNNB1, FBXW7, FGF2, GNAS, HRAS, KRAS) and elevation of 11 exemplary protein markers in serum beyond a threshold (CA19-9 (> 92 U / ml), CEA (> 7,507 pg / ml), CA125 (> 577 U / ml), AFP (> 21,321 pg / ml), Prolactin (> 145,345 pg / ml), HGF (> 899 pg / ml), OPN (> 157,772 pg / ml), TIMP-1 (> 176,989 pg / ml), Folistatin (> 1,970 pg / ml), G-CSF (> 800 pg / ml), and CA15-3 (> 98 U / ml)) can be tested in the assay. In some modalities, genetic biomarkers (for example, ctDNA mutations) in 16 exemplary genes (KT1, APC, BRAF, CDKN2, CTNNB1, FBXW7, FGFR2, GNAS, HRAS, KRAS, PPP2R1A, TP53, PTEN, PIK3CA, EGFR and NRAS) and elevation of 11 exemplary protein biomarkers in serum beyond a threshold (CA19-9 (> 92 U / ml), CEA (> 7,507 pg / ml), CA125 (> 577 U / ml), AFP (> 21,321 pg / ml), Prolactin (> 145,345 pg / ml), HGF (> 899 pg / ml), OPN (> 157,772 pg / ml), TIMP-1 (> 176,989 pg / ml), Folistatin (> 1,970 pg / ml), G-CSF (> 800 pg / ml), and CA15-3 (> 98 U / ml)) can be tested in the assay. In some modalities, the presence of a genetic biomarker (for example, a mutation) or an elevation beyond the threshold of any of the protein biomarkers constitutes a positive result (for example, identification of cancer in an individual). In some modalities, the presence of genetic biomarkers (for example, mutations) or elevations in two or more protein biomarkers is a positive result. For example, the presence of genetic biomarkers (for example, mutations) in two, three, four, five, six, seven, eight, nine or ten exemplary genes and / or elevations in two, three, four, five , six, seven, eight, nine, ten or eleven protein markers are a positive result.

[211] In some embodiments, proteins (eg, protein biomarkers) can be tested from a variety of biological samples obtained from an individual (eg, a human individual) including, but not limited to, blood, plasma , serum, urine, cerebrospinal fluid, saliva, sputum, bronchoalveolar lavage, bile, lymphatic fluid, cyst fluid, feces, ascites and combinations of the same. Proteins (for example, protein biomarkers) found in large quantities in cancers can be tested for amounts of proteins that do not occur in healthy humans. Examples of proteins (for example, protein biomarkers), any, two, three, four, five, six, seven, eight, nine, ten or eleven of which can be tested include, without limitation, the antigen of carbohydrates 19-9 (CA19-9), carcinoembryonic antigen (CEA), hepatocyte growth factor (HGF), osteopontin (OPN), CA125, AFP, prolactin, TIMP-1, folistatin, G-CSF and CA15-3 . Any protein biomarker known in the art can be used when a threshold value is obtained above which normal, healthy human individuals do not fall, but human individuals with cancer do fall.

[212] In some embodiments, a CA19-9 threshold level can be at least about 92 U / mL (for example, about 92 U / mL). In some embodiments, a CA19-9 threshold level may be 92 U / mL. In some embodiments, a threshold level of CEA may be at least about 7,507 pg / ml (for example, about 7,507 pg / ml

ml). In some embodiments, a threshold level of CEA may be 7.5 ng / mL. In some embodiments, a threshold level of HGF can be at least about 899 pg / ml (for example, about 899 pg / ml). In some embodiments, a threshold level of HGF can be 0.92 ng / mL. In some embodiments, an OPN threshold level can be at least about 157,772 pg / ml (for example, about 157,772 pg / ml). In some embodiments, an OPN threshold level may be 158 ng / mL. In some embodiments, a CA125 threshold level can be at least about 577 U / ml (for example, about 577 U / ml). In some embodiments, a CA125 threshold level may be 577 U / mL. In some embodiments, a threshold level of AFP may be at least about 21,321 pg / ml (for example, about 21,321 pg / ml). In some embodiments, a threshold level of AFP may be 21,321 pg / ml. In some embodiments, a threshold level of prolactin may be at least about 145,345 pg / ml (for example, about 145,345 pg / ml). In some embodiments, a threshold level of prolactin may be

145,345 pg / ml. In some embodiments, a threshold level of TIMP-1 may be at least about 176,989 pg / ml (for example, about 176,989 pg / ml). In some embodiments, a threshold level of TIMP-1 may be 176,989 pg / ml. In some embodiments, a threshold level of follistatin may be at least about 1,970 pg / ml (for example, about 1,970 pg / ml). In some embodiments, a threshold level of follistatin may be 1,970 pg / ml. In some embodiments, a threshold level of G-CSF can be at least about 800 pg / ml (for example, about 800 pg / ml). In some embodiments, a threshold level of G-CSF can be 800 pg / ml. In some embodiments, a CA15-3 threshold level can be at least about 98 U / ml (for example, about 98 U / ml). In some embodiments, a CA15-3 threshold level may be 98 U / ml. In some embodiments, a threshold level of CA19-9, CEA and / or OPN can be 5%, 10%, 15%, 20%, 25%, 30%,

35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 100% or more above the threshold levels listed above (for example, greater than a threshold level of 92 U / ml for CA-19-9, 7,507 pg / ml for CEA, 899 pg / ml for HGF, 157,772 pg / ml for OPN, 577 U / ml for CA125, 21,321 pg / ml for AFP, 145,345 pg / ml for pro- lactin, 176,989 pg / ml for TIMP-1, 1,970 pg / ml for follistatin, 800 pg / ml for G-CSF and / or 98 U / ml for CA15-3 ).

[213] In some embodiments, a protein biomarker threshold level may be higher than the levels that are typically tested for diagnostic or clinical purposes. For example, the CA19-9 threshold level can be greater than about 37 U / ml (for example, greater than about 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95 or more U / mL). In addition or alternatively, the threshold level of CEA may be greater than about 2.5 µg / L (for example, greater than 3.0, 3.5, 4.0, 4.5, 5.0, 5 , 5, 6.0, 6.5, 7.0, 7.5 or more ug / L). Additionally or alternatively, the CA125 threshold level can be greater than about 35 U / mL (for example, greater than about 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90 , 95, 100, 150, 200, 250, 300, 350, 400, 450, 500, 550 or more U / ml). In addition or alternatively, the AFP threshold level may be greater than about 21 ng / mL (for example, greater than about 25, 30, 40, 50, 60, 70, 80, 90, 100, 150, 200 , 250, 300, 350, 400 or more ng / L). Additionally or alternatively, the threshold level of TIMP-1 may be greater than about 2300 ng / mL (for example, greater than about

2,500, 3,000, 4,000, 5,000, 6,000, 7,000, 8,000, 9,000, 10,000, 15,000,

20,000, 25,000, 30,000, 35,000, 40,000 or more ng / L). Additionally or alternatively, the threshold level of follistatin may be greater than about 2 µg / mL (for example, greater than 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 5 , 5, 6.0, 6.5, 7.0, 7.5 or more ug / L). Additionally or alternatively, the CA15-3 threshold level can be greater than about 30 U / mL (for example, greater than about 35, 40, 45, 50, 55, 60, 65, 70, 75, 80 ,

85, 90, 95 or more U / ml). In some modalities, the detection of one or more protein biomarkers at threshold levels higher than that normally tested during traditional clinical or diagnostic tests can improve the sensitivity of cancer detection.

[214] In some embodiments, an assay includes detecting threshold biomarkers of proteins in a biological sample (for example, any biological sample disclosed here, such as plasma) without detecting genetic biomarkers (for example, mutations in the Circulating tumor DNA (ctDNA)). For example, an assay may include detecting one or more of CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1, follistatin, G-CSF and / or CA15-3 in a biological sample. In some embodiments, an assay may include detecting one or more of CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1, follistatin, G-CSF and / or CA15-3 in a biological sample in any one of the threshold levels disclosed here. In some embodiments, once an assay that includes detecting threshold protein biomarkers in a biological sample is performed, subsequent testing or monitoring is performed (for example, any of the varieties of additional diagnostic tests or au monitoring techniques) - mentioned here disclosed). In some embodiments, once an assay that includes detecting threshold protein markers in a biological sample is performed, a second assay that includes detecting a genetic biomarker (for example, a genetic biomarker present in DNA without cells (for example , ctDNA)) can be performed (for example, detecting any of several genetic changes in the genetic biomarkers that are present in the DNA or in the cell-free ctDNA, as described here).

[215] In some embodiments, an assay includes detecting a genetic biomarker in the circulating tumor DNA (ctDNA) in a biological sample (for example, any biological sample disclosed here, such as plasma), without detecting protein biomarkers with lipids. meow.

For example, an assay may include detecting genetic biomarkers (eg, genetic changes) in one or more of the genes disclosed herein, including, without limitation, CDKN2A, FGF2, GNAS, ABL1, EVI1, MYC, APC, IL2, TNFAIP3 , ABL2, EWSR1, MYCL1, ARHGEF12, JAK2, TP53, AKT1, FEV, MYCN, ATM, MAP2K4, TSC1, AKT2, FGFR1, NCOA4, BCL11B, MDM4, TSC2, ATF1, FGFR1OP, NFKB2, BLM, , FGFR2, NRAS, BMPR1A, MLH1, WRN, BCL2, FUS, NTRK1, BRCA1, MSH2, WT1, BCL3, GOLGA5, NUP214, BRCA2, NF1, BCL6, GOPC, PAX8, CARS, NF2, BCR, HMGA1, PDG, HCR , NOTCH1, BRAF, HMGA2, PIK3CA, CDH1, NPM1, CARD11, HRAS, PIM1, CDH11, NR4A3, CBLB, IRF4, PLAG1, CDK6, NUP98, CBLC, JUN, PPARG, SMAD4, PALB2, CCND1, KIT, PTPN11, CEBPA , PML, CCND2, KRAS, RAF1, CHEK2, PTEN, CCND3, LCK, REL, CREB1, RB1, CDX2, LMO2, RET, CREBBP, RUNX1, CTNNB1, MAF, ROS1, CYLD, SDHB, DDB2, MAFB, SMO, DDX5 , SDHD, DDIT3, MAML2, SS18, EXT1, SMARCA4, DDX6, MDM2, TCL1A, EXT2, SMARCB1, DEK, MET, TET2, FBXW7, SOCS1, EGFR, MITF, TFG, FH, STK 11, ELK4, MLL, TLX1, FLT3, SUFU, ERBB2, MPL, TPR, FOXP1, SUZ12, ETV4, MYB, USP6, GPC3, SYK, ETV6, IDH1, and / or TCF3. In some embodiments, an assay may include detecting genetic biomarkers (eg, genetic changes) in one or more of AKT1, APC, BRAF, CDKN2A, CTNNB1, FBXW7, FGF2, GNAS, HRAS, KRAS.

In some modalities, an assay may include detecting genetic biomarkers (eg, genetic changes) in one or more of KT1, APC, BRAF, CDKN2, CTNNB1, FBXW7, FGFR2, GNAS, HRAS, KRAS, PPP2R1A, TP53 , PTEN, PIK3CA, EGFR and NRAS.

In some embodiments, since an assay that includes detecting genetic biomarkers present in ctDNA in a biological sample is performed, subsequent testing or monitoring is performed (for example, any of the varieties of additional diagnostic tests or enhanced monitoring techniques disclosed herein ). In some modalities, since an assay that includes detecting a genetic biomarker present in ctDNA in a biological sample is performed, a second assay that includes detecting protein biomarkers at high thresholds can be performed (for example, detecting any of the varieties of protein biomarkers described herein including, but not limited to, carbohydrate antigen 19-9 (CA19-9), carcinoembryonic antigen (CEA), hepatocyte growth factor (HGF), osteopontin (OPN), CA125, AFP, prolactin, TIMP-1, follistatin, G-CSF and CA15-3 and combinations thereof).

[216] In some embodiments, at least two codons or at least two amplicons from a tumor suppressor gene or an oncogene can be tested. In some embodiments, at least three, at least four, or at least five or more codons or amplicons from an individual tumor suppressor gene or oncogene can be tested. In some modalities, the more distributed the mutations are in a gene, the more codons or amplicons may be desired.

[217] Examples of codons for tumor suppressor genes and oncogenes that can be tested include, without limitation, one or more of the following codons and their surrounding splicing sites: AKT1 codons 16-18; codons 1304-1311, 1450-1459 from APC; BRAF codons 591-602; codons 51-58, 76-88 of CDKN2A; codons 31-39, 38-47 of CTNNB1; codons 856-868 of EGFR; codons 361-371, 464-473, 473-483, 498-507 of FBXW7; codons 250-256 of FGFR2; codons 199-208 of GNAS; HRAS codons 7-19; codons 7-14, 57-65, 143-148 of KRAS; codons 3-15, 54-63 of NRAS; codons 80-90, 343-348, 541-551, 1038-1050 of PIK3CA; codons 175-187 of PPP2R1A; codons 90-98,

125-132, 133-146, 145-154 of PTEN; and codons 10-22, 25-32, 33-40, 40- 52, 52-64, 82-94, 97-110, 112-125, 123-125, 126-132, 132-142, 150-163, 167-177, 175-186, 187-195, 195-206, 207-219, 219-224, 226-237, 232-245, 248-261, 261-268, 272-283, 279-290, 298- 307, 307-314, 323- 331, 333-344, 344-355, 367-375, 374-386 of TP53. All or some of these regions can be tested. In some modalities, the mutation can be in KRAS, for example, in codon 12 or 61. In other modalities, the mutation can be in other KRAS codons. In some embodiments, the mutation may be in CDKN2A (for example, any of the CDKN2A mutations identified in Example 2). In some embodiments, the mutation may be in tumor suppressors or oncogenes, including but not limited to ABL1; EVI1; MYC; APC; IL2; TNFAIP3; ABL2; EWSR1; MYCL1; ARHGEF12; JAK2; TP53; AKT1; FEV; MYCN; ATM; MAP2K4; TSC1; AKT2; FGFR1; NCOA4; BCL11B; MDM4; TSC2; ATF1; FGFR1OP; NFKB2; BLM; MEN1; VHL; BCL11A; FGFR2; NRAS; BMPR1A; MLH1; WRN; BCL2; FUS; NTRK1; BRCA1; MSH2; WT1; BCL3; GOLGA5; NUP214; BRCA2; NF1; BCL6; GOPC; PAX8; CARS; NF2; BCR; HMGA1; PDGFB; CBFA2T3; NOTCH1; BRAF; HMGA2; PIK3CA; CDH1; NPM1; CARD11; HRAS; PIM1; CDH11; NR4A3; CBLB; IRF4; PLAG1; CDK6; NUP98; CBLC; JUN; PPARG; SMAD4; PALB2; CCND1; KIT; PTPN11; CEBPA; PML; CCND2; KRAS; RAF1; CHEK2; PTEN; CCND3; LCK; REL; CREB1; RB1; CDX2; LMO2; RET; CREBBP; RUNX1; CTNNB1; MAF; ROS1; CYLD; SDHB; DDB2; MAFB; SMO; DDX5; SDHD; DDIT3; MAML2; SS18; EXT1; SMARCA4; DDX6; MDM2; TCL1A; EXT2; SMARCB1; DEK; MET; TET2; FBXW7; SOCS1; EGFR; MITF; TFG; FH; STK11; ELK4; MLL; TLX1; FLT3; SUFU; ERBB2; MPL; TPR; FOXP1; SUZ12; ETV4; MYB; USP6; GPC3; SYK; ETV6; HDI1; and TCF3. Tests can include amplification and / or sequencing.

[218] In some embodiments, sequence determination with a high degree of precision can be advantageous when analytes are present in low amounts and / or fractions. High precision sequence determination can employ oligo-nucleotide, endogenous or exogenous barcodes. These can be introduced into a model analyte by amplification, for example, in the case of an exogenous bar code. Alternatively, an endogenous oligonucleotide barcode can be used by attaching an oligonucleotide adapter molecule to it, for example. The adapter molecule may contain an initiation site for DNA synthesis and / or for hybridization to a solid surface. The adapter can be immediately adjacent to the endogenous bar code or to a fixed number of nucleotides in the endogenous bar code.

[219] In some embodiments, oligonucleotide barcodes allow individual model molecules to be labeled in the sample prior to processing, in particular amplification. For example, by requiring that all or a high proportion or a threshold proportion of family members (with the same oligonucleotide barcode) exhibit a mutation, it is possible to filter or minimize false-positive mutations that arise during amplification and / or other DNA synthesis or processing. See, for example, Kinde I, Wu J, Papadopoulos N, Kinzler KW, & Vogelstein B (2011) Detection and quantification of rare mutations with massively parallel sequencing. Proc Natl Acad Sci U S A 108 (23): 9530-9535, the content of which is explicitly incorporated by reference. Additionally or alternatively, a threshold for mutation calling for a mutation to occur in two different families. Multiple filters of this nature can be applied.

[220] In some embodiments, the methods provided here may be used to detect a genetic biomarker (for example, a genetic change (for example, one or more genetic changes)) in the circulating tumor DNA present in DNA without cells , where the

Cellless DNA is present in an amount of less than about 1500 ng, for example, less than about 1400 ng, less than about 1300 ng, less than about 1200 ng, less than about 1100 ng, less than about 1000 ng, less than about 900 ng, less than about 800 ng, less than about 700 ng, less than about 600 ng, less than about 500 ng, less than about 400 ng, less than about 300 ng, less than about 200 ng, less than about 150 ng, less than about 100 ng, less than about 95 ng, less than about 90 ng, less than about 85 ng, less than about 80 ng , less than about 75 ng, less than about 70 ng, less than about 65 ng, less than about 60 ng, less than about 55 ng, less than about 50 ng, less than about 45 ng, less than about 40 ng, less than about 35 ng, less than about 30 ng, less than about 25 ng, less than about 20 ng, less than about 15 ng, less than about 10 n g or less than about 5 ng.

In some embodiments, the methods provided here can be used to detect a genetic biomarker (for example, a genetic alteration (for example, one or more genetic alterations)) in the circulating tumor DNA present in DNA without cells, where the Circulating tumor DNA represents 100% of DNA without cells.

In some embodiments, the methods provided here can be used to detect a genetic biomarker (eg, a genetic alteration (eg, one or more genetic alterations)) in the circulating tumor DNA present in the cellless DNA, where the tumor DNA circulating represents less than 100% of DNA without cells, for example, about 95%, about 90%, about 85%, about 80%, about 75%, about 70%, about 65%, about about 60%, about 55%, about 50%, about 45%, about 40%, about 35%, about 30%, about 25%, about 20%, about 15%, about 10%, about 5%, about 4%, about 3%, about 2%, about 1%, about 0.95%, about 0.90%, about 0.85%, about 0.80%, about 0.75%, about 0.70%, about

0.65%, about 0.60%, about 0.55%, about 0.50%, about 0.45%, about 0.40%, about 0.35%, about 0 , 30%, about 0.25%, about 0.20%, about 0.15%, about 0.10%, about 0.09%, about 0.08%, about 0, 07%, about 0.06%, about 0.05% of DNA without cells, or less.

[221] In some embodiments, one or more genetic biomarkers present in cellless DNA (eg, ctDNA) and / or one or more protein biomarkers can be tested from a variety of biological samples isolated or obtained from a individual (for example, a human being in question) including, without limitation, blood, plasma, serum, urine, cerebrospinal fluid, saliva, sputum, bronchoalveolar lavage, bile, lymphatic fluid, cyst fluid, feces, asites and combinations thereof. In some embodiments, one or more genetic biomarkers present in DNA without cells (for example, ctDNA) and one or more protein biomarkers can be tested from the same sample. For example, a single sample can be isolated or obtained from an individual, whose single sample can be tested for one or more genetic biomarkers present in DNA without cells (for example, ctDNA), one or more protein biomarkers, or both. One or more genetic biomarkers present in DNA without cells (for example, ctDNA) and one or more protein biomarkers can be tested from the sample at the same time or at different times. For example, the sample can be tested for one or more genetic biomarkers present in DNA without cells (eg ctDNA) for the first time and for one or more protein biomarkers the second time or vice versa. In some embodiments, the sample can be refrigerated, frozen or stored for future testing. In some embodiments, one or more genetic biomarkers present in DNA without cells (eg, ctDNA) and one or more protein biomarkers can be tested from different samples. For example, a first sample can be isolated or obtained from an individual and tested for one or more genetic biomarkers present in DNA without cells (for example, ctDNA), and a second sample can be isolated or obtained from the individual and tested for one or more more protein biomarkers. The first and second samples can be of the same type (for example, plasma or serum) or of different types. The first and / or second samples can be refrigerated, frozen or stored for future testing.

[222] In some embodiments, any of the assay varieties disclosed herein can be repeated to increase the accuracy of mutation detection. The tests can be done in duplicate or triplicate, for example. In some modalities, positive tests can be repeated on the same initial sample as a patient. Additionally or alternatively, a second sample can be obtained from a patient later, for example, when positive results are found. Any of the assay varieties described here, including ctDNA and / or protein biomarkers, can be repeated or performed in parallel replicates.

[223] In some modalities, a radiological, ultrasound or other technique can be applied to any individual (for example, a human individual) in which a mutation is detected. The technique can be applied to the entire body, to a single organ or to a region of the body. The technique can be used, for example, to check for the presence of a specific type of cancer, to confirm the presence of a cancer or to identify the location of a cancer in the body. In some modalities, the technique is a scan. In some modalities, the scan is a computed tomography (CT), a computed tomography angiography (CTA), an esophagram (a barium swallow), a barium enema, a magnetic resonance imaging (MRI), a PET tomography (PET), an ultrasound (for example, an endobronchial ultrasound, an endoscopic ultrasound), an X-ray, a DEXA scan, or positron emission tomography and computed tomography (PET-CT). In some modalities, the technique is a physical examination, such as an anoscopy, a bronchoscopy (for example, an autofluorescence bronchoscopy, a white light bronchoscopy, a navigation bronchoscopy), a colonoscopy, a digital breast tomosynthesis, a retrograde endoscopic cholangiopancreatography (ERCP), an ensophagogastroduodenoscopy, a mammography, a Pap smear or a pelvic exam. In some modalities, the technique is a biopsy (for example, a bone marrow aspiration, a tissue biopsy). In some modalities, biopsy is performed by fine needle aspiration or surgical excision. In some embodiments, the technique also includes obtaining a biological sample (for example, a tissue sample, a urine sample, a blood sample, a swab, a saliva sample, a mucosal sample (eg, sputum , bronchial secretion), a nipple aspiration, a secretion or excretion). In some modalities, the technique includes determining exosomal proteins (eg, an exosomal surface protein (eg, CD24, CD147, PCA-3)) (Soung et al. (2017) Cancers 9 (1): pii: E8). In some modalities, the diagnostic test method is an oncotype DX® test (Baehner (2016) Ecancermedicalscience 10: 675).

[224] In some modalities, several methods described here can be used to detect cancers selected from the group consisting of: pancreatic cancer, colon cancer, esophageal cancer, stomach cancer, ovarian cancer, liver cancer, lung cancer and breast cancer and combinations thereof.

[225] In some embodiments, methods provided here (for example, methods in which genetic biomarkers present in cellless DNA (eg, ctDNA) and high threshold protein biomarkers are detected in a biological sample isolated from the individual) be used to select a treatment for an individual.

For example, once an individual has been determined to have cancer (for example, pancreatic cancer, colon cancer, esophageal cancer, stomach cancer, ovarian cancer, liver cancer, lung cancer or breast cancer) by any of the various methods disclosed herein, an appropriate treatment can be selected (for example, any of the various therapeutic interventions described here). In some embodiments, the methods provided here (for example, methods in which genetic biomarkers present in cellless DNA (eg, ctDNA) and high-threshold protein biomarkers are detected in a biological sample isolated from the individual) can be used to select an individual for treatment.

For example, once an individual has been determined to have cancer (for example, pancreatic cancer, colon cancer, esophageal cancer, stomach cancer, ovarian cancer, liver cancer, lung cancer or cancer of the breast) by any of the various methods disclosed herein, that individual can be identified as an appropriate individual to receive treatment (for example, any of the various therapeutic interventions described here). In some modalities, methods provided here (for example, methods in which genetic biomarkers present in DNA without cells (eg, ctDNA) and high threshold protein biomarkers are detected in an isolated biological sample from the individual) used to select a treatment for an individual for increased monitoring.

For example, once an individual has been determined to have cancer (for example, pancreatic cancer, colon cancer, esophageal cancer, stomach cancer, ovarian cancer, liver cancer, lung cancer or breast cancer) by any of the various methods disclosed herein, that individual can be identified as an appropriate individual to receive increased monitoring (for example, any of the various monitoring techniques described here). In some modalities, the methods provided here (for example, methods in which genetic biomarkers present in DNA without cells (eg, ctDNA) and high-threshold protein biomarkers are detected in a biological sample isolated from the individual) be used to select an individual for additional diagnostic tests. For example, once an individual has been determined to have cancer (for example, pancreatic cancer, colon cancer, esophageal cancer, stomach cancer, ovarian cancer, liver cancer, lung cancer or breast cancer) by any of the various methods disclosed herein, that individual can be identified as an appropriate individual to receive additional diagnostic tests (for example, any of the various diagnostic techniques described here).

[226] In some modalities, the methods provided here may be used to detect the presence of cancer (for example, pancreatic cancer, colon cancer, esophageal cancer, stomach cancer, ovarian cancer, liver cancer, lung cancer or breast cancer) in a period prior to the diagnosis of the individual with early cancer and / or in a period prior to the individual exhibiting symptoms associated with cancer. For example, the methods provided here can be used when an individual has not been diagnosed with cancer and / or when an individual is not known to harbor a cancer cell.

[227] In some modalities, certain protein biomarkers can be detected at high threshold levels to detect specific types of cancer. For example, high levels of CA19-9 can be detected to indicate the presence of pancreatic cancer. Additionally or alternatively, high levels of CEA threshold can be detected to indicate the presence of, for example, colon, gastric, pancreatic, lung and / or breast cancer. In addition or alternatively, high CA-125 threshold levels can be detected to indicate the presence of, for example, ovarian cancer. In addition or alternatively, high AFP threshold levels can be detected to indicate the presence of liver cancer. In addition or alternatively, high levels of prolactin threshold can be detected to indicate the presence of, for example, ovarian, breast and / or lung cancer. Additionally or alternatively, high HFG threshold levels can be detected to indicate the presence of, for example, esophageal, gastric and / or liver cancer. In addition or alternatively, high OPN threshold levels can be detected to indicate the presence of, for example, ovarian, breast and / or lung cancer. In addition or alternatively, high TIMP-1 threshold levels can be detected to indicate the presence of, for example, colon and / or pancreatic cancer. In addition or alternatively, high levels of follistatin threshold can be detected to indicate the presence of, for example, ovarian, breast and / or lung cancer. In addition or alternatively, high G-CSF threshold levels can be detected to indicate the presence of, for example, ovarian cancer. Additionally or alternatively, high threshold levels of CA-15-3 can be detected to indicate the presence of, for example, breast cancer. Exemplary protein biomarkers detected in various types of cancer are shown in Example 2.

[228] In some embodiments, assays for genetic biomarkers (eg, genetic alterations) can be combined with assays for elevated protein biomarkers to increase the sensitivity of a blood test for low-stage pancreatic cancer . In some modalities, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95% or more of these cancers can be detected by this combination test, including some patients with a favorable prognosis. In some modalities, 64% of these cancers can be detected through this combination test, including some patients with a favorable prognosis. One of the design characteristics of certain studies established here was the inclusion of only patients with resectable pancreatic cancer and patients with advanced disease (ie, Stage III or IV) were excluded. Although this exclusion reduces the sensitivity that could be achieved by evaluating all patients with pancreatic cancer, regardless of stage, resectable cases represent a promising group with advantageous clinical relevance in relation to the evaluation of a screening technology. In some modalities, the methods provided here can be used to detect all pancreatic cancers in individuals (for example, humans).

[229] Whether the combination of genetic biomarkers present in ctDNA and protein markers could increase sensitivity to any of them was not known before the present disclosure. In fact, it was conceivable that the same patients with detectable circulating protein markers would widely overlap those who release DNA into the circulation. This was of particular concern for patients with early-stage cancer, because ctDNA and protein-based markers are known to be considerably higher in patients with advanced cancer compared to those with earlier cancer (Lennon AM & Gog- gins M (2010) Diagnostic and Therapeutic Response Markers. Pancreatic Cancer, (Springer New York, New York, NY), pp 675-701, Locker GY, et al. (2006) ASCO 2006 update of recommendations for the use of tumor markers in gastrointestinal cancer. J Clin Oncol 24 (33): 5313-5327, Bettegowda C, et al. (2014) Detection of circulating tumor DNA in early- and late-stage human malignancies. Science translational medicine 6 (224) : 224ra224).

[230] In some modalities of the methods provided here, a very high specificity (for example, 99.5%: 95% CI 97-100%) can be achieved.

For example, only a false positive among 182 healthy individuals with an average age of 64 years was observed in the studies established here.

Given the relatively low frequency of cancer in the general population, the specificity of any potentially useful blood screening test for pancreatic cancer is preferably high, for example, preferably> 99%. Otherwise, the number of false positives would greatly exceed the number of true positives (that is, it has subideal positive predictive value) (Lennon AM, et al. (2014) The Early Detection of Pancreatic Cancer: What Will It Take to Diagnose and Treat Curable Pancreatic Neoplasia - Cancer Res 74 (13): 3381- 3389). Such rigor for screening tests is not normally necessary for tests to monitor diseases in patients with known cancer.

For monitoring, specificity may be somewhat relaxed, in the interest of obtaining greater sensitivity.

High specificity has been achieved with several methods disclosed here in at least two ways.

First, ctDNA was used as one of the components of the test.

Mutations in KRAS are exquisitely specific for neoplasms and their specificity is traditionally limited by technical, rather than biological, factors.

The incorporation of the molecular barcode in several assays described here (for example, using a Safe-SeqS technique) can minimize the false positive results of sequencing that have traditionally been the main technical problems faced by any assays based on ctDNA.

Mutations in KRAS are particularly suitable for early detection strategies, because they are rarely found in clones that arise during age-related clonal hematopoiesis.

Such clones, which may represent early forms of myelodysplasia, are a potential source of false positive ctDNA assays.

The vast majority of these mutations occur in nine genes (DNMT3A, TET2, JAK2, ASXL1, TP53, GNAS, PPM1D, BCORL1 and SF3B1) (48-50), posing challenges to the use of these genes as biomarkers in ctDNA-based assays. Second, high thresholds were used to mark protein markers as positive. These thresholds were based on previous studies in the literature or on an independent set of controls, allowing the avoidance of positive scores in the vast majority of healthy patients (Kim JE, et al. (2004) Clinical usefulness of carbohydrate antigen 19-9 as a screening test for pancreatic cancer in an asymptomatic population. J Gastroenterol Hepatol 19 (2): 182-186). In some modes, these high thresholds can be used without a general reduction in sensitivity because the ctDNA assay added sensitivity by itself and positive cases of ctDNA only partially overlapped positive cases of protein biomarkers (See, for example, Fig. 9, Fig. 10 and Example 2).

[231] Protein biomarkers have been combined with each other in the past for greater sensitivity (Dong T, Liu CC, Petricoin EF, & Tang LL (2014) Combining markers with and without the limit of detection. Stat Med 33 (8): 1307-132). For example, it was shown that the combination of CA19-9 and TIMP-1 was more sensitive to the detection of PDAC than any of the biomarkers alone (Zhou W, et al. (1998) Identifying markers for pancreatic cancer by gene expression analysis Cancer Epidemiol Biomarkers Prev 7 (2): 109-112). More recently, it was shown that the combination of CA19-9, TIMP-1 and LRG-1 was more sensitive for detecting early PDAC than CA19-9 alone (Dong T, Liu CC, Petricoin EF, & Tang LL (2014) Combining markers with and without the limit of detection (Stat Med 33 (8): 1307-1320). The combination of protein biomarkers with ultra-sensitive ctDNA, as disclosed here, is different. A recent study evaluated a

combination of ctDNA and CA19-9 for pancreatic cancer, but found no benefit in combining biomarkers with only CA19-9. Without being limited by theory, it is possible that this conclusion has been reached due to the inadequate sensitivity of the test used to detect mutations in KRAS (Le Calvez-Kelm F, et al. (2016) KRAS mutations in blood circulating cell -free DNA: the pancreatic cancer case-control (Oncotarget 7 (48): 78827-78840). In addition, the specificity for ctDNA achieved in this study was relatively low, reducing its suitability for screening.

[232] In some modalities, the methods provided here can be used to detect resectable cancers through a non-invasive blood test in most patients.

[233] In some modalities, the results obtained using any of the various methods disclosed herein may underestimate the survival benefits of early detection. Most of the patients studied here, despite having resectable cancer, were symptomatic and their cancers were discovered only because of their symptoms. Consequently, 77% of the patients in the cohort described here were in stage IIB and the average size of the tumors in these patients was 3 cm. In some modalities, in a study of screening for asymptomatic individuals, a greater proportion of patients at an early stage, with smaller tumors, can be discovered using any of the various forms of methods disclosed here. In some modalities, any of the several methods disclosed here may be more sensitive for detecting patients with larger tumors and with a worse prognosis than for patients with smaller tumors, even though all tumors may be surgically resectable (See, for example, Fig. 9B and Example 2). In some modalities, mutations in KRAS can be found in the circulation of patients with types of cancer other than those of the pancreas, mainly those of the lung (Herbst RS, Heymach JV, & Lippman SM (2008) Lung cancer. N Engl J Med 359 (13): 1367-1380), and CA19-9, CEA, HGF, and OPN expression can be elevated in several other cancer types (Kim JE, et al. (2004) Clinical usefulness of carbohydrate antigen 19-9 as a screening test for pancreatic cancer in an asymptomatic population. J Gastroenterenterol Hepatol 19 (2): 182-186; Thomas DS, et al. (2015) Evaluation of serum CEA, CYFRA21-1 and CA125 for the early detection of colorectal cancer using longitudinal preclinical samples.Br J Cancer 113 (2): 268-274; Di Renzo MF, et al. (1995) Overexpression and amplification of the met / HGF receptor gene during the progression of colorec- such cancer.Clin Cancer Res 1 (2): 147-154; El-Tanani MK, et al. (2006) The regulation and role of osteopontin in malignant transformation and cancer. Cytok ine Growth Factor Rev 17 (6): 463-474). Thus, in some modalities, patients with a positive test using any of the various forms disclosed herein may undergo additional appropriate imaging studies to identify the location of the tumor.

[234] In some modalities, the methods provided here establish a basis for the evaluation of patients at high risk of PDAC and for the implementation of early detection strategies (Klinich M, et al. (2017) An RNA- based signature enables high specificity detection of circulating tumor cells in hepatocellular carcinoma Proc Natl Acad Sci USA 114 (5): 1123-1128). As an example, it is known that recent onset diabetes is associated with an increased risk of pancreatic cancer. Approximately 1% of diabetic patients aged 50 and over are diagnosed with pancreatic cancer within 3 years after first meeting the criteria for diabetes (Chari ST, et al. (2005) Probability of pancreatic cancer following diabetes: the population -based study, Gastroenterology 129 (2): 504-511). With an incidence of 1%, it is expected that the PPV / VPN of certain combination tests disclosed here will be 54% and 99.6%, respectively, in this population, which is well within the range of the tests screening tests currently approved for cancer.

[235] The available evidence indicates that many cancers have detectable genetic biomarkers present in ctDNA in their early stages, generally more commonly than that seen in pancreatic cancer (Bettegowda C, et al. (2014) Detection of circulating tumor DNA in early- and late-stage human malignancies.Science trans- national medicine 6 (224): 224ra224). Likewise, a large number of protein biomarkers have been described for the detection of various types of cancer (Liotta LA & Petricoin EF, 3rd (2003) The promise of proteomics. Clin Adv Hematol Oncol 1 (8): 460 -462). These protein biomarkers can be thresholded according to any of the various forms described here, allowing the use of ctDNA-protein combinations to detect a variety of cancers (Bette- gowda C, et al. (2014) Detection of circulating tumor DNA in early- and late-stage human malignancies.Science translational medicine 6 (224): 224ra224).

[236] In certain respects, assays are provided here that can be used as a cancer screening test with improved sensitivity while maintaining specificity. In some modalities, the assays combine to detect mutations in genetic biomarkers present in the circulating tumor DNA (ctDNA) with the detection of protein biomarkers with plasma threshold. In some modalities, ctDNA is tested only for the presence of genetic biomarkers. In some modalities, protein biomarkers are tested alone. In some modalities, the combination of genetic biomarkers present in the ctDNA and protein markers can be superior to any single marker. For example, in some modalities, the combination can detect almost two thirds of pancreatic cancers that do not show evidence of distant metastasis at the time of surgical resection. In some embodiments, sequence determination with a high degree of precision can be advantageous when analytes are present in low amounts and / or fractions. High precision sequence determination can employ oligonucleotide, endogenous or exogenous barcodes. These can be introduced into a model analyte by amplification, for example, in the case of an exogenous bar code. Alternatively, an endogenous oligonucleotide barcode can be used by attaching an oligonucleotide adapter molecule to it, for example. The adapter molecule may contain an initiation site for DNA synthesis and / or for hybridization to a solid surface. The adapter can be immediately adjacent to the endogenous barcode or to a fixed number of nucleotides in the endogenous barcode.

[237] In some embodiments, the oligonucleotide barcodes allow the marking of individual model molecules in the sample before processing, in particular amplification. For example, by requiring that all or a high proportion or a threshold proportion of family members (with the same oligonucleotide barcode) exhibit a mutation, it is possible to filter or minimize false-positive mutations that arise during amplification and / or other DNA synthesis or processing. See, for example, Kinde I, Wu J, Papadopoulos N, Kinzler KW, & Vogelstein B (2011) Detection and quantification of rare mutations with massively parallel sequencing. Proc Natl Acad Sci U S A 108 (23): 9530-9535, the content of which is explicitly incorporated by reference. Additionally or alternatively, a threshold for mutation calling for a mutation to occur in two different families. Multiple filters of this nature can be applied.

[238] In some embodiments, the methods provided here may be used to detect a genetic change (for example, one or more genetic changes) in the circulating tumor DNA present in cellless DNA, where cellless DNA is present in a less than about 1500 ng, for example, less than about 1400 ng, less than about 1300 ng, less than about 1200 ng, less than about 1100 ng, less than about 1000 ng, less than about 900 ng, less than about 800 ng, less than about 700 ng, less than about 600 ng, less than about 500 ng, less than about 400 ng, less than about 300 ng, less than about 200 ng, less than about 150 ng, less than about 100 ng, less than about 95 ng, less than about 90 ng, less than about 85 ng, less than about 80 ng, less than about 75 ng , less than about 70 ng, less than about 65 ng, less than about 60 ng, less than about 55 ng, less than about 50 ng, less than about 45 ng, less than about 40 ng, less than about 35 ng, less than about 30 ng, less than about 25 ng, less than about 20 ng, less than about 15 ng, less than about 10 ng or less than about 5 ng.

In some embodiments, the methods provided here can be used to detect a genetic change (for example, one or more genetic changes)) in the circulating tumor DNA present in the cellless DNA, where the circulating tumor DNA represents 100% of the cellless DNA.

In some embodiments, the methods provided here can be used to detect a genetic change (for example, one or more genetic changes) in the circulating tumor DNA present in the cellless DNA, where the circulating tumor DNA represents less than 100% of DNA without cells, for example, about 95%, about 90%, about 85%, about 80%, about 75%, about 70%, about 65%, about 60%, about 55%, about 50%, about 45%, about 40%, about 35%, about 30%, about 25%, about 20%, about 15%, about 10%, about 5%, about 4%, about 3%, about 2%, about 1%, about 0.95%, about 0.90%, about 0.85%, about 0.80% , about 0.75%, about 0.70%, about 0.65%, about 0.60%, about 0.55%, about

0.50%, about 0.45%, about 0.40%, about 0.35%, about 0.30%, about 0.25%, about 0.20%, about 0 , 15%, about 0.10%, about 0.09%, about 0.08%, about 0.07%, about 0.06%, about 0.05% of DNA without cells, or less.

[239] In some embodiments, the presence of genetic biomarkers (for example, mutations in DNA without cells (for example, ctDNA)) can be tested from a variety of biological samples isolated or obtained from an individual ( for example, a human individual) including, but not limited to, blood, plasma, serum, urine, cerebrospinal fluid, saliva, sputum, bronchoalveolar lavage, bile, lymphatic fluid, cyst fluid, feces, ascites and combinations of even. In some modalities, one or more genetic biomarkers present in DNA without cells (for example, ctDNA) and one or more bi-markers of proteins can be tested from the same sample. For example, a single sample can be isolated or obtained from an individual, whose single sample can be tested for genetic biomarkers present in DNA without cells (for example, ctDNA), one or more protein biomarkers, or both. The presence of one or more genetic biomarkers in DNA without cells (for example, ctDNA) and the presence of one or more protein biomarkers can be tested from the sample at the same time or at different times. For example, the sample can be tested for the presence of one or more genetic biomarkers in DNA without cells (eg, ctDNA) for the first time and for the presence of one or more protein biomarkers in the second time or vice versa. In some modalities, the sample can be chilled, frozen or stored for future testing. In some ways, the presence of genetic biomarkers in DNA without cells (for example, ctDNA) and the presence of one or more protein biomarkers can be tested from different samples. For example, a first sample can be isolated or obtained from an individual and tested for the presence of genetic biomarkers in cellless DNA (for example, ctDNA), and a second sample can be isolated or obtained from the individual and tested for the presence of one or more protein biomarkers. The first and second samples can be of the same type (for example, plasma or serum) or of different types. The first and / or second samples can be refrigerated, frozen or stored for future testing.

[240] In some embodiments, any of the assay varieties disclosed herein can be repeated to increase the accuracy of mutation detection. The tests can be done in duplicate or triplicate, for example. In some modalities, positive tests can be repeated on the same initial sample as a patient. Additionally or alternatively, a second sample can be obtained from a patient later, for example, when positive results are found. Any of the assay varieties described here, including ctDNA and / or protein biomarkers, can be repeated or performed in parallel replicates.

[241] In some embodiments, a genetic biomarker (for example, a mutation in DNA without cells) may be in a tumor suppressor gene or in an oncogene. For example, genetic biomarkers (for example, mutations) may be at access points for mutations, for example, sites that are frequently silenced in tumors or other cancers. In some embodiments, the mutation may be in the KRAS, for example, in codon 12 or 61. In other embodiments, the genetic biomarker (eg, mutation) may be in other KRAS codons. In some embodiments, the genetic biomarker (for example, mutation) can be on CDKN2A (for example, any of the CDKN2A mutations identified in Example 2). In some embodiments, the genetic biomarker (for example, mutation) may be in tumor suppressor genes or oncogenes, including but not limited to ABL1; EVI1; MYC; APC; IL2; TNFAIP3; ABL2; EWSR1; MYCL1; ARHGEF12; JAK2; TP53; AKT1; FEV; MYCN; ATM; MAP2K4; TSC1; AKT2; FGFR1; NCOA4; BCL11B; MDM4; TSC2; ATF1; FGFR1OP; NFKB2; BLM; MEN1; VHL; BCL11A; FGFR2; NRAS; BMPR1A; MLH1; WRN; BCL2; FUS; NTRK1; BRCA1; MSH2; WT1; BCL3; GOLGA5; NUP214; BRCA2; NF1; BCL6; GOPC; PAX8; CARS; NF2; BCR; HMGA1; PDGFB; CBFA2T3; NOTCH1; BRAF; HMGA2; PIK3CA; CDH1; NPM1; CARD11; HRAS; PIM1; CDH11; NR4A3; CBLB; IRF4; PLAG1; CDK6; NUP98; CBLC; JUN; PPARG; SMAD4; PALB2; CCND1; KIT; PTPN11; CEBPA; PML; CCND2; KRAS; RAF1; CHEK2; PTEN; CCND3; LCK; REL; CREB1; RB1; CDX2; LMO2; RET; CREBBP; RUNX1; CTNNB1; MAF; ROS1; CYLD; SDHB; DDB2; MAFB; SMO; DDX5; SDHD; DDIT3; MAML2; SS18; EXT1; SMARCA4; DDX6; MDM2; TCL1A; EXT2; SMARCB1; DEK; MET; TET2; FBXW7; SOCS1; EGFR; MITF; TFG; FH; STK11; ELK4; MLL; TLX1; FLT3; SUFU; ERBB2; MPL; TPR; FOXP1; SUZ12; ETV4; MYB; USP6; GPC3; SYK; ETV6; HDI1; and TCF3. In some modalities, protein biomarkers (for example, protein biomarkers) can be tested from a variety of biological samples isolated or obtained from an individual (for example, a human individual) including, but not limited to blood, plasma, serum, urine, cerebrospinal fluid, saliva, sputum, bronchoalveolar lavage, bile, lymphatic fluid, cyst fluid, feces, ascites and combinations thereof.

Protein biomarkers found in large quantities in cancer can be tested for amounts of protein biomarkers that do not occur in healthy humans.

Examples of protein biomarkers, one, two, three or four of which can be tested, include, without limitation, carbohydrate antigen 19-9 (CA19-9), carcinoembryonic antigen (CEA), hepatocyte growth factor ( HGF) and osteopontin (OPN). In some embodiments, a CA19-9 threshold level can be at least about 100 U / ml (for example, about 100 U / ml). In some embodiments, a CA19-9 threshold level may be 100 U / mL. In some embodiments, a threshold level of CEA may be at least about 7.5 ng / mL (for example, about 7.5 ng / mL). In some embodiments, a threshold level of CEA may be 7.5 ng / mL. In some embodiments, a threshold level of HGF can be at least about 0.92 ng / ml (for example, about 0.92 ng / ml). In some embodiments, a threshold level of HGF can be 0.92 ng / mL. In some embodiments, an OPN threshold level can be at least about 158 ng / ml (for example, about 158 ng / ml). In some embodiments, an OPN threshold level may be 158 ng / mL. In some embodiments, a threshold level of CA19-9, CEA and / or OPN can be 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55 %, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 100% or more above the threshold levels listed above (for example, greater than a 100 U / mL level for CA-19-9, 7.5 ng / ml for CEA, 0.92 ng / ml for HGF and / or 158 ng / ml for OPN).

[242] Any protein biomarker known in the art can be used when a threshold value is obtained above which normal, healthy human subjects do not fall, but human subjects with cancer do fall. Non-limiting examples of such protein biomarkers include Translation elongation factor (EEF1A1); Glyceraldehyde-3-phosphate dehydrogenase (GAPDH); Gamma actin (ACTG1); Ferritin, heavy polypeptide 1 (FTH1); Eukaryotic translation elongation factor 1 gamma (EEF1G); Ribosomal protein, large subunit, P0 (RPLP0); 90 kDa alpha heat shock protein (cytosolic), member of class B 1 (HSP90AB1); Muscle pyruvate kinase (PKM2); Ferritin, light polypeptide (FTL); and L3 ribosomal protein (RPL3). Protein biomarkers known to be overexpressed in serum include, but are not limited to, Transferrin, α-1 antitrypsin, apolipo 1 protein, c3a complement, Caveolin-1, Kallikrein 6, glucose-8 regulated protein, α -1 defense, -2, -3, serum C peptide, alpha protein -2-HS glycol, Catenin, Defensin α 6, MMPs, Cyclin D, S100 P, filament protein Lamina A / C and Txl-2 (thioredoxin-like protein-2 ).

[243] In some embodiments, an assay includes detecting threshold biomarkers of proteins in a biological sample (for example, any biological sample disclosed here, such as plasma) without detecting genetic biomarkers (for example, mutations in the Circulating tumor DNA (ctDNA)). For example, an assay may include detecting one or more of CA19-9, CEA, HGF and / or OPN in a biological sample. In some embodiments, an assay may include detecting one or more of CA19-9, CEA, HGF, and / or OPN in a biological sample at any of the threshold levels disclosed here. In some modalities, once an assay that includes detecting threshold biomarkers of proteins in a biological sample is performed, subsequent testing or monitoring is performed (for example, any of the varieties of additional or technical diagnostic tests enhanced monitoring features disclosed here). In some ways, once an assay that includes detecting threshold biomarkers of proteins in a biological sample is performed, a second assay that includes detecting one or more genetic biomarkers present in DNA without cells (eg, ctDNA) can be performed (for example, detecting any of the various genetic changes that are present in the DNA or in the ctDNA without cells, as described here).

[244] In some embodiments, an assay includes detecting one or more genetic biomarkers present in the circulating tumor DNA (ctDNA) in a biological sample (for example, any biological sample disclosed here, such as plasma), without detecting protein biomarkers with threshold. For example, an assay may include detecting genetic biomarkers (eg, genetic changes) in one or more of the genes disclosed herein, including, without limitation, CDKN2A, FGF2, GNAS, ABL1, EVI1, MYC, APC, IL2, TNFAIP3, ABL2 , EWSR1, MYCL1, ARHGEF12, JAK2, TP53, AKT1, FEV, MYCN, ATM, MAP2K4, TSC1, AKT2, FGFR1, NCOA4, BCL11B, MDM4, TSC2, ATF1, FGFR1OP, NFKB2, BLM, FG1, VH , NRAS, BMPR1A, MLH1, WRN, BCL2, FUS, NTRK1, BRCA1, MSH2, WT1, BCL3, GOLGA5, NUP214, BRCA2, NF1, BCL6, GOPC, PAX8, CARS, NF2, BCR, HMGA1, PDGTB, CBFA , BRAF, HMGA2, PIK3CA, CDH1, NPM1, CARD11, HRAS, PIM1, CDH11, NR4A3, CBLB, IRF4, PLAG1, CDK6, NUP98, CBLC, JUN, PPARG, SMAD4, PALB2, CCND1, KIT, PTPN11, CEBPA, PML , CCND2, KRAS, RAF1, CHEK2, PTEN, CCND3, LCK, REL, CREB1, RB1, CDX2, LMO2, RET, CREBBP, RUNX1, CTNNB1, MAF, ROS1, CYLD, SDHB, DDB2, MAFB, SMO, DDX5, SDHD , DDIT3, MAML2, SS18, EXT1, SMARCA4, DDX6, MDM2, TCL1A, EXT2, SMARCB1, DEK, MET, TET2, FBXW7, SOCS1, EGFR, MITF, TFG, FH, STK11 , ELK4, MLL, TLX1, FLT3, SUFU, ERBB2, MPL, TPR, FOXP1, SUZ12, ETV4, MYB, USP6, GPC3, SYK, ETV6, IDH1, and / or TCF3. In some embodiments, an assay may include detecting genetic changes in KRAS (for example, KRAS codons 12 and / or 61). In some embodiments, since an assay that includes detecting one or more genetic biomarkers present in ctDNA in a biological sample is performed, subsequent testing or monitoring is performed (for example, any of the varieties of additional diagnostic tests or techniques monitoring system disclosed here). In some embodiments, since an assay that includes detecting one or more genetic biomarkers present in ctDNA in a biological sample is performed, a second assay that includes detecting one or more protein biomarkers at high thresholds (for example , detect any of the varieties of protein biomarkers described here including, but not limited to, carbohydrate antigen 19-9 (CA19-9), carcinoma embryonic antigen (CEA), hepatocyte growth factor (HGF), osteopontine (OPN) and combinations thereof).

[245] In some embodiments, one or more genetic biomarkers present in cellless DNA (eg, ctDNA) and / or one or more protein biomarkers can be tested from a variety of biological samples isolated or obtained from a individual (for example, a human being in question) including, without limitation, blood, plasma, serum, urine, cerebrospinal fluid, saliva, sputum, bronchoalveolar lavage, bile, lymphatic fluid, cyst fluid, feces, asites and combinations thereof. In some embodiments, one or more genetic biomarkers present in DNA without cells (for example, ctDNA) and one or more protein biomarkers can be tested from the same sample. For example, a single sample can be isolated or obtained from an individual, whose single sample can be tested for the presence of one or more genetic biomarkers in DNA without cells (for example, ctDNA), the presence of one or more biomarkers of proteins or both. Genetic biomarkers present in cellless DNA (eg, ctDNA) and one or more protein biomarkers can be tested from the sample at the same time or at different times. For example, the sample can be tested for the presence of one or more genetic biomarkers in DNA without cells (for example, ctDNA) for the first time and for one or more protein biomarkers the second time or vice versa. In some embodiments, the sample can be refrigerated, frozen or stored for future testing. In some modes, the presence of one or more genetic biomarkers in DNA without cells (for example, ctDNA) and the presence of one or more biomarkers of proteins can be tested from different samples. For example, a first sample can be isolated or obtained from an individual and tested for the presence of one or more genetic biomarkers in DNA without cells (for example, ctDNA), and a second sample can be isolated or obtained from the individual and tested for the presence of one or more protein biomarkers. The first and second samples can be of the same type (for example, plasma or serum) or of different types. The first and / or second samples can be refrigerated, frozen or stored for future testing.

[246] In some embodiments, several codons or genetic regions in a tumor suppressor gene or oncogene can be tested to identify a genetic biomarker (for example, a mutation). For example, at least two, at least three, at least four, at least five or more codons or genetic regions can be tested on a gene. In addition or alternatively, multiple genes can be tested for mutations to increase the scope of an assay for more types of cancer or more cancers within a single type.

[247] In some modalities, a radiological, ultrasonographic or other technique can be applied to any individual (for example, a human individual) in which a genetic biomarker (for example, a mutation) is detected. The technique can be applied to the entire body, to a single organ or to a region of the body. The technique can be used, for example, to check for the presence of a specific type of cancer, to confirm the presence of a cancer or to identify the location of a cancer in the body. In some modalities, the technique is a sweep. In some modalities, the scan is a computerized tomography (CT), a computed tomography angiography (CTA), an esophagus (a barium swallow), a barium enema, a magnetic resonance imaging (MRI), a tomography PET (PET), an ultrasound (for example, an endobronchial ultrasound, an endoscopic ultrasound), an X-ray, a DEXA scan, or positron emission tomography and computed tomography (PET-

CT). In some modalities, the technique is a physical examination, such as an anoscopy, a bronchoscopy (for example, an autofluorescence bronchoscopy, a white light bronchoscopy, a navigation bronchoscopy), a colonoscopy, a digital breast tomosynthesis, a retrograde endoscopic cholangiopancreatography (ERCP), an entagagogastroduodenoscopy, a mammogram, a Pap smear or a pelvic exam. In some modalities, the technique is a biopsy (for example, a bone marrow aspiration, a tissue biopsy). In some modalities, biopsy is performed by fine needle aspiration or surgical excision. In some embodiments, the technique also includes obtaining a biological sample (for example, a tissue sample, a urine sample, a blood sample, a swab, a saliva sample, a mucosal sample (eg, sputum , bronchial secretion), a nipple aspirate, a secretion or excretion). In some embodiments, the technique includes determining exosomal proteins (for example, an exosomal surface protein (for example, CD24, CD147, PCA-3)) (Soung et al. (2017) Cancer 9 (1): pii: E8). In some modalities, the diagnostic test method is an oncotype DX® test (Baehner (2016) Ecancermedicalscience 10: 675).

[248] In some modalities, cancers from organs other than pancreatic cancer can be detected according to any of the several methods described here.

[249] In some embodiments, methods provided here (for example, methods in which the presence of one or more genetic biomarkers in DNA without cells (for example, ctDNA) and the presence of one or more high threshold protein biomarkers are detected in an isolated biological sample from the individual) can be used to select a treatment for an individual. For example, once an individual has been determined to have cancer (for example,

pancreatic cancer) by any of the various methods disclosed herein, an appropriate treatment can be selected (for example, any of the various therapeutic interventions described here). In some embodiments, the methods provided here (for example, methods in which the presence of one or more genetic biomarkers in DNA without cells (for example, ctDNA) and the presence of one or more high threshold protein biomarkers are detected in an individual's isolated biological sample) can be used to select an individual for treatment.

For example, once an individual has been determined to have cancer (for example, pancreatic cancer) by any of the various methods disclosed herein, that individual can be identified as an appropriate individual to receive treatment. (for example, any of the various therapeutic interventions described here). In some embodiments, the methods provided here (for example, methods in which the presence of one or more genetic biomarkers in DNA without cells (for example, ctDNA) and the presence of one or more high threshold protein biomarkers are detected in a individual biological sample) can be used to select an individual for increased monitoring.

For example, once an individual has been determined to have cancer (for example, pancreatic cancer) by any of the various methods disclosed here, that individual can be identified as an appropriate individual to receive increased monitoring (for example, any of the various monitoring techniques described here). In some embodiments, the methods provided here (for example, methods in which the presence of one or more genetic biomarkers in DNA without cells (for example, ctDNA) and the presence of one or more high threshold protein biomarkers are detected in a biological sample isolated from the individual) can be used to select an individual for further diagnostic testing.

For example, once an individual has been determined to have cancer

(for example, pancreatic cancer) by any of the various forms disclosed herein, that individual can be identified as an appropriate individual to receive additional diagnostic tests (for example, any of the various diagnostic techniques described here).

[250] In some modalities, the methods provided here may be used to detect the presence of cancer (for example, pancreatic cancer) in a period prior to the diagnosis of the individual with an early stage cancer and / or in a moment before the individual showing symptoms associated with cancer. For example, the methods provided here can be used when an individual has not been diagnosed with cancer and / or when an individual is not known to harbor a cancer cell.

[251] In some modalities of any of the methods described here, the individual can be administered with single or multiple doses (for example, two, three, four, five, six, seven, eight, nine or ten doses) of any of the therapeutic interventions described here.

[252] In some embodiments, assays for genetic biomarkers (eg, genetic alterations) can be combined with assays for high protein biomarkers to increase the sensitivity of a blood test for low-stage pancreatic cancer. In some modalities, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95% or more of these cancers can be detected by this combination test, including some patients with a favorable prognosis. In some modalities, 64% of these cancers can be detected through this combination test, including some patients with a favorable prognosis. One of the design characteristics of certain studies established here was the inclusion of only patients with resectable pancreatic cancer and patients with advanced disease (i.e., Stage III or IV) were excluded. Although this exclusion reduces the sensitivity that could be achieved by assessing all patients with pancreatic cancer, regardless of the stage, resectable cases represent a promising group with advantageous clinical relevance in relation to the evaluation of a technology of screening. In some embodiments, the methods provided here can be used to detect all pancreatic cancers in individuals (for example, humans).

[253] Whether the combination of genetic biomarkers present in the biomarkers of ctDNA and protein markers could increase sensitivity to any of them was not known before the present disclosure. In fact, it was conceivable that the same patients with detectable circulating protein biomarkers would vastly outnumber those who release DNA into the circulation. This was of particular concern for patients with early-stage cancer, because both ctDNA and protein-based biomarkers are known to be considerably higher in patients with advanced cancer compared to those with early-stage cancer (Lennon AM & Goggins M (2010) Diagnostic and Therapeutic Response Markers. Pancreatic Cancer, (Springer New York, New York, NY), pp 675-701; Locker GY, et al. (2006) ASCO 2006 update of recommendations for the use of tumor markers in gastrointestinal cancer. J Clin Oncol 24 (33): 5313-5327; Bettegowda C, et al. (2014) Detection of circulating tumor DNA in early- and late-stage human malignancies. Science translational medicine 6 (224): 224ra224).

[254] In some modalities of the methods provided here, a very high specificity (for example, 99.5%: CI 95% 97-100%) can be achieved. For example, only one false positive among 182 healthy individuals with an average age of 64 years was observed in the studies established here. Given the relative low frequency of cancer in the general population, the specificity of any blood screening test potentially useful for pancreatic cancer is preferably high, for example, preferably> 99%. Otherwise, the number of false positives would greatly exceed the number of true positives (that is, it has subideal positive predictive value) (Lennon AM, et al. (2014) The Early Detection of Pancreatic Cancer: What Will It Take to Diagnose and Treat Curable Pancreatic Neoplasia - Cancer Res 74 (13): 3381-3389). Such rigor for screening tests is not normally necessary for tests to monitor diseases in patients with known cancer.

For monitoring, the specificity may be somewhat relaxed, in the interest of obtaining greater sensitivity.

High specificity was achieved with methods disclosed here in at least two ways.

First, ctDNA was used as one of the components of the test.

NoKRAS mutations are exquisitely specific for neoplasms and their specificity is traditionally limited by technical, rather than biological, factors.

The incorporation of the molecular bar code in several assays described here (for example, using a Safe-SeqS technique) can minimize the false-positive results of sequencing that have traditionally been the main technical problems faced by any assays based on ctDNA.

Mutations in KRAS are particularly suitable for early detection strategies, because they are rarely found in clones that arise during age-related clonal hematopoiesis.

Such clones, which may represent early forms of myelodysplasia, are a potential source of false positive ctDNA assays.

The vast majority of these mutations occur in nine genes (DNMT3A, TET2, JAK2, ASXL1, TP53, GNAS, PPM1D, BCORL1 and SF3B1) (48-50), posing challenges to the use of these genes as biomarkers in ctDNA-based assays.

Second, high thresholds were used to mark protein biomarkers as positive.

These thresholds were based on previous studies in the literature or on an independent set of controls, allowing the avoidance of positive scores in the vast majority of healthy patients (Kim JE, et al. (2004) Clinical usefulness of carbohydrate antigen 19-9 as a screening test for pancreatic cancer in an asymptomatic population. J Gastroenterol Hepatol 19 (2): 182-186). In some embodiments, these high thresholds can be used without a general reduction in sensitivity because the ctDNA assay added sensitivity by itself and positive cases of ctDNA only partially overlapped positive cases of protein biomarkers (See, for example, Fig 9, Fig. 10 and Example 2).

[255] Protein biomarkers have been combined with each other in the past to achieve greater sensitivity (Dong T, Liu CC, Petricoin EF, & Tang LL (2014) Combining markers with and without the limit of detection. Stat Med 33 (8): 1307-1320). For example, it was shown that the combination of CA19-9 and TIMP-1 was more sensitive to PDAC detection than any of the biomarkers alone (Zhou W, et al. (1998) Identifying markers for pancreatic cancer by gene expression analysis Cancer Epidemiol Biomarkers Prev 7 (2): 109-112). More recently, it was shown that the combination of CA19-9, TIMP-1 and LRG-1 was more sensitive for the detection of early PDAC than just CA19-9 (Capello M, et al. (2017) Sequential Validation of Blood- Based Protein Biomarker Candidates for Early-Stage Pancreatic Cancer, J Natl Cancer Inst 109 (4)). The combination of protein biomarkers with ultrasensitive ctDNA, as disclosed here, is different. A recent study evaluated a combination of ctDNA and CA19-9 for pancreatic cancer, but found no benefit in combining biomarkers with only CA19-9. Without being limited by theory, it is possible that this conclusion was reached due to the inadequate sensitivity of the test used to detect KRAS mutations (Le Calvez-Kelm F, et al. (2016) KRAS mutations in blood circulating cell-free DNA: a pancreatic cancer case-control.

7 (48): 78827-78840). In addition, the specificity for ctDNA achieved in this study was relatively low, reducing its suitability for screening.

[256] In some modalities, the methods provided here can be used to detect resectable pancreatic cancers through a non-invasive blood test in most patients.

[257] In some modalities, the results obtained using any of the several methods disclosed herein may underestimate the survival benefits of early detection. Most of the patients studied here, despite having resectable cancer, were symptomatic and their cancers were discovered only because of their symptoms. Consequently, 77% of the patients in the cohort described here were in stage IIB and the average size of the tumors in these patients was 3 cm. In some modalities, in a study of screening for asymptomatic individuals, a greater proportion of patients at an early stage, with smaller tumors, can be discovered using any of the various forms of methods disclosed here. In some modalities, any of the several methods disclosed here may be more sensitive for the detection of patients with larger tumors and with a worse prognosis than for patients with smaller tumors, even though all tumors may be surgically resectable (See, for example). example, Fig. 9B and Example 2). In some modalities, mutations in KRAS can be found in the circulation of patients with cancers other than pancreas, especially lung cancer (Herbst RS, Heymach JV, & Lippman SM (2008) Lung cancer. N Engl J Med 359 (13 ): 1367-1380), and the expression of CA19-9, CEA, HGF and OPN can be elevated in several other types of cancer (Kim JE, et al. (2004) Clinical usefulness of carbohydrate antigen 19-9 as a screening test for pancreatic cancer in an asymptomatic population. J Gastroenterol Hepatol 19 (2): 182-186; Thomas DS, et al. (2015) Evaluation of serum

CEA, CYFRA21-1 and CA125 for the early detection of colorectal cancer using longitudinal preclinical samples. Br J Cancer 113 (2): 268-274; Di Renzo MF, et al. (1995) Overexpression and amplification of the met / HGF receptor gene during the progression of colorectal cancer. Clin Cancer Res 1 (2): 147-154; El-Tanani MK, et al. (2006) The regulation and role of osteopontin in malignant transformation and cancer. Cytokine Growth Factor Rev 17 (6): 463-474). Thus, in some modalities, patients with a positive test using any of the various forms disclosed herein may undergo additional appropriate imaging studies to identify the location of the tumor.

[258] In some modalities, the methods provided here establish a basis for the assessment of patients at high risk for PDAC and for the implementation of early detection strategies (Klinich M, et al. (2017) An RNA- based signature enables high specificity detection of circulating tumor cells in hepatocellular carcinoma Proc Natl Acad Sci USA 114 (5): 1123-1128). As an example, it is known that recent onset diabetes is associated with an increased risk of pancreatic cancer. Approximately 1% of diabetic patients aged 50 and over are diagnosed with pancreatic cancer within 3 years after first meeting the criteria for diabetes (Chari ST, et al. (2005) Probability of pancreatic cancer following diabetes: a population-based Gastroenterology 129 (2): 504-511). With an incidence of 1%, it is expected that the PPV / VPN of certain combination tests disclosed here will be 54% and 99.6%, respectively, in this population, which is well within the range of the screening tests. currently approved for cancer.

[259] Available evidence indicates that many cancers have detectable ctDNA in the early stages, generally more commonly than seen in pancreatic cancer (Bettegowda C, et al. (2014) Detection of circulating tumor DNA in early- and late-stage human malignancies.Science translational medicine 6 (224): 224ra224). Likewise, a large number of protein biomarkers have already been described for the detection of various types of cancer (Liotta LA & Petricoin EF, 3rd (2003) The promise of proteomics. Clin Adv Hematol Oncol 1 (8): 460-462 ). These protein biomarkers can be thresholded according to any of the various forms described here, allowing the use of ctDNA-protein combinations to detect a variety of types of cancer (Bettegowda C, et al. (2014) Detection of circulating tumor DNA in early- and late-stage human malignancies.Science translational medicine 6 (224): 224ra224). Genetic biomarkers in combination with aneuploidy

[260] In one aspect, methods and materials are provided here to detect the presence of one or more members of a panel of genetic biomarkers and the presence of aneuploidy in one or more samples obtained from an individual. In another aspect, methods and materials are provided here to diagnose or identify the presence of a disease in an individual (for example, identifying the individual as having cancer) by detecting the presence of one or more members of a panel of genetic biomarkers and the presence of aneuploidy in one or more samples obtained from the individual. In another aspect, methods and materials are provided here to identify an individual as being at risk (for example, increased risk) of having or developing a disease (for example, cancer) by detecting the presence of one or more members of a panel of genetic biomarkers and the presence of aneuploidy in one or more samples obtained from the individual. In another aspect, methods and materials are provided here to treat an individual who has been diagnosed or identified as having a disease (eg cancer) or who has been identified as being at risk (eg increased risk) of having or developing a disease (for example, cancer) detecting the presence of one or more members of a panel of genetic biomarkers and the presence of aneuploidy in one or more samples obtained from the individual. In another aspect, methods and materials are provided here to identify a treatment for an individual who has been diagnosed or identified as having a disease (eg, cancer) or who has been identified as being at risk (eg, increased risk) ) to have or develop a disease (for example, cancer) by detecting the presence of one or more members of a panel of genetic biomarkers and the presence of aneuploidy in one or more samples obtained from the individual. In another aspect, methods and materials are provided here to identify an individual who will or will likely respond to a treatment by detecting the presence of one or more members of a panel of genetic biomarkers and the presence of aneuploidy in one or more samples obtained from the individual. In another aspect, methods and materials are provided here to identify an individual as a candidate for additional diagnostic tests by detecting the presence of one or more members of a panel of genetic biomarkers and the presence of aneuploidy in one or more samples obtained from the individual. In another aspect, methods and materials are provided here to identify an individual as a candidate for increased monitoring by detecting the presence of one or more members of a panel of genetic biomarkers and the presence of aneuploidy in one or more samples obtained from the individual.

[261] In some embodiments, the methods provided here that include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of aneuploidy in one or more samples obtained from an individual provide high sensitivity in the detection or diagnosis. cancer diagnosis (for example, a high frequency or incidence of correctly identifying an individual as having cancer). In some modalities, the methods provided here that include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of aneuploidy in one or more samples obtained from an individual provide sensitivity in the detection or diagnosis cancer (for example, a high frequency or incidence of correctly identifying an individual as having cancer) that is superior to the sensitivity provided by the separate detection of the presence of one or more members of a panel of genetic biomarkers or the presence of aneuploidy.

In some embodiments, the methods and materials provided here that include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of aneuploidy in one or more samples obtained from an individual provide a sensitivity of at least about 70% at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99% or greater.

In some modalities, methods and materials provided here that include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of aneuploidy in one or more samples obtained from an individual, provide high sensitivity in detecting a single type cancer.

In some modalities, methods and materials provided here that include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of aneuploidy in one or more samples obtained from an individual provide high sensitivity in detecting two or more types of cancer.

Any of the various types of cancer can be detected using the methods and materials provided here (see, for example, the section entitled "Cancers"). In some modalities, cancers that can be detected using methods and materials that include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of aneuploidy in one or more samples obtained from an individual include pan cancer - creases. In some modalities, cancers that can be detected using methods and materials that include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of aneuploidy in one or more samples obtained from an individual include cancer liver, ovarian cancer, esophageal cancer, stomach cancer, pancreatic cancer, colorectal cancer, lung cancer or breast cancer. In some modalities, cancers that can be detected using methods and materials that include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of aneuploidy in one or more samples obtained from an individual include cancers of the female reproductive system (for example, cancer of the cervix, cancer of the endometrium, ovarian cancer or cancer of the fallopian tubes). In some embodiments, cancers that can be detected using methods and materials that include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of aneuploidy in one or more samples obtained from an individual include bladder or carcinoma cancer - but upper urothelial tract.

[262] In some embodiments, the methods provided here that include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of aneuploidy in one or more samples obtained from an individual provide high specificity in detection or diagnosis cancer (for example, a low frequency or incidence of incorrectly identifying an individual as having cancer when that individual does not have cancer). In some embodiments, the methods provided here that include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of aneuploidy in one or more samples obtained from an individual provide specificity in the detection or diagnosis of cancer

(for example, a high frequency or incidence of correctly identifying an individual as having cancer) that is greater than the specificity provided by the separate detection of the presence of one or more members of a panel of genetic biomarkers or the presence of aneuploidy. In some embodiments, the methods and materials provided here that include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of aneuploidy in one or more samples obtained from an individual provide a specificity of at least about 70% at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99% or greater. As will be understood by those skilled in the art, a specificity of 99% means that only 1% of individuals who do not have cancer are incorrectly identified as having cancer. In some modalities, the methods and materials provided here that include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of aneuploidy in one or more samples obtained from an individual provide high specificity in the detection of a single cancer (for example, there is a low probability of incorrectly identifying that individual as having this type of cancer). In some embodiments, the methods and materials provided here that include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of aneuploidy in one or more samples obtained from an individual provide high specificity in detecting two or more cancers (for example, there is a low likelihood of incorrectly identifying that individual as having these two or more types of cancer).

[263] In some embodiments, the methods provided here that include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of aneuploidy in one or more samples obtained from an individual include detecting the presence of: 1) a or more genetic biomarkers in one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 or 18) of the following genes: NRAS, PTEN, FGFR2, KRAS, POLE, AKT1, TP53, RNF43, PPP2R1A, MAPK1, CTNNB1, PIK3CA, FBXW7, PIK3R1, APC, EGFR, BRAF and / or CDKN2A and 2) the presence of aneuploidy. In some modalities, the methods provided here that include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of aneuploidy in one or more samples obtained from an individual include detecting the presence of: 1) one or more genetic biomarkers in each of the following genes: NRAS, PTEN, FGFR2, KRAS, POLE, AKT1, TP53, RNF43, PPP2R1A, MAPK1, CTNNB1, PIK3CA, FBXW7, PIK3R1, APC, EGFR, BRAF and CDKN2A and 2) the presence of aneuploidy. In some types of methods provided here, which include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of aneuploidy in one or more samples obtained from an individual include detecting the presence of: 1) one or more than one more genetic biomarkers in one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 or 18) of the following gene items: NRAS, PTEN, FGFR2, KRAS, POLE, AKT1, TP53, RNF43, PPP2R1A, MAPK1, CTNNB1, PIK3CA, FBXW7, PIK3R1, APC, EGFR, BRAF and / or CDKN2A and 2) in the presence of aneuploidy, the individual is determined to have (for example, diagnosed as having) or is determined to be (for example, diagnosed as being) at high risk of having or developing one of the following types of cancer: cervical cancer, endometrial cancer, ovarian cancer or fallopian tube cancer.

[264] In some embodiments, the methods provided here include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of protein biomarkers in one or more samples obtained of an individual include detecting the presence of: 1) one or more genetic biomarkers in one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9 or 10) of the following genes: TP53, PIK3CA, FGFR3, KRAS, ERBB2, CDKN2A, MLL, HRAS, MET and / or VHL, 2) the presence of a TERT promoter mutation (for example, a genetic biomarker in a TERT promoter) and 3) the presence of aneuploidy.

In some modalities, the methods provided here that include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of aneuploidy in one or more samples obtained from an individual include detecting the presence of: 1) one or more genetic biomarkers in each of the following genes: TP53, PIK3CA, FGFR3, KRAS, ERBB2, CDKN2A, MLL, HRAS, MET and VHL, 2) the presence of a TERT promoter mutation (for example, a genetic biomarker) in a TERT promoter) and 3) the presence of aneuploidy.

In some modalities of methods provided here, which include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of aneuploidy in one or more samples obtained from an individual include detecting the presence of: 1) one or more than one more genetic biomarkers in one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9 or 10) of the following genes: TP53, PIK3CA, FGFR3, KRAS, ERBB2, CDKN2A, MLL, HRAS, MET and / or VHL, 2) the presence of a TERT promoter mutation (for example, a genetic biomarker in a TERT promoter) and 3) the presence of aneuploidy, the individual is determined to have (for example, diagnosed as having) or is determined to be (for example, diagnosed as being) at high risk of having or developing cancer, one of the following types of cancer: bladder cancer or upper end urothelial carcinoma.

[265] A sample obtained from an individual can be any of the variety of samples described here that contain DNA (for example, ctDNA in the blood or DNA present in bladder, cervical, endometrial or uterine samples) and / or proteins. In some embodiments, the DNA (for example, DNA without cells (for example, ctDNA) or DNA present in the bladder, cervix, endometrium or uterus samples) and / or proteins in a sample obtained from the individual are derived - veins of a tumor cell. In some embodiments, the DNA (eg, cellless DNA (eg, ctDNA) in a sample taken from the individual includes one or more genetic biomarkers and or aneuploidy DNA. In some embodiments, the proteins in a sample obtained from the individual include one or more protein biomarkers, non-limiting examples of samples in which genetic biomarkers and / or protein biomarkers and / or aneuploidies can be detected include a blood sample, a plasma sample, a sample of serum, a urine sample, an endometrial sample, a cervical sample and a uterine sample. In some modalities, the presence of one or more genetic biomarkers and the presence of aneuploidy in a single sample obtained from the individual. the presence of one or more genetic biomarkers is detected in a first sample obtained from an individual, and the presence of aneuploidy is detected in a second sample obtained from the individual.

[266] In some embodiments, when an individual is determined to have (for example, diagnosed as having) cancer or determined to be (for example, diagnosed as being) at high risk of having or developing cancer (for example, detec - with the presence of: 1) one or more genetic biomarkers in one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 , 16, 17 or 18) of the following gene items: NRAS, PTEN, FGFR2, KRAS,

POLE, AKT1, TP53, RNF43, PPP2R1A, MAPK1, CTNNB1, PIK3CA, FBXW7, PIK3R1, APC, EGFR, BRAF and / or CDKN2A and 2) the presence of aneuploidy) the individual is selected as a candidate for (for example, is selected for) additional diagnostic tests (for example, any of several other forms of additional diagnostic tests described here), the individual is selected as a candidate for (for example, is selected for) additional diagnostic tests (for example example, any of several other forms of diagnostic methods described here), the individual is selected as a candidate for (for example, is selected for) increased monitoring (for example, any of the various augmented monitoring methods described here), the individual is identified as an individual who will respond or is likely to respond to a treatment (for example, any of the various therapeutic interventions described here), the individual is selected as a candidate for (for example, is selected for a treatment, a treatment (for example, any of the varieties of therapeutic interventions described here) is selected for the individual and / or a treatment (for example, any of the various therapeutic interventions described here) is administered to the individual.

In some modalities, when an individual is determined to have (for example, diagnosed as having) cancer or determined to be (for example, diagnosed as being) at high risk of having or developing cancer (for example, detecting the presence of: 1) one or more genetic biomarkers in one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9 or 10) of the following genes: TP53, PIK3CA, FGFR3, KRAS, ERBB2, CDKN2A, MLL, HRAS, MET and / or VHL, 2) the presence of a TERT promoter mutation (for example, a genetic biomarker on a TERT promoter) and 3) the presence of aneuploidy), the individual is selected as a candidate for (for example, selected for) additional diagnostic tests (for example, any of the various additional diagnostic test methods described here), the individual is selected as a candidate for (for example, is selected for) additional diagnostic tests (for example, any of several other forms of diagnostic methods described here), the individual is selected as a candidate for (for example, selected for) increased monitoring (for example, any of the various augmented monitoring methods described here), the individual is identified as an individual who respond or likely respond to a treatment (for example, any of the various therapeutic interventions described here), the individual is selected as a candidate for (for example, selected for) a treatment, a treatment (for example, any of the varieties of therapeutic interventions described here) is selected for the individual and / or a treatment (for example, any of the various therapeutic interventions described here) is administered to the individual.

For example, when an individual is determined to have (for example, diagnosed as having) cancer or determined to be (for example, diagnosed as being) at high risk of having or developing cancer, the individual may undergo diagnostic tests additional, such additional diagnostic tests can confirm the presence of cancer in the individual.

In addition or alternatively, the individual can be monitored more frequently.

In some modalities of an individual determined to have (for example, diagnosed as having) cancer or determined to be (for example, diagnosed as being) at high risk of having or developing cancer in which the individual is tested for additional diagnosis and / or increased monitoring, the individual can additionally be administered with a therapeutic intervention.

In some modalities, after an individual is administered with a therapeutic intervention, the individual undergoes further additional diagnostic tests (for example, the same type of additional diagnostic test that was performed previously and / or a different type of test additional diagnostics) and / or continuous increased monitoring (for example, increased monitoring at the same frequency or at a different frequency than previously performed). In modalities, after an individual is administered with a therapeutic intervention and the individual undergoes further additional diagnostic tests and / or additional increased monitoring, the individual is administered with another therapeutic intervention (for example, the same therapeutic intervention that was previously administered and / or a different therapeutic intervention). In some ways, after administering a therapeutic intervention, the individual is tested for the presence of: 1) one or more genetic biomarkers in one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 or 18) of the following genes: NRAS, PTEN, FGFR2, KRAS, POLE, AKT1, TP53, RNF43, PPP2R1A, MAPK1, CTNNB1, PIK3CA, FBXW7, PIK3R1, APC, EGFR, BRAF and / or CDKN2A and 2) the presence of aneuploidy. In some modalities, after an individual has been administered a therapeutic intervention, the individual is tested for the presence of: 1) one or more genetic biomarkers in one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9 or 10) of the following genes: TP53, PIK3CA, FGFR3, KRAS, ERBB2, CDKN2A, MLL, HRAS, MET and / or VHL, 2) the presence of a TERT promoter mutation (for example, a genetic biomarker in a TERT promoter) and 3) the presence of aneuploidy.

[267] In some embodiments, the methods provided here that include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of aneuploidy also include detecting the presence of one or more members of a panel of biomarkers from proteins in one or more samples obtained from an individual (for example, the same sample is used to detect one or more of the presence of one or more members of a panel of genetic biomarkers and the presence of aneuploidy or a different sample). Any of a variety of protein biomarkers can be detected (for example, any variety of protein biomarkers and / or panels of protein biomarkers described here).

[268] In some modalities of methods provided here, which include detecting in one or more samples obtained from an individual, the presence of: 1) one or more genetic biomarkers in one or more (for example, 1, 2, 3, 4 , 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 or 18) of the following genes: NRAS, PTEN, FGFR2, KRAS, POLE, AKT1, TP53, RNF43, PPP2R1A , MAPK1, CTNNB1, PIK3CA, FBXW7, PIK3R1, APC, EGFR, BRAF and / or CDKN2A and 2) the presence of aneuploidy, the methods also include detecting the presence of one or more members of a panel of protein biomarkers in a sample obtained from the individual (for example, the same sample is used to detect one or more of the presence of one or more members of a panel of genetic biomarkers and the presence of aneuploidy or a different sample). In some types of methods provided here, which include detecting in one or more samples obtained from an individual, the presence of: 1) one or more genetic biomarkers in each of the following genes: NRAS, PTEN, FGFR2, KRAS, POLE , AKT1, TP53, RNF43, PPP2R1A, MAPK1, CTNNB1, PIK3CA, FBXW7, PIK3R1, APC, EGFR, BRAF and CDKN2A and 2) the presence of aneuploidy, the methods also include detecting the presence of one or more members of a country level of protein biomarkers in a sample obtained from the individual (for example, the same sample is used to detect one or more of the presence of one or more members of a panel of genetic biomarkers and the presence of aneuploidy or a different sample) . In some modalities of methods provided here, which include detecting in one or more samples obtained from an individual the presence of: 1)

one or more than one more genetic biomarkers in one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 or 18) of the following gene items: NRAS, PTEN, FGFR2, KRAS, POLE, AKT1, TP53, RNF43, PPP2R1A, MAPK1, CTNNB1, PIK3CA, FBXW7, PIK3R1, APC, EGFR, BRAF and / or CDKN2A 2) in the presence of aneuploidy , and 3) the presence of one or more members of a panel of protein biomarkers, the presence of one or more members of, the individual is determined to have (for example, diagnosed as having) or is determined to be being (for example, diagnosed as being) at high risk of having or developing one of the following types of cancer: cervical cancer, endometrial cancer, ovarian cancer or fallopian tube cancer.

[269] In some modalities or methods provided here, which include detecting in one or more samples obtained from an individual, the presence of: 1) one or more genetic biomarkers in one or more (for example, 1, 2, 3, 4 , 5, 6, 7, 8, 9 or 10) of the following genes: TP53, PIK3CA, FGFR3, KRAS, ERBB2, CDKN2A, MLL, HRAS, MET and / or VHL, 2) the presence of a TERT promoter mutation ( eg, a genetic biomarker on a TERT promoter) and 3) the presence of aneuploidy, the methods further include detecting the presence of one or more members of a panel of protein biomarkers in a sample obtained from the individual (for example, at the same time) use of sample to detect or the presence of one or more members of a panel of genetic biomarkers and the presence of aneuploidy or a different sample). In some modalities of methods provided here, which include detecting in one or more samples obtained from an individual the presence of: 1) one or more genetic biomarkers in each of the following genes: TP53, PIK3CA, FGFR3, KRAS, ERBB2, CDKN2A, MLL, HRAS, MET and VHL, 2) the presence of a TERT promoter mutation (for example, a genetic biomarker on a TERT promoter)

and 3) the presence of aneuploidy, the methods also include detecting the presence of one or more members of a panel of protein biomarkers in a sample obtained from the individual (for example, at the same sample use to detect one or both the presence of one or more members of a panel of genetic biomarkers and the presence of aneuploidy or a different sample). In some modalities of methods provided here, which include detecting in one or more samples obtained from an individual the presence of: 1) one or more genetic biomarkers in one or more (for example, 1, 2, 3, 4 , 5, 6, 7, 8, 9 or 10) of the following genes: TP53, PIK3CA, FGFR3, KRAS, ERBB2, CDKN2A, MLL, HRAS, MET and / or VHL, 2) the presence of a TERT promoter mutation ( for example, a genetic biomarker in a TERT promoter), 3) the presence of aneuploidy, and 4) the presence of one or more members of a panel of protein biomarkers, the individual is determined to have (for example, diagnosed as having) or is determined to be (for example, diagnosed as being) at high risk of having or developing cancer, one of the following types of cancer: bladder cancer or upper tract urothelial carcinoma.

[270] In some modalities of methods provided here, which include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of aneuploidy, one or more (for example, 1, 2, 3, 4, 5, 6, 7 or 8) of the following protein biomarkers can still be detected: CA19-9, CEA, HGF, OPN, CA125, prolactin, TIMP-1 and / or myeloperoxidase (MPO). In some modalities of the methods provided here, which include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of aneuploidy, each of the following protein biomarkers can still be detected: CA19-9, CEA, HGF, OPN, CA125, pro-lactin, TIMP-1 and myeloperoxidase (MPO).

[271] In some modalities of methods provided here, which include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of aneuploidy, one or more (for example, 1, 2, 3, 4, 5 , 6, 7, 8, 9, 10, or 11) of the following protein biomarkers can still be detected: CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1, follistatin, G- CSF and / or CA15-3. In some modalities of the methods provided here, which include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of aneuploidy, each of the following protein biomarkers can still be detected: CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1, follistatin, G-CSF and CA15-

3.

[272] In some modalities of methods provided here, which include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of aneuploidy, one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8 or 9) of the following protein biomarkers can still be detected: CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1 and / or CA15-3. In some modalities of the methods provided here, which include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of aneuploidy, each of the following protein biomarkers can still be detected: CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1 and CA15-3.

[273] In some modalities of methods provided here, which include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of aneuploidy, one or more (for example, 1, 2, 3 or 4) of the following Protein biomarkers can also be detected: CA19-9, CEA, HGF and / or OPN. In some modalities of the methods provided here, which include detecting the

presence of one or more members of a panel of genetic biomarkers and the presence of aneuploidy, each of the following protein biomarkers can still be detected: CA19-9, CEA, HGF, and OPN.

[274] In some embodiments, any of the variety of methods provided here that includes detecting the presence of one or more members of a panel of genetic biomarkers and the presence of aneuploidy in one or more samples obtained from an individual also includes detecting the presence of one or more members of one or more additional classes of biomarkers. Non-limiting examples of these additional classes of biomarkers include: changes in copy number, changes in DNA methylation, other nucleic acids (for example, mRNAs, miRNAs, lncRNAs, circRNA, mtDNA, telomeric DNA, translocation and genomic rearrangements), peptides and / or metabolites.

[275] In some embodiments, the one or more additional classes of biomarkers include a metabolite biomarker. In some modalities, an individual is determined to be at high risk of having or developing cancer if the biological sample contains one or more metabolites indicative of cancer. In some modalities, an individual is determined to have cancer if the biological sample contains one or more metabolites indicative of cancer. Non-limiting examples of cancer-indicating metabolites include: 5-methylthioadenosine (MTA), Reduced glutathione (GSH), N-acetylglutamate, Lactose, N-acetylneuraminate, UDP-acetylglucosamine, UDP-Acetylgalacto-samine, UDP-glucuronate , Pantothenate, Araquidonate (20: 4n6), Choline, Cytidine 5′-diphosphocholine, Di-homo-linolenate (20: 3n3), Docosapentaeno-act (DPA 22: 5n3), Eicosapentaenoate (EPA 20: 5n3), Glycerophosphoryloline (GPC), Docosahexaenoate (DHA 22: 6n3), Linoleate (18: 2n6), cytidine 5'-monophosphate (5′-CMP), Gamma-glutamylglutamate, X - 14577, X - 11583, Isovalerylcarnitine, Phosphocreatine, 2-aminoadipic acid, Gluconic acid, O-Acetylcarnitine, aspartic acid, Deamido-NAD +,

glutamic acid, Isobutyrylcarnitine, Carnitine, Pyridoxal, Citric acid, Adenosine, ATP, valine, XC0061, Isoleucine, γ-Butyrobetaine, Lactic acid, alanine, phenylalanine, Gluconolactone, leucine, Glutathione, Gutathin + , UTP, creatine, Theobromine, CTP, GTP, 3-Methylhistidine, Succinic Acid, Glycerol 3-phosphate, glutamine, 5-Oxoproline, Thiamine, Butyrylcarnitine, 4-acetamidobutanoic acid, UDP-Glucose, UDP-Galactose, threonine, N-Acetylglycine, proline, ADP, Choline, Malic Acid, S-Adenosylmethionine, Pantothenic acid, Cysteine-sulfinic acid, 6-Aminohexanoic acid, Homocysteic acid, Hydroxyproline, Methionine sulfoxide, 3-guanidinopropionic acid, 3-guanidinopropionic acid, 3-guanidinopropionic acid , Phenaceturic acid, Threonic acid, tryptophan, Pyridoxine, N-acetylaspartic acid, 4-guanidinobutyric acid, serine, Citrulline, Betaine, N-acetylasparagine, 2-hydroxyglutamic acid, arginine, Glutathione (GSH), Glutathione (GSH) , Dihydroxyacetone phosphate, histidine, glycine, Glucose 1-phosphate, N-formylglycine, Ketoprofen, lysine, beta-alanine, N-acetylglutamic acid, 2-Amino-2- (hydroxymethyl) -1,3-propanediol, Ornithine, Phosphorylcholine, Glycerophosphocholine, Terephthalic acid, Glyceral 3-phosphate, Gly-Asp, Taurine, fructose 1,6-diphosphate, 3-Aminoisobutyric acid, Sperididine, GABA, Triethanamine, Glycerol, N-acetylserine, N-Acetylornithine, Diethanolamine, AMP, Disulphetus glutathione cysteine, Streptomycin sulfate + divalent H2O, Trans-glutaconic acid, nicotinic acid, Isobutylamine, Betaine aldehyde + H2O, Urocanic acid, 1-Aminocyclopropane-1-carboxylic acid Homoserinalac-tona, 5-Aminic acid , Ethanolamine, Isovaleric acid, N-methylglutamic acid, Cystathionine, Spermine, Carnosine, 1-Methylnicotinamide, N-Acetylneuramine acid, Sarcosine, GDP, N-Methylalanine, palmitic acid, 1,2-dioleoyl-sn-glycerine -3-phospho-rac-glycerolcholesterol 5α, 6α epoxidelanosterol, lignoceric acid, 1oleoil_rac_GL , cholesterol_epoxide, erucic acid, T-LCA, oleoyl-L-carnitine, oleanolic acid, 3-phosphoglycerate, 5-hydroxynorvaline, 5-methoxytrypptamine, adenosine-5-monophosphate, alpha-keto-glutarate, asparagine, benzoic acid, hypoxanthine, maltose, maltotriosis,

methionine sulfoxide, nornicotine, phenol, phosphoethanolamine, pyrophosphate, pyruvic acid, quininic acid, taurine, uric acid, inosine, lactamide, 5-hydroxynorvaline NIST, cholesterol, deoxypentitol, 2-hydroxyestrone, 2-hydroxyestradiol, 2-methoestrestrone, 2-methoestrestrone metolxiestradiol, 2-hydroxyestrone-3-methyl ether, 4-hydroxestrone, 4-metolxestrone, 4-methoxyestradiol, 16alpha-hydroxyestrone, 17-epiestriol, estriol, 16-ketoestradiol, 16-epiestriol, acyl-carnitine C18: 1, citrulline amino acids and trans-4-hydroxyproline, hydroxyproline, glycerophospholipids PC aa C28: 1, PC aa and C30: 0 and PC aa and C30: 2, and sphingolipid SM (OH) C14: 1. See, for example, Halama et al., Nesting of colon and ovarian cancer cells in the endothelial niche is associated with alterations in glycan and lipid metabolism, Scientific Reports volume 7, Article number: 39999 (2017); Hur et al., Systems approach to characterize the metabolism of liver cancer stem cells expressing CD133, Sci Rep., 7: 45557, doi: 10.1038 / srep45557, (2017); Eliassen et al., Urinary Estrogens and Estrogen Metabolites and Subsequent Risk of Breast Cancer among Premenopausal Women, Cancer Res; 72 (3); 696–706 (2011); Gangi et al., Metabolomic profile in pancreatic cancer patients: a consensus-based approach to identify highly discriminating metabolites, Oncotarget, Feb 2; 7 (5): 5815–5829 (2016); Kumar et al., Serum and Plasma Metabolomic Biomarkers for Lung Cancer, Bioinformation, 13 (6): 202–208, doi: 10.6026 / 97320630013202 (2017); Schmidt et al., Pre-diagnostic metabolite concentrations and prostate cancer risk in 1077 cases and 1077 matched controls in the European Prospective Investigation into Cancer and Nutrition, BMC Med., 15: 122, doi:

10.1186 / s12916-017-0885-6 (2017); each of which is incorporated herein by reference in its entirety.

[276] In some embodiments, the one or more additional classes of biomarkers include a peptide (for example, a peptide that is distinct from the various protein biomarkers described herein as useful in one or more methods). In some modalities, an individual is determined to be at high risk of having or developing cancer if the biological sample contains one or more peptides indicating cancer.

In some modalities, an individual is determined to have cancer if the biological sample contains one or more peptides indicative of cancer.

In some embodiments, a peptide is derived from a protein (for example, the peptide includes an amino acid sequence present in a protein biomarker or a different protein). Non-limiting examples of cancer-specific peptides include the following peptides and peptides derived from the following proteins: CEACAM, CYFRA21-1, CA125, PKLK, Pro-GRP, NSE, TPA 6, TPA 7, TPA 8, NRG, NRG 100 , CNDP, APOВ100, SCC, VEGF, EGFR, PIK3CA, HER2, BRAF, ROS, RET, NRAS, MET, MEK1, HER2, C4.4A, PSF3, FAM83B, ECD, CTNNB, VIM, S100A4, S100A7, COX2, MUC1 , KLKB1, SAA, HP-β chain, C9, Pgrmc1, Ciz1, Transferrin, α-1 antitrypsin, apolipo 1 protein, complement c3a, Cavolin-1, Kallikrein 6, Glucose-regulated protein-8, α-defense , -2, -3, serum C-peptide, Alpha-2-HS glycol protein, Tryptic KRT 8 peptide, plasma glycol protein, Catenin, Defensin α 6, MMPs, Cyclin D, S100 P, Lamina A / C filament protein, heat shock protein, aldehyde dehydrogenase, Txl-2 (thioredoxin-like protein-2), P53, nm23, u-PA, VEGF, Eph B4, CRABP2, WT-1, Rab-3D, Mesothelin, ERα, ANXA4, PSAT1 , SPB5, CEA5, CEA6, A1AT, SLPI, APOA4, VDBP, HE4, IL-1, -6, -7, -8, -10, -11, -12, -16, -18, -21, -23, -28A, -33, LIF, TNFR1–2, HVEM (TNFRSF14), IL1R-a, IL1R-b, IL-2R, M-CSF , MIP-1a, TNF-α, CD40, RANTES, CD40L, MIF, IFN-β, MCP-4 (CCL13), MIG (CXCL9), MIP-1δ (CCL15), MIP3a (CCL20), MIP-4 (CCL18) ), MPIF-1, SDF-1a + b (CXCL12), CD137 / 4–1BB, lymphotactin (XCL1), eotaxin-1 (CCL11), eotaxin-2 (CCL24), 6Ckine / CCL21), BLC (CXCL13), CTACK (CCL27), BCA-1 (CXCL13), HCC4 (CCL16), CTAP-3 (CXCL7), IGF1, VEGF, VEGFR3, EGFR, ErbB2, CTGF, PDGF AA, BB, PDGFRb, bFGF,

TGFbRIII, β-cellulin, IGFBP1–4, 6, BDNF, PEDF, angiopoietin-2, renin, lysophosphatidic acid, β2-microglobulin, sialyl TN, ACE, CA 19–9, CEA, CA 15–3, CA- 50, CA 72–4, OVX1, mesothelin, sialyl TN, MMP-2, - 3, -7, -9, VAP-1, TIMP1–2, tenascin C, VCAM-1, osteopontin, KIM-1, NCAM, tetranectin, nidogen-2, cathepsin L, prostasin, matriptase, calcicin 2, 6, 10, cystatin C, claudin, spondin2, SLPI, bHCG, urinary gonadotropin peptide, inhibin, leptin, adiponectin, GH, TSH, ACTH, PRL, FSH, LH, cortisol, TTR, osteocalcin, insulin, ghrelin, GIP, GLP-1, amylin, glucagon, YY peptide, follistatin, hepcidin, CRP, Apo A1, CIII, H, transthyretin, SAA, SAP, complement C3 , 4, complement factor H, albumin, ceruloplasmin, haptoglobin, β-hemoglobin, transferrin, ferritin, fibrinogen, thrombin, von Willebrand factor, myoglobin, immunosuppressive acid protein, lipid-associated sialic acid, S100A12 (EN- RAGE), fetuin A, clusterin, α1-antitrypsin, a2-macroglobul ina, serpin1 (human plasminogen activating inhibitor-1), Cox-1, Hsp27, Hsp60, Hsp80, Hsp90, oxidized LDL receptor of lectin type 1, CD14, lipocalin 2, ITIH4, sFasL, Cyfra21-1, TPA, perforin , DcR3, AGRP, creatine kinase-MB, human milk fat globule 1-2, NT-Pro-BNP, neuron-specific enolase, CASA, NB / 70K, AFP, afamine, collagen, prohibitin, keratin-6, PARC , B7-H4, YK-L40, AFP-L3, DCP, GPC3, OPN, GP73, CK19, MDK, A2, 5-HIAA, CA15-3, CA19-9, CA27.29, CA72-4, calcitonin, CGA , BRAF V600E, BAP, BCT-ABL fusion protein, KIT, KRAS, PSA, Lactate dehydrogenase, NMP22, PAI-1, uPA, fibrin D-dimer, S100, TPA, thyroglobulin, CD20, CD24, CD44, RS / DJ-1, p53, alpha-2-HS glycoprotein, lipophilin B, beta-globin, hemopexin, UBE2N, PSMB6, PPP1CB, CPT2, COPA, MSK1 / 2, Pro-NPY, Secernina-1, Vinculina, NAAA, PTK7 , TFG, MCCC2, TRAP1, IMPDH2, PTEN, POSTN, EPLIN, eIF4A3, DDAH1, ARG2, PRDX3 & 4, P4HB, YWHAG, Enoyl CoA-hydrase, PHB, TUBB, KRT2, DES, HSP71, ATPD LMNA, EZH2, AMACR, FABP5,

PPA2, EZR, SLP2, SM22, Bax, Smac / Diablo phosphorylated Bcl2, STAT3 and Smac / Diablo expression, PHB, PAP, AMACR, PSMA, FKBP4, PRDX4, KRT7 / 8/18, GSTP1, NDPK1, MTX2, GDF15, PCa-24, Caveololine-2, Prothrombin, Antithrombin III, Haptoglobin, serum amyloid protein A-1, ZAG, ORM2, APOC3, CALML5, IGFBP2, MUC5AC, PNLIP, PZP, TIMP1, AMBP, heavy chain inhibitor alpha-trypsin H1 chain, alpha-trypsin H2 inhibitor heavy chain, alpha-trypsin H3 inhibitor heavy chain, type V proton ATPase B subunit, renal isoform, hepatocyte growth factor protein, component Serum amyloid P, acylglycerol kinase, protein 9 containing leucine-rich repeat, Beta-2-glycoprotein 1, Plasma C1 protease inhibitor, protein 1 containing the homology domain of lipoxygenase, Protocerherin alfa-13. See, for example, Kuppusamy et al., Volume 24, Issue 6, September 2017, Pages 1212-1221; Elzek and Rodland, Cancer Metastasis Rev. 2015 Mar; 34 (1): 83–96; Noel and Lokshin, Future Oncol. 2012 Jan; 8 (1): 55–71; Tsuchiya et al., World J Gastroenterol. 2015 Oct 7; 21 (37): 10573-10583; Lou et al., Biomark Cancer. 2017; 9: 1–9; Park et al., Oncotarget. 2017 Jun 27; 8 (26): 42761–42771; Saraswat et al., Cancer Med. 2017 Jul; 6 (7): 1738–1751; Zamay et al., Cancers (Basel). 2017 Nov; 9 (11): 155; Tanase et al., Oncotarget. 2017 Mar 14; 8 (11): 18497–18512, each of which is incorporated herein by reference in its entirety.

[277] In some embodiments, the one or more additional classes of biomarkers include lesions or variations of nucleic acid (for example, an injury or variation of nucleic acid that is distinct from the various genetic biomarkers described herein as being useful in one or more more methods). In some embodiments, an individual is determined to be at high risk of developing or developing cancer if the biological sample contains one or more nucleic acid lesions or variations indicative of cancer. In some embodiments, an individual is determined to have cancer if the biological sample contains one or more nucleic acid lesions or indicative variations in cancer. Non-limiting examples of lesions or variations in nucleic acids include changes in copy number, changes in DNA methylation and / or other nucleic acids (for example, mRNAs, miRNAs, lncRNAs, circRNA, mtDNA, telomeric DNA, translocation and rearrangements genomic). Translocations and genomic rearrangements have been correlated with various types of cancer (eg, prostate, glioma, lung cancer, non-small cell lung cancer, melanoma and thyroid cancer) and used as biomarkers for years (for example , Demeure et al., 2014, World J Surg., 38: 1296-305; Hogenbirk et al., 2016, PNAS USA, 113: E3649-56; Gasi et al., 2011, PLoS One, 6: e16332; Ogiwara et al., 2008, Oncogene, 27: 4788-97; US Patent

9,745,632; and U.S. Patent 6,576,420). In addition, changes in the number of copies were used as biomarkers for various types of cancer, including, without limitation, head and neck squamous cell carcinoma, lymphoma (eg, non-Hodgkin's lymphoma) and colorectal cancer (Kumar et al ., 2017, Tumor Biol, 39: 1010428317740296; Kumar et al., 2017, Tumor Biol., 39: 1010428317736643; Henrique et al., 2014, Ex-Rev. Rev. Mol. Diagn., 14: 419-22; and US patent 9,816,139). DNA methylation and changes in DNA methylation (eg, hypomethylation, hypermethylation) are also used as biomarkers in cancer. For example, hypomethylation has been associated with hepatocellular carcinoma (see, for example, Henrique et al., 2014, Expert Rev. Mol. Diagn., 14: 419-22), esophageal carcinogenesis (see, for example, Alvarez et al ., 2011, PLoS Genet., 7: e1001356) and gastric and liver cancer (see, for example, US Patent 8,728,732), and hypermethylation has been associated with colorectal cancer (see, for example, US Patent 9,957. 570;).

In addition to changes in methylation across the genome, specific changes in methylation within specific genes can be indicative of specific cancers (see, for example, U.S. Patent 8,150,626). Li et al. (2012, J.

Epidemiol., 22: 384-94) provides a review of the association between various types of cancer (eg, breast, bladder, gastric, lung, prostate, head and neck squamous cell and nasopharynx) and aberrant methylation.

In addition or alternatively, additional types of nucleic acids or characteristics of nucleic acids have been associated with several types of cancer.

Non-limiting examples of such nucleic acids or characteristics of nucleic acids include the presence or absence of several microRNAs (miRNAs) that have been used in the diagnosis of colon, prostate, colorectal and ovarian cancer (see, for example, D'Souza et al., 2018, PLos One, 13: e0194268; Fukagawa et al., 2017, Cancer Sci., 108: 886-96; Giraldez et al., 2018, Methods Mol.

Biol., 1768: 459-74; U.S. Patent 8,343,718; 9,410,956; and 9,074,206). For a review of the specific association of miR-22 with cancer, see Wang et al. (2017, Int.

J.

Oncol. 50: 345-55); the abnormal expression of long noncoding RNAs (lncRNAs) has also been used as a biomarker in cancers such as prostate cancer, colorectal cancer, cervical cancer, melanoma, non-small cell lung cancer, gastric cancer, endometrial carcinoma and hepatocellular carcinoma (see, for example, Wang et al., 2017, Oncotarget, 8: 58577086; Wang et al., 2018, Mol.

Cancer, 17: 110; Yu et al., 2018, Eur.

Rev.

Med.

Pharmacol.

Sci., 22: 4812-9; Yu et al., 2018, Eur.

Rev.

Med.

Pharmacol.

Sci., 22: 993-1002; Zhang et al., 2018, Eur.

Rev.

Med.

Pharmacol.

Sci., 22: 4820-7; Zhang et al., 2018, Eur.

Rev.

Med.

Pharmacol.

Sci., 22: 2304-9; Xie et al., 2018, EBioMedicine, 33: 57-67; and U.S. Patent 9,410,206); the presence or absence of circular RNA (circRNA) has been used as a biomarker in lung cancer, breast cancer, gastric cancer, colorectal cancer and liver cancer (for example, Geng et al., 2018, J.

Hematol. Oncol., 11:98) and melanoma (e.g., Zhang et al., 2018, Oncol. Lett. 16: 1219-25); changes in telomeric DNA (for example, in length or heterozygosity) or in centromeric DNA (for example, changes in the expression of centromeric genes) were also associated with cancers (for example, prostate, breast, lung, lymphoma and Ewing's sarcoma) (see for example, Baretton et al., 1994, Cancer Res., 54: 4472-80; Liscia et al., 1999, Br. J. Cancer, 80: 821-6; Proctor et al., 2009, Biochim. Biophys. Acta, 1792: 260-74; and Sun et al., 2016, Int. J. Cancer, 139: 899-907); various mutations (eg, deletions), rearrangements and / or changes in the number of copies in mitochondrial DNA (mtDNA) have been used prognostically and diagnostically for various types of cancer (eg, prostate cancer, melanoma, breast cancer, cancer lung and colorectal cancer). See, for example, Maragh et al., 2015, Cancer Biomark., 15: 763-73; Shen et al., 2010, Mitochondrion, 10: 62-68; Hosgood et al., 2010, Carcinogen., 31: 847-9; Thyagarajan et al., 2012, Cancer Epid. Biomarkers & Prev., 21: 1574-81; and U.S. Patent 9,745,632; and the abnormal presence, absence or quantity of messenger RNAs (mRNAs) have also been correlated with various types of cancer, including, without limitation, breast cancer, Wilms' tumors and cervical cancer (see, for example, Guetschow et al. , 2012, Anal. Bioanaly. Chem., 404: 399-406; Schwienbacher et al., 2000, Cancer Res., 60: 1521-5; and Ngan et al., 1997, Genitourin Med., 73: 54- 8 ). Each of these quotes is incorporated by reference in its entirety here.

[278] In certain respects, methods for detecting diseases in an individual (for example, a human being) are provided here. Several methods disclosed in this document provide a widely applicable approach for noninvasive cancer detection in individuals (for example, a cancer such as, without limitation, endometrial or ovarian cancer). Several methods disclosed in this document provide an approach widely applicable to the treatment of an individual with or with suspected cancer after non-invasive detection of cancer in individuals (for example, a cancer such as, without limitation, endometrial or ovarian cancer).

[279] In some embodiments, the methods provided here include detecting genetic biomarkers (eg, mutations) in one or more cell genes present in a sample (eg, a cervical or endometrial sample) obtained from an individual. For example, the methods provided here can be used to detect the presence of one or more genetic biomarkers (for example, mutations) in one or more genes selected from the group consisting of: NRAS, PTEN, FGFR2, KRAS, POLE, AKT1, TP53, RNF43, PPP2R1A, MAPK1, CTNNB1, PIK3CA, FBXW7, PIK3R1, APC, EGFR, BRAF and CDKN2A, in which the presence of one or more genetic biomarkers (for example, mutations) in one or more genes is indicative of the presence of ovarian or endometrial cancer in the individual. In some ways, the methods provided here include detecting in a sample obtained from an individual the presence of aneuploidy (for example, monoomatous or trisomy), in which the presence of aneuploidy is indicative of the presence of ovarian cancer or endometrium in the individual. In some embodiments, the methods provided here include detecting, in a sample obtained from an individual, the presence of one or more genetic biomarkers (for example, mutations) in one or more genes (for example, NRAS, PTEN, FGFR2, KRAS, POLE, AKT1, TP53, RNF43, PPP2R1A, MAPK1, CTNNB1, PIK3CA, FBXW7, PIK3R1, APC, EGFR, BRAF and / or CDKN2A) and the presence of aneuploidy (for example, mo- moomy or trisomy). In some modalities, methods that include detecting in a sample obtained from an individual each one of the presence of one or more genetic biomarkers (for example, mutations) in one or more genes (for example, NRAS, PTEN, FGFR2,

KRAS, POLE, AKT1, TP53, RNF43, PPP2R1A, MAPK1, CTNNB1, PIK3CA, FBXW7, PIK3R1, APC, EGFR, BRAF and / or CDKN2A) and the presence of aneuploidy (for example, monosomy or trisomy) provide a better indication that the individual has cancer (for example, endometrial or ovarian cancer) than methods in which either aspect is tested individually.

[280] In some modalities, a sample to detect the presence of a cancer (for example, an ovarian or endometrial cancer) can be collected using a Pap brush. In some modalities of any of the several methods provided here, a sample to detect the presence of a cancer (for example, an ovarian or endometrial cancer) can be collected using a Tao brush.

[281] In some embodiments, the methods provided here also include testing a sample obtained from an individual (for example, a plasma sample) for genetic biomarkers in nucleic acids that are present as circulating tumor DNA (ctDNA ). For example, a sample (for example, a plasma sample) can be tested to detect genetic biomarkers in nucleic acids that harbor one or more mutations in one or more of the following genes: AKT1, APC, BRAF, CDKN2A, CTNNB1, EGFR, FBXW7, FGFR2, GNAS, HRAS, KRAS, NRAS, PIK3CA, PPP2R1A, PTEN and / or TP53.

[282] In various modalities of methods provided here, in which one or more genetic biomarkers (eg, mutations) in genes in the cells present in a sample (eg, a cervical or endometrial sample) obtained from an individual are detected , genetic biomarkers (for example, mutations) in one or more NRAS, PTEN, FGFR2, KRAS, POLE, AKT1, TP53, RNF43, PPP2R1A, MAPK1, CTNNB1, PIK3CA, FBXW7, PIK3R1, APC, EGFR, BRAF and / or CDKN2A can be detected. In some embodiments, one or more genetic biomarkers (for example, mutations) in 1, 2, 3, 4, 5, 6, 7,

8, 9, 10, 11, 12, 13, 14, 15, 16 or 17 of these genes can be detected.

In some modalities, one or more genetic biomarkers (for example, mutations) in all 18 of these genes can be detected.

In some modalities, the one or more genetic biomarkers (for example, mutations) in these genes can be a mutation shown in Table 15. In some modalities, the one or more genetic biomarkers (for example, mutations) in these genes can - may be a mutation shown in Table 16. In some modalities, the one or more genetic biomarkers (for example, mutations) in these genes may be a mutation shown in Table 17. In some embodiments, the one or more genetic biomarkers ( for example, mutations) in these genes may be a mutation in a gene shown in Table 15. In some embodiments, the one or more genetic biomarkers (eg, mutations) in these genes may be a mutation in a gene shown in Table 16 In some modalities, the one or more genetic biomarkers (for example, mutations) in these genes may be a mutation in a gene shown in Table 17. In some modalities, methods provided here to detect the presence of a cancer d and ovary or endometrial, detecting the presence of one or more genetic biomarkers (for example, mutations) in one or more of the following genes: NRAS, PTEN, FGFR2, KRAS, POLE, AKT1, TP53, RNF43, PPPF2R1A, MAPK1 , CTNNB1, PIK3CA, FBXW7, PIK3R1, APC, EGFR, BRAF and / or CDKN2A can be combined with the detection of aneuploidy, the detection of genetic biomarkers (for example, mutations) present in ctDNA, or both.

In some modalities, combining with the detection of aneuploidy, the detection of genetic biomarkers (for example, mutations) present in ctDNA, or both, can increase the specificity and / or sensitivity of the detection of ovarian or endometrial cancer.

In some embodiments, the sample is collected using a Pap brush. In some embodiments, the sample is collected using a Tao brush.

[283] In some modalities, the methods provided here can be used to detect the presence of endometrial cancer. For example, the methods provided here can be used to detect the presence of one or more genetic biomarkers (for example, mutations) in one or more of the following genes: PTEN, TP53, PIK3CA, PIK3R1, CTNNB1, KRAS, FGFR2, POLE, APC, FBXW7, RNF43 and / or PPP2R1A, in which the presence of one or more genetic biomarkers (for example, mutations) in one or more genes is indicative of the presence of endometrial cancer in the individual. In some modes, one or more genetic biomarkers (for example, mutations) in 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or 11 of these genes can be detected. In some modalities, one or more genetic biomarkers (for example, mutations) in all 12 of these genes can be detected. In some embodiments, the one or more genetic biomarkers (for example, mutations) in these genes can be any mutation described here (for example, a mutation as shown in any of Tables 15, 16 or 17). In some embodiments, the methods provided here to detect the presence of endometrial cancer by detecting the presence of one or more genetic biomarkers (for example, mutations) in one or more of the following genes: PTEN, TP53, PIK3CA, PIK3R1, CTNNB1 , KRAS, FGFR2, POLE, APC, FBXW7, RNF43 and / or PPP2R1A can be combined with the detection of aneuploidy, the detection of genetic biomarkers (for example, mutations) present in the ctDNA or both. In some modalities, combining with the detection of aneuploidy, the detection of genetic biomarkers (for example, mutations) present in the ctDNA, or both, can increase the specificity and / or sensitivity of the detection of endometrial cancer. In some embodiments, the sample is collected using a Pap brush. In some modalities, the sample is collected using a Tao brush.

[284] In some modalities, the methods provided here can be used to detect the presence of ovarian cancer. For example, the methods provided here can be used to detect the presence of one or more genetic biomarkers (for example, mutations) in TP53, where the presence of one or more genetic biomarkers (for example, mutations) in one or more genes is indicative of the presence of ovarian cancer in the individual. In some modalities, an ovarian cancer detected by detecting the presence of a genetic biomarker (eg, mutation) in TP53 can be a high-grade ovarian cancer. In some embodiments, the one or more genetic biomarkers (for example, mutations) in TP53 can be any TP53 mutation described here (for example, a TP53 mutation as shown in any of Tables 15, 16 or 17). In some embodiments, the methods provided here to detect the presence of an endometrial cancer by detecting the presence of one or more genetic biomarkers (for example, mutations) in TP53 can be combined with the detection of aneuploidy, the detection of biomarkers - genetic pains (for example, mutations) present in the ctDNA or in both. In some modalities, combining with the detection of aneuploidy, the detection of mutations present in the ctDNA, or both, can increase the specificity and / or sensitivity of the detection of ovarian cancer. In some embodiments, the sample is collected using a Pap brush. In some embodiments, the sample is collected using a Tao brush.

[285] Genetic biomarkers (for example, mutations) in one or more of the genes described here can be detected by any of the exemplary techniques for detecting mutations described here. In addition, those skilled in the art will be aware of other methods suitable for detecting genetic biomarkers (for example, mutations) in these genes.

[286] In some embodiments, the methods provided here (for example, method including the detection of one or more genetic biomarkers (for example, mutations) in any of the genes described here, the detection of aneuploidy or both) further include testing for a sample obtained from an individual (for example, a plasma sample) for genetic biomarkers in nucleic acids that are present as circulating tumor DNA (ctDNA). In some modalities, the sample includes nucleic acids that harbor one or more of genetic biomarkers (for example, mutations) in one or more genes (for example, NRAS, PTEN, FGFR2, KRAS, POLE, AKT1, TP53, RNF43, PPP2R1A, MAPK1, CTNNB1, PIK3CA, FBXW7, PIK3R1, APC, EGFR, BRAF and / or CDKN2A) whose nucleic acids can be analyzed according to any of the several methods disclosed here. In some embodiments, the plasma sample includes nucleic acids that harbor one or more of the genetic biomarkers (for example, mutations) in one or more genes (for example, AKT1, APC, BRAF, CDKN2A, CTNNB1, EGFR, FBXW7, FGFR2, GNAS, HRAS, KRAS, NRAS, PIK3CA, PPP2R1A, PTEN and / or TP53) whose nucleic acids can be analyzed according to any of the several methods disclosed herein. In some embodiments, the presence of one or more genetic biomarkers (for example, mutations) in a gene listed in Table 15 can be detected in a sample (for example, a plasma sample). In some modalities, the presence of one or more genetic biomarkers (for example, mutations) listed in Table 15 can be detected in a sample (for example, a plasma sample). In some embodiments, the presence of one or more genetic biomarkers (for example, mutations) in a gene listed in Table 16 can be detected in a sample (for example, a plasma sample). In some modalities, the presence of one or more genetic biomarkers (for example, mutations) listed in Table 16 can be detected in a sample (for example, a plasma sample). In some embodiments, the presence of one or more genetic biomarkers (for example, mutations) in a gene listed in Table 17 can be detected in a sample (for example, a plasma sample). In some modalities, the presence of one or more genetic biomarkers (for example, mutations) listed in Table 17 can be detected in a sample (for example, a plasma sample). As will be appreciated by those skilled in the art, this ctDNA may represent nucleic acids that are eliminated from cancer cells (eg, cervical cancer, endometrial cancer cells, ovarian cancer cells and / or fallopian tube cancer cells) and, as such, it can be tested using any of the various methods provided here to determine the presence of cancer in the individual. In some embodiments, the sample to detect the presence of one or more mutations in the ctDNA is or may include blood (for example, whole blood, serum or plasma), anion, tissue, urine, cerebrospinal fluid, saliva, sputum, bronchus - alveolar lavage, bile, lymphatic fluid, cyst fluid (eg, ovarian cyst fluid), feces, ascites, pap smears, peritoneal fluid, peritoneal lavage, uterine lavage and combinations thereof. Mutations in ctDNA can be detected by any of the exemplary techniques for detecting mutations described here. In addition, those skilled in the art will be aware of other suitable methods for detecting mutations in the ctDNA.

[287] In some embodiments, the methods provided here include detecting, in a sample (for example, a cervical or endometrial sample) obtained from an individual, the presence of one or more genetic biomarkers (for example, mutations) in one or more genes

(e.g. NRAS, PTEN, FGFR2, KRAS, POLE, AKT1, TP53, RNF43, PPP2R1A, MAPK1, CTNNB1, PIK3CA, FBXW7, PIK3R1, APC, EGFR, BRAF and / or CDKN2A) and / or aneuploidy (for example, monosome or trisomy). In some embodiments, the sample is a cervical sample. In some modalities, the sample is an endometrial sample. In some embodiment, the sample comprises tissue or cells from each cervix and endometrium. In some embodiments, a sample is obtained with a Pap brush. In some embodiments, a sample is obtained with a Tao brush. In some embodiments, the methods include isolating cells from the rest of the sample. For example, the cells can be completely isolated from other components of the sample or they can be isolated to a degree such that the isolated cells include only small amounts of other sample material. In some embodiments, nucleic acids present in cells isolated from a sample can be tested using any of the various methods provided here. For example, the nucleic acids present in the sample cells can be isolated and analyzed.

[288] In some embodiments, the methods provided here include detecting in a sample (for example, a cervical or endometrial sample) obtained from an individual the presence of one or more genetic biomarkers (for example, mutations) in a or more of the following genes: NRAS, PTEN, FGFR2, KRAS, POLE, AKT1, TP53, RNF43, PPP2R1A, MAPK1, CTNNB1, PIK3CA, FBXW7, PIK3R1, APC, EGFR, BRAF and / or CDKN2A, where at least one of the genetic biomarkers (for example, at least one of the mutations) is present at low frequency in the sample. For example, the methods provided here can detect a genetic biomarker (for example, a mutation) when the genetic biomarker (for example, the mutation) is present in 0.1%, 0.2%, 0.3%, 0, 4%, 0.5%, 0.6%, 0.7%, 0.8%, 0.9%, 1% or less of the sample cells. In some embodiments, the methods provided here can detect a genetic biomarker (for example, a mutation) when the genetic biomarker (for example, the mutation) is present in less than 0.1%, 0.2%, 0.3% , 0.4%, 0.5%, 0.6% 0.7%, 0.8%, 0.9% or 1% of the total nucleic acid present in the sample.

[289] In some modalities of any of the several methods disclosed here, in which the presence of one or more genetic biomarkers (for example, mutations) in one or more genes (for example, NRAS, PTEN, FGFR2, KRAS, POLE, AKT1, TP53, RNF43, PPP2R1A, MAPK1, CTNNB1, PIK3CA, FBXW7, PIK3R1, APC, EGFR, BRAF and / or CDKN2A) and / or the presence of aneuploidy (for example, monosomy or trisomy), cytology can be performed in combination with or regardless of the method. For example, cytology can be performed in combination with any of the various methods disclosed herein to improve the detection of a cancer (for example, an ovarian or endometrial cancer) in the individual. In some modalities, perform cytology in combination with the detection of the presence of one or more genetic biomarkers (for example, mutations) in one or more genes (for example, NRAS, PTEN, FGFR2, KRAS, POLE, AKT1, TP53, RNF43, PPP2R1A, MAPK1, CTNNB1, PIK3CA, FBXW7, PIK3R1, APC, EGFR, BRAF and / or CDKN2A) and / or the presence of aneuploidy (eg, monosomy or trisomy) increases the sensitivity of the assay (for example, at least 10%, 20%, 30%, 40%, 50%, 60% or more). In some modalities, perform cytology in combination with the detection of the presence of one or more genetic biomarkers (for example, mutations) in one or more genes (for example, NRAS, PTEN, FGFR2, KRAS, POLE, AKT1, TP53, RNF43, PPP2R1A, MAPK1, CTNNB1, PIK3CA, FBXW7, PIK3R1, APC, EGFR, BRAF and / or CDKN2A) and / or the presence of aneuploidy (for example, monosomy or trisomy) increases the specificity of the assay (for example, at least 10%, 20%, 30%, 40%, 50%, 60% or more). In some modalities, perform cytology in combination with the detection of the presence of one or more genetic biomarkers (for example, mutations) in one or more genes (for example, NRAS, PTEN, FGFR2, KRAS, POLE, AKT1, TP53, RNF43, PPP2R1A, MAPK1, CTNNB1, PIK3CA, FBXW7, PIK3R1, APC, EGFR, BRAF and / or CDKN2A) and / or the presence of aneuploidy (for example, monosomy or trisomy) allows the detection of cancers that - laugh undetectable or rarely detectable with cytology alone (for example, low grade tumors). As another example, cytology can be performed independently to confirm the presence of a cancer (for example, ovarian or endometrial cancer), since its presence is determined by detecting the presence of one or more genetic biomarkers (for example, example, mutations) in one or more genes (for example, NRAS, PTEN, FGFR2, KRAS, POLE, AKT1, TP53, RNF43, PPP2R1A, MAPK1, CTNNB1, PIK3CA, FBXW7, PIK3R1, APC, EGFR, BRAF and / or CDKN2A) and / or aneuploidy (for example, monosome or trisomy). In some embodiments, the methods provided here include detecting the presence of one or more genetic biomarkers (for example, mutations) in one or more genes (for example, NRAS, PTEN, FGFR2, KRAS, POLE, AKT1, TP53, RNF43, PPP2R1A, MAPK1, CTNNB1, PIK3CA, FBXW7, PIK3R1, APC, EGFR, BRAF and / or CDKN2A) and the presence of aneuploidy (for example, monosomy or trisomy) and perform cytology.

[290] In some embodiments, any of the various methods disclosed herein can be performed on individuals who have previously undergone cancer treatments (for example, an ovarian or endometrial cancer). In some modalities, the methods provided here can be used to determine the effectiveness of the treatment. For example, an individual with ovarian or endometrial cancer may receive treatment (also referred to here as "therapeutic intervention"), after which the continued presence of cancer or the amount of cancer (or lack thereof) is determined by detection of the presence of one or more genetic biomarkers (for example, mutations) in one or more genes (for example, NRAS, PTEN, FGFR2, KRAS, POLE, AKT1, TP53, RNF43, PPP2R1A, MAPK1, CTNNB1, PIK3CA, FBXW7, PIK3R1, APC, EGFR, BRAF and / or CDKN2A) and / or the presence of aneuploidy (for example, monosomy or trisomy).

[291] In certain respects, methods of detecting disease in an individual (eg, a human being) are provided here. Several methods disclosed in this document provide a widely applicable approach to non-invasive cancer detection (for example, an early cancer, such as, without limitation, bladder cancer or upper tract urothelial carcinoma (UTUC)). In some modalities, the disease detected is cancer. In some modalities, the cancer detected is malignant. In some modalities, the disease detected is related to the urinary tract. In some modalities, the disease detected is a cancer that affects the urinary tract. In some modalities, the disease detected is bladder cancer. In some modalities, the disease detected is related to the renal pelvis. In some modalities, the disease detected is a cancer that affects the renal pelvis. In some modalities, the disease detected is a UTUC.

[292] In some embodiments, the methods provided here include detecting mutations in one or more genes in a sample (eg, a urine sample) obtained from an individual. For example, the methods provided here can be used to detect the presence of one or more genetic biomarkers (for example, one or more mutations) in one or more of the following genes: TP53, PIK3CA, FGFR3, KRAS, ERBB2, CDKN2A, MLL , HRAS, MET and / or VHL. In some embodiments, the methods provided here include detecting the presence of at least one genetic biomarker (eg, mutation) in a TERT promoter in a sample obtained from an individual. In some embodiments, the methods provided here include detecting the presence of aneuploidy (for example, monosomy or trisomy) in a sample obtained from an individual.

In some embodiments, the methods provided here include detecting in a sample obtained from an individual two or more of: genetic biomarkers (eg, mutations) in one or more genes (eg, TP53, PIK3CA, FGFR3, KRAS , ERBB2, CDKN2A, MLL, HRAS, MET and / or VHL), the presence of aneuploidy (eg, monosomy or trisomy) and the presence of at least one genetic biomarker (eg, mutation) in a TERT promoter.

In some embodiments, the methods provided here include detecting in a sample obtained from an individual each presence of one or more of genetic biomarkers (for example, one or more mutations) in one or more genes (for example, TP53, PIK3CA, FGFR3, KRAS, ERBB2, CDKN2A, MLL, HRAS, MET and / or VHL), the presence of aneuploidy (for example, monosomy or trisomy) and the presence of at least one genetic biomarker (for example, mutation) in a TERT promoter.

In some modalities, methods that include detecting in a sample obtained from an individual each one the presence of one or more genetic biomarkers (for example, mutations) in one or more genes (for example, TP53, PIK3CA, FGFR3, KRAS, ERBB2, CDKN2A, MLL, HRAS, MET and / or VHL), the presence of aneuploidy (eg, monosomy or trisomy) and the presence of at least one genetic biomarker (eg, mutation) in a TERT promoter provides a better indication that the individual has cancer (for example, bladder cancer or UTUC) than methods in which less than all three of these parameters are tested.

In some embodiments, methods that include detecting in a sample obtained from an individual each of the presence of one or more genetic biomarkers (for example, mutations) in one or more genes (for example, TP53, PIK3CA, FGFR3, KRAS, ERBB2, CDKN2A, MLL, HRAS, MET and / or VHL), the presence of aneuploidy (for example,

monosomy or trisomy) and the presence of at least one genetic biomarker (for example, mutation) in a TERT promoter can increase the specificity and / or sensitivity of detecting ovarian or endometrial cancer (for example, bladder cancer or UTUC ).

[293] In various modalities of methods provided here, in which one or more genetic biomarkers (for example, one or more mutations) in the genes of a sample (for example, a urine sample) obtained from an individual are detected, genetic biomarkers - cos (for example, mutations) in one or more of TP53, PIK3CA, FGFR3, KRAS, ERBB2, CDKN2A, MLL, HRAS, MET and / or VHL can be detected. In some embodiments, genetic biomarkers (for example, mutations) in 1, 2, 3, 4, 5, 6, 7, 8, or 9 of these genes can be detected. In some modalities, genetic biomarkers (for example, mutations) in all 10 of these genes can be detected. In some embodiments, the one or more genetic biomarkers (for example, mutations) in these genes may be any mutations disclosed here. For example, the one or more genetic biomarkers (for example, mutations) in these genes may be a mutation shown in Table 19 or Table 29. In some embodiments, at least one genetic biomarker (for example, mutations) in one of the TR53 or FGFR3 is detected. In some modalities, at least one genetic biomarker (for example, at least one mutation) in each of the TR53 or FGFR3 is detected.

[294] In some embodiments, the methods provided here include detecting the presence of at least one genetic biomarker (eg, mutation) in a TERT promoter in a sample (eg, a urine sample) obtained from an individual . Any genetic bio-marker (for example, mutation) in a TERT promoter disclosed in this document can be detected. For example, any of the varieties of genetic biomarkers of the TERT promoter

(for example, mutations) shown in Table 26 or Table 30 can be detected using the methods provided here. In some modalities, the TERT promoter genetic biomarkers (for example, mutations) that can be detected according to various methods provided here include the g.1295228 C> T and / or g.1295250 C mutations > T. In some embodiments, the TERT promoter genetic biomarkers (for example, mutations) that can be detected according to the various methods provided here include mutations in positions hg1295228 and / or hg 1295250, which are 66 and 88 bp upstream of the initial transcription site, respectively. In some embodiments, a genetic biomarker (for example, a mutation) in a TERT promoter is identified using a single-plex PCR assay. In some embodiments, a genetic biomarker (for example, a mutation) in a TERT promoter is identified using a multiplex PCR assay. In some embodiments, the single amplification primer can be used to amplify a segment containing the TERT promoter region known to harbor genetic biomarkers (eg, mutations) in cancer (eg, bladder cancer or UTUC).

[295] As used herein, the term "TERT" refers to the gene and / or the protein encoded by the gene, which is the telomerase reverse transcriptase, a catalytic subunit of the telomerase enzyme that, together with the RNA component telomerase (TERC), comprises the most important unit of the telomerase complex. High rates of activating mutations in the upstream promoter of the TERT gene are found in most BC, as well as in other types of cancer. Mutations in the TERT promoter generally affect two access points: g.1295228 C> T and g.1295250 C> T. These mutations lead to the generation of CCGGAA / T or GGAA / T motifs by altering the binding site for ETS transcription factors and subsequently increasing the activity of the TERT promoter. TERT promoter mutations occur in up to

80% of invasive urothelial carcinomas of the bladder and upper urinary tract, as well as in several of its histological variants. In addition, TERT promoter mutations occur in 60-80% of BC precursors, including low malignant potential urothelial papillary neoplasms, low-grade non-invasive non-invasive papillary carcinoma, high-grade non-invasive papillary carcinoma and "flat" carcinoma in situ (CIS), as well as in the urinary cells of a subset of these patients. The TERT promoter mutations were thus established as a common genetic alteration in BC. Human TERT promoter sequences are known in the art.

[296] Genetic biomarkers (for example, mutations) in one or more of the genes described here can be detected by any of the exemplary techniques for detecting mutations described here. In addition, those skilled in the art will be aware of other methods suitable for detecting genetic biomarkers (for example, mutations) in these genes.

[297] In some embodiments, the methods provided here include detecting in a sample (for example, a urine sample) obtained from an individual the presence of one or more genetic biomarkers (for example, mutations) in one or more of the following genes: TP53, PIK3CA, FGFR3, KRAS, ERBB2, CDKN2A, MLL, HRAS, MET and / or VHL, the presence of at least one genetic biomarker (for example, at least one mutation) in a TERT promoter, or both. In some embodiments, the sample is a urine sample. In some embodiments provided here, the methods include isolating such cells from the rest of the sample. For example, cells can be completely isolated from other components of the sample or they can be isolated to such an extent that the isolated cells include only small amounts of other material (s) in the sample. In some modalities, the presence of genetic biomarkers in nucleic acids present in cells isolated from a sample and / or the presence of aneuploidy in cells isolated from a sample can be tested using any of the several methods provided here. For example, nucleic acids present in the sample cells can be isolated and analyzed for the presence of one or more genetic biomarkers and / or the presence of aneuploidy. In some embodiments, cells are not isolated from the sample before isolating their nucleic acids for analysis. In some embodiments, the sample includes nucleic acids that harbor one or more genetic biomarkers (for example, mutations) in one or more genes (for example, TP53, PIK3CA, FGFR3, KRAS, ERBB2, CDKN2A, MLL, HRAS, MET and / or VHL), at least one genetic biomarker (for example, a mutation) in a TERT and / or aneuploidy promoter (for example, monosomy or trisomy), whose nucleic acids are analyzed according to any of the several methods here disclosed. As will be appreciated by those skilled in the art, these nucleic acids can represent nucleic acids that are eliminated from cancer cells (for example, bladder cancer cells or UTUC cells) and, as such, can be tested using any one of the several methods provided here to determine the presence of cancer in the individual.

[298] In some embodiments, the methods provided here include detecting in a sample (for example, a urine sample) obtained from an individual the presence of one or more genetic biomarkers (for example, mutations) in one or more of the following genes: TP53, PIK3CA, FGFR3, KRAS, ERBB2, CDKN2A, MLL, HRAS, MET and / or VHL, the presence of at least one genetic biomarker (for example, a mutation) in a TERT promoter, or both , in which at least one of the genetic biomarkers (for example, at least one of the mutations) is present at low frequency in the sample. For example, the methods provided here can detect a genetic biomarker (for example, a mutation) when the genetic biomarker (for example, the mutation) is present at 0.01%, 0.02%, 0.03%, 0, 04%, 0.05%, 0.06%, 0.07%, 0.08%, 0.09%, 0.1%, 0.2%, 0.3%, 0.4%, 0, 5%, 0.6%, 0.7%, 0.8%, 0.9%, 1% or less of the sample cells. In some embodiments, the methods provided here can detect a genetic biomarker (eg, a mutation) when the genetic biomarker (eg, the mutation) is present in 0.03% or less of the cells in the sample. In some modalities, the methods provided here can detect a genetic biomarker (for example, a mutation) when the genetic biomarker (for example, the mutation) is present in less than 0.01%, 0.02%, 0, 03%, 0.04%, 0.05%, 0.06%, 0.07%, 0.08%, 0.09%, 0.1%, 0.2%, 0.3%, 0, 4%, 0.5%, 0.6%, 0.7%, 0.8%, 0.9% or 1% of the total nucleic acid present in the sample.

[299] In some modalities of any of the several methods disclosed here, in which the presence of one or more genetic biomarkers (for example, mutations) in one or more genes (for example, TP53, PIK3CA, FGFR3, KRAS, ERBB2, CDKN2A, MLL, HRAS, MET and / or VHL), the presence of aneuploidy (eg, monosomy or trisomy) and / or the presence of at least one genetic biomarker (eg, a mutation) in a TERT promoter is detected, cytology can be performed in combination with or regardless of the method. For example, cytology can be performed in combination with any of the various methods disclosed herein to improve the detection of a cancer (for example, a bladder cancer or UTUC) in the individual. In some modalities, perform cytology in combination with the detection of the presence of one or more genetic biomarkers (for example, mutations) in one or more genes (for example, TP53, PIK3CA, FGFR3, KRAS, ERBB2, CDKN2A, MLL, HRAS, MET and / or VHL), the presence of aneuploidy (for example, monosomy or trisomy) and / or the presence of at least one genetic biomarker (for example, a mutation) in a TERT promoter increases the sensitivity of the assay in compared to isolated cytology (for example, at least 10%, 20%, 30%, 40%, 50%, 60% or more). In some modalities, perform cytology in combination with the detection of the presence of one or more genetic biomarkers (for example, mutations) in one or more genes (for example, TP53, PIK3CA, FGFR3, KRAS, ERBB2, CDKN2A, MLL, HRAS, MET and / or VHL), the presence of aneuploidy (for example, monosomy or trisomy) and / or the presence of at least one mutation in a TERT promoter (for example, a genetic biomarker in a TERT promoter ) increases the specificity of the assay compared to cytology only (for example, by at least 10%, 20%, 30%, 40%, 50%, 60% or more). In some modalities, perform cytology in combination with the detection of the presence of one or more genetic biomarkers (for example, mutations) in one or more genes (for example, TP53, PIK3CA, FGFR3, KRAS, ERBB2, CDKN2A, MLL, HRAS, MET and / or VHL), the presence of aneuploidy (for example, monosome or trisomy) and / or the presence of at least one genetic biomarker (for example, a mutation) in a TERT promoter allows to detect cancers that they would be undetectable or only rarely detectable with cytology alone (eg low grade tumors). As another example, cytology can be performed independently to confirm the presence of a cancer (for example, a bladder cancer or a UTUC) since its presence is determined by detecting the presence of one or more genetic biomarkers (for example, mutations) in one or more genes (for example, TP53, PIK3CA, FGFR3, KRAS, ERBB2, CDKN2A, MLL, HRAS, MET and / or VHL), aneuploidy (for example, monosomy or trisomy) and / or the presence of at least one genetic biomarker (for example, a mutation) in a TERT promoter.

In some modalities, the methods provided here include detecting each presence of one or more of genetic biomarkers (for example, mutations) in one or more genes (for example, TP53, PIK3CA, FGFR3, KRAS, ERBB2, CDKN2A, MLL, HRAS, MET and / or VHL), the presence of aneuploidy (eg, monosomy or trisomy), presence of at least one genetic biomarker (eg, mutation) in a TERT promoter and perform cytology.

[300] In some modalities, any of the various methods disclosed here can be performed on individuals who have previously undergone cancer treatments (for example, bladder cancer or UTUC). In some embodiments, the methods provided here can be used to determine the effectiveness of the treatment. For example, an individual with bladder cancer or UTUC may receive a treatment (also referred to here as "therapeutic intervention"), after which the continued presence of cancer or the amount of cancer (or lack thereof) is determined by detecting the presence of one or more genetic biomarkers (for example, mutations) in one or more genes (for example, TP53, PIK3CA, FGFR3, KRAS, ERBB2, CDKN2A, MLL, HRAS, MET and / or VHL) , presence of aneuploidy (eg, monosomy or trisomy) and / or the presence of at least one genetic biomarker (eg, a mutation) in a TERT promoter.

[301] Some modalities of the methods provided here include testing cytological samples for cancer. In some modalities, one or more cytological tests are used for diagnosis or screening. In some modalities, one or more cytological tests are used to diagnose cancer. In some modalities, one or more cytological tests are used to screen for cancer. In some modalities, one or more cytological tests are used to classify a disease or condition. In some modalities, one or more cytological tests are used to classify cancer.

[302] Various methods can be used to collect a sample including, but not limited to, aspiration cytology (e.g.

fine needle aspiration), exfoliative cytology (eg, printing smears and tissue scraping), cystoscopy.

[303] In some modalities, the cytological test includes a general examination. In some modalities, the cytological test includes a histological examination. In some modalities, the cytological test includes a frozen section exam. In some modalities, the cytological test is administered in conjunction with another method or test. In some modes, the other method or test includes a histochemical stain. In some embodiments, the other method or test includes immunohistochemical staining. In some embodiments, the other method or test includes electron microscopy. In some embodiments, the other method or test includes flow cytometry. In some embodiments, the other method or test includes image cytometry. In some embodiments, the other method or test includes genetic testing. For example, genetic testing may include, but is not limited to, a cytogenetic test, a fluorescent in situ hybridization test (FISH) and / or a molecular genetic test.

[304] In some embodiments, a molecular genetic test is used on a cytological sample (for example, a sample on which cytology is also performed) to detect the presence of one or more genetic biomarkers (for example, mutations) in PTEN, TP53, PIK3CA, PIK3R1, CTNNB1, KRAS, FGFR2, POLE, APC, FBXW7, RNF43 and / or PPP2R1A. In some modalities, a molecular genetic test is used on a cytological sample (for example, a sample in which cytology is also performed) to detect the presence of one or more genetic biomarkers (for example, mutations) in TP53, PIK3CA , FGFR3, KRAS, ERBB2, CDKN2A, MLL, HRAS, MET and / or VHL. In some embodiments, a molecular genetic test is used on a cytological sample (for example, a sample on which cytology is also performed) to detect the presence of one or more genetic biomarkers (for example, mutations) in NRAS, PTEN, FGFR2 , KRAS, POLE, AKT1, TP53, RNF43, PPP2R1A, MAPK1, CTNNB1, PIK3CA, FBXW7, PIK3R1, APC, EGFR, BRAF or CDKN2A. In some embodiments, a molecular genetic test is used on a cytological sample (for example, a sample on which cytology is also performed) to detect the presence of one or more genetic biomarkers (for example, mutations) in TP53 . Protein biomarkers in combination with aneuploidy

[305] In one aspect, methods and materials for detecting the presence of one or more members of a panel of protein biomarkers and the presence of aneuploidy in one or more samples obtained from an individual are provided here. In another aspect, methods and materials are provided here to diagnose or identify the presence of a disease in an individual (for example, identifying the individual as having cancer) by detecting the presence of one or more members of a panel of protein biomarkers and the presence of aneuploidy in one or more samples obtained from the individual. In another aspect, methods and materials are provided here to identify an individual as being at risk (for example, increased risk) of having or developing a disease (for example, cancer) by detecting the presence of one or more panel members of protein biomarkers and the presence of aneuploidy in one or more samples obtained from the individual. In another aspect, methods and materials are provided here to treat an individual who has been diagnosed or identified as having a disease (eg, cancer) or who has been identified as being at risk (eg, increased risk) of having or developing a disease (for example, cancer) detecting the presence of one or more members of a panel of protein biomarkers and the presence of aneuploidy in one or more samples obtained from the individual. In another aspect, methods and materials are provided here to identify a treatment for an individual who has been diagnosed or identified as having a disease (for example, cancer) or who has been identified as being at risk (for example, increased risk) of having or develop a disease (for example, cancer) by detecting the presence of one or more members of a panel of protein biomarkers and the presence of aneuploidy in one or more samples obtained from the individual. In another aspect, methods and materials are provided here to identify an individual who will or will likely respond to a treatment by detecting the presence of one or more members of a panel of protein biomarkers and the presence of aneuploidy in one or more samples obtained from the individual. In another aspect, methods and materials are provided here to identify an individual as a candidate for additional diagnostic tests by detecting the presence of one or more members of a panel of protein biomarkers and the presence of aneuploidy in a or more samples obtained from the individual. In another aspect, methods and materials are provided here to identify an individual as a candidate for increased monitoring by detecting the presence of one or more members of a panel of protein biomarkers and the presence of aneuploidy in one or more samples. - of the individual.

[306] In some embodiments, the methods provided here that include detecting the presence of one or more members of a panel of protein biomarkers and the presence of aneuploidy in one or more samples obtained from an individual provide high sensitivity in detection or diagnosis cancer (for example, a high frequency or incidence of correctly identifying an individual as having cancer). In some embodiments, the methods provided here that include detecting the presence of one or more members of a panel of protein biomarkers and the presence of aneuploidy in one or more samples obtained from an individual provide sensitivity in the detection or in the diagnosis of cancer (for example, a high frequency or incidence of correctly identifying an individual as having cancer) that is superior to the sensitivity provided by the separate detection of the presence of one or more members of a panel of protein biomarkers. nas or the presence of aneuploidy.

In some embodiments, the methods and materials provided here that include detecting the presence of one or more members of a panel of protein biomarkers and the presence of aneuploidy in one or more samples obtained from an individual provide a hair sensitivity. at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99% or higher .

In some modalities, methods and materials provided here that include detecting the presence of one or more members of a panel of protein biomarkers and the presence of aneuploidy in one or more samples obtained from an individual provide high sensitivity in detecting a single type of cancer.

In some modalities, methods and materials provided here that include detecting the presence of one or more members of a panel of protein biomarkers and the presence of aneuploidy in one or more samples obtained from an individual provide high sensitivity in detecting two or more types of cancer.

Any of the various types of cancer can be detected using the methods and materials provided here (see, for example, the section entitled "Cancers"). In some embodiments, cancers that can be detected using methods and materials that include detecting the presence of one or more members of a panel of protein biomarkers and the presence of aneuploidy in one or more samples obtained from an individual include pancreatic cancer.

In some modalities, cancers that can be detected using methods and materials that include detecting the presence of one or more members of a panel of protein biomarkers and the presence of aneuploidy in one or more samples obtained from an individual include liver cancer, ovarian cancer, esophageal cancer, stomach cancer, pancreatic cancer, colorectal cancer, lung cancer or breast cancer. In some modalities, cancers that can be detected using methods and materials that include detecting the presence of one or more members of a panel of protein biomarkers and the presence of aneuploidy in one or more samples obtained from a include cancers of the female reproductive system (eg, cervical cancer, endometrial cancer, ovarian cancer or fallopian tube cancer). In some modalities, cancers that can be detected using methods and materials that include detecting the presence of one or more members of a panel of protein biomarkers and the presence of aneuploidy in one or more samples obtained from an individual include bladder cancer or urothelial carcinomas of the upper tract.

[307] In some embodiments, the methods provided here that include detecting the presence of one or more members of a panel of protein biomarkers and the presence of aneuploidy in one or more samples obtained from an individual provide high specificity in the detection or diagnosis of cancer (for example, a low frequency or incidence of incorrectly identifying an individual as having cancer when that individual does not have cancer). In some modalities, the methods provided here that include detecting the presence of one or more members of a panel of protein biomarkers and the presence of aneuploidy in one or more samples obtained from an individual provide specificity in the detection or in the cancer diagnosis (for example, a high frequency or incidence of correctly identifying an individual as having cancer) that is superior to the specificity provided by the separate detection of the presence of one or more members of a panel of protein biomarkers or the presence of aneuploidy . In some modalities, the methods and materials provided here that include detecting the presence of one or more members of a panel of protein biomarkers and the presence of aneuploidy in one or more samples obtained from an individual provide a specificity of at least at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99% or greater. As will be understood by those skilled in the art, a specificity of 99% means that only 1% of individuals who do not have cancer are incorrectly identified as having cancer. In some embodiments, the methods and materials provided here that include detecting the presence of one or more members of a panel of protein biomarkers and the presence of aneuploidy in one or more samples obtained from an individual provide high specificity in detecting a single cancer (for example, there is a low probability of incorrectly identifying this individual as having this type of cancer). In some embodiments, the methods and materials provided here that include detecting the presence of one or more members of a panel of protein biomarkers and the presence of aneuploidy in one or more samples obtained from an individual provide high specificity in detecting two or more cancer (for example, there is a low probability of incorrectly identifying this individual as having these two or more types of cancer).

[308] In some embodiments, the methods provided here that include detecting the presence of one or more members of a panel of protein biomarkers and the presence of aneuploidy in one or more samples obtained from an individual include detecting the presence of:

1) one or more (for example, 1, 2, 3, 4, 5, 6, 7 or 8) of the following protein biomarkers: CA19-9, CEA, HGF, OPN, CA125, prolactin, TIMP-1 and / or myeloperoxidase (MPO), and 2) the presence of aneuploidy. In some embodiments, the methods provided here that include detecting the presence of one or more members of a protein biomarker panel and the presence of aneuploidy in one or more samples obtained from an individual include detecting the presence of : 1) each of the following biomarker proteins: CA19-9, CEA, HGF, OPN, CA125, prolactin, TIMP-1 and myeloperoxidase (MPO) and presence of aneuploidy. In some modalities of methods provided here, which include detecting the presence of one or more members of a panel of protein biomarkers and the presence of aneuploidy in one or more samples obtained from an individual, detecting the presence of: 1) one or more (for example ,, 1, 2, 3, 4, 5, 6, 7 or 8) of the following protein biomarkers: CA19-9, CEA, HGF, OPN, CA125, prolactin, TIMP-1 and / or myeloperoxidase (MPO), and 2) in the presence of aneuploidy, the individual is determined to have (for example, diagnosed as having) or is determined to be (for example, diagnosed as having) at high risk of having or developing a of the following types of cancer: liver cancer, ovarian cancer, esophageal cancer, stomach cancer, pancreatic cancer, colorectal cancer, lung cancer and / or breast cancer.

[309] In some embodiments, the methods provided here that include detecting the presence of one or more members of a panel of protein biomarkers and the presence of aneuploidy in one or more samples obtained from an individual include detecting the presence of: 1) one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or 11) of the following protein biomarkers: CA19-9, CEA, HGF, OPN, CA125, AFP , prolactin, TIMP-1, follistatin, G-CSF and / or CA15-3 and 2) the presence of aneuploidy. In some embodiments, the methods provided here that include detecting the presence of one or more members of a panel of protein biomarkers and the presence of aneuploidy in one or more samples obtained from an individual include detecting the presence of: 1) each of the following biomarker proteins: CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1, follistatin, G-CSF and CA15-3 and 2) the presence of aneuploidy. In some modalities of methods provided here, which include detecting the presence of one or more members of a panel of protein biomarkers and the presence of aneuploidy in one or more samples obtained from an individual, detecting the presence of: 1) one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or 11) of the following protein biomarkers: CA19-9, CEA, HGF, OPN, CA125, AFP , prolactin, TIMP-1, follistatin, G-CSF and / or CA15-3 and 2) in the presence of aneuploidy, the individual is determined to have (for example, diagnosed as having) or is determined to be (for example, diagnosed as being) at high risk of having or developing cancer one of the following types of cancer: liver cancer, ovarian cancer, esophageal cancer, stomach cancer, pancreatic cancer, colorectal cancer, lung cancer and / or cancer breast.

[310] In some embodiments, the methods provided here that include the presence of one or more members of a panel of protein biomarkers and the presence of aneuploidy in one or more samples obtained from an individual include detecting the presence of: 1) a or more (for example, 1, 2, 3, 4, 5, 6, 7, 8, or 9) of the following protein biomarkers: CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1 , and / or CA15-3 and 2) the presence of aneuploidy. In some embodiments, the methods provided here that include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of one or more members of a panel of protein biomarkers in one or more samples obtained from an individual include detecting the presence of: 1) each of the following biomarker proteins: CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1, and CA15-3 and 2) the presence of aneuploidy. In some modalities of methods provided here, which include detecting the presence of one or more members of a panel of protein biomarkers and the presence of aneuploidy in one or more samples obtained from an individual, detecting the presence of: 1) one or more (for example ,, 1, 2, 3, 4, 5, 6, 7, 8 or 9) of the following protein biomarkers: CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1 and / or CA15-3, and 2) in the presence of aneuploidy, the individual is determined to be (for example, diagnosed as having) or is determined to be (for example, diagnosed as being) at high risk of having or develop one of the following types of cancer: liver cancer, ovarian cancer, esophageal cancer, stomach cancer, pancreatic cancer, colorectal cancer, lung cancer and / or breast cancer.

[311] In some embodiments, the methods provided here that include detecting the presence of one or more members of a panel of protein biomarkers and the presence of aneuploidy in one or more samples obtained from an individual include detecting the presence of: 1) one or more (for example, 1, 2, 3 or 4) of the following protein biomarkers: CA19-9, CEA, HGF and / or OPN and 2) the presence of aneuploidy. In some embodiments, the methods provided here that include detecting the presence of one or more members of a panel of protein biomarkers and the presence of aneuploidy in one or more samples obtained from an individual include detecting the presence of: 1) each one of the following biomarker proteins: CA19-9, CEA, HGF and OPN and 2) the presence of aneuploidy. In some modalities of methods provided here, which include detecting the presence of one or more members of a panel of protein biomarkers and the presence of aneuploidy in one or more samples obtained from an individual, in detecting the presence of 1 ) one or more (for example, 1, 2, 3 or 4) of the following protein biomarkers: CA19-9, CEA, HGF and / or OPN and 2) the presence of aneuploidy, an individual is determined to have (for example , diagnosed as having) or is determined to be (for example, diagnosed as being) at high risk of having or developing pancreatic cancer.

[312] A sample obtained from an individual can be any of the variety of samples described here that contain DNA (for example, ctDNA in the blood or DNA present in bladder, cervical, endometrial or uterine samples) and / or proteins. In some embodiments, the DNA (for example, DNA without cells (for example, ctDNA) or DNA present in the bladder, cervix, endometrium or uterus samples) and / or proteins in a sample obtained from the individual are derived - veins of a tumor cell. In some embodiments, the DNA (eg, cellless DNA (eg, ctDNA) in a sample taken from the individual includes one or more genetic biomarkers and or aneuploidy DNA. In some embodiments, the proteins in a sample obtained from the individual include one or more protein biomarkers, non-limiting examples of samples in which genetic biomarkers and / or protein biomarkers and / or aneuploidies can be detected include a blood sample, a plasma sample, a sample of serum, a urine sample, an endometrial sample, a cervical sample and a uterine sample In some modalities, the presence of one or more protein biomarkers and the presence of aneuploidy is detected in a single sample obtained from the individual. modalities, the presence of one or more protein biomarkers is detected in a first sample obtained from an individual, and the presence of aneuploidy is detected in a second sample obtained from the individual.

[313] In some modalities, methods provided here that include detecting the presence of one or more members of a panel of protein biomarkers (for example, each member of a panel of protein biomarkers) and the presence of aneuploidy in a or more samples taken from an individual, a high level of one or more members of the protein biomarker panel can be detected.

For example, a high level of a protein biomarker may be higher than the reference level.

A reference level can be any level of the protein biomarker that is not associated with the presence of cancer.

For example, a reference level for a protein biomarker may be present in a reference individual who does not have cancer or does not harbor a cancer cell.

A reference level for a protein biomarker can be the average level that is present in a plurality of reference individuals who do not have cancer or do not harbor a cancer cell.

A reference level for a protein biomarker in an individual determined to have cancer may be the level that was present in the individual before the cancer started.

In some embodiments, a panel of protein biomarkers in which one or more panel members is present at a high level includes one or more of (for example, 1, 2, 3, 4, 5, 6, 7 or each of ): CA19-9, CEA, HGF, OPN, CA125, prolactin, TIMP-1 and / or myeloperoxidase (MPO). In some embodiments, a panel of protein biomarkers in which one or more panel members is present at a high level includes one or more of (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9 , 10, or each of): CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1, follistatin, G-CSF and / or CA15-3. In some embodiments, a panel of protein biomarkers in which one or more panel members are present at a high level includes one or more of (for example, 1, 2, 3, 4, 5, 6, 7 , 8 or each of): CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1, and / or CA15-3. In some embodiments, a panel of protein biomarkers in which one or more panel members is present at a high level includes one or more of (for example, 1, 2, 3 or each of): CA19-9, CEA, HGF and / or OPN.

[314] In some embodiments, methods provided here that include detecting the presence of one or more members of a panel of protein biomarkers (for example, each member of a panel of protein biomarkers) and the presence of aneuploidy in a or more samples taken from an individual, a reduced level of one or more members of the protein biomarkers panel can be detected. For example, a reduced level of a protein biomarker may be lower than the reference level. A reference level can be any level of the protein biomarker that is not associated with the presence of cancer. For example, a reference level for a protein biomarker may be present in a reference person who does not have cancer or does not harbor a cancer cell. A reference level for a protein biomarker can be the average level that is present in a plurality of reference individuals who do not have cancer or do not harbor a cancer cell. A reference level for a protein biomarker in an individual determined to have cancer may be the level that was present in the individual before the cancer started. In some modalities, a panel of protein biomarkers in which one or more panel members is present at a high level includes one or more of (for example, 1, 2, 3, 4, 5, 6, 7 or each one of): CA19-9, CEA, HGF, OPN, CA125, prolactin, TIMP-1 and / or myeloperoxidase (MPO). In some embodiments, a panel of protein biomarkers in which one or more panel members is present at a reduced level includes one or more of (for example, 1, 2, 3, 4, 5, 6, 7 , 8, 9, 10, or each of): CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1, folistatin, G-CSF and / or CA15-3. In some embodiments, a panel of protein biomarkers in which one or more panel members is present at a reduced level includes one or more of (for example, 1, 2, 3, 4, 5, 6, 7, 8 or each of): CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1, and / or CA15-3. In some embodiments, a panel of protein biomarkers in which one or more panel members is present at a reduced level includes one or more of (for example, 1, 2, 3 or each of): CA19-9, CEA, HGF and / or OPN.

[315] In some embodiments, when an individual is determined to have (for example, diagnosed as having) cancer or determined to be (for example, diagnosed as being) at high risk of having or developing cancer (for example, detec - namely: 1) the presence of aneuploidy, and 2) the presence of one or more protein biomarkers in any of the panels described here as being useful in conjunction with the presence of aneuploidy), the individual is selected as a candidate for (for example, is selected for) additional diagnostic tests (for example, any of several other forms of additional diagnostic tests described here), the individual is selected as a candidate for (for example, is selected for) additional diagnostic tests (for example, any of several other forms of diagnostic methods described here), the individual is selected as a candidate for (for example, selected for) increased monitoring (for example, wed any of the various augmented monitoring methods described here), the individual is identified as an individual who will respond or likely respond to a treatment (for example, any of the various therapeutic interventions described here), the individual is selected as candidate (for example, is selected for) a treatment, a treatment (for example, any of the varieties of therapeutic interventions described here) is selected for the individual and / or a treatment (for example, any of the various therapeutic interventions described here) is administered to the individual. For example, when an individual is determined to have (for example, diagnosed as having) cancer or determined to be (for example, diagnosed as being) at high risk of having or developing cancer, the individual may be tested additional diagnostic tests, such additional diagnostic tests can confirm the presence of cancer in the individual.

Additionally or alternatively, the individual can be monitored more frequently.

In some modalities of an individual determined to have (for example, diagnosed as having) cancer or determined to be (for example, diagnosed as being) at high risk of having or developing cancer in which the individual is tested for cancer. additional diagnoses and / or increased monitoring, the individual can additionally be administered with a therapeutic intervention.

In some modalities, after an individual is administered with a therapeutic intervention, the individual undergoes further additional diagnostic tests (for example, the same type of additional diagnostic test that was performed previously and / or a different type additional diagnostic test) and / or continuous augmented monitoring (for example, augmented monitoring at the same or different frequency than previously performed). In modalities, after an individual is administered with a therapeutic intervention and the individual undergoes further additional diagnostic tests and / or additional increased monitoring, the individual is administered with another therapeutic intervention (for example, the same therapeutic intervention that was previously administered and / or a different therapeutic intervention). In some modalities, after an individual is administered with a therapeutic intervention, the individual is tested for 1) the presence of one or more (for example, 1, 2, 3, 4, 5, 6, 7 or 8) of the following protein biomarkers: CA19-9, CEA, HGF, OPN, CA125, prolactin, TIMP-1 and / or myeloperoxidase (MPO) and 2) the presence of aneuploidy.

In some modalities, after administering a therapeutic intervention, the individual is tested for 1) the presence of one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or 11 ) of the following protein biomarkers: CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1, follistatin, G-CSF and / or CA15-3 and 2) the presence of aneuploidy. In some modalities, after an individual is administered with a therapeutic intervention, the individual is tested for 1) the presence of one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8 or 9) of the following protein biomarkers: CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1 and / or CA15-3 and 2) the presence of aneuploidy. In some modalities, after an individual is administered with a therapeutic intervention, the individual is tested for 1) the presence of one or more (for example, 1, 2, 3, or 4) of the following protein biomarkers : CA19-9, CEA, HGF and / or OPN and 2) the presence of aneuploidy.

[316] In some modalities of the methods provided here that include detecting the presence of one or more members of a panel of protein biomarkers and the presence of aneuploidy, the methods also include detecting the presence of one or more members of a panel of protein biomarkers in one or more samples obtained from an individual (for example, the same sample is used to detect one or more of the presence of one or more members of a panel of protein biomarkers and the presence of aneuploidy or a sample) different). Any of a variety of genetic biomarkers can be detected (for example, any variety of genetic biomarkers and / or biomarker panels described here).

[317] In some modalities, methods provided here that include detecting in one or more samples obtained from an individual the presence of: 1) one or more (for example, 1, 2, 3, 4, 5, 6, 7 or 8) of the following protein biomarkers: CA19-9, CEA, HGF, OPN, CA125, prolactin, TIMP-1 and / or myeloperoxidase (MPO) and 2) the presence of aneuploidy, the methods also include detecting the presence of a or more members of a panel of genetic biomarkers in one or more samples obtained from an individual (for example, the same sample use to detect one or both the presence of one or more members of a panel of protein biomarkers and the presence of aneuploidy or a different sample). In some embodiments, methods provided here that include detecting in one or more samples obtained from an individual the presence of: 1) each of the following protein biomarkers: CA19-9, CEA, HGF, OPN, CA125, prolactin, TIMP - 1, and myeloperoxidase (MPO) and the presence of aneuploidy, the methods also include detecting the presence of one or more members of a panel of genetic biomarkers in one or more samples obtained from an individual (for example, at the same use sample to detect one or both of the presence of one or more members of a panel of protein biomarkers and the presence of aneuploidy or a different sample). In some modalities of methods provided here, which include detecting in one or more samples obtained from an individual the presence of: 1) one or more (for example, 1, 2, 3, 4, 5, 6, 7 or 8) of following protein biomarkers: CA19-9, CEA, HGF, OPN, CA125, prolactin, TIMP-1 and / or myeloperoxidase (MPO), 2) in the presence of aneuploidy, and 3) the presence of one or more panel members of genetic biomarkers, the individual is determined to have (for example, diagnosed as having) or is determined to be (for example, diagnosed as being) at high risk of having or developing one of the following types of cancer: liver cancer, cancer ovarian cancer, esophageal cancer, stomach cancer, pancreatic cancer, colorectal cancer, lung cancer and / or breast cancer.

[318] In some modalities of methods provided here that include detecting in one or more samples obtained from an individual the presence of: 1) one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8 , 9, 10 or 11) of the following protein biomarkers: CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1, follistatin, G-CSF, and / or CA15-3 and 2) the presence aneuploidy, the methods also include detecting the presence of one or more members of a panel of genetic biomarkers in one or more samples obtained from an individual (for example, the same sample use to detect one or both the presence of one or more members of a panel of protein biomarkers and the presence of aneuploidy or a different sample). In some modalities of methods provided here that include detecting in one or more samples obtained from an individual the presence of: 1) each of the following protein biomarkers: CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP -1, follistatin, G-CSF and CA15-3 and 2) the presence of aneuploidy, the methods also include detecting the presence of one or more members of a panel of genetic biomarkers in one or more samples obtained from an individual ( for example, the same sample use to detect one or both of the presence of one or more members of a panel of protein biomarkers and the presence of aneuploidy or a different sample). In some modalities of methods provided here, which include detecting in one or more samples obtained from an individual the presence of: 1) one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8 , 9, 10 or 11) of the following protein biomarkers: CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1, follistatin, G-CSF and / or CA15-3, 2) in presence of aneuploidy, and 3) the presence of one or more members of a panel of genetic biomarkers, the individual is determined to be (for example, diagnosed as having) or is determined to be (for example, diagnosed as being) at high risk of having or developing cancer one of the following types of cancer: liver cancer, ovarian cancer, esophageal cancer, stomach cancer, pancreatic cancer, colorectal cancer, cancer of the lung and / or breast cancer.

[319] In some modalities of methods provided here that include detecting in one or more samples obtained from an individual the presence of: 1) one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8 or 9) of the following protein biomarkers: CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1 and / or CA15-3 and 2) the presence of aneuploidy, the methods also include detecting the presence of one or more members of a panel of genetic biomarkers in one or more samples obtained from an individual (for example, the same sample use to detect one or both the presence of one or more members of a panel of biomarkers of proteins and the presence of aneuploidy or a different sample). In some modalities I give methods provided here that include detecting in one or more samples obtained from an individual the presence of: 1) each of the following protein biomarkers: CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin , TIMP-1, and CA15-3 and 2) the presence of aneuploidy, the methods also include detecting the presence of one or more members of a panel of genetic biomarkers in one or more samples obtained from an individual (for example, the same sample use to detect one or both of the presence of one or more members of a panel of protein biomarkers and the presence of aneuploidy or a different sample). In some modalities of methods provided here, which include in one or more samples obtained from an individual the presence of: 1) one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8 or 9) of the following protein biomarkers: CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1, and / or CA15-3, 2) in the presence of aneuploidy, and 3) the presence of one or more members of a panel of genetic biomarkers, the individual is determined to have (for example, diagnosed as having) or is determined to be (for example, diagnosed as having) at high risk of having or developing one of the following types of cancer: liver cancer, ovarian cancer, esophageal cancer, stomach cancer, pancreatic cancer, colorectal cancer, lung cancer and / or breast cancer.

[320] In some modalities of methods provided herein which include detecting in one or more samples obtained from an individual the presence of: 1) one or more (for example, 1, 2, 3 or 4) of the following protein biomarkers: CA19 -9, CEA, HGF and / or OPN and 2) the presence of aneuploidy, the methods also include detecting the presence of one or more members of a panel of genetic biomarkers in one or more samples obtained from an individual (for example , at the same sample use to detect one or both of the presence of one or more members of a panel of protein biomarkers and the presence of aneuploidy or a different sample). In some types of methods provided here, which include detecting in one or more samples obtained from an individual, the presence of: 1) each of the following protein biomarkers: CA19-9, CEA, HGF and OPN and 2) a presence of aneuploidy. In some modalities of the methods provided here, which include detecting in one or more samples obtained from an individual the presence of 1) one or more (for example, 1, 2, 3 or 4) of the following protein biomarkers: CA19-9, CEA, HGF and / or OPN, 2) the presence of aneuploidy and 3) the presence of one or more members of a panel of genetic biomarkers, an individual is determined to have (for example, diagnosed as having) or is determined as being (for example, diagnosed as being) at high risk of having or developing pancreatic cancer.

[321] In some modalities of methods provided here, which include detecting the presence of one or more members of a panel of protein biomarkers and the presence of aneuploidy, one or more genetic biomarkers in one or more (for example, 1 , 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 or 16) of the following genes can still be detected: NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR , BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A and / or GNAS. In some modalities of methods provided here, which include detecting the presence of one or more members of a panel of protein biomarkers and the presence of aneuploidy, one or more genetic biomarkers in each of the following genes can still be detected: NRAS , CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A and GNAS.

[322] In some modalities of methods provided here, which include detecting the presence of one or more members of a panel of protein biomarkers and the presence of aneuploidy, one or more genetic biomarkers in one or more (for example, 1, 2 , 3 or 4) of the following genes can still be detected: KRAS (for example, genetic biomarkers in codons 12 and / or 61), TP53, CDKN2A and / or SMAD4. In some modalities of methods provided here, which include detecting the presence of one or more members of a panel of protein biomarkers and the presence of aneuploidy, one or more genetic biomarkers in each of the following genes can still be detected: KRAS (for example, genetic biomarkers at codons 12 and / or 61), TP53, CDKN2A and SMAD4.

[323] In some modalities of methods provided here, which include detecting the presence of one or more members of a panel of protein biomarkers and the presence of aneuploidy, one or more genetic biomarkers in one or more (for example, 1, 2 , 3, 4, 5, 6, 7, 8, 9 or 10) of the following genes can still be detected: TP53, PIK3CA, FGFR3, KRAS, ERBB2, CDKN2A, MLL, HRAS, MET and / or VHL. In some modalities of methods provided here, which include detecting the presence of one or more members of a panel of protein biomarkers and the presence of aneuploidy, one or more genetic biomarkers in each of the following genes can still be detected: TP53 , PIK3CA, FGFR3, KRAS, ERBB2, CDKN2A, MLL, HRAS, MET and VHL.

[324] In some modalities of methods provided here, which include detecting the presence of one or more members of a panel of protein biomarkers and the presence of aneuploidy, one or more genetic biomarkers in one or more (for example, 1, 2 , 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 or 18) of the following genes can still be detected: NRAS, PTEN, FGFR2, KRAS, POLE , AKT1, TP53, RNF43, PPP2R1A, MAPK1, CTNNB1, PIK3CA, FBXW7, PIK3R1, APC, EGFR, BRAF and / or CDKN2A. In some modalities of methods provided here, which include detecting the presence of one or more members of a panel of protein biomarkers and the presence of aneuploidy, one or more genetic biomarkers in each of the following genes can still be detected : NRAS, PTEN, FGFR2, KRAS, POLE, AKT1, TP53, RNF43, PPP2R1A, MAPK1, CTNNB1, PIK3CA, FBXW7, PIK3R1, APC, EGFR, BRAF, and CDKN2A.

[325] In some modalities of methods provided here, which include detecting the presence of one or more members of a panel of protein biomarkers and the presence of aneuploidy, one or more genetic biomarkers in one or more (for example, 1, 2 , 3, 4, 5, 6, 7, 8, 9, 10, 11 or 12) of the following genes can also be detected: PTEN, TP53, PIK3CA, PIK3R1, CTNNB1, KRAS, FGFR2, POLE, APC, FBXW7 , RNF43 and / or PPP2R1A. In some modalities of methods provided here, which include detecting the presence of one or more members of a panel of protein biomarkers and the presence of aneuploidy, one or more genetic biomarkers in each of the following genes can still be detected : PTEN, TP53, PIK3CA, PIK3R1, CTNNB1, KRAS, FGFR2, POLE, APC, FBXW7, RNF43, and PPP2R1A.

[326] In some modalities of methods provided here, which include detecting the presence of one or more members of a panel of protein biomarkers and the presence of aneuploidy, one or more genetic biomarkers in one or more (for example, 1, 2 , 3, 4, 5, 6,

7, 8, 9, 10, 11, 12, 13, 14, 15 or 16) of the following genes can still be detected: AKT1, APC, BRAF, CDKN2A, CTNNB1, EGFR, FBXW7, FGFR2, GNAS, HRAS, KRAS, NRAS, PIK3CA, PPP2R1A, PTEN, and / or TP53. In some modalities of methods provided here, which include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of aneuploidy, one or more protein biomarkers in each of the following genes can still be detected : AKT1, APC, BRAF, CDKN2A, CTNNB1, EGFR, FBXW7, FGFR2, GNAS, HRAS, KRAS, NRAS, PIK3CA, PPP2R1A, PTEN, and TP53.

[327] In some embodiments, any of the variety of methods provided here that includes detecting the presence of one or more members of a panel of protein biomarkers and the presence of aneuploidy in one or more samples obtained from an individual further includes detect the presence of one or more members of one or more additional classes of biomarkers. Non-limiting examples of these additional classes of biomarkers include: changes in the number of copies, changes in DNA methylation, other nucleic acids (for example, mRNAs, miRNAs, lncRNAs, circRNA, mtDNA, telomeric DNA, translocation and genomic rearrangements ), peptides and / or metabolites.

[328] In some embodiments, the one or more additional classes of biomarkers include a metabolite biomarker. In some embodiments, an individual is determined to be at high risk of having or developing cancer if the biological sample contains one or more metabolites indicative of cancer. In some modalities, an individual is determined to have cancer if the biological sample contains one or more metabolites indicative of cancer. Non-limiting examples of cancer-indicating metabolites include: 5-methylthioadenosine

(MTA), Reduced Glutathione (GSH), N-acetylglutamate, Lactose, N-acetylneuraminate, UDP-acetylglucosamine, UDP-Acetylgalactosamine, UDP-glucuronate, Pantothenate, Araquidonate (20: 4n6), Choline, Cytidine 5′-di - phosphocholine, Di-homo-linolenate (20: 3n3), Docosapentaenoate (DPA 22: 5n3), Eicosapentaenoate (EPA 20: 5n3), Glycerophosphorylcholine (GPC), Docosahexaenoate (DHA 22: 6n3), Linoleate (18: 2n6 ), Cytidine 5'-monophosphate (5′-CMP), Gamma-glutamylglutamate, X - 14577, X - 11583, Iso-valerylcarnitine, Phosphocreatine, 2-aminoadipic acid, Gluconic acid, O-Acetylcarnitine, aspartic acid, Deamide NAD +, Glutamic Acid, Isobutyrylcarnitine, Carnitine, Pyridoxal, Citric Acid, Adenosine, ATP, Valine, XC0061, Isoleucine, γ-Butyrobetaine, Lactic Acid, Alanine, Phenylalanine, Gluconolactone, Leucine, Gluconolate, , NAD +, XC0016, UTP, creatine, Theobromine, CTP, GTP, 3-Methylhistidine, Succinic Acid, Glycerol 3-phosphate, glutamine, 5-Oxoproline, Thiamine, Butyrylca ritin, 4-acetamidobutanoic acid, UDP-Glucose, UDP-Galactose, threonine, N-Acetylglycine, proline, ADP, Choline, Malic Acid, S-Adenosylmethionine, Pantothenic acid, Cystein sulfinic acid, 6-Amino-hexane acid homocysteine, hydroxyproline, methionine sulfoxide, 3-guanidinopropionic acid, glucose 6-phosphate, phenaceturic acid, threonic acid, tryptophan, pyridoxine, N-acetyl-aspartic acid, 4-guanidinobutyric acid, serine, betaine, serine, betaine acetyl-asparagine, 2-hydroxyglutaric acid, arginine, Glutathione (GSH), creatinine, Dihydroxyacetone phosphate, histidine, glycine, Glucose 1- phosphate, N-formylglycine, Ketoprofen, lysine, beta-alanine, N-acetylglutamic acid, 2 - Amino-2- (hydroxymethyl) -1,3-propanediol, Ornithine, Phosphorylcholine, Glycerophosphocholine, Terephthalic acid, Glyceraldehyde 3-phosphate, Gly-Asp, Taurine, Fructose 1,6-diphosphate, 3-Amino-butyric acid, Sperididine, GABA, Triethanolamine, Glycerol, N-acetylserine, N-Acetylornithine, Diethanol amine, AMP, Glutathione cysteine disulfide, Streptomycin sulfate + divalent H2O, Trans-glutaconic acid, nicotinic acid, Isobutylamine, Betaine aldehyde + H2O, Urocanic acid, 1-aminocycloprop-1-carboxylic acid Homoserinalactone, 5 -aminovaleric, 3- Acid

hydroxybutyric acid, ethanolamine, isovaleric acid, N-methylglutamic acid, cystathionine, spermine, carnosine, 1-methylnicotinamide, N-acetylneuraminic acid, sarcosine, GDP, N-methylalanine, palmitic acid, 1,2-dio-leoil-sn- glycero-3-phospho-rac-glycerolcholesterol 5α, 6α epoxidelanosterol, lignoceric acid, 1oleoil_rac_GL, cholesterol_epoxide, erucic acid, T-LCA, oleoyl-L-carnitine, oleanolic acid, 3-phosphoglycerate, 5-hydroxinor-valetamine, 5-hydroxy , adenosine-5-monophosphate, alpha-ketogluta-rat, asparagine, benzoic acid, hypoxanthine, maltose, maltotriose, methionine sulphide, nornicotine, phenol, phosphoethanolamine, pyrophosphate, pyruvic acid, quininic acid, taurine, uric acid , lactamide, 5-hydroxynorvaline NIST, cholesterol, deoxypentitol, 2-hydroxyestrone, 2-hydroxyestradiol, 2-metolestrone, 2-methoxyestradiol, 2-hydroxyestrone-3-methyl ether, 4-hydroxestrone, 4-metolxestrone, 4-methoxyestradiol, 16alpha - hydroxyestrone, 17-epiestriol, estriol, 16-keto stradiol, 16-epiestriol, acyl-carnitine C18: 1, amino acids citrulline and trans-4-hydroxyproline, hydroxyproline, glycerophospholipids PC aa C28: 1, PC ae C30: 0 and PC ae C30: 2, and sphingolipid SM (OH ) C14: 1. See, for example, Halama et al., Nesting of colon and ovarian cancer cells in the endothelial niche is associated with alterations in glycan and lipid metabolism, Scientific Reports volume 7, Article number: 39999 (2017); Hur et al., Systems approach to characterize the metabolism of liver cancer stem cells expressing CD133, Sci Rep., 7: 45557, doi: 10.1038 / srep45557, (2017); Eliassen et al., Urinary Estrogens and Estrogen Metabolites and Subsequent Risk of Breast Cancer among Premenopausal Women, Cancer Res; 72 (3); 696–706 (2011); Gangi et al., Metabolomic profile in pancreatic cancer patients: a consensus-based approach to identify highly discriminating metabolites, Oncotarget, Feb 2; 7 (5): 5815–5829 (2016); Kumar et al., Serum and Plasma Metabolomic Biomarkers for Lung Cancer, Bioinformation, 13 (6): 202–208, doi: 10.6026 / 97320630013202 (2017); Schmidt et al., Pre-diagnostic metabolite concentrations and prostate cancer risk in 1077 cases and 1077 matched controls in the European Prospective Investigation into Cancer and Nutrition, BMC Med., 15: 122, doi:

10.1186 / s12916-017-0885-6 (2017); each of which is incorporated herein by reference in its entirety.

[329] In some embodiments, the one or more additional classes of biomarkers include a peptide (for example, a peptide that is distinct from the various protein biomarkers described herein as useful in one or more methods). In some modalities, an individual is determined to be at high risk of having or developing cancer if the biological sample contains one or more peptides indicating cancer. In some modalities, an individual is determined to have cancer if the biological sample contains one or more peptides indicative of cancer. In some embodiments, a peptide is derived from a protein (for example, the peptide includes an amino acid sequence present in a protein biomarker or a different protein). Non-limiting examples of cancer-specific peptides include the following peptides and peptides derived from the following proteins: CEACAM, CYFRA21-1, CA125, PKLK, Pro-GRP, NSE, TPA 6, TPA 7, TPA 8, NRG, NRG 100 , CNDP, APOВ100, SCC, VEGF, EGFR, PIK3CA, HER2, BRAF, ROS, RET, NRAS, MET, MEK1, HER2, C4.4A, PSF3, FAM83B, ECD, CTNNB, VIM, S100A4, S100A7, COX2, MUC1 , KLKB1, SAA, HP-β chain, C9, Pgrmc1, Ciz1, Transferrin, α-1 antitrypsin, apolipo 1 protein, complement c3a, Caevolin-1, Kallikrein 6, Glucose-regulated protein-8, α defense -1 , -2, -3, serum C-peptide, Alpha-2-HS glycol protein, Tryptic KRT 8 peptide, plasma glycol protein, Catenin, Defensin α 6, MMPs, Cyclin D, S100 P, Lamina A / C filament protein, heat shock protein, aldehyde dehydrogenase, Txl-2 (thioredoxin-like protein-2), P53, nm23, u-PA, VEGF, Eph B4, CRABP2, WT-1, Rab-3D, Mesothelin, ERα, ANXA4, PSAT1 , SPB5, CEA5, CEA6, A1AT, SLPI, APOA4, VDBP,

HE4, IL-1, -6, -7, -8, -10, -11, -12, -16, -18, -21, -23, -28A, -33, LIF, TNFR1–2, HVEM ( TNFRSF14), IL1R-a, IL1R-b, IL-2R, M-CSF, MIP-1a, TNF-α, CD40, RANTES, CD40L, MIF, IFN-β, MCP-4 (CCL13), MIG (CXCL9) , MIP-1δ (CCL15), MIP3a (CCL20), MIP-4 (CCL18), MPIF-1, SDF-1a + b (CXCL12), CD137 / 4–1BB, lymphotactin (XCL1), eotaxin-1 (CCL11) , eotaxin-2 (CCL24), 6Ckine / CCL21), BLC (CXCL13), CTACK (CCL27), BCA-1 (CXCL13), HCC4 (CCL16), CTAP-3 (CXCL7), IGF1, VEGF, VEGFR3, EGFR, ErbB2, CTGF, PDGF AA, BB, PDGFRb, bFGF, TGFbRIII, β-cellulin, IGFBP1–4, 6, BDNF, PEDF, angiopoietin-2, renin, lysophosphatidic acid, β2-microglobulin, sialyl TN, ACE, CA 19–9, CEA, CA 15–3, CA-50, CA 72–4, OVX1, mesothelin, sialyl TN, MMP-2, - 3, -7, -9, VAP-1, TIMP1–2, tenascin C , VCAM-1, osteopontin, KIM-1, NCAM, tetranectin, nidogen-2, cathepsin L, prostasin, matriptase, calcicrein 2, 6, 10, cystatin C, claudin, spondin2, SLPI, bHCG, urinary gonadotropin peptide , inhibin, leptin, adiponectin, GH, TSH, ACTH, PRL, FSH, LH, cortisol, TTR, osteocalcin, insulin, ghrelin, GIP, GLP-1, amylin, glucagon, YY peptide, follistatin, hepcidin, CRP, Apo A1, CIII, H, transthyretin, SAA, SAP, complement C3,4, complementary factor H, albumin, ceruloplasmin, haptoglobin, β-hemoglobin, transferrin, ferritin, fibrinogen, thrombin, von Willebrand factor, myoglobin, immunosuppressive acidic protein, lipid-associated sialic acid, S100A12 (EN- RAGE), feuine A, clusterin, α1-antitrypsin, a2-macroglobulin, serpin1 (human plasminogen activator inhibitor-1), Cox-1, Hsp27, Hsp60, Hsp80, Hsp90, oxidized LDL receptor of the lectin type 1, CD14, lipocalin 2, ITIH4, sFasL, Cyfra21-1, TPA, perforin, DcR3, AGRP, creatine kinase-MB, human milk fat globule 1-2, NT-Pro-BNP, neuron specific enolase, HOME , NB / 70K, AFP, afamine, collagen, prohibitin, keratin-6, PARC, B7-H4, YK-L40, AFP-L3, DCP, GPC3, OPN, GP73, CK19, MDK, A2, 5-HIAA, CA15 -3, CA19-9, CA27.29, CA72- 4 , calcitonin, CGA, BRAF V600E, BAP, fusion protein BCT-ABL, KIT, KRAS, PSA, Lactate dehydrogenase, NMP22, PAI-1, uPA, fibrin D-dimer, S100, TPA, thyroglobulin, CD20, CD24, CD44 , RS / DJ-1, p53, alpha-2-HS glycoprotein, lipophilin B, beta-globin, hemopexin, UBE2N, PSMB6, PPP1CB, CPT2, COPA, MSK1 / 2, Pro-NPY, Secernina-1, Vin-culina , NAAA, PTK7, TFG, MCCC2, TRAP1, IMPDH2, PTEN, POSTN, EPLIN, eIF4A3, DDAH1, ARG2, PRDX3 & 4, P4HB, YWHAG, Enoyl Cohydrase, PHB, TUBB, KRT2, DES, HSP, HSP HSPD1, LMNA, EZH2, AMACR, FABP5, PPA2, EZR, SLP2, SM22, Bax, Smac / Phosphorylated Bcl2, STAT3 and Smac / Diablo expression, PHB, PAP, AMACR, PSMA, FKBP4, PRDX4 / KRT7 / 8 18, GSTP1, NDPK1, MTX2, GDF15, PCa-24, Caveolin-2, Prothrombin, Antithrombin III, Haptoglobin, serum amyloid protein A-1, ZAG, ORM2, APOC3, CALML5, IG-FBP2, MUC5AC, PNLIP, PZP, TIMP1, AMBP, alpha-trypsin inhibitor heavy chain H1, alpha-trypsin inhibitor heavy chain H2, heavy chain inhibitor H1 alpha-trypsin H3, B subunit of type V proton ATPase, renal isoform, hematocyte growth factor protein, serum amyloid P component, acylglycerol kinase, protein 9 containing leucine-rich repeat, Beta-2- glycoprotein 1, Plasma C1 protease inhibitor, protein 1 containing the homology domain of lipoxygenase, Protocadherina alfa-13. See, for example, Kuppusamy et al., Volume 24, Issue 6, September 2017, Pages 1212-1221; Elzek and Rodland, Cancer Metastasis Rev. 2015 Mar; 34 (1): 83–96; Noel and Lokshin, Future Oncol. 2012 Jan; 8 (1): 55–71; Tsuchiya et al., World J Gastroenterol. 2015 Oct 7; 21 (37): 10573-10583; Lou et al., Biomark Cancer. 2017; 9: 1–9; Park et al., Oncotarget. 2017 Jun 27; 8 (26): 42761–42771; Saraswat et al., Cancer Med. 2017 Jul; 6 (7): 1738–1751; Zamay et al., Cancers (Basel). 2017 Nov; 9 (11): 155; Tanase et al., Oncotarget. 2017 Mar 14; 8 (11): 18497–18512, each of which is incorporated here by reference in its entirety.

[330] In some embodiments, the one or more additional classes of biomarkers include lesions or variations of nucleic acid

(for example, an injury or variation of nucleic acid that is distinct from the various genetic biomarkers described herein as being useful in one or more methods). In some embodiments, an individual is determined to be at high risk of developing or developing cancer if the biological sample contains one or more nucleic acid lesions or variations indicative of cancer. In some embodiments, an individual is determined to have cancer if the biological sample contains one or more nucleic acid lesions or indicative variations in cancer. Non-limiting examples of lesions or variations in nucleic acids include changes in copy number, changes in DNA methylation and / or other nucleic acids (for example, mRNAs, miRNAs, lncRNAs, circRNA, mtDNA, telomeric DNA, translocation and rearrangements genomic). Translocations and genomic rearrangements have been correlated with various types of cancer (eg, prostate, glioma, lung cancer, non-small cell lung cancer, melanoma and thyroid cancer) and used as biomarkers for years (for example , Demeure et al., 2014, World J Surg., 38: 1296-305; Hogenbirk et al., 2016, PNAS USA, 113: E3649-56; Gasi et al., 2011, PLoS One, 6: e16332; Ogiwara et al., 2008, Oncogene, 27: 4788-97; US Patent

9,745,632; and U.S. Patent 6,576,420). In addition, changes in the number of copies were used as biomarkers for various types of cancer, including, without limitation, head and neck squamous cell carcinoma, lymphoma (eg, non-Hodgkin's lymphoma) and colorectal cancer (Kumar et al ., 2017, Tumor Biol, 39: 1010428317740296; Kumar et al., 2017, Tumor Biol., 39: 1010428317736643; Henrique et al., 2014, Ex-Rev. Rev. Mol. Diagn., 14: 419-22; and US patent 9,816,139). DNA methylation and changes in DNA methylation (eg, hypomethylation, hypermethylation) are also used as biomarkers in cancer. For example, hypomethylation has been associated with hepatocellular carcinoma (see, for example, Henrique et al., 2014, Expert Rev. Mol.

Diagn., 14: 419-22), esophageal carcinogenesis (see, for example, Alvarez et al., 2011, PLoS Genet., 7: e1001356) and gastric and liver cancer (see, for example, US Patent 8,728,732) , and hypermethylation has been associated with colorectal cancer (see, for example, US Patent 9,957,570;). In addition to changes in methylation across the genome, specific changes in methylation within specific genes can be indicative of specific cancers (see, for example, U.S. Patent 8,150,626). Li et al. (2012, J.

Epidemiol., 22: 384-94) provides a review of the association between various types of cancer (eg, breast, bladder, gastric, lung, prostate, head and neck squamous cell and nasopharynx) and aberrant methylation.

In addition or alternatively, additional types of nucleic acids or characteristics of nucleic acids have been associated with several types of cancer.

Non-limiting examples of such nucleic acids or characteristics of nucleic acids include the presence or absence of several microRNAs (miRNAs) that have been used in the diagnosis of colon, prostate, colorectal and ovarian cancer (see, for example, D'Souza et al., 2018, PLos One, 13: e0194268; Fukagawa et al., 2017, Cancer Sci., 108: 886-96; Giraldez et al., 2018, Methods Mol.

Biol., 1768: 459-74; U.S. Patent 8,343,718; 9,410,956; and 9,074,206). For a review of the specific association of miR-22 with cancer, see Wang et al. (2017, Int.

J.

Oncol. 50: 345-55); the abnormal expression of long noncoding RNAs (lncRNAs) has also been used as a biomarker in cancers such as prostate cancer, colorectal cancer, cervical cancer, melanoma, non-small cell lung cancer, gastric cancer, endometrial carcinoma and hepatocellular carcinoma (see, for example, Wang et al., 2017, Oncotarget, 8: 58577086; Wang et al., 2018, Mol.

Cancer, 17: 110; Yu et al., 2018, Eur.

Rev.

Med.

Pharmacol.

Sci., 22: 4812-9; Yu et al., 2018, Eur.

Rev.

Med.

Pharmacol.

Sci., 22: 993-1002; Zhang et al., 2018, Eur.

Rev.

Med.

Pharmacol.

Sci., 22: 4820-7; Zhang et al., 2018, Eur.

Rev.

Med.

Pharmacol.

Sci., 22: 2304-

9; Xie et al., 2018, EBioMedicine, 33: 57-67; and U.S. Patent 9,410,206); the presence or absence of circular RNA (circRNA) has been used as a biomarker in lung cancer, breast cancer, gastric cancer, colorectal cancer and liver cancer (e.g., Geng et al., 2018, J. Hematol. Oncol. , 11:98) and melanoma (e.g., Zhang et al., 2018, Oncol. Lett. 16: 1219-25); changes in telomeric DNA (for example, in length or heterozygosity) or in centromeric DNA (for example, changes in the expression of centromeric genes) were also associated with cancers (for example, prostate, breast, lung, lymphoma and Ewing's sarcoma) (see for example, Baretton et al., 1994, Cancer Res., 54: 4472-80; Liscia et al., 1999, Br. J. Cancer, 80: 821-6; Proctor et al., 2009, Biochim. Biophys. Acta, 1792: 260-74; and Sun et al., 2016, Int. J. Cancer, 139: 899-907); various mutations (eg, deletions), rearrangements and / or changes in the number of copies in mitochondrial DNA (mtDNA) have been used prognostically and diagnostically for various types of cancer (eg, prostate cancer, melanoma, breast cancer, cancer lung and colorectal cancer). See, for example, Maragh et al., 2015, Cancer Biomark., 15: 763-73; Shen et al., 2010, Mitochondrion, 10: 62-68; Hosgood et al., 2010, Carcinogen., 31: 847-9; Thyagarajan et al., 2012, Cancer Epid. Biomarkers & Prev., 21: 1574-81; and U.S. Patent 9,745,632; and the abnormal presence, absence or quantity of messenger RNAs (mRNAs) have also been correlated with various types of cancer, including, without limitation, breast cancer, Wilms' tumors and cervical cancer (see, for example, Guetschow et al. , 2012, Anal. Bioanaly. Chem., 404: 399-406; Schwienbacher et al., 2000, Cancer Res., 60: 1521-5; and Ngan et al., 1997, Genitourin Med., 73: 54- 8 ). Each of these quotes is incorporated by reference in its entirety here. Unique class of biomarkers or aneuploidy

[331] In one aspect, methods and materials are provided here to detect the presence of one or more members of a single class of biomarkers (for example, genetic biomarkers or protein biomarkers) or the presence of aneuploidy in one or more samples obtained from an individual.

In another aspect, methods and materials are provided here to diagnose or identify the presence of a disease in an individual (for example, identifying the individual as having cancer) by detecting the presence of one or more members of a single class of biomarkers (for example , genetic biomarkers or protein biomarkers) or the presence of aneuploidy in one or more samples obtained from an individual.

In another aspect, methods and materials are provided here to identify an individual as being at risk (for example, increased risk) of having or developing a disease (for example, cancer) by detecting the presence of one or more members of a single class of biomarkers (for example, genetic biomarkers or protein biomarkers) or the presence of aneuploidy in one or more samples obtained from an individual.

In another aspect, methods and materials are provided here to treat an individual who has been diagnosed or identified as having a disease (for example, cancer) or who has been identified as being at risk (for example, increased risk) of having or developing a disease (for example, cancer) detecting the presence of one or more members of a single class of biomarkers (for example, genetic biomarkers or protein biomarkers) or the presence of aneuploidy in one or more samples obtained from an individual.

In another aspect, methods and materials are provided here to identify a treatment for an individual who has been diagnosed or identified as having a disease (eg, cancer) or who has been identified as being at risk (eg, increased risk) of having or developing a disease (for example, cancer) by detecting the presence of one or more members of a single class of biomarkers (for example,

genetic biomarkers or protein biomarkers) or the presence of aneuploidy in one or more samples obtained from an individual. In another aspect, methods and materials are provided here to identify an individual who will or will likely respond to a treatment by detecting the presence of one or more members of a single class of biomarkers (for example, genetic biomarkers or protein biomarkers) or the presence of aneuploidy in one or more samples obtained from an individual. In another aspect, methods and materials are provided here to identify an individual who will or will likely respond to a treatment by detecting the presence of one or more members of a single class of biomarkers (for example, genetic biomarkers or biomarkers). protein pain) or the presence of aneuploidy in one or more samples obtained from an individual. In one aspect, methods and materials are provided here to identify an individual as a candidate for increased monitoring by detecting the presence of one or more members of a single class of biomarkers (for example, genetic biomarkers or protein biomarkers) or the presence of aneuploidy in one or more samples obtained from an individual.

[332] In some embodiments, the methods provided here that include detecting the presence of one or more members of a single class of biomarkers (for example, genetic biomarkers or protein biomarkers) or the presence of aneuploidy in one or more samples obtained from an individual provides high sensitivity in detecting or diagnosing cancer (for example, a high frequency or incidence of correctly identifying an individual as having cancer). In some embodiments, methods provided here that include detecting the presence of one or more members of a single class of biomarkers (for example, genetic biomarkers or protein biomarkers) or the presence of aneuploidy in one or more samples obtained from an individual provide a sensitivity of at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99% or higher.

In some embodiments, the methods provided here that include detecting the presence of one or more members of a single class of biomarkers (for example, genetic biomarkers or protein biomarkers) or the presence of aneuploidy in one or more samples obtained from a provide high sensitivity in detecting a single type of cancer.

In some ways, the methods provided here that include detecting the presence of one or more members of a single class of biomarkers (for example, genetic biomarkers or protein biomarkers) or the presence of aneuploidy in one or more samples obtained from an individual provide high sensitivity in detecting two or more types of cancer.

Any of the various types of cancer can be detected using the methods and materials provided here (see, for example, the section entitled "Cancers"). In some modalities, cancers that can be detected using methods and materials that include detecting the presence of one or more members of a single class of biomarkers (for example, genetic biomarkers or protein biomarkers) or the presence of aneuploidy in one or more samples obtained from an individual include liver cancer, ovarian cancer, esophageal cancer, stomach cancer, pancreatic cancer, colorectal cancer, lung cancer or breast cancer.

In some modalities, cancers that can be detected using methods and materials that include detecting the presence of one or more members of a single class of biomarkers (for example, genetic biomarkers or protein biomarkers) or the presence of aneuploidy in one or more samples taken from an individual includes pancreatic cancer. In some modalities, cancers that can be detected using methods and materials that include detecting the presence of one or more members of a single class of biomarkers (for example, genetic biomarkers or protein biomarkers) or the presence of aneuploidy in one or more samples taken from an individual include cancers of the female reproductive system (for example, cervical cancer, endometrial cancer, ovarian cancer or fallopian tube cancer). In some modalities, cancers that can be detected using methods and materials that include detecting the presence of one or more members of a single class of biomarkers (for example, genetic biomarkers or protein biomarkers) or the presence of aneuploidy in one or more samples taken from an individual include bladder cancer or urothelial carcinomas of the upper tract.

[333] In some embodiments, the methods provided here that include detecting the presence of one or more members of a single class of biomarkers (for example, genetic biomarkers or protein biomarkers) or the presence of aneuploidy in one or more samples obtained of an individual

[334] In some embodiments, the methods provided here that include detecting the presence of one or more members of a single class of biomarkers (for example, genetic biomarkers or protein bi-markers) or the presence of aneuploidy in one or more samples obtained from an individual provide high specificity in the detection or diagnosis of cancer (for example, a low frequency or incidence of incorrectly identifying an individual as having cancer when that individual does not have cancer). In some modalities, methods provided here that include detecting the presence of one or more members of a single class of biomarkers (for example,

genetic biomarkers or protein biomarkers) or the presence of aneuploidy in one or more samples obtained from an individual provides a specified of at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99% or higher. As will be understood by those skilled in the art, a specificity of 99% means that only 1% of individuals who do not have cancer are incorrectly identified as having cancer. In some modalities, methods provided here that include detecting the presence of one or more members of a single class of biomarkers (for example, genetic biomarkers or protein biomarkers) or the presence of aneuploidy in one or more samples obtained from an individual provides high specificity in detecting a single cancer (for example, there is a low probability of incorrectly identifying that individual as having that type of cancer). In some embodiments, methods provided here that include detecting the presence of one or more members of a single class of biomarkers (for example, genetic biomarkers or protein biomarkers) or the presence of aneuploidy in one or more samples obtained from an individual provide high specificity in detecting two or more cancers (for example, there is a low probability of incorrectly identifying that individual as having these two or more types of cancer).

[335] In some embodiments, the methods provided here that include detecting the presence of one or more members of a single class of biomarkers include detecting the presence of one or more members of a panel of genetic biomarkers.

[336] In some embodiments, the methods provided here, which include detecting the presence of one or more members of a panel of genetic biomarkers, include detecting one or more genetic biomarkers in one or more (for example, 1, 2, 3, 4 , 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 or 16) of the following genes: NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS , KRAS, AKT1, TP53, PPP2R1A and / or GNAS. In some embodiments, the methods provided here that include detecting the presence of one or more members of a panel of genetic biomarkers include detecting one or more genetic biomarkers in each of the following genes: NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A and GNAS.

[337] In some embodiments, the methods provided here that include detecting the presence of one or more members of a panel of genetic biomarkers include detecting one or more genetic biomarkers in one or more (for example, 1, 2, 3 or 4) of the following genes: KRAS (for example, genetic biomarkers in codons 12 and / or 61), TP53, CDKN2A and / or SMAD4. In some embodiments, the methods provided here that include detecting the presence of one or more members of a panel of genetic biomarkers include detecting one or more genetic biomarkers in each of the following genes: KRAS (eg, genetic biomarkers in codons 12 and / or 61), TP53, CDKN2A and SMAD4.

[338] In some embodiments, the methods provided here that include detecting the presence of one or more members of a panel of genetic biomarkers include detecting one or more genetic biomarkers in one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 or 18) of the following genes: NRAS, PTEN, FGFR2, KRAS, POLE, AKT1, TP53, RNF43, PPP2R1A, MAPK1, CTNNB1, PIK3CA, FBXW7, PIK3R1, APC, EGFR, BRAF and / or CDKN2A.

In some embodiments, the methods provided here that include detecting the presence of one or more members of a panel of genetic biomarkers include detecting one or more genetic biomarkers in each of the following genes: NRAS, PTEN, FGFR2, KRAS, POLE, AKT1, TP53, RNF43, PPP2R1A, MAPK1, CTNNB1, PIK3CA, FBXW7, PIK3R1, APC, EGFR, BRAF, and CDKN2A. In some embodiments, the methods provided here that include detecting the presence of one or more members of a panel of genetic biomarkers include detecting one or more genetic biomarkers in one or more (for example, 1, 2, 3, 4 , 5, 6, 7, 8, 9, 10, 11 or 12) of the following genes: PTEN, TP53, PIK3CA, PIK3R1, CTNNB1, KRAS, FGFR2, POLE, APC, FBXW7, RNF43 and / or PPP2R1A. In some embodiments, the methods provided here that include detecting the presence of one or more members of a panel of genetic biomarkers include detecting one or more genetic biomarkers in each of the following genes: PTEN, TP53, PIK3CA, PIK3R1, CTNNB1, KRAS, FGFR2, POLE, APC, FBXW7, RNF43, and PPP2R1A. In some embodiments, the methods provided here that include detecting the presence of one or more members of a panel of genetic biomarkers include detecting one or more genetic biomarkers in TP53

[339] In some embodiments, the methods provided here that include detecting the presence of one or more members of a panel of genetic biomarkers include detecting one or more genetic biomarkers in one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9 or 10) of the following genes: TP53, PIK3CA, FGFR3, KRAS, ERBB2, CDKN2A, MLL, HRAS, MET and / or VHL. In some embodiments, the methods provided here that include detecting the presence of one or more members of a panel of genetic biomarkers include detecting one or more genetic biomarkers in each of the following genes: TP53, PIK3CA, FGFR3, KRAS, ERBB2, CDKN2A, MLL, HRAS, MET and VHL.

[340] In some embodiments, the methods provided here, which include detecting the presence of one or more members of a panel of protein biomarkers, include detecting one or more (for example, 1, 2, 3, 4, 5, 6, 7 or 8) of the following protein biomarkers: CA19-9, CEA, HGF, OPN, CA125, prolactin, TIMP-1 and / or myeloperoxidase (MPO). In some embodiments, the methods provided here that include detecting the presence of one or more members of a panel of protein biomarkers include detecting each of the following protein biomarkers: CA19-9, CEA, HGF, OPN, CA125, prolac - tubin, TIMP-1, and myeloperoxidase (MPO). In some modalities of methods provided here, which include the detection of the presence of one or more members of a panel of genetic biomarkers and the presence of aneuploidy, one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or 11) of the following protein biomarkers: CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1, follistatin, G-CSF and / or CA15-3. In some embodiments, the methods provided here that include the detection of the presence of one or more members of a panel of protein biomarkers include the detection of each of the following protein biomarkers: CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1, follistatin, G-CSF and CA15-3. In some methods of methods provided here, which include detecting the presence of one or more members of a panel of genetic biomarkers and the presence of aneuploidy, one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8 or 9) of the following protein biomarkers: CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1, and / or CA15-3. In some embodiments, the methods provided here that include detecting the presence of one or more members of a panel of protein biomarkers include detecting each of the following protein biomarkers: CA19-9, CEA, HGF, OPN, CA125, AFP, pro-lactin, TIMP-1, and CA15-3. In some embodiments, the methods provided here, which include detecting the presence of one or more members of a panel of protein biomarkers, include detecting one or more (for example, 1, 2, 3, or 4) of the following protein biomarkers proteins: CA19-9, CEA, HGF, and / or OPN. In some embodiments, the methods provided herein that include detecting the presence of one or more members of a panel of protein biomarkers include detecting each of the following protein biomarkers: CA19-9, CEA, HGF, OPN.

[341] In some modalities, the methods provided here that include detecting the presence of aneuploidy include detecting aneuploidy in one or more of the 5q, 8q and / or 9p chromosomal arms. In some modalities, the methods provided here that include detecting the presence of aneuploidy include detecting aneuploidy in one or more of the 4p, 7q, 8q and / or 9q chromosomal arms.

[342] In some embodiments, the methods provided here that include detecting the presence of one or more members of a single class of biomarkers include detecting the presence of one or more members of a class of biomarkers, including, without limitation: changes in the number copies, changes in DNA methylation, other nucleic acids (for example, mRNAs, miRNAs, lncRNAs, circRNA, mtDNA, telomeric DNA, translocation and genomic rearrangements), peptides or metabolites.

[343] In some embodiments, the methods provided here include detecting the presence of one or more metabolites. In some modes, an individual is determined to be at high risk of having or developing cancer if the biological sample contains one or more metabolites indicative of cancer. In some modalities, an individual is determined to have cancer if the biological sample contains one or more metabolites indicative of cancer. Non-limiting examples of cancer-indicating metabolites include: 5-methylthioadenosine

(MTA), Reduced Glutathione (GSH), N-acetylglutamate, Lactose, N-acetylneuraminate, UDP-acetylglucosamine, UDP-Acetylgalactosamine, UDP-glucuronate, Pantothenate, Araquidonate (20: 4n6), Choline, Cytidine 5′-di - phosphocholine, Di-homo-linolenate (20: 3n3), Docosapentaenoate (DPA 22: 5n3), Eicosapentaenoate (EPA 20: 5n3), Glycerophosphorylcholine (GPC), Docosahexaenoate (DHA 22: 6n3), Linoleate (18: 2n6 ), Cytidine 5'-monophosphate (5′-CMP), Gamma-glutamylglutamate, X - 14577, X - 11583, Iso-valerylcarnitine, Phosphocreatine, 2-aminoadipic acid, Gluconic acid, O-Acetylcarnitine, aspartic acid, Deamide NAD +, Glutamic Acid, Isobutyrylcarnitine, Carnitine, Pyridoxal, Citric Acid, Adenosine, ATP, Valine, XC0061, Isoleucine, γ-Butyrobetaine, Lactic Acid, Alanine, Phenylalanine, Gluconolactone, Leucine, Gluconolate, , NAD +, XC0016, UTP, creatine, Theobromine, CTP, GTP, 3-Methylhistidine, Succinic Acid, Glycerol 3-phosphate, glutamine, 5-Oxoproline, Thiamine, Butyrylca ritin, 4-acetamidobutanoic acid, UDP-Glucose, UDP-Galactose, threonine, N-Acetylglycine, proline, ADP, Choline, Malic Acid, S-Adenosylmethionine, Pantothenic acid, Cystein sulfinic acid, 6-amino-hexaic acid - noic, homocystic acid, hydroxyproline, methionine sulfoxide, 3-guanidinopropionic acid, glucose 6-phosphate, phenaceturic acid, threonic acid, tryptophan, pyridoxine, N-acetyl-aspartic acid, 4-guanidine acid, sericinic acid, 4-guanidine acid Citrulline, Betaine, N-acetyl-asparagine, 2-hydroxyglutaric acid, arginine, Glutatin (GSH), creatinine, Dihydroxyacetone phosphate, histidine, glycine, Glucose 1-phosphate, N-formylglycine, Ketoprofen, lysine, beta-alanine, N-acetylglutamic acid, 2-Amino-2- (hydroxymethyl) -1,3-propanediol, Ornithine, Phosphorylcholine, Glycerophospho-choline, Terephthalic acid, Glyceraldehyde 3-phosphate, Gly-Asp, Taurine, 1 , Fructose 6-diphosphate, 3-aminoisobutyric acid, Sperididine, GABA, Triethanolamine, Glycerol, N-acetyl-serine, N-Acetylornithine, Diethanolamine, AMP, Glutathione cysteine disulfide, Streptomycin sulfate + divalent H2O, Trans-glutaconic acid, nicotinic acid, Isobutylamine, Betaine aldehyde + H2O, Urocanic acid, 1-aminocyclopropane-1-carboxylic acid Homoserinalactone, 5

aminovaleric, 3-hydroxybutyric acid, ethanolamine, isovaleric acid, N-methylglutamic acid, cystathionine, spermine, carnosine, 1-methylnicotinamide, N-acetylneuraminic acid, sarcosine, GDP, N-methylalanine, palmitic acid, 1,2 -dioleoyl-sn-glycero-3-phospho-rac-glycerol-cholesterol, 5α, 6α epoxidelanosterol, lignoceric acid, 1oleoil_rac_GL, coleste-rol_epoxide, erucic acid, T-LCA, oleoyl-L-carnitine, oleanolic acid, 3-phosphoglycerate, 5-hydroxynorvaline, 5-methoxytryptamine, adenosine-5-monophosphate, alpha-ketoglutarate, asparagine, benzoic acid, hypoxanthine, maltose, maltotriose, methionine sulfoxide, nornicotine, phenol, phosphoethanolamine, pyrophosphate, pyruvic acid , taurine, uric acid, inosine, lactamide, 5-hydroxynorvaline NIST, cholesterol, deoxypententol, 2-hydroxyestrone, 2-hydroxyestradiol, 2-metholestrone, 2-methyldestrestriol, 2-hydroxyestrone-3-methyl ether, 4- hydroxestrone, 4-metolxestrone, 4-methoxyestradiol, 16alpha-hydroxyestrone, 17-ep iestriol, estriol, 16-keto-tradiol, 16-epiestriol, acylcarnitine C18: 1, amino acids citrulline and trans-4-hydroxyproline, hydroxyproline, glycerophospholipids PC aa C28: 1, PC ae C30: 0 and PC ae C30: 2, and sphingolipid SM (OH) C14: 1. See, for example, Halama et al., Nesting of colon and ovarian cancer cells in the en- dothelial niche is associated with alterations in glycan and lipid metabolism, Scientific Reports volume 7, Article number: 39999 (2017); Hur et al., Systems ap-proach to characterize the metabolism of liver cancer stem cells expressing CD133, Sci Rep., 7: 45557, doi: 10.1038 / srep45557, (2017); Eliassen et al., Urinary Estrogens and Estrogen Metabolites and Subsequent Risk of Breast Cancer among Premenopausal Women, Cancer Res; 72 (3); 696–706 (2011); Gangi et al., Metabolomic profile in pancreatic cancer patients: a consensus-based ap-proach to identify highly discriminating metabolites, Oncotarget, Feb 2; 7 (5): 5815–5829 (2016); Kumar et al., Serum and Plasma Metabolomic Biomarkers for Lung Cancer, Bioinformation, 13 (6): 202–208, doi: 10.6026 / 97320630013202 (2017); Schmidt et al., Pre-diagnostic metabolite concentrations and prostate cancer risk in 1077 cases and 1077 matched controls in the European

Prospective Investigation into Cancer and Nutrition, BMC Med., 15: 122, doi: 10.1186 / s12916-017-0885-6 (2017); each of which is incorporated here by reference in its entirety.

[344] In some embodiments, the methods provided here include detecting the presence of one or more peptides (for example, one or more peptides that are distinct from the various protein biomarkers described here as being useful in one or more methods ). In some embodiments, an individual is determined to be at high risk of having or developing cancer if the biological sample contains one or more peptides indicative of cancer. In some embodiments, an individual is determined to have cancer if the biological sample contains one or more peptides indicative of cancer. In some modalities, a peptide is derived from a protein (for example, the peptide includes a sequence of amino acids present in a protein biomarker or in a different protein). Non-limiting examples of cancer-indicating peptides include the following peptides and peptides derived from the following proteins: CEACAM, CYFRA21-1, CA125, PKLK, ProGRP, NSE, TPA 6, TPA 7, TPA 8, NRG, NRG 100, CNDP , APOВ100, SCC, VEGF, EGFR, PIK3CA, HER2, BRAF, ROS, RET, NRAS, MET, MEK1, HER2, C4.4A, PSF3, FAM83B, ECD, CTNNB, VIM, S100A4, S100A7, COX2, MUC1, KLKB1 , SAA, HP-β chain, C9, Pgrmc1, Ciz1, Transferrin, α-1 antitrypsin, apolipo 1 protein, complement c3a, Caveolin-1, Kallikrein 6, Glucose-regulated protein-8, α defense -1, - 2, -3, Serum C-peptide, Alpha-2-HS glycol protein, Tryptic KRT 8 peptide, plasma glycol protein, Catenin, Defensin α 6, MMPs, Cyclin D, S100 P, Lamina A / C filament protein, protein heat shock ine, aldehyde dehydrogenase, Txl-2 (thioredoxin-like protein-2), P53, nm23, u-PA, VEGF, Eph B4, CRABP2, WT- 1, Rab-3D, Mesothelin, ERα, ANXA4 , PSAT1, SPB5, CEA5, CEA6, A1AT, SLPI, APOA4, VDBP, HE4, IL-1, -6, -7, -8, - 10, -11, -12, -16, -18,

-21, -23, -28A, -33, LIF, TNFR1–2, HVEM (TNFRSF14), IL1R-a, IL1R-b, IL-2R, M-CSF, MIP-1a, TNF-α, CD40, RANTES , CD40L, MIF, IFN-β, MCP-4 (CCL13), MIG (CXCL9), MIP-1δ (CCL15), MIP3a (CCL20), MIP-4 (CCL18), MPIF-1, SDF-1a + b ( CXCL12), CD137 / 4–1BB, lymphotactin (XCL1), eotaxin-1 (CCL11), eotaxin-2 (CCL24), 6Ckine / CCL21), BLC (CXCL13), CTACK (CCL27), BCA-1 (CXCL13), HCC4 (CCL16), CTAP-3 (CXCL7), IGF1, VEGF, VEGFR3, EGFR, ErbB2, CTGF, PDGF AA, BB, PDGFRb, bFGF, TGFbRIII, β-cellulin, IGFBP1–4, 6, BDNFiet, PEDF, angio -2, renin, lysophosphatidic acid, β2-microglobulin, sialyl TN, ACE, CA 19–9, CEA, CA 15–3, CA-50, CA 72–4, OVX1, mesothelin, sialyl TN, MMP-2, - 3, -7, -9, VAP-1, TIMP1–2, tenascin C, VCAM-1, osteopontin, KIM-1, NCAM, tetranectin, nidogen-2, cathepsin L, prostine, matriptase, kallikrein 2 , 6, 10, cystatin C, claudin, spondin2, SLPI, bHCG, urinary gonadotropin peptide, inhibin, leptin, adiponectin, GH, TSH, ACTH, PRL, FSH, LH, cortisol, TTR, osteocalc ina, insulin, ghrelin, GIP, GLP-1, amylin, glucagon, YY peptide, follistatin, hepcidin, CRP, Apo A1, CIII, H, transthyretin, SAA, SAP, complement C3,4, complement factor H, albumin, ceruloplasmin , haptoglobin, β-hemoglobin, transferrin, ferretin, fibrinogen, thrombin, von Willebrand factor, myoglobin, immunosuppressive acidic protein, lipid-associated sialic acid, S100A12 (EN-RAGE), pheokine A, clusterin, α1- antitrypsin, a2- macroglobulin, serpin1 (human plasminogen activating inhibitor-1), Cox-1, Hsp27, Hsp60, Hsp80, Hsp90, oxidized LDL receptor of the type lectin 1, CD14, lipocalin 2, ITIH4, sFasL, Cyfra21-1 , TPA, perforin, DcR3, AGRP, creatine kinase-MB, human milk fat globule 1-2, NT-Pro-BNP, neuron specific enolase, CASA, NB / 70K, AFP, afamine, collagen, prohibitin, keratin -6, PARC, B7-H4, YK-L40, AFP- L3, DCP, GPC3, OPN, GP73, CK19, MDK, A2, 5-HIAA, CA15-3, CA19-9, CA27.29, CA72-4 , calcitonin, CGA, BRAF V600E, BAP, protein d and fusion BCT-ABL, KIT, KRAS, PSA, Lactate dehydrogenase, NMP22, PAI-1, uPA, fibrin D-dimer, S100, TPA, thyroglobulin, CD20, CD24, CD44, RS / DJ-

1, p53, alpha-2-HS glycoprotein, lipophilin B, beta-globin, hemopexin, UBE2N, PSMB6, PPP1CB, CPT2, COPA, MSK1 / 2, Pro-NPY, Sencolin-1, Vinculin, NAAA, PTK7, TFG, MCCC2, TRAP1, IMPDH2, PTEN, POSTN, EPLIN, eIF4A3, DDAH1, ARG2, PRDX3 & 4, P4HB, YWHAG, Enoyl CoA-hydrase, PHB, TUBB, KRT2, DES, HSP71, ATP5B, C2 , AMACR, FABP5, PPA2, EZR, SLP2, SM22, Bax, Smac / Phosphorylated diablo Bcl2, STAT3 and Smac / Diablo expression, PHB, PAP, AMACR, PSMA, FKBP4, PRDX4, KRT7 / 8/18, GSTP1, NDPK1 , MTX2, GDF15, PCa-24, Caveolin-2, Prothrombin, Antithrombin III, Haptoglobin, serum amyloid protein A-1, ZAG, ORM2, APOC3, CALML5, IGFBP2, MUC5AC, PNLIP, PZP, TIMP1, AMBP, ca - alpha-trypsin inhibitor heavy chain H1, alpha-trypsin inhibitor heavy chain H2, alpha-trypsin inhibitor heavy chain H3, type V proton ATPase subunit, renal isoform, hepatocyte growth, serum amyloid P component, acylglucolole kinase, protein 9 co having leucine-rich repeat, Beta-2-glycoprotein 1, plasma C1 protease inhibitor, protein 1 containing the homology domain of lipoxygenase, Protocadherina alfa-13. See, for example, Kuppusamy et al., Volume 24, Issue 6, September 2017, Pages 1212-1221; Elzek and Rodland, Cancer Metastasis Rev. 2015 Mar; 34 (1): 83–96; Noel and Lokshin, Future Oncol. 2012 Jan; 8 (1): 55–71; Tsuchiya et al., World J Gastroenterol. 2015 Oct 7; 21 (37): 10573-10583; Lou et al., Biomark Cancer. 2017; 9: 1–9; Park et al., Oncotarget. 2017 Jun 27; 8 (26): 42761–42771; Saraswat et al., Cancer Med. 2017 Jul; 6 (7): 1738–1751; Zamay et al., Cancers (Basel). 2017 Nov; 9 (11): 155; Tanase et al., Oncotarget. 2017 Mar 14; 8 (11): 18497–18512, each of which is incorporated herein by reference in its entirety.

[345] In some embodiments, the methods provided here include detecting the presence of one or more lesions or variations of nucleic acid (for example, one or more lesions or variations of nucleic acid that are distinct from the various genetic biomarkers here described as being useful in one or more methods). In some modalities, an individual is determined to be at high risk of having or developing cancer if the biological sample contains one or more nucleic acid lesions or variations indicative of cancer.

In some embodiments, an individual is determined to have cancer if the biological sample contains one or more nucleic acid lesions or variations indicative of cancer.

Non-limiting examples of lesions or variations in nucleic acids include changes in the number of copies, changes in DNA methylation and / or other nucleic acids (eg, mRNAs, miRNAs, lncRNAs, circRNA, mtDNA, telomeric DNA, translocation and genomic rearrangements). Translocations and genomic rearrangements have been correlated with several types of cancer (for example, prostate, glioma, lung cancer, non-small cell lung cancer, melanoma and thyroid cancer) and used as biomarkers for years (for example, Demeure et al., 2014, World J Surg., 38: 1296-305; Hogenbirk et al., 2016, PNAS USA, 113: E3649-56; Gasi et al., 2011, PLoS One, 6: e16332; Ogiwara et al ., 2008, Oncogene, 27: 4788-97; US Patent 9,745,632; and US Patent 6,576,420). In addition, changes in the number of copies have been used as biomarkers for various types of cancer, including, without limitation, head and neck squamous cell carcinoma, lymphoma (eg, non-Hodgkin's lymphoma) and colorectal cancer (Kumar et al., 2017, Tumor Biol, 39: 1010428317740296; Kumar et al., 2017, Tumor Biol., 39: 1010428317736643; Henrique et al., 2014, Expert Rev.

Mol.

Diagn., 14: 419-22; and U.S. Patent 9,816,139). DNA methylation and changes in DNA methylation (eg, hypomethylation, hypermethylation) are also used as biomarkers in cancer.

For example, hypomethylation has been associated with hepatocellular carcinoma (see, for example, Henrique et al., 2014, Expert Rev.

Mol.

Diagn., 14: 419-22), esophageal carcinogenesis (see, for example, Alvarez et al., 2011, PLoS Genet., 7: e1001356) and gastric and liver cancer (see, for example, US Patent 8,728,732) , and hypermethylation has been associated with colorectal cancer (see, for example, US Patent 9,957,570;). In addition to changes in methylation across the genome, specific changes in methylation within specific genes can be indicative of specific cancers (see, for example, U.S. Patent 8,150,626). Li et al. (2012, J.

Epidemiol., 22: 384-94) provides a review of the association between various types of cancer (eg, breast, bladder, gastric, lung, prostate, squamous cell of the head and neck and nasopharynx) and aberrant methylation.

In addition or alternatively, additional types of nucleic acids or characteristics of nucleic acids have been associated with various types of cancer.

Non-limiting examples of such nucleic acids or characteristics of nucleic acids include the presence or absence of several microRNAs (miRNAs) that have been used in the diagnosis of colon, prostate, colorectal and ovarian cancer (see, for example, D'Souza et al. , 2018, PLos One, 13: e0194268; Fukagawa et al., 2017, Cancer Sci., 108: 886-96; Giraldez et al., 2018, Methods Mol.

Biol., 1768: 459-74; U.S. Patent 8,343,718; 9,410,956; and 9,074,206). For a review of the specific association of miR-22 with cancer, see Wang et al. (2017, Int.

J.

Oncol. 50: 345-55); abnormal expression of long noncoding RNAs (lncRNAs) has also been used as a biomarker in cancers such as prostate cancer, colorectal cancer, cervical cancer, melanoma, non-small cell lung cancer, gastric cancer, endometrial carcinoma and hepatocellular carcinoma (see, for example, Wang et al., 2017, Oncotarget, 8: 58577086; Wang et al., 2018, Mol.

Cancer, 17: 110; Yu et al., 2018, Eur.

Rev.

Med.

Pharmacol.

Sci., 22: 4812-9; Yu et al., 2018, Eur.

Rev.

Med.

Pharmacol.

Sci., 22: 993-1002; Zhang et al., 2018, Eur.

Rev.

Med.

Pharmacol.

Sci., 22: 4820-7;

Zhang et al., 2018, Eur.

Rev.

Med.

Pharmacol.

Sci., 22: 2304-9; Xie et al., 2018, EBioMedicine, 33: 57-67; and U.S. Patent 9,410,206); the presence or absence of circular RNA (circRNA) has been used as a biomarker in lung cancer, breast cancer, gastric cancer, colorectal cancer and liver cancer (for example, Geng et al., 2018, J.

Hematol.

Oncol., 11:98) and melanoma (e.g., Zhang et al., 2018, Oncol.

Lett. 16: 1219-25); changes in telomeric DNA (for example, in length or heterozygosity) or in centromeric DNA (for example, changes in the expression of centromeric genes) were also associated with cancers (for example, prostate, breast, lung, lymphoma and Ewing's sarcoma) (see for example, Baretton et al., 1994, Cancer Res., 54: 4472-80; Liscia et al., 1999, Br.

J.

Cancer, 80: 821-6; Proctor et al., 2009, Biochim.

Biophys.

Acta, 1792: 260-74; and Sun et al., 2016, Int.

J.

Cancer, 139: 899-907); various mutations (eg, deletions), rearrangements and / or changes in the number of copies in mitochondrial DNA (mtDNA) have been used prognostically and diagnostically for various types of cancer (eg, prostate cancer, melanoma, breast cancer, cancer lung and colorectal cancer). See, for example, Maragh et al., 2015, Cancer Biomark., 15: 763-73; Shen et al., 2010, Mitochondrion, 10: 62-68; Hosgood et al., 2010, Carcinogen., 31: 847-9; Thyagarajan et al., 2012, Cancer Epid.

Biomarkers & Prev., 21: 1574-81; and U.S. Patent 9,745,632; and the abnormal presence, absence or quantity of messenger RNAs (mRNAs) have also been correlated with various types of cancer, including, without limitation, breast cancer, Wilms' tumors and cervical cancer (see, for example, Guetschow et al. , 2012, Anal.

Bioanaly.

Chem., 404: 399-406; Schwienbacher et al., 2000, Cancer Res., 60: 1521-5; and Ngan et al., 1997, Genitourin Med., 73: 54-8). Each of these quotes is incorporated by reference in its entirety here.

Validation of detected genetic biomarkers

[346] In some embodiments, the methods provided here can be used to check whether a genetic biomarker detected in the circulating tumor DNA present in the cellless DNA indicates the presence of a cancer cell in the individual. In some modalities, the methods provided here can be used to check whether a genetic alteration (for example, one or more genetic alterations) detected in the circulating tumor DNA present in the cellless DNA indicates the presence of a cancer cell in the individual . For example, certain genetic biomarkers (eg, genetic changes) present in cancer cells also occur in other non-cancer cells in the body. Such non-cancerous cells include, without limitation, white blood cell clones that arise during age-associated clonal hematopoiesis (for example, clonal hematopoietic expansion (also known as undetermined potential clonal hematopoiesis or CHIP) or myelodysplasia). As a result, these clones, which may represent early forms of myelodysplasia, are a potential source of false positives in assays based on ctDNA. In such cases, the detection of a genetic biomarker (for example, a genetic alteration) in DNA without cells can lead to a false diagnosis of cancer, since the genetic biomarker (for example, genetic alteration) arises from hematopoietic white blood cells , not a cancer (for example, a solid tumor). The methods provided here can reduce or eliminate these false diagnoses of cancer.

[347] The methods provided here can be used to reduce or eliminate false cancer diagnoses, determining whether one or more genetic biomarkers (eg, genetic alterations) detected in cell-free DNA originate from hematopoietic white blood cells instead of a cancer cell. For example, DNA can be isolated or obtained from an individual's white blood cells, which can be tested to determine the presence or absence of a genetic biomarker (for example, a genetic alteration) that has been identified in the individual's cell-free DNA , whose genetic biomarker (eg, genetic alteration) is associated with cancer. In some modalities, if the genetic biomarker (eg, genetic alteration) is identified in the DNA from a white blood cell, it is indicative that the genetic biomarker (eg, genetic alteration) identified in the DNA without cells originates in the blood cells white, not a cancer cell present in the individual. In some modalities, if the genetic biomarker (eg, genetic alteration) is not identified in the DNA from the white blood cells, it is indicative that the genetic biomarker (eg genetic alteration) identified in the DNA without cells has its origin in a cancer cell present in the individual, and not a white blood cell. Methods of testing DNA isolated or obtained from white blood cells for the presence or absence of a genetic biomarker (for example, a genetic mutation) associated with cancer, in order to determine whether that genetic biomarker (for example, genetic alteration) originating from a cancer cell in the individual are here generically described as "checking for a genetic change against white blood cells", "checking for a genetic change against white blood cell DNA", "checking for white blood cells" and similar phrases.

[348] Any genetic biomarker (eg, genetic alteration) associated with cancer can be verified using the methods described here. Examples of genetic biomarkers (eg, genes with genetic alterations) associated with cancer include, without limitation, ABCA7, ABL1, ABL2, ACVR1B, ACVR2A, AJUBA, AKT1, AKT2, ALB, ALDOB, ALK, AMBRA1, AMER1 , AMOT, ANKRD46, APC, AR, ARHGAP35, ARHGEF12, ARID1A, ARID1B, ARID2, ARID4B, ARL15, ARMCX1, ASXL1, ASXL2, ATAD2, ATF1, ATG14, ATG5, ATM, ATRX, ATXN2, BXA1, BXA1 , BCL11B, BCL2, BCL3, BCL6,

BCL9, BCLAF1, BCOR, BCR, BIRC6, BIRC8, BLM, BLVRA, BMPR1A, BRAF, BRCA1, BRCA2, BRD7, BRE, BRWD3, BTBD7, BTRC, C11orf70, C12orf57, C2CD5, C3orf62, C8orfG, CAMD CARS, CASP8, CBFA2T3, CBFB, CBLC, CBX4, CCAR1, CCDC117, CCDC88A, CCM2, CCNC, CCND1, CCND2, CCND3, CCR3, CD1D, CD79B, CDC73, CDCP1, CDH1, CDH11, CDK12, CDK4, CDK1 CDKN1B, CDKN2A, CDX2, CEBPA, CELF1, CENPB, CEP128, CHD2, CHD4, CHD8, CHEK2, CHRDL1, CHUK, CIC, CLEC4C, CMTR2, CNN2, CNOT1, CNOT4, COL11A1, COPS4, CREB1, COPS4, CREB1 CSMD3, CTCF, CTDNEP1, CTNNB1, CUL1, CUL2, CYB5B, CYLD, DACH1, DCHS1, DCUN1D1, DDB2, DDIT3, DDX3X, DDX5, DDX6, DEK, DHX15, DHX16, DICER1, DKD2, DISK, DISK, DISK DNER, DNM1L, DNMT3A, EED, EGFR, EIF1AX, EIF2AK3, EIF2S2, EIF4A1, EIF4A2, ELF3, ELK4, EMG1, EMR3, EP300, EPB41L4A, EPHA2, EPS8, ERBB2, ERBB3, ERVI, EVI, EVI, EFI, EWSR1, EXO5, EXT1, EXT2, EZH2, F5, FANCM, FAT1, FBN2, FBXW7, FCER1G, FEV, FGF2, FGFR1, FGFR1OP, FGFR2, FGFR3, FH, FLT3, FN1, FOXA1, FOXP 1, FUBP1, FUS, GALNTL5, GATA3, GGCT, GIGYF2, GK2, GLIPR2, GNAS, GNP-TAB, GNRHR, GOLGA5, GOLM1, GOPC, GOT2, GPC3, GPS2, GPX7, GRK1, GSE1, GZMA, HDAC1, HZMA HERC4, HGF, HIST1H2BO, HLA-A, HLA-B, HMCN1, HMGA1, HMGA2, HNRNPA1, HRAS, HSP90AB1, ID3, IDH1, IDH2, IFNGR2, IFT88, IKZF2, IL2, INO80, INPP4, INPP4 JAK1, JAK2, JUN, KANSL1, KAT8, KAT-NAL1, KBTBD7, KCNMB4, KDM5C, KDM6A, KEAP1, KIAA1467, KIT, KLF4, KMT2A, KMT2B, KMT2C, KMT2D, KMT2E, LRAS, KRAS, LAM LCK, LMO2, LPAR2, LYN, MAF, MAFB, MAML2, MAP2K1, MAP2K2, MAP2K4, MAP3K1, MAP4K3, MAPK1, MAX, MB21D2, MBD1, MBD6, MBNL1, MBNL3, MDM2, MDM4, MED12, MED23, MEN1 MGA, MITF, MKLN1, MLH1, MLL, MLLT4,

MOAP1, MORC4, MPL, MS4A1, MSH2, MSI1, MTOR, MYB, MYC, MYCL1, MYCN, MYD88, MYL6, MYO1B, MYO6, NAA15, NAA25, NAP1L2, NAP1L4, NCOA2, NCOA4, NOR1, NKF, NOR NFE2L2, NFE2L3, NFKB2, NIPBL, NIT1, NKX3-1, NME4, NOTCH1, NOTCH2, NPM1, NR4A3, NRAS, NSD1, NTRK1, NUP214, NUP98, PALB2, PAX8, PBRM1, PCBP1, PCFP3 PIK3CB, PIK3R1, PIM1, PLAG1, PML, POLA2, POT1, PPARD, PPARG, PPM1D, PPP2R1A, PPP6C, PRKACA, PRKCI, PRPF40A, PSIP1, PTEN, PTH2, PTMS, PTN, PTPN11, RABF, RAC, RAPGEF6, RASA1, RB1, RBBP6, RBM10, RBM26, RC3H2, REL, RERE, RET, RFC4, RHEB, RHOA, RIMS2, RIT1, RNF111, RNF43, ROS1, RPL11, RPL5, RQCD1, RRAS, RUN1, RX2 SCAF11, SDHB, SDHD, SEC22A, SENP3, SENP8, SETD1B, SETD2, SF3A3, SF3B1, SFPQ, SIN3A, SKAP2, SMAD2, SMAD3, SMAD4, SMARCA4, SMARCB1, SMARCC2, SMO, SNCB, SOX, SOCS1, SP3, SPEN, SPOP, SPSB2, SS18, STAG2, STK11, STK31, SUFU, SUFU, SUZ12, SYK, TAF1A, TARDBP, TAS2R30, TBL1XR1, TBX3, TCF12, TCF3, TCF7L2, TCL1A, TET2, TEX11, DP2, TFG, TGFBR2, THRAP3, TLX1, TM9SF1, TMCO2, TMED10, TMEM107, TMEM30A, TMPO, TNFAIP3, TNFRSF9, TNRC6B, TP53, TP53BP1, TPR, TRAF3, TRIM8, TRIP12, TSC1, TSC2, TTK U2AF1, UBE2D3, UBR5, UNC13C, UNKL, UPP1, USO1, USP28, USP6, USP9X, VHL, VN1R2, VPS33B, WAC, WDR33, WDR47, WRN, WT1, WWP1, XPO1, YOD1, ZC3HX, ZF3, ZF ZFP36L2, ZGRF1, ZMYM3, ZMYM4, ZNF234, ZNF268, ZNF292, ZNF318, ZNF345, ZNF600, ZNF750, and / or ZNF800. In some modalities, genetic biomarkers (eg, genes with genetic alterations) associated with cancer that can be checked against the DNA of isolated or obtained white blood cells from an individual include tumor suppressor genes or oncogenes. In some modalities, one or more codons and / or their surrounding splice sites can be tested according to the methods disclosed here. Examples of codons of tumor suppressor genes and oncogenes that can be tested include, without limitation, one or more of the following codons and their surrounding splicing sites: AKT1 codons 16-18; codons 1304-1311, 1450-1459 from APC; BRAF codons 591-602; codes 51-58, 76-88 of CDKN2A; codons 31-39, 38-47 of CTNNB1; codons 856-868 from EGFR; codons 361-371, 464-473, 473-483, 498-507 of FBXW7; codons 250-256 of FGFR2; codons 199-208 of GNAS; HRAS codes 7-19; codons 7-14, 57-65, 143-148 of KRAS; codons 3-15, 54-63 of NRAS; codons 80-90, 343-348, 541-551, 1038-1050 of PIK3CA; codons 175-187 of PPP2R1A; PTEN codons 90-98, 125-132, 133-146, 145-154; and codons 10-22, 25-32, 33-40, 40-52, 52-64, 82- 94, 97-110, 112-125, 123-125, 126-132, 132-142, 150-163, 167-177, 175-186, 187-195, 195-206, 207-219, 219-224, 226-237, 232-245, 248- 261, 261-268, 272-283, 279-290, 298- 307, 307-314, 323-331, 333-344, 344-355, 367-375, 374-386 of TP53. In some embodiments, the genetic biomarkers (for example, genes with genetic alterations) associated with cancer that can be checked against the DNA of white blood cells isolated or obtained from an individual include one or more of the genetic biomarkers (for example, genetic changes) identified in Table 11 or Table 12. The DNA test isolated or obtained from white blood cells may include amplification and / or sequencing of that DNA (for example, using any of the methods described here, including, without limitation, Safe-SeqS methods). The use of a technique such as Safe-SeqS provides corresponding advantages when testing DNA isolated or obtained from white blood cells, as well as when testing the individual's DNA without cells.

[349] In some modalities, a single genetic biomarker

(for example, genetic alteration) is checked against DNA isolated or obtained from white blood cells. In some modalities, more than one genetic biomarker (for example, genetic alterations) is checked against DNA isolated or obtained from white blood cells. For example, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more genetic biomarkers (eg, genetic changes) can be checked against isolated DNA or obtained from white blood cells using the methods here described. In some embodiments, one or more genetic biomarkers (for example, genetic changes) are checked against DNA isolated or obtained from white blood cells using a plurality of samples that are isolated or obtained from white blood cells. For example, one or more genetic biomarkers (eg, genetic changes) can be checked against isolated or obtained white blood cell DNA, isolating the DNA from two isolated or obtained white blood cell samples from the individual. Verification of genetic biomarkers (eg, genetic alterations) against DNA isolated or obtained from white blood cells using a plurality of samples can increase the sensitivity of the tests, leading to a more accurate diagnosis.

[350] In some modalities, one or more genetic biomarkers (for example, genetic alterations) can be determined as originating from a cancer cell in the individual (or not), checking the biomarker (s) genetic (s) (eg genetic alteration (s)) against DNA isolated or obtained from white blood cells in the absence of additional diagnostic testing methods. In some modalities, one or more genetic biomarkers (for example, genetic changes) can be determined as originating from a cancer cell in the individual (or not), by checking the genetic biomarker (s) (for example) , genetic change (s) against DNA isolated or obtained from white blood cells in combination with additional diagnostic test methods. In some embodiments, these additional diagnostic test methods may include one or more of the diagnostic test methods described herein.

In some embodiments, these additional diagnostic test methods may include testing a protein biomarker (for example, one or more of the protein biomarkers disclosed herein). In some embodiments, these additional diagnostic testing methods may include testing a protein biomarker (for example, one or more of the protein biomarkers disclosed herein) at a certain threshold level.

Examples of protein biomarkers that can be combined with the verification of white blood cells include, without limitation, carbohydrate antigen 19-9 (CA19-9), carcinoembryonic antigen (CEA), hepatocyte growth factor ( HGF), osteopontin (OPN), CA125, AFP, pro-lactin, TIMP-1, follistatin, G-CSF and CA15-3. Any of the range of thresholds for these protein biomarkers disclosed here can be used in combination with the white blood cell check for genetic biomarkers (eg, genetic alteration (s)) found in the DNA without cells.

Exemplary and non-limiting thresholds for certain protein biomarkers include: CA19-9 (> 92 U / ml), CEA (> 7,507 pg / ml), CA125 (> 577 U / ml), AFP (> 21,321 pg / ml) , Prolactin (> 145,345 pg / ml), HGF (> 899 pg / ml), OPN (> 157,772 pg / ml), TIMP-1 (> 176,989 pg / ml), Folistatin (> 1,970 pg / ml), G- CSF (> 800 pg / ml), and CA15-3 (> 98 U / ml). In some embodiments, the threshold levels for protein biomarkers may be higher (for example, about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80%, about 90%, about 100% or higher) at the exemplary threshold levels described herein.

In some embodiments, the threshold levels for protein biomarkers may be lower (for example, about 10%, about 20%, about 30%, about 40%, about 50% or less) than than the exemplary threshold levels described here.

In certain embodiments, a test for a single protein biomarker is combined with the verification of white blood cells for genetic biomarkers (eg, genetic alteration (s)) found in cellless DNA. In certain modalities, the testing of more than one protein biomarker (two, three, four, five, six, seven, eight, nine, ten, eleven or more protein biomarkers) is combined with the verification of white blood cells of the genetic biomarkers (for example, genetic change (s)) found in DNA without cells.

[351] In some embodiments, a plurality of genetic biomarkers (for example, a panel of genetic biomarkers) is checked against DNA isolated or obtained from white blood cells. In some embodiments, a plurality of genetic biomarkers that are checked against DNA isolated or obtained from white blood cells include one or more NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A or GNAS. In some embodiments, a plurality of genetic biomarkers that are checked against DNA isolated or obtained from white blood cells each includes NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A and GNAS. In some embodiments, one or more genetic biomarkers (for example, genetic changes) that include one or more of (for example, each) NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS , KRAS, AKT1, TP53, PPP2R1A or GNAS can be determined as originating from a cancer cell in the individual (or not), by checking the genetic biomarker (s) against DNA isolated or obtained from blood cells in combination with additional diagnostic test methods. Such additional diagnostic testing methods include, without limitation, testing for the presence of one or more protein biomarkers and / or the presence of aneuploidy. In some embodiments, the one or more protein biomarkers can be one or more of (for example, each) CA19-9, CEA, HGF,

OPN, CA125, prolactin, TIMP-1 or myeloperoxidase (MPO). In some embodiments, the one or more protein biomarkers can be one or more of (for example, each) CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1 or CA15-3. In some embodiments, the one or more protein biomarkers may be one or more of (for example, each) CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1, follistatin, G- CSF or CA15 -3.

[352] In some embodiments, a plurality of genetic biomarkers that are checked against DNA isolated or obtained from white blood cells include one or more of KRAS, TP53, CDKN2A or SMAD4. In some embodiments, a plurality of genetic biomarkers that are checked against DNA isolated or obtained from white blood cells each includes KRAS, TP53, CDKN2A and SMAD4. In some embodiments, one or more genetic biomarkers (eg, genetic alterations) that include one or more of (for example, each) KRAS, TP53, CDKN2A, or SMAD4 can be determined to originate from a cancer cell in the individual (or not), checking the genetic biomarker (s) against DNA isolated or obtained from white blood cells in combination with additional diagnostic test methods. Such additional methods of diagnostic testing include, without limitation, testing for the presence of one or more protein biomarkers and / or the presence of aneuploidy. In some embodiments, the one or more protein biomarkers can be one or more of (for example, each) CA19-9, CEA, HGF or OPN.

[353] In some embodiments, a plurality of genetic biomarkers that are checked against DNA isolated or obtained from white blood cells include one or more of NRAS, PTEN, FGFR2, KRAS, POLE, AKT1, TP53, RNF43, PPP2R1A, MAPK1, CTNNB1, PIK3CA, FBXW7, PIK3R1, APC, EGFR, BRAF or CDKN2A. In some embodiments, a plurality of genetic biomarkers that are checked against DNA isolated or obtained from white blood cells each includes NRAS, PTEN, FGFR2, KRAS, POLE, AKT1, TP53, RNF43, PPP2R1A, MAPK1, CTNNB1, PIK3CA, FBXW7, PIK3R1, APC, EGFR, BRAF and CDKN2A. In some embodiments, one or more genetic biomarkers (for example, genetic changes) that include one or more of (for example, each of) NRAS, PTEN, FGFR2, KRAS, POLE, AKT1, TP53, RNF43, PPP2R1A, MAPK1, CTNNB1, PIK3CA, FBXW7, PIK3R1, APC, EGFR, BRAF, or CDKN2A can be determined as originating from a cancer cell in the individual (or not), by checking the genetic biomarker (s) against isolated DNA or obtained from white blood cells in combination with additional diagnostic test methods. Such additional diagnostic testing methods include, without limitation, testing for the presence of one or more protein biomarkers and / or the presence of aneuploidy (eg, aneuploidy on one or more chromosomes or chromosomal arms that are associated with the presence of cancer). In some modalities, the presence of aneuploidy can be detected in one or more of the 4p, 7q, 8q or 9q chromosomal arms.

[354] In some embodiments, a plurality of genetic biomarkers that are checked against DNA isolated or obtained from white blood cells include one or more of TP53, PIK3CA, FGFR3, KRAS, ERBB2, CDKN2A, MLL, HRAS, MET, or VHL. In some modes, a plurality of genetic biomarkers that are checked against DNA isolated or obtained from white blood cells each include TP53, PIK3CA, FGFR3, KRAS, ERBB2, CDKN2A, MLL, HRAS, MET, and VHL . In some embodiments, one or more genetic biomarkers (for example, genetic changes) that include one or more of (for example, each of) TP53, PIK3CA, FGFR3, KRAS, ERBB2, CDKN2A, MLL, HRAS, MET, or VHL CDKN2A can be determined as originating from a cancer cell in the individual (or not), by checking the genetic biomarker (s) against DNA isolated or obtained from white blood cells in combination with test methods additional diagnostic tools. Such additional diagnostic testing methods include, but are not limited to, testing for the presence of one or more protein biomarkers and / or the presence of aneuploidy (for example, aneuploidy on one or more chromosomes or chromosomal arms that are associated with the presence of cancer). In some modalities, the presence of aneuploidy can be detected in one or more of the chromosomal arms 5q, 8q, or 9p.

[355] In some embodiments, a sample isolated or obtained from the individual used to isolate or obtain white blood cell DNA may be the same sample isolated or obtained from the individual used to test genetic biomarkers in cellless DNA and / or biomarkers of pro - theines. For example, the sample may be a blood sample (for example, whole blood), the blood sample of which is subsequently separated into a plasma fraction and a fraction of white blood cells. This separation can be achieved, for example, by centrifuging a density gradient in which the plasma is separated from the white blood cells, which are typically found in the buffy coating. After this separation, the white blood cell DNA in the buffy coating can be isolated and tested, while genetic biomarkers in the DNA without plasma fraction cells can also be isolated and tested. In some modalities, the plasma fraction is allowed to clot to obtain a serum fraction. In some modalities, the sample is frozen, chilled or otherwise stored before and / or after testing and / or fractionation. In some embodiments, one or more fractions of the sample are frozen, refrigerated or otherwise stored before and / or after the test.

[356] In some embodiments, a sample isolated or obtained from the individual used to isolate or obtain white blood cell DNA may be different from the sample isolated or obtained from the individual used to test genetic biomarkers in cellless DNA and / or protein biomarkers. For example, a first sample can be isolated or obtained from the individual, whose first sample is used to test genetic biomarkers in DNA without cells and / or protein biomarkers. Before, simultaneously or after the first sample is isolated or obtained, a second sample can be isolated or obtained from the individual, the second sample of which is used to isolate or obtain white blood cell DNA to check one or more genetic biomarkers (for example, genetic changes) identified in DNA without cells. The second sample can be fractionated as described here (for example, by density gradient centrifugation). In some modalities, the first and / or the second sample (or fractions thereof) can be frozen, chilled or otherwise stored before and / or after testing and / or fractionation.

[357] In some embodiments, once a genetic biomarker (eg, a genetic alteration) has been verified against isolated DNA or obtained from white blood cells and the genetic biomarker (eg, a genetic alteration) is determined to be not present in DNA isolated from white blood cells (for example, the genetic biomarker (for example, a genetic alteration) is determined to originate from a cancer cell in the individual), the individual may experience more diagnostic testing or enhanced monitoring (for example, using one or more of the diagnostic testing and / or monitoring methods disclosed herein). In some modalities, once a genetic biomarker (for example, a genetic alteration) has been verified against isolated DNA or obtained from white blood cells and the genetic biomarker (for example, a genetic alteration) is determined to be no. present in DNA isolated from white blood cells (for example, the genetic biomarker (for example, a genetic alteration) is determined to originate from a cancer cell in the individual), the individual may receive a therapeutic intervention (for example, one or more therapeutic interventions disclosed here). Sample classification

[358] The present disclosure provides methods for identifying the presence of cancer in an individual based on one or more protein biomarkers (for example, protein concentrations in whole blood or plasma). Various methods can be used to determine whether the individual has cancer and / or the likelihood that the individual will have cancer. These methods involve several types of techniques and statistical methods described here, including, for example, a regression model, a logistic regression model, a neural network, a grouping model, principal component analysis, component analysis correlated, classifier analysis of nearest neighbors, linear discrimination analysis, quadratic discrimination analysis, a support vector machine, a decision tree, Random Forest, a genetic algorithm, classifier optimization using bagging, classifier optimization using impulse, classifier optimization using the Random Subspace method, a search for genetic projection and programming and weighted voting, etc.

[359] In some modalities, regression analysis can be used to determine whether an individual has cancer. Regression analysis can be performed on a panel of protein biomarkers, a panel of genetic biomarkers (eg, mutations) and / or a panel includes protein biomarkers and genetic biomarkers. In some modalities, regression analysis is performed on the pontuação score for one or more mutations (eg, higher mutation) and / or a panel of protein biomarkers.

[360] In some modalities, regression analysis is performed based on a mathematical model in the form:

V = α + Σβiƒ (Xi)

[361] In this model form, V is a value that indicates an individual's probability score for cancer. In some modalities, the probability score is indicative of the probability that a test subject will have cancer. Xi represents the value of each biomarker (for example, pontuação score, plasma protein concentrations, etc.). βi is a coefficient for ƒ (Xi), which is a variable that corresponds to the value of the biomarker. The function f (x) is a function that provides a corresponding value of x. In some embodiments, f (x) = x. Thus, the mathematical model can have the form V = α + Σβi Xi. In some other modalities, f (x) can be a function for normalization or standardization. In some modalities, the formula may include additional parameters to explain age, sex and race category.

[362] In some modalities, V is a value that indicates an individual's probability score of having cancer. In some modalities, V is a real probability (a number ranging from 0 to 1). In other modalities, V is a value from which a probability can be derived.

[363] In some modalities, the mathematical model is a regression model, for example, a logistic regression model or a linear regression model. The regression model can be used to test multiple sets of biomarkers.

[364] In the case of linear regression models, the model can be used to analyze expression data from a test subject and provide a result indicative of a quantitative measure of the test subject, for example, the probability of the individual have cancer.

[365] In general, a linear regression equation is expressed as Y = α + β1X1 + β2X2 + ... + βkXk + ε

[366] Y, the dependent variable, indicates a quantitative measure of a biological characteristic (for example, probability of having or not having cancer). The dependent variable Y depends on k explanatory variables (the characteristic values measured for the biomarkers), plus an error term that includes several omitted factors not specified. In the model identified above, the parameter β1 measures the effect of the first explanatory variable X1 on the dependent variable Y. β2 gives the effect of the explanatory variable X2 on Y.

[367] A logistic regression model is a nonlinear transformation of linear regression. The logistic regression model is often called the “logit” model and can be expressed as ln [p / (1 − p)] = α + β1X1 + β2X2 + ... + βkXk + ε where, α is a constant; ε is an error term; ln is the natural logarithm, log (e), where e = 2.71828 ..., p is the probability that the event Y will occur, p / (1-p) are the "probabilities" ln [p / (1 -p)] is the probability of the log, or “logit”.

[368] It will be appreciated by those skilled in the art that α and ε can be folded into a single constant and expressed as α. In some embodiments, a single term α is used and ε is omitted. The "logistical" distribution is an S-shaped distribution function. The logit distribution restricts the estimated probabilities (p) between 0 and 1.

[369] In some modalities, the logistic regression model is expressed as Y = α + Σβi Xi

[370] Here, Y is a value (for example, a probability score) indicating whether the set of biomarkers for a given individual should be classified with the group of cases (for example, groups of individuals with cancer), as opposed to control group (for example, groups of individuals without cancer). The probability that the set of biomarkers is classified with the group of cases, as opposed to the control group, therefore, the probability that the individual will have cancer can be derived from Y. The higher the score, the greater the probability that the individual will have cancer.

[371] Xi is the value of the i-th biomarker. In some modalities, it may be plasma protein concentrations, gender, age, or a score derived from genetic markers (for example, Ω score). βi is a coefficient of the logistic regression equation for the biomarker, α is a constant in the logistic regression equation that can be zero and βi and α are the result of applying logistic regression analysis to the case group and the control group .

[372] In some modalities, the logistic regression model is adjusted by the maximum likelihood estimate (MLE). The coefficients (for example, α, β1, β2, ...) are determined by the maximum likelihood. A likelihood is a conditional probability (for example, P (Y | X), the probability of Y given X). The likelihood function (L) measures the probability of observing the specific set of dependent variable values (Y1, Y2, .., Yn) that occur in the sample data set. In some embodiments, it is written as the product of the probability of observing Y1, Y2, ..., Yn: L = Prob (Y1, Y2, ..., Yn) = Prob (Y1) * Prob (Y2) * .. Prob (Yn)

[373] The greater the likelihood function, the greater the probability of observing the Ys in the sample. The MLE involves finding the coefficients (α, β1, β2, ...) that make the log of the likelihood function (LL <0) as large as possible or -2 times the log of the likelihood function (-2LL) as small as possible. In MLE, some initial estimates of parameters α, β1, β2 and so on are made. Then, the likelihood of the data, given these parameter estimates, is calculated. Parameter estimates are improved, the probability of the data is recalculated.

This process is repeated until the parameter estimates remain substantially unchanged (for example, a change less than 0.01 or 0.001). Examples of suitable logistic regression and logistic regression models are found in Hastie, The Elements of Statistical Learning, Springer, NY, 2001, pp. 95-100.

[374] After the coefficients of the logistic regression equation and the constant of the logistic regression equation are determined, the model can be easily applied to a test subject to obtain Y. In some modalities, Y can be used to calculate the probability (p) by solving the function Y = ln (p / (1-p)).

[375] In some modalities, explanatory variables are standardized or standardized before being adjusted in the model. Standardized coefficients (or beta coefficients) are the estimates resulting from a standardized regression analysis so that the variations of the dependent and explanatory variables are 1. Therefore, the standardized coefficients represent how many standard deviations a dependent variable will change, for increase in standard deviation in the explanatory variable. For univariate regression, the absolute value of the standardized coefficient is equal to the correlation coefficient. Standardization of the coefficient is usually performed to identify which of the explanatory variables have the greatest effect on the dependent variable in a multiple regression analysis. In some modalities, the variables are standardized or normalized before fitting a logistic regression model. Standardized logistic regression coefficients (or standardized beta coefficients) are the estimates resulting from performing a logistic regression analysis on standardized variables. In some modes, only explanatory variables are standardized, and in some other modalities, only dependent variables are standardized. In addition, in some modalities, both explanatory variables and dependent variables are standardized. In some modalities, the standardized regression coefficient is equal to the corresponding non-standardized coefficient multiplied by the std (Xi) / std (Y) ratio, where "std" indicates standard deviation.

[376] In some modalities, the omega score (for example, the omega score for the superior mutation) can be used as an explanatory variable in a logistic regression. In some modes, logistic regression may include one or more other explanatory variables (for example, protein concentrations). In some modalities, protein biomarkers can be selected based on the Mann-Whitney-Wilcoxon test. In some modalities, the selected protein biomarkers have higher median values in cancer samples than in normal samples. In some modalities, a direct selection is used to select explanatory variables for all biomarkers (including genetic biomarkers and protein biomarkers).

[377] The application of a mathematical model to the data can generate one or more classifiers. The classifiers are a mathematical model with appropriate parameters (for example, β coefficient in the regression model). These parameters can be determined by applying a mathematical model to a set of training data, for example, a data set that includes control subjects and a group of individuals with cancer.

[378] A classifier can be assessed for its ability to adequately characterize each individual in a data set (for example, a training data set or a validation data set) using methods known to someone skilled in the art. Various statistical criteria can be used, for example, area under the curve (AUC), percentage of correct predictions, sensitivity and / or specificity. In some modalities, the classifier is evaluated by cross validation, Cross Validation Leave One OUT

(LOOCV), cross-validation n times and analysis by a pocket knife. In some modalities, each classifier is assessed for its ability to adequately characterize those individuals in a set of data not used to generate the classifier (a "test data set").

[379] In some modalities, the method used to evaluate the classifier for its ability to adequately characterize each individual in a data set is a method that assesses the classifier's sensitivity (true positive fraction) and specificity 1 (negative fraction true). In some modalities, the method used to test the classifier is a Receiver Operational Characteristic (ROC), which provides several parameters to assess the sensitivity and specificity of the result of the generated equation. In some modalities, the ROC area (area under the curve) is used to evaluate the equations. A ROC area greater than 0.5, 0.6, 0.7, 0.8, 0.9 is preferred. A perfect ROC score of 1.0 is indicative of 100% sensitivity and 100% specificity. In some modalities, classifiers are selected based on the assessment score. In some embodiments, the evaluation scoring system used is a score for the receiver operating characteristic (ROC) curve determined by the area under the ROC curve. In some modalities, classifiers with scores greater than 0.95, 0.9, 0.85, 0.8, 0.7, 0.65, 0.6, 0.55 or 0.5 are chosen. In some modalities, where specificity is important for the use of the classifier, a threshold of sensitivity can be defined and classifiers classified based on specificity are chosen. For example, classifiers with a cut-off point for specificity greater than 0.95, 0.9, 0.85, 0.8, 0.7, 0.65, 0.6, 0.55 0.5 or 0, 45 can be chosen. Likewise, the specificity threshold can be defined and classifiers classified based on sensitivity (for example, greater than 0.95,

0.9, 0.85, 0.8, 0.7, 0.65, 0.6, 0.55 0.5 or 0.45) can be chosen. Thus, in some modalities, only the ten main classification classifiers, the twenty main classification classifiers or the first 100 classification classifiers are selected. The ROC curve can be calculated by various statistical tools, including, but not limited to, statistical analysis software (SAS®), R and CORExpress®.

[380] As someone skilled in the art would understand, the usefulness of the combinations and classifiers determined by a mathematical model will depend on some characteristics (for example, race, age, gender, medical history) of the population used to generate the data for entry into the model. You can select individually identified biomarkers or subsets of the individually identified genes and test all possible combinations of the selected biomarkers to identify useful combinations of biomarker sets.

[381] In some modality, an individual's likelihood score (for example, the Y value in a logistic regression) can be used to determine whether an individual is likely to have cancer. Thus, if the likelihood score is greater than a predetermined reference threshold, the individual is likely to have cancer. In some modality, if the likelihood score is less than a reference threshold, it is likely that the individual does not have cancer. One skilled in the art will appreciate that the appropriate reference threshold for each classifier can be different and can be optimized for various statistical measures (for example, sensitivity, specificity, percentage of correct predictions). In some modalities, the reference threshold is determined by experiments or in a clinical trial.

[382] In some modalities, several classifiers are created that are satisfactory for the specified purpose (for example, all have sufficient AUC and / or sensitivity and / or specificity). In some modalities, a formula is generated that uses more than one classifier. For example, a formula can be generated that uses classifiers in series. Other possible combinations and classifier weights would be understood and are covered here.

[383] In some modalities, an individual's likelihood of having cancer can be derived from the likelihood score. Thus, if the probability is greater than a predetermined threshold, for example, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9 , the individual will be administered cancer treatment. In some modalities, if the probability is less than a predetermined threshold, for example, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8 , 0.9, the individual will not be administered cancer treatment. In some modalities, the predetermined threshold for treatment is 0.4, 0.5 or 0.6, and the predetermined threshold for not treating or for discontinuing treatment is 0.4, 0.5 or 0.6 . In some modalities, the predetermined threshold is determined by experiments or in a clinical trial.

[384] One skilled in the art will also appreciate that the sensitivity and specificity of the method depend on the reference threshold (or cut-off point). When the reference threshold is increased, sensitivity decreases, but specificity increases. In some modes, the reference limit can be optimized for sensitivity, specificity or the percentage of correct predictions. Determining the source tissue

[385] The present disclosure provides methods for determining the type of cancer or the origin of a cancer cell or tumor cell. Mathematical models can be applied to several biomarkers, as described here (for example, protein concentrations for several biomarkers, genetic mutations in several biomarkers).

[386] Useful mathematical models according to disclosure include those using supervised and unsupervised learning techniques. In some modalities, the chosen mathematical model uses supervised learning in conjunction with a set of training data to assess each possible combination of biomarkers. Various mathematical models can be used, for example, a regression model, a logistic regression model, a neural network, a clustering model, principal component analysis, correlated component analysis, classification analysis of closest neighbors, analysis linear discrimination, quadratic discrimination analysis, a support vector machine, a decision tree, Random Forest, a genetic algorithm, classifier optimization using bagging, classifier optimization using impulse, classifier optimization using the Random Subspace method, a search for genetic projection and programming and weighted voting, etc.

[387] In some modalities, a supervised learning model is used to determine the origin of a cancer cell or tumor cell. The supervised learning model refers to a model that learns a function that maps an input to an output based on example input-output pairs. You can infer a function from labeled training data that consists of a set of training examples. In supervised learning, each example is a pair that consists of an input object (usually a vector, for example, a vector of protein biomarkers) and a desired output value (also called a supervision signal, for example, type of cancer or tissue of origin). A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used to map the biomarkers obtained from a test subject. Many supervised machine learning methods can be used. Methods include, but are not limited to, Support Vector Machines, regression analysis, linear regression, logistic regression, naive Bayes, linear discrimination analysis, decision trees, k nearest neighbor algorithm, neural networks ( for example, multilayer perceptron).

[388] In some modalities, a supervised learning model is used to determine the origin of a cancer cell or tumor cell. Unsupervised machine learning refers to the machine learning task of inferring a function that describes the structure of "unlabeled" data (that is, data that has not been classified or categorized). This is done under the assumption that the relevant biomarkers will be more similar if the samples have the same origin. Unsupervised machine learning can identify these shared characteristics and apply these models to biomarkers obtained from a test subject, thereby determining the origin of a cancer or tumor cell in the individual. Many unsupervised machine learning methods can be used. Methods include, but are not limited to, grouping (for example, k-means grouping, mixing model grouping and hierarchical grouping, etc.), anomaly detection, unsupervised neural networks (for example, autocoders, deep belief networks, Hebrew learning, adversary networks and self-organized map etc.).

[389] In some modalities, the Random Forest is used. Random forest refers to a learning method for classification, regression and other tasks, which operate by building several decision trees at the time of training and generating the class that is the mode of the classes (classification) or average prediction ( regression) of individual trees. Random Forest can be implemented by various programs, for example, by the Random Forest package (Liaw, Andy and Matthew Wiener. "Classification and regression by randomForest." R news 2.3 (2002): 18-22). In some modalities, Random Forest identifies the presence of cancer in an individual based on one or more protein biomarkers (for example, concentrations of proteins in whole blood or plasma). In some modalities, more than ten 10-fold cross-validation rounds can be performed.

[390] In some embodiments, support vector machines (SVM) can be used to determine the source tissue. SVM begins with a set of training examples, each marked as belonging to one or the other of two categories (for example, type of cancer). The SVM training algorithm builds a model that assigns new examples to one category or another, making it a non-probabilistic binary linear classifier (for example, cancer of a specific origin and cancer that is not of a specific origin) . An SVM model is a representation of the examples as points in space, mapped so that the examples in the separate categories are divided by a clear gap as wide as possible.

[391] These mathematical models can be applied to various biomarkers or panels of biomarkers (for example, protein biomarkers), as described here to determine the origin of the cancer cell. In some modalities, these methods are applied only to individuals predicted to have cancer.

[392] In some modalities, once an individual is determined to have or is likely to have cancer, a tissue of origin for the cancer is determined as shown in Figure 68. For example, a decision can be made on each branch of the tree shown in Figure 68 and, once a terminal is reached (for example, there are no more decisions to be made), the source tissue can be predicted or determined as shown. The tree shown in Figure 68 provides a framework for a practitioner to predict or determine the tissue from which a cancer originates (for example, breast cancer, colorectal cancer, liver cancer, lung cancer, ovarian cancer, pancreatic cancer and cancer gastrointestinal).

[393] The tree shown in Figure 68 is exemplary and not limiting. For example, at some decision points, the level of a protein biomarker (for example, one or more protein biomarkers) can be determined (for example, the level of a protein biomarker present in a sample obtained of an individual) and the level of that protein biomarker can be compared to the level indicated in the tree shown in Figure 68. In some embodiments, the level of a protein biomarker is determined (for example, the level of a protein biomarker present in a sample taken from an individual), and the level of that protein biomarker is compared to the level that differs from the level indicated in the tree shown in Figure 68. In some embodiments, the different level of a protein biomarker (for example, one or more protein biomarkers) in the tree shown in Figure 68 is about 10% different from (for example, about 10% greater than or 10% less than), about 15% different from (for example, about 15% greater than or 15% less than) , about 20% different from (for example, about 20% greater than or 20% less than), about 25% different from (for example, about 25% greater than or 25% less than), about 30 % different from (for example, about 30% greater than or 30% less than), about 35% different from (eg, about 35% greater than or 35% less than), approximately 40% different from (for example, about 40% greater than or 40% less than), about 45% different from (for example, about 45% greater than or 45% less than), about 50% different from (for example, about 50% greater than or 50% less than) the level of this protein biomarker shown at one or more decision points in the tree shown in Figure 68. At some decision points, the gender (for example, the biological gender) of the individual is determined and compared to gender at one or more decision points indicated in the tree shown in Figure 68. At some decision points, the individual can be determined as a woman. At some decision points, the individual can be determined as a man.

[394] In some modalities, once an individual is determined to have or is likely to have cancer, a tissue of origin for the cancer is determined according to the list of rules shown in Figure 69 and the table below. For example, the level of a protein biomarker (for example, one or more protein biomarkers) can be determined (for example, the level of a protein biomarker present in a sample obtained from an individual) and the level of that individual. protein biomarker can be compared to the level indicated in Figure 69 and the table below. As a non-limiting example, the rule [condition = CA125> 102.76 & sFas <= 830.345 & gender% in% c ('F'), prediction = ovary] means that if a woman (% in% c ('F ')) has CA125> 102.76 and sFas <= 830,345, the woman has or is predicted to have ovarian cancer. A person skilled in the art will be able to detect the level of one or more protein biomarkers in an individual and / or determine the gender (eg biological gender) of an individual and determine whether the individual has or is likely to have a type of cancer listed in Figure 69. In some embodiments, once an individual is determined to have or is likely to have cancer, a tissue of origin for the cancer is determined to be colorectal when the level of one or more biomarkers of protein in an individual and / or the gender (for example, biological gender) of an individual does not follow any of the specific rules shown in Figure 69 and the table below. In some embodiments, the level of a protein biomarker is determined (for example, the level of a protein biomarker present in a sample obtained from an individual), and the level of that protein biomarker is compared to the level that differs from the level shown in Figure 69 and the table below.

In some embodiments, the different level of a protein biomarker (for example, one or more of the protein biomarkers) in the tree shown in Figure 69 is about 10% different from (for example, about 10% greater than or 10% less than), about 15% different from (for example, about 15% greater than or 15% less than), about 20% different from (for example, about 20% greater than or 20% less than ), about 25% different from (for example, about 25% greater than or 25% less than), about 30% different from (for example, about 30% greater than or 30% less than), about 35% different from (for example, about 35% greater than or 35% less than), approximately 40% different from (for example, about 40% greater than or 40% less than), about 45% different from ( for example, about 45% greater than or 45% less than), about 50% different from (for example, about 50% greater than or 50% less than) the level of this protein biomarker shown at one or more points decision tree n— shown in Figure 69. In some modalities, the gender (for example, the biological gender) of the individual is determined and compared to gender in one or more of the rules shown in Figure 69 and in the table below.

In some modalities, the individual can be determined to be a woman (for example, "gender% in% c ('F')"). In some modalities, the individual can be determined as a man (for example, "gender% in% c ('M')"). For the table below, exemplary and non-limiting rules and predictions of types of cancer are indicated. "Other" indicates that if none of the other exemplary rules are met, the cancer can be predicted to be colorectal.

Exemplary rules Prediction CA125> 102.76 & sFas <= 830.345 & gender% in% c ('F') Ovarian CA199> 37.65 & CYFRA211 <= 14640.67 & CD44> 15.67 & Midkine> 289.485 & PAR> 4580.45 & Pancreatic sHER2> 6935.375 AFP> 17774.49 & TIMP2 <= 61777.34 & Galectin3 <= 16.72 & Mesothelin <= 27.71 Liver CA199 <= 71.53 & Leptin> 12927.9 & sFas> 411,525 & TIMP1 <= 59700,835 & Mama TIMP2 <= 37667,97 & gender% in% c ('F')

CA125 <= 104.41 & CA153 <= 16.22 & CA199 <= 117.275 & HGF <= 465.81 & Leptin <= 7244.265 CRC & SHBG> 29.53 Prolactin> 37214.25 & sFas> 1046.435 & TIMP1 <= 93517,645 & DKK1 <= 1,095 & Lung sHER2 <= 6054,825 & gender% in% c ('M') CA153 <= 15,285 & IL8> 39,235 & IL8 <= 163,64 & sFas <= 1098,015 & Myeloperoxidase> 19,325 & Stomach / sHER2 <= 5172,43 Esophagus AFP <= 2867,07 & CA153> 9,505 & Prolactin> 37962,925 & sFas> 688,47 & Myeloperoxi- Lung dase <= 11,935 & Thrombospondin2 < = 3273,685 AFP <= 36492,58 & CA125 <= 10,475 & Prolactin <= 275955,405 & sFas <= 1351,39 & CRC AXL> 1999,77 & sHER2 <= 10172,47 EAA <= 1892,955 & sFas <= 1076.05 & TIMP> 240306,87 & CD44 <= 26,915 & sHER2 <= 9959,43 Ovarian & gender% in% c ('F') Leptin <= 5186,325 & OPN> 94670,57 & TIMP1 > 75617,725 & SHBG <= 141,115 & Stomach / sHER2 <= 8369,265 & Thrombospondin2 <= 17040,89 Esophagus AFP <= 40375,115 & CA153 <= 20,835 & EAA> 3057,27 & Leptin <= 237948,935 & CRC OPN <= 250628.405 & TIMP2 <= 68136.905 AFP <= 299906.424 & sFas> 1100.55 & TIMP1 <= 85064.65 & TIMP2 <= 53195.905 & Mama DKK1 <= 1.285 & gender% in% c ('F') Leptin> 8839.01 & sFas <= 1745.34 & TIMP1> 63205.505 & CD44 <= 19.735 & Mesote- CRC lina <= 39.705 & gender% in% c (' F ') AFP <= 5369,16 & CA153 <= 16,21 & CEA> 1374,755 & Myeloperoxidase <= 368,56 & Mid-CRC kine <= 4401,495 & sHER2 <= 10713,16 TIMP2 <= 61631 , 2 & Myeloperoxidase> 23,725 & SHBG <= 96,985 & DKK1 <= 1,335 & Mid- Stomach / kine <= 348,515 & sHER2 <= 5523,84 Esophagus CA125 <= 56,21 & HGF <= 928,54 & Prolactin> 65977 , 55 & sFas <= 2849,195 & Mesothelin> 13.68 Lung & AXL <= 2093.75 CA199 <= 36.795 & CEA <= 115717.2 & HE4 <= 15657.71 & Leptin> 15287.95 & sFas> 716,205 & Breast gender% in% c ('F') Other CRC

[395] For Figures 68 and 69 and the table shown above, the units of certain protein biomarkers are indicated in the key below: CA19-9 U / ml CEA pg / ml CA125 U / ml AFP pg / ml Prolactin pg / ml HGF pg / ml OPN pg / ml TIMP-1 pg / ml TIMP-2 pg / ml Mesothelin ng / ml Midkine pg / ml Kallikrein-6 pg / ml CD44 ng / ml

Angiopoietin-2 pg / ml Endogline pg / ml Folistatin pg / ml G-CSF pg / ml GDF15 ng / ml DKK1 ng / ml NSE ng / ml OPG ng / ml AXL pg / ml sHER2 / sEGFR2 / sErbB2 pg / ml Trombospondin- 2 pg / ml sEGFR pg / ml PAR pg / ml sPECAM-1 pg / ml CA15-3 U / ml Leptin pg / ml IL-6 pg / ml IL-8 pg / ml sFas pg / ml FGF2 pg / ml CYFRA 21 -1 pg / ml HE4 pg / ml TGFa pg / ml Galectin-3 ng / ml Myeloperoxidase ng / ml SHBG nM Detecting genetic biomarkers

[396] Any of several techniques can be used to detect the presence of one or more genetic biomarkers (for example, mutations) present in a sample (for example, cervical, endometrial, urine, saliva, ctDNA , blood, serum and / or plasma sample) obtained from an individual. Non-limiting examples of such techniques include a PCR-based multiplex assay, a digital PCR assay, a digital drop PCR assay (ddPCR), a PCR-based singleplex PCR assay, a Sanger sequencing assay, a sequencing assay last generation, a quantitative PCR assay, binding assay and microarray assay. Those skilled in the art will be aware of other techniques suitable for detecting the presence of one or more genetic biomarkers (for example, mutations) present in a sample obtained from an individual.

[397] In some modalities of methods provided here, the presence of one or more genetic biomarkers (for example, mutations) present in a sample (for example, a cervical, endometrial sample, urine, saliva, ctDNA, blood , serum and / or plasma) obtained from an individual is detected using a method that can increase the sensitivity of massively parallel sequencing instruments with an error reduction technique. For example, these techniques may allow the detection of rare mutant alleles in a range of 1 mutant model between 100 to 1,000,000 wild-type models (for example, 500 to 1,000,000 wild-type models). In some modalities, these techniques may allow the detection of rare mutant alleles when they are present as a low fraction of the total number of models (for example, when rare mutant alleles when they are present in a fraction less than about 1%, less than about 0.1%, less than about 0.01%, less than about 0.001%, less than about 0.00001% of the total models, or less, or any fraction between these exemplary fractions). In some embodiments, the presence of one or more genetic biomarkers (for example, mutations) present in a sample obtained from an individual is detected by amplifying DNA (for example, DNA obtained from cells in a sample or DNA without cells) to form amplicon families in which each member of a family is derived from a single model molecule in the cellless DNA, where each member of a family is marked by a common oligonucleotide barcode and where each family is marked by a code of distinct oligo-nucleotide bars. For example, the presence of one or more genetic biomarkers (for example, mutations) present in a sample obtained from an individual can be detected by molecularly assigning a unique identifier (UID) to each model molecule, amplifying each model molecule markedly to create UID families and redundantly sequence the amplification products. In some embodiments, the oligonucleotide bar code is introduced into the model molecule by an amplification step with a population of primers that collectively contain a plurality of oligonucleotide bar codes. In some embodiments, the oligonucleotide bar code is endogenous to the model molecule and an adapter including a DNA synthesis initiation site is attached to one end of the model molecule adjacent to the oligonucleotide bar code. See, for example, Kinde I, Wu J, Papadopoulos N, Kinzler KW, Vogelstein B (2011) Detection and quantification of rare mutations with massively parallel sequencing. Proc Natl Acad Sci USA 108: 9530-9535, the contents of which are hereby incorporated by reference in their entirety.

[398] In some modalities of the methods provided here, the presence of one or more genetic biomarkers (for example, mutations) present in a sample (for example, a cervical, endometrial or plasma sample) obtained from an individual is detected using sequencing technology (for example, next generation sequencing technology). A variety of sequencing technologies are known in the art. For example, a variety of technologies for detecting and characterizing tumor DNA circulating in cellless DNA are described in Haber and Velculescu, Blood-Based Analyzes of Cancer: Circulating Tumor Cells and Circulating Tumor DNA, Cancer Discov., Jun ; 4 (6): 650-61. doi: 10.1158 / 2159-

8290.CD-13-1014, 2014, incorporated herein by reference in its entirety. Non-limiting examples of such techniques include SafeSeqs (Kinde et. Al, Detection and quantification of rare mutations with massively parallel sequencing, Proc Natl Acad Sci USA; 108, 9530-5, 2011), OnTarget (Forshew et al., Noninvasive identification and monitoring of cancer mutations by targeted deep sequencing of plasma DNA, Sci Transl Med; 4: 136ra68, 2012), and TamSeq (Thompson et al., Winnowing DNA for rare sequences: highly specific sequence and methylation ba-sed enrichment. PLoS ONE, 7: e31597, 2012), each of which is incorporated herein by reference in its entirety. In some modalities, the presence of one or more mutations present in a sample obtained from an individual is detected using digital droplet PCR (ddPCR), a method that is known to be highly sensitive to mutation detection. In some modalities, the presence of one or more mutations present in a sample obtained from an individual is detected using other sequencing technologies, including, among others, chain termination techniques, shotgun techniques, synthesis sequencing methods, methods using microfluidics, other capture technologies, or any of the other sequencing techniques known in the art that are useful for detecting small amounts of DNA in a sample (eg, ctDNA in a DNA sample without cells ).

[399] In some embodiments, the presence of one or more genetic biomarkers (for example, mutations) present in a sample (for example, a cervical, endometrial, urine, saliva, ctDNA, blood, serum and / or plasma sample ) obtained from an individual is detected using array-based methods. For example, the step of detecting a genetic change (for example, one or more genetic changes) in DNA without cells can be performed using a DNA microarray. In some embodiments, a DNA microarray can detect more than a plurality of genetic biomarkers (for example, mutations in cancer cells). In some embodiments, DNA without cells is amplified before the detection of the genetic biomarker (for example, genetic alteration). Non-limiting examples of array-based methods that can be used in any of the methods described here include: a complementary DNA microarray (cDNA) (Kumar et al. (2012) J. Pharm. Bioallied Sci. 4 (1) : 21-26; Laere et al. (2009) Methods Mol. Biol. 512: 71-98; Mackay et al. (2003) Oncogene 22: 2680-2688; Alizadeh et al. (1996) Nat. Genet. 14: 457-460), an oligonucleotide microarray (Kim et al. (2006) Carcinogeneisis 27 (3): 392-404; Lodes et al. 2009 PLoS One 4 (7): e6229), a clone chip of the artificial bacterial chromosome (BAC) (Chung et al. (2004) Genome Res. 14 (1): 188-196; Thomas et al. (2005) Genome Res. 15 (12): 1831-1837), a single nucleotide polymorphism microarray (SNP) (Mao et al. (2007) Curr. Genomics 8 (4): 219-228; Jasmine et al. (2012) PLoS One 7 (2): e31968), a comparative genomic hybridization arrangement based on microarrays (CGH arrangement) (Beers and Nederlof (2006) Breast Cancer Res. 8 (3): 210; Pinkel et al. (2005) Nat. Genetics 37: S11 -S17; Michels et al. (2007) Genet. Med. 9: 574-584), a molecular inversion probe (MIP) assay (Wang et al. (2012) Cancer Genet 205 (7-8): 341-55; Lin et al. (2010) BMC Genomics 11: 712). In some embodiments, the cDNA microarray is an Affymetrix microarray (Irizarry (2003) Nucleic Acids Res 31: e15; Dalma-Weiszhausz et al. (2006) Methods Enzymol. 410: 3-28), a NimbleGen microarray (Wei et al. (2008) Nucleic Acids Res 36 (9): 2926-2938; Albert et al. (2007) Nat. Methods 4: 903-905), an Agilent microarray (Hughes et al. (2001) Nat. Biotechnol. 19 (4): 342-347), or a BeadArray arrangement (Liu et al. (2017) Biosens Bioelectron 92: 596-601). In some ways, the oligonucleotide microarray is a DNA placement arrangement (Mockler and Ecker (2005) Genomics 85 (1): 1-15; Bertone et al. (2006) Genome Res 16 (2): 271-281 ). Other suitable arrangement-based methods are known in the art.

[400] In some embodiments, once an individual has been determined to have cancer (for example, ovarian cancer, endometrial cancer, bladder cancer or UTUC),

the individual can be additionally monitored or selected for increased monitoring.

In some modalities, the methods provided here can be used to select an individual for increased monitoring in a period prior to the period when conventional techniques are able to diagnose the individual with early-stage cancer.

For example, the methods provided here to select an individual for increased monitoring can be used when an individual has not been diagnosed with cancer by conventional methods and / or when an individual is not known to harbor cancer.

In some modalities, an individual selected for increased monitoring may be administered a diagnostic test (for example, any of the diagnostic tests disclosed here) with an increased frequency compared to an individual who was not selected for increased monitoring.

For example, an individual selected for increased monitoring can be administered with a diagnostic test at a frequency of twice a day, daily, biweekly, weekly, bi-monthly, monthly, quarterly, semi-annually, or any frequency at the same time. .

In some modalities, an individual selected for increased monitoring can be administered with one or more additional diagnostic tests compared to an individual who was not selected for increased monitoring.

For example, an individual selected for increased monitoring can receive two diagnostic tests, while an individual who was not selected for increased monitoring receives only a single diagnostic test (or no diagnostic test). In some modalities, an individual who has been selected for increased monitoring can also be selected for additional diagnostic tests.

Once the presence of a cancer cell has been identified (for example, by any of the methods disclosed here), it may be beneficial for the individual to undergo increased monitoring (for example, to assess the progression of the tumor or cancer in the individual and / or assess the development of genetic biomarkers (for example, mutations in cancer cells) and / or aneuploidy) and further diagnostic tests (for example, to determine the exact size and / or location of the tumor that houses the cancer cell). In some modalities, a therapeutic intervention is administered to the individual who is selected for increased monitoring after the detection of a genetic biomarker (for example, a mutation in cancer cells) and / or aneuploidy.

Any of the therapeutic interventions disclosed herein or known in the art can be administered.

For example, an individual who has been selected for increased monitoring can be monitored later and a therapeutic intervention can be administered if the presence of the cancer cell is maintained throughout the increased monitoring period.

In addition or alternatively, an individual who has been selected for increased monitoring can be administered with a therapeutic intervention and monitored additionally as the therapeutic intervention progresses.

In some modalities, after administering a therapeutic intervention to an individual who has been selected for increased monitoring, the increased monitoring will reveal one or more genetic biomarkers (for example, one or more additional mutations in cancer cells) and / or aneuploidy.

In some embodiments, one or more genetic biomarkers (for example, one or more additional cancer cell mutations) and / or aneuploidy will provide a cause for administering a different therapeutic intervention (for example, a resistance mutation may arise in a cancer cell during therapeutic intervention, whose cancer cell harboring the resistance mutation is a resistance to the original therapeutic intervention).

[401] In some embodiments, once an individual has been determined to have cancer (for example, ovarian cancer, endometrial cancer, bladder cancer or UTUC), the individual can be administered with additional tests or selected for additional diagnostic tests.

In some modalities, the methods provided here can be used to select an individual for increased monitoring in a period prior to the period when conventional techniques are able to diagnose the individual with early-stage cancer.

For example, the methods provided here to select an individual for additional diagnostic tests can be used when an individual has not been diagnosed with cancer by conventional methods and / or when an individual is not known to harbor cancer.

In some modalities, an individual selected for additional diagnostic tests may be administered a diagnostic test (for example, any of the diagnostic tests disclosed here) with an increased frequency compared to an individual who has not been selected for additional diagnostic tests.

For example, an individual selected for additional diagnostic tests can be administered with a diagnostic test twice a day, daily, biweekly, weekly, bimonthly, monthly, quarterly, semi-annually, or any frequency. in the same.

In some modalities, an individual selected for additional diagnostic tests may be administered with one or more additional diagnostic tests compared to an individual who was not selected for additional diagnostic tests.

For example, an individual selected for additional diagnostic tests can receive two diagnostic tests, while an individual who was not selected for additional diagnostic tests receives only a single diagnostic test (or no diagnostic test). In some modalities, the diagnostic test method can determine the presence of the same type of cancer as the cancer that was originally detected.

Additionally or alternatively, the diagnostic test method can determine the presence of a different type of cancer such as the cancer originally detected.

In some embodiments, the diagnostic test method is a scan.

In some modalities, the scan is a computed tomography (CT), a computed tomography angiography (CTA), an esophagram (a barium swallow), a barium enema, a magnetic resonance imaging (MRI), a PET tomography , an ultrasound (for example, an endobronchial ultrasound, an endoscopic ultrasound), an X-ray, a DEXA scan.

In some modalities, the diagnostic test method is a physical examination, such as an anoscopy, a bronchoscopy (for example, an autofluorescence bronchoscopy, a white light bronchoscopy, a navigation bronchoscopy), a colonoscopy, a digital breast mossynthesis, a retrograde endoscopic cholangiopancreatography (ERCP), an entopagogastroduodenoscopy, a mammography, a Pap smear, a pelvic exam, a positron emission tomography and computed tomography (PET-CT) scan. In some modalities, an individual who has been selected for further diagnostic tests can also be selected for increased monitoring.

Once the presence of a cancer cell is identified (for example, by any of the methods disclosed here), it may be beneficial for the individual to undergo increased monitoring (for example, to assess the progression of the tumor or cancer in the individual and / or evaluate the development of genetic biomarkers (for example, additional mutations in cancer cells) and / or aneuploidy) and further diagnostic tests (for example, to determine the size and / or the exact location of the tumor it houses the cancer cell). In some embodiments, a therapeutic intervention is administered to the individual who is selected for additional diagnostic tests after the detection of a genetic biomarker (for example, a mutation in cancer cells) and / or aneuploidy. Any of the therapeutic interventions disclosed herein or known in the art can be administered. For example, an individual who has been selected for additional diagnostic tests can be administered with an additional diagnostic test and a therapeutic intervention can be administered if the presence of the cancer cell is confirmed. Additionally or alternatively, an individual who has been selected for additional diagnostic tests can be administered with a therapeutic intervention and can be monitored additionally as the therapeutic intervention progresses. In some modalities, after an individual who has been selected for additional diagnostic testing has been administered with a therapeutic intervention, the additional test will reveal one or more additional genetic biomarkers (for example, mutations in cancer cells) and / or aneuploidy . In some embodiments, one or more additional genetic biomarkers (for example, cancer cell mutations) and / or aneuploidy will provide the cause to administer a different therapeutic intervention (for example, a resistance mutation may appear in a cancer cell during therapeutic intervention, whose cancer cell harboring the resistance mutation is a resistance to the original therapeutic intervention).

[402] In some embodiments, the presence of one or more genetic biomarkers (for example, mutations) present in a sample (for example, a cervical, endometrial, urine, saliva, ctDNA, blood, serum and / or plasma sample ) obtained from an individual is detected using any of the various bottleneck sequencing system methods described in International Patent Application Publication Number WO 2017/132438, the content of which is incorporated by reference here in its entirety. The Bottleneck Sequencing System (BotSeqS) is a state-of-the-art sequencing method that simultaneously quantifies rare somatic point mutations in the mitochondrial and nuclear genomes. BotSeqS combines molecular bar code with a simple dilution step just before the library is amplified. BotSeqS can be used to show accumulations of rare mutations, dependent on age and tissue, and to demonstrate that the somatic mutational load in normal tissues can vary by several orders of magnitude, depending on biological and environmental factors. BotSeqS has been used to show major differences between the mutational patterns of the mitochondrial and nuclear genomes in normal tissues. BotSeqS showed that the mutation spectra of normal tissues were different from each other, but similar to the cancers that appeared in them.

[403] According to some modalities of BotSeqS, a method is provided to obtain a DNA sequence. The adapters are attached to the ends of the random fragments of a population of DNA to form a library of fragments linked to the adapter, so that after amplification of a fragment in the library of fragments linked to the adapter, each end of the fragment has a distinct end. The library of fragments attached to the adapter is diluted to form diluted fragments, attached to the adapter. At least a portion of the diluted fragments attached to the adapter are amplified to form families from a single strand of a fragment attached to the adapter. Family members are sequenced to obtain the nucleotide sequence of a plurality of members of a fragment family attached to the adapter.

[404] According to some modalities of BotSeqS, a method is provided for sequencing DNA. The adapters are connected to the ends of a population of fragmented double-stranded DNA molecules to form a library of fragments linked to the adapter, so that after amplification of a fragment in the library of fragments linked to the adapter, each end fragment has a distinct end. The library of fragments attached to the adapter is diluted to form diluted fragments, attached to the adapter. At least a portion of the diluted fragments attached to the adapter is amplified to form families from a single strand of a fragment attached to the adapter. Family members are sequenced to obtain the nucleotide sequence of a plurality of family members of a fragment attached to the adapter. The nucleotide sequence of a member of a first family is aligned with a reference sequence. A difference is identified between the first family member and the reference sequence. The difference is identified as a potential rare or potential non-clonal mutation, if found in a second family from a strip opposite the single strip of the fragment connected to the adapter.

[405] According to some modalities of BotSeqS, a method is provided for sequencing DNA. A double-stranded DNA population of a sample is randomly fragmented to form a fragment library. Adapters are attached to the ends of the fragments to form a library of fragments linked to the adapter, so that, after amplifying a fragment in the library of fragments connected to the adapter, each end of the fragment has a distinct end. The library of fragments attached to the adapter is diluted to form diluted fragments, attached to the adapter. At least a portion of the diluted fragments attached to the adapter are amplified to form families from a single strand of a fragment attached to the adapter. Family members are sequenced to obtain the nucleotide sequence of a plurality of family members of a fragment attached to the adapter. The nucleotide sequence of a member of a first family is aligned with a reference sequence. A difference is identified between the first family member and the reference sequence. The difference is identified as a potential rare or non-clonal mutation, if found in a second family from a tape opposite the simple tape of the fragment connected to the adapter.

[406] In some modalities for detecting the presence of one or more genetic biomarkers (for example, mutations) present in a sample (for example, a cervical, endometrial sample, urine, saliva, ctDNA, blood, serum and / or plasma) obtained from an individual, rare somatic point mutations are quantified in the mitochondrial and nuclear genomes. One or more modalities of such methods are referred to informally as BotSeqS, short for Bottleneck Sequencing System. Using molecular barcode (exogenous or endogenous) and a simple dilution step immediately before the amplification of the library, the method allows, for example, to determine the mutational load based on the age or tissue type of normal tissues. Various BotSeqS methods described here can also be used to demonstrate the effect of mutagens and environmental insults on the mutation rate. Several BotSeqS methods described here are designed to accurately detect rare point mutations in any molecular barcode library in a completely impartial manner.

[407] BotSeqS can be used with any molecular bar code strategy, such as bar codes demarcated by endogenous position, described in Kinde, I, et al., Detection and quantification of rare mutations with massively parallel sequencing. Proceedings of the National Academy of Sciences of the United States of America 108, 9530-9535 (2011), and combined bar codes added exogenously (Kinde, I, et al., Detection and quantification of rare mutations with massively parallel sequencing Proceedings of the National Academy of Sciences of the United States of America 108, 9530-9535 (2011); Jabara et al., Accurate sampling and deep sequencing of the

HIV-1 protease gene using a Primer ID. Proceedings of the National Academy of Sciences of the United States of America 108, 20166-20171 (2011); Schmitt, M.W. et al. Detection of ultra-rare mutations by next-generation sequencing. Proceedings of the National Academy of Sciences of the United States of America 109, 14508-14513 (2012); Hiatt et al., Single molecule molecular inversion probes for targeted, high-accuracy detection of low-frequency variation. Genome research 23, 843-854 (2013); Kivioja, T. et al. Counting absolute numbers of molecules using unique molecular identifiers. Nature methods 9, 72-74 (2012); Kinde, I. et al. Evaluation of DNA from the Papanicolaou test to detect ovarian and endometrial cancers. Science translational medicine 5, 167ral64 (2013); Kumar, A. et al. Deep sequencing of multiple regions of glial tumors reveals spatial heterogeneity for mutations in clinically relevant genes. Genome biology 15, 530 (2014); Keys, J.R. et al. Primer ID Informs Next-Generation Sequencing Platforms and Reveals Preexisting Drug Resistance Mutations in the HIV-1 Reverse Transcriptase Coding Domain. AIDS Res Hum Retroviruses 31, 658-668 (2015)). In some modalities, BotSeqS measures very rare mutations, across the entire genome, in a completely impartial way, while SafeSeqS measures relatively frequent, but not clonal (ie, "subclonal") mutations in predefined targeted loci.

[408] Conceptually, BotSeqS can be seen as achieving low coverage of randomly sampled genomic loci, while Safe-SeqS works through the ultra high coverage of a targeted locus.

[409] Low genomic coverage, which can be seen as a characteristic of the various BotSeqS methods described here, allows rare mutations to constitute a major portion of the signal in that genomic position, contributing to the sensitivity of the method. The applications of such methods are varied. They can be used to measure very rare somatic mutations.

They can be used to assess somatic mosaicism, cell line development, theories about aging, environmental exposure to carcinogens and cancer risk assessment. Many of these applications are demonstrated in the examples here.

[410] Various filters can be applied to the data generated with various BotSeqS sequencing methods. A filter that can be applied is for mtDNA only; Watson AND Crick duplicates families only, excluding models that include high frequency mutations (ie homopolymers,> 1 mutation per model) and excluding models that are mapped to repeatMasker. Another filter that can be applied is only for nuclear DNA; Watson AND Crick duplicates only families, excluding models that include high-frequency mutations (ie, homopolymers,> 1 mutation per model) and excluding models that map repetitive DNA or structural variants. Another filter that can be applied is only for average quality scores only mtDNA, only single base substation, greater than or equal to 30, Read 1> = 2 Watson duplicates with> = 90% only of the mutation fraction, Read 2> = 2 duplicates of Crick with> = 90% mutation fraction only, exclude all variants called in WGS, exclude all variants in dbSNP142, exclude calls that are mapped to repeatMasker, exclude visual artifacts and high frequency mutations (ie homopolymers, cycle 6 and 7, change> 1 per model> 1 model per change). Yet another filter that can be applied is quality score Average only nuclear DNA, only single base replacement> = 30, Read 1> = 2 duplicated PCR with> = 90% of the mutation fraction only, Read 2> = 2 Duplicate PCR with> = 90% of the mutation fraction only, exclude all variants named in WGS, exclude all variants in dbSNP130 and dbSNP142, exclude calls that map repetitive DNA or structural variants, exclude visual artifacts and high frequency mutations (or ie homopolymers, cycle 6 and 7,> 1 change per model).

[411] Several databases were used to align and filter the data, including: dbSNP build 130, Genomic Variant Database, Segment Duplications, Broken Repetition Fragments, Simple Tandem Repeats, Repetition Masker, dbSNP build 142, Updated Genomic Variant Database, updated Genomic Variant Database, updated Segment Duplications, updated Interrupted Repetition Fragments, updated Simple Tandem Repetitions, updated Repetition Masker. The GRCh37 / hgl9 genome set from the USCS human genome browser was used.

[412] Double-stranded DNA fragments can be made from longer-chain polymers, using any technique known in the art, including, but not limited to, enzymatic digestion, punching and shearing. Alternatively, some sources of DNA are already fragmented into suitable sizes. Such sources include, without limitation, saliva, sputum, urine, plasma and feces. If the DNA source is already the right size, the fragments do not need to be fragmented. Desirably, the fragmentation process, whether endogenous or by human action, is random. The desirable size of the fragments may depend on the length of the sequencing readings. The fragments can be less than 2 kbp, less than 1500 bp, less than 1 kbp, less than 500 bp, less than 400 bp, less than 200 bp or less than 100 bp. It is desirable that fragments are greater than twice the reading duration, for example. The fragments can be at least 50 bp, at least 100 bp, at least 150 bp, at least 200 bp, at least 300 bp, at least 400 bp, at least 500 bp, for example.

[413] In some modalities, fragments are connected to adapters. In some embodiments, each end of a fragment has different adapters. This can be a laborious process, which can involve a lot of sorting and processing to obtain fragments with two different adapters at each end. One way to achieve this goal is to use Y, U or hairpin adapters that contain or can be processed to contain non-complementary strings in Watson and Crick strings. If there is a non-complementary region in an adapter, amplifying the fragment attached to the adapter will generate fragments of double strand with different orientation from the adapter in fragments derived from each strand, when amplified.

[414] Dilution of libraries of fragments attached to the adapter can be performed using any dilution level appropriate for the source. Less concentrated samples may be less diluted and more concentrated samples may be more diluted. The complexity of a sample will also take into account the desired degree of dilution. Any series of dilutions can be used as convenient, such as two-fold dilutions, five-fold dilutions, ten-fold dilutions, etc. In some modalities, a dilution level is chosen that will produce 5 to 10 members of a family per fragment connected to the adapter. This is influenced by how many fragments are sequenced. For example, at a specific dilution, sequencing -20 million clusters will yield 1-4 members, but sequencing 75 million clusters will yield 5-10 (see FIG. 55). The more molecules are sequenced, the greater the number of members that will be found per family. After the sequencing of family members derived from the diluted fragments, attached to the adapter, a nucleotide sequence of 4-100 family members of a fragment linked to the adapter is desirably obtained.

[415] The dilution can beneficially achieve a relatively low level of genome coverage. That is, the genome can be sampled instead of sequentially exhaustively and repeatedly. In some embodiments, the dilution is sufficient for less than 10 families of nuclear DNA to include 20 or more overlapping nucleotides in the non-adapting portion. In some embodiments, the dilution is sufficient for less than 5 nuclear DNA families to include 20 or more overlapping nucleotides in the non-adapting portion. In some modalities, the dilution is sufficient for less than 10 families to understand the rare potential or non-clonal potential difference detected between a test sequence and a reference sequence. In some modalities, the dilution is sufficient for less than 5 families to understand the rare potential or non-clonal potential difference detected between a test sequence and a reference sequence.

[416] In some modalities, the dilution performs three characteristics. First, dilution can achieve a lower coverage of representative loci for one or a few molecules to "discover" rare mutations. Second, dilution can increase the chances that the two strands of the initial molecules will be sequenced redundantly. Third, dilution can facilitate random sampling of the genome with a minimum amount of sequencing.

[417] Amplification can be performed by any technique known in the art. Typically, the polymerase chain reaction will be used. Other techniques, linear or logarithmic, can be used.

[418] Typically, primers will be used in the amplification that are complementary to the adapter sequences.

[419] Sequencing can be performed by any technique known in the art. A next generation sequencing method can be used. The fragments sequences can be aligned with a reference sequence. They can be grouped into families based on an endogenous or exogenous barcode.

An endogenous bar code normally comprises the N nucleotides that are adjacent to the adapter. The value of N can be chosen as appropriate and provides sufficient diversity / complexity. Exogenous bar codes can be added in a separate connection step, by amplification initiators or can be part of the adapters. In some embodiments, bar codes are random. In some modalities, 2 to 1000 family members are sequenced. In some modalities, less than 100 family members are sequenced. In some modalities, at least 4 family members are sequenced. In some modalities, 4 to 10 family members are sequenced.

[420] According to the various methods described here, it is not necessary to physically separate or analyze the nuclear and mitochondrial genomes separately. This allows comparing rates in the two genomes in the same cells.

[421] The exogenous barcode can be used to identify fragments, samples, tissues, patients, etc. individual. Although the examples provided here employ endogenous bar codes, this can be supplemented or replaced with exogenous bar codes. In some modalities, the complexity of the population of bar codes is greater than the complexity of the population of fragments to be encoded with bar codes, so that the bar code represents a specific fragment. Barcodes can be added to a population of fragments using any technique known in the art, including, without limitation, by amplification or ligation, or as part of adapter molecules that are added by ligation. The differences that can be detected between a given nucleotide sequence and a reference nucleotide sequence include, without limitation, mutations, such as point mutations, indels (for example, 1-6 base insertions or deletions) and / or substitutions. If the same mutation is found in two different families, a greater degree of certainty will be associated with it (for example, it is more likely to have arisen in the biological sample, rather than in experimental processing). The two families can have identical sequences derived from the double strand fragments, but they can have a different orientation with respect to the adapter sequences. To achieve a higher degree of certainty, it may be required that at least two members from each of the two families have the difference in sequence. To achieve a higher degree of certainty, 90% or more of the members of a family may be required to have the sequence difference.

[422] As a means of filtering mutations in the germline or clonal lineage, libraries of fragments that have not been amplified and that are from the same sample can be sequenced. Mutations in the germline and clonal lineage will be evident on inspection due to their repeated occurrences.

[423] BotSeqS methods are simple, NGS-based approaches that can accurately measure rare point mutations impartially and across the genome. Using BotSeqS, several important objectives were achieved: (i) estimates of rare frequencies of mutations were defined in the entire genome; (ii) rare mutations in the nuclear and mitochondrial genomes of the same population of cells were evaluated simultaneously; (iii) rare frequencies of mutations were compared between various normal tissues of individuals of different age groups, DNA repair capacity or exposure history; and (iv) the spectra of rare mutations in normal tissues were identified, allowing their comparison with those of clonal mutations in cancers.

[424] Data presented here show that mutations increase with age, a result that is largely consistent with the literature (Kennedy, S.R., Loeb, L.A. & Herr, A.J. Somatic mutations in aging,

cancer and neurodegeneration.

Mech Aging Dev 133, 118-126 (2012); Vijg, J.

Somatic mutations, genome mosaicism, cancer and aging.

Curriculum opinion in genetics & development 26, 141-149 (2014). The rate of increase in mutations is not as great in the brain as in the colon or kidney, probably because the colon and kidney are self-renewing tissues throughout adulthood, whereas the brain is not.

On the other hand, the fact that the frequency of mutation increased after childhood was surprising, given that the main types of cells in the prefrontal cortex are generally considered post-mitotic (Spalding et al., Retrospective birth dating of cells in humans.

Cell 122, 133-143 (2005)). Without being limited by theory, there are several potential explanations for this increase.

A small number of cells that are replicating more actively than neurons or glia may be responsible for the increase.

Such cells may include microglia or infiltrating lymphocytes or other inflammatory cells.

Alternatively, these mutations may represent the results of spontaneous DNA damage, regardless of DNA replication.

A recent study of sequencing single cells from human neurons suggested that spontaneous damage occurs during transcription (Lodato, M.A. et al.

The somatic mutation in isolated human neurons follows the history of development and transcription.

Science 350, 94-98 (2015)). However, unlike single cell sequencing, BotSeqS measures the mutations found in both strands.

Thus, for the explanation of spontaneous DNA damage to be plausible, the mutations identified by BotSeqS would have to be subject to DNA repair.

Consistent with this possibility, it is known that DNA repair processes are active in post-mitotic neurons and glia (Madabhushi, R., Pan, L. & Tsai, L.H.

DNA damage and its links to neurodegeneration.

Neuron 83, 266-282 (2014)).

[425] A third possibility is that these mutations are a feature of the procedure we use to detect them.

It is fascinating that this formal possibility is essentially impossible to exclude because the mutations were probably found in only one cell of the studied tissue, and the DNA of that cell is no longer available for subsequent evaluation.

In addition, there is no other technique available to observe these mutations with the sensitivity achieved by the various BotSeqS methods described here.

Currently, sensitivity is limited only by the amount of sequencing dedicated to the project.

It is easy to detect mutations that occur between 6 x 10-8 per bp, using a small fraction of a HiSeq ™ 2500 flow cell. It is estimated that mutations can be detected at <10-9 per bp using a flow cell entire.

The only other method that approximates this sensitivity has been described by Loeb and colleagues (Schmitt, MW et al.

Detection of ultra-rare mutations by next-generation sequencing.

Proceedings of the National Academy of Sciences of the United States of America 109, 14508-14513 (2012); Kennedy, S.R. et al.

Detecting ultralow-frequency mutations by Duplex Sequencing.

Nature protocols 9, 2586- 2606 (2014)), but this is applicable only to pre-defined regions (-0.001%) of the genome.

In the absence of direct confirmation, we are forced to use correlations and other approaches to support the accuracy of the technology described here.

These correlations include the following, as detailed in Table 51: similar frequencies and mutation spectra identified in different aliquots of DNA from the same samples; similar frequencies and spectra of mutation identified in the same tissues from different individuals of similar age; expected increases in the frequency of mutation with age; specific tissue differences in age-dependent increases in mutation frequencies; higher frequencies of mutation in normal tissues deficient in incompatibility repair or exposed to environmental mutagens; and mutation spectra in normal tissues consistent with those previously seen in cancers of the same tissues. Other in silico and experimental approaches used to assess the accuracy of BotSeqS are described in Example 7.

[426] It was also possible to compare mutation frequencies in the mitochondrial and nuclear genomes of the same tissues. In normal individuals, in the absence of exposure to mutagens, the frequency of mutation was much higher in the mitochondria than in the nuclear genome (median ratio of 26.2). This is consistent with the relatively low efficiency of DNA repair in mitochondria compared to the nuclear genome. Equally important, however, is that the proportion of mitochondrial to nuclear mutation frequencies was much lower (median of 1.3) in the normal kidneys of individuals exposed to cigarette smoke or AA. This finding is not consistent with the known and less efficient repair of DNA in mitochondria. In addition, there was a change in the mutational signature of AA, A: T to T: A transversions, in the nuclear DNA of normal kidneys in individuals exposed to AA, but virtually none in mtDNA. Without being limited by theory, one possibility is that the higher prevalence of mutations in mtDNA may be hiding the effect of environmental mutagens on the mitochondrial genome compared to its effect on the nuclear genome. Another possibility is that there are unexpected and pronounced differences in the ways in which these mutagens cause damage to the DNA in these two organelles.

[427] Another new discovery of the data established here is the discovery that the mutation spectra differed between normal tissues, even in the absence of exposures to known mutagens. It is not known whether these differences reflect varying exposures to commonly unidentified mutagens or specific tissue repair processes. In some modalities, the rare spectra of mutation in normal tissues were similar to the clonal mutations found in cancers. Although the varying mutation spectra in cancers have often been attributed to specific cancer processes, the data set forth here suggest that at least a subset of these mutations does indeed reflect specific tissue processes. This concept is consistent with the idea that a substantial fraction of the mutations found in cancers occur in normal stem cells (Tomasetti, C. & Vogelstein, B. Cancer etiology. The variation in cancer risk between tissues can be explained by the number of stem cell divisions Science 347, 78-81 (2015); Tomasetti, C, Vogelstein, B. & Parmigiani, G. Half or more of the somatic mutations in cancers of self-renewing tissues originate prior to tumor Proceedings of the National Academy of Sciences of the United States of America 110, 1999-2004 (2013). Several BotSeqS approaches described here, which can easily measure very rare mutations in any type of tissue or cell of interest, will apply to issues of broad biomedical interest.

[428] In some embodiments, the presence of one or more genetic biomarkers (for example, mutations) present in a sample (for example, a cervical, endometrial, urine, saliva, ctDNA, blood, serum and / or plasma sample ) obtained from an individual is detected using any of the various methods described in US Patent Application Number 9,476,095, the content of which is incorporated by reference here in its entirety. The identification of mutations present in a small fraction of the DNA models is advantageous for progress in several areas of biomedical research. Although massively parallel sequencing instruments are, in principle, suitable for this task, the error rates on these instruments are generally too high to allow reliable identification of rare variants. An approach is provided here that can substantially increase the sensitivity of massively parallel sequencing instruments for that purpose. An example of this approach, called “Safe-SeqS” for (Safe-Sequencing System) includes (i) the assignment of a unique identifier (UID) for each model molecule; (ii) amplification of each model molecule uniquely marked to create UID families; and (iii) redundant sequencing of the amplification products. PCR fragments with the same UID are truly mutants ("super mutants") if ≧ 95% of them contain the same mutation. This approach is useful, for example, to determine the fidelity of a polymerase, the precision of oligonucleotides synthesized in vitro and the prevalence of mutations in the nuclear and mitochondrial genomes of normal cells.

[429] In some modalities of the methods provided here, the presence of one or more genetic biomarkers (for example, mutations) present in a sample (for example, a cervical, endometrial sample, urine, saliva, ctDNA, blood, serum and / or plasma) obtained from an individual is detected using a unique identifier (UID) nucleic acid sequence attached to a first end of each of a plurality of analyte nucleic acid fragments to form identified analyte nucleic acid fragments uniquely, the nucleotide sequence of a uniquely identified analyte nucleic acid fragment is determined redundantly, in which determined nucleotide sequences that share a UID form a family of members and a nucleotide sequence is identified as accurately representing a nucleic acid fragment from the analyte when at least 1% of the family members contain the sequence.

[430] In some modalities of methods provided here, the presence of one or more genetic biomarkers (for example, mutations) present in a sample (for example, a cervical, endometrial sample, urine, saliva, ctDNA, blood , serum and / or plasma) obtained from an individual is detected using a unique identifier sequence

(UID) connected to a first end of each of a plurality of analyte DNA fragments using at least two amplification cycles with the first and second primers to form uniquely identified analytical DNA fragments. In such modalities, UIDs can exceed the DNA fragments of the analyte during amplification, the first primers can include a first segment complementary to a desired amplicon; a second segment containing the UID; and a third segment containing a universal initiation site for subsequent amplification, the second initiators can include a universal initiation site for subsequent amplification, each amplification cycle can attach a universal initiation site to a tape, fragments of Uniquely identified analyte DNA can be amplified to form a family of uniquely identified analyte DNA fragments from each uniquely identified analyte DNA fragment, and nucleotide sequences from a plurality of family members can determined.

[431] In some modalities of the methods provided here, the presence of one or more genetic biomarkers (for example, mutations) present in a sample (for example, a cervical, endometrial sample, urine, saliva, ctDNA, blood, serum and / or plasma) obtained from an individual is detected using endogenous unique identifier sequences (UIDs). For example, fragmented analyte DNA that includes fragments from 30 to 2000 bases, can be obtained. Each end of a fragment can form an endogenous UID for the fragment. Adapter oligonucleotides can be attached to the ends of the fragments to form adapted fragments. Fragments representing one or more selected genes can optionally be enriched by capturing a subset of the fragments using capture oligonucleotides complementary to the selected genes in the DNA of the analyte or by amplifying fragments complementary to the selected genes. Adapted fragments can be amplified using primers complementary to adapter oligonucleotides to form adapted fragment families. Nucleotide sequences can be determined for a plurality of members of a family. The nucleotide sequences of the plurality of family members can be compared. A nucleotide sequence can be identified as accurately representing an analyte DNA fragment when at least 1% of family members contain the sequence.

[432] In some embodiments, compositions including populations of primer pairs are provided here, where each pair includes a first and a second primer to amplify and identify a gene or gene portion. The first primer can include a first portion (for example, 10 to 100 nucleotides) complementary to the gene or portion of gene and a second portion of (for example, 10 to 100 nucleotides) including a site for hybridization with a third initiator. The second primer may include a first portion of (for example, 10 to 100 nucleotides) complementary to the gene or portion of gene and a second portion (for example, 10 to 100 nucleotides) including a fourth hybridization site initiator. In some embodiments, interposed between the first portion and the second portion of the second primer is a third portion consisting of 2 to 4000 nucleotides forming a unique identifier (UID). Unique identifiers in the population can have at least 4 different strings. The first and second primers can be complementary to the opposite strands of the gene or portion of the gene. A kit can include the population of primers and the third and fourth primers complementary to the second portions of each of the first and second primers.

[433] In some modalities of the methods provided here, the presence of one or more genetic biomarkers (for example, mutations) present in a sample (for example, a cervical, endometrial sample, urine, saliva, ctDNA, blood, serum and / or plasma) obtained from an individual is detected using an approach called “Safe-SeqS” (from the Safe-Sequencing System). In one embodiment, Safe-SeqS involves two basic steps (FIG. 61): the first step is the assignment of a Unique Identifier (UID) to each model nucleic acid molecule to be analyzed, and the second step is the amplification of each model marked exclusively, so that many daughter molecules with the identical sequence are generated (defined as a UID family). If a mutation pre-exists in the model molecule used for amplification, that mutation must be present in a certain proportion, or even in all daughter molecules that contain this UID (except for subsequent replication or sequencing errors). A UID family in which each member of the family (or a certain predetermined proportion) has an identical mutation is called a "super mutant". Mutations that do not occur in the original models, such as those that occur during the amplification steps or through errors in the base call, should not give rise to super mutants (for example, they will not be present at the predetermined frequency in a UID family ). In some modalities, amplification is not necessary.

[434] Any of the various Safe-SeqS approaches can be used for any purpose, where you want to obtain a very high level of precision and sensitivity from the sequence data. As described here, the approach can be used to assess the fidelity of a polymerase, the accuracy of synthesis of nucleic acids synthesized in vitro, and the prevalence of mutations in the nuclear or mitochondrial nucleic acids of normal cells. The approach can be used to detect and / or quantify somatic mosaics and mutations.

[435] In some modalities of Safe-SeqS approaches provided here, nucleic acid fragments can be obtained using a random fragment formation technique, such as mechanical shear, sonication or subjecting nucleic acids to other physical or chemical stresses . Fragments may not be strictly random, as some sites may be more susceptible to stress than others. Endonucleases that fragment randomly or specifically can also be used to generate fragments. In some modes of any of the various Safe-SeqS approaches, nucleic acid fragments can be obtained using a technique that does not result in random fragments. The size of the fragments can vary, but desirably varies between 30 and 5,000 base pairs, between 100 and 2,000, between 150 and 1,000, or within ranges with different combinations of these end points. The nucleic acids can be, for example, RNA or DNA. Modified forms of RNA or DNA can also be used.

[436] In some embodiments of the Safe-SeqS approaches provided here, the attachment of an exogenous UID to a nucleic acid fragment of the analyte can be accomplished by any means known in the art, including enzymatic, chemical or biological. One medium employs a polymerase chain reaction. Another medium employs a ligase enzyme. The enzyme can be mammalian or bacterial, for example. The ends of the fragments can be repaired before joining with other enzymes, such as the Klenow fragment of DNA polymerase T4. Other enzymes that can be used for ligation are other polymerase enzymes. A UID can be added to either or both ends of the fragments. A UID can be contained within a nucleic acid molecule that contains other regions for other intended functionality. For example, a universal initiation site can be added to allow for further amplification. Another additional site may be a region of complementarity with a particular region or gene in the analyte nucleic acids. A UID can be from 2 to 4,000, from 100 to 1000, from 4 to 400, bases in length, for example. In some modalities, a UID has 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35 , 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 150, 200, 250, 300, 350, 400, 450, 500, 600, 700, 800, 900 , 1000 nucleotides or more in length, or can be any length between those lengths. In the modalities in which two or more UIDs are used, the UIDs can be of equal or different lengths.

[437] In some modalities of Safe-SeqS approaches provided here, UIDs can be made using random addition of nucleotides to form a short sequence to be used as an identifier. At each addition position, a selection of one of the four deoxyribonucleotides can be used. Alternatively, a selection of one of three, two or a deoxyribonucleotide can be used. Thus, the UID can be completely random, somewhat random or non-random in certain positions. Another way to make UIDs uses predetermined nucleotides mounted on a chip. In this way, complexity is achieved in a planned manner. In some modalities, it may be advantageous to attach a UID to each end of a fragment, increasing the complexity of the UID population in the fragments.

[438] A polymerase chain reaction cycle for the addition of exogenous UID refers to the thermal denaturation of a double stranded molecule, the hybridization of a first primer with a resulting single strand, the extension of the primer to form a new second ribbon hybridized to the original single ribbon. A second cycle concerns the denaturation of the second new strand from the original single strand, the hybridization of a second initiator to the second new strand, and the extension of the second initiator to form a third new strand, hybridized to the new second strand. Several cycles can be used to increase efficiency, for example, when the analyte is diluted or there are inhibitors.

[439] In the case of endogenous UIDs, adapters can be added to the ends of the fragments by any of a variety of methods, including, without limitation, connection. In some modes, the complexity of the analyte fragments can be reduced by a capture step, either in the solid or in the liquid step. In some embodiments, the capture step employs hybridization with probes that represent a gene or set of genes of interest. In some embodiments, if in a solid phase, fragments that do not bind are separated from the binding fragments. Suitable solid phases known in the art include filters, membranes, spheres, columns, etc. In some modalities, if in a liquid phase, a capture reagent that binds to the probes can be added, for example, through a biotin-avidin interaction. After capture, the desired fragments can be eluted for further processing. The order to add adapters and capture is not critical. Another non-limiting means of reducing the complexity of the analyte fragments involves the amplification of one or more specific genes or regions. An exemplary way to achieve this is to use reverse PCR. Primers that are specific to the gene can be used, thus enriching when forming libraries. Optionally, specific gene primers can contain graft sequences for subsequent binding to a massively parallel sequencing platform.

[440] As in some modalities, endogenous UIDs provide a limited number of unique possibilities, depending on the size of the fragment and the length of the sequencing reading, combinations of endogenous and exogenous UIDs can be used. In some embodiments, the introduction of additional strings when amplifying increases the available UIDs and thus increases the sensitivity. For example, before amplification, the model can be divided into 96 wells and 96 different primers can be used during amplification; this would effectively increase the available UIDs by 96 times, because up to 96 models with the same endogenous UID could be distinguished. This technique can also be used with exogenous UIDs, so that the primers in each well add a unique and very specific sequence to amplification products, which can also improve the specificity of detecting rare models.

[441] In some modalities of Safe-SeqS approaches provided here, amplification of fragments containing a UID can be performed according to known techniques to generate fragment families. The polymerase chain reaction can be used. Other amplification methods can also be used, as appropriate. Reverse PCR can be used, as well as rotating circle amplification. The amplification of fragments is usually done using primers complementary to the initiation sites that are attached to the fragments at the same time as the UIDs. In some modalities, the initiation sites are distal to the UIDs, so that the amplification includes the UIDs. In some modalities, the amplification forms a family of fragments, each member of the family sharing the same UID. Since the diversity of UIDs generally far exceeds the diversity of fragments, each family must be derived from a single fragment molecule in the analyte. The primers used for amplification can be chemically modified to make them more resistant to exonucleases. Non-limiting examples of such modifications include the use of phosphorothioate bonds between one or more 3 'nucleotides and boranophosphates.

[442] In some modalities of the Safe-SeqS approaches provided here, family members are sequenced and compared to identify any disagreements within a family. In some modalities, sequencing is performed on a massively parallel sequencing platform, many of which are commercially available. If the sequencing platform employs a "grafting" sequence, that is, connection to the sequencing device, that sequence can be added during the addition of UIDs or adapters or separately. A graft sequence can be part of a UID primer, a universal primer, a specific gene target primer, the amplification primers used to form a family or to separate. Redundant sequencing refers to the sequencing of a plurality of members of a single family.

[443] A threshold can be defined to identify a genetic biomarker (for example, a mutation) in an analyte. If the "mutation" appears in all members of a family, it is derived from the analyte. If it appears in less than all members, it may have been entered during the review. Thresholds for calling a mutation can be set, for example, at 1%, 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, 97%, 98% or 100%. Thresholds can be set based on the number of family members that are sequenced and the specific objective and situation.

[444] In some modalities of Safe-SeqS approaches provided here, populations of primer pairs are used to connect exogenous UIDs. For example, the first primer may include a first portion (for example, 10 to 100 nucleotides) complementary to the gene or gene portion and a second portion (for example, 10 to 100 nucleotides) including a site for hybridization with a third initiator. In some embodiments, the first portion and / or second potion of the first initiator is 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90 , 95 or 100 nucleotides in length, or any length between them. The second primer can include a first portion (for example, 10 to 100 nucleotides) complementary to the gene or gene portion and a second portion (for example, 10 to 100 nucleotides) including a site for hybridization with a fourth primer. In some embodiments, the first portion and / or second potion of the second initiator is 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90 , 95 or 100 nucleotides in length, or any length between them. In some modalities, interposed between the first portion and the second portion of the second primer is a third portion (for example, from 2 to 4,000 nucleotides, for example, 2, 3, 4, 5, 6, 7, 8, 9 , 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90 , 95, 100, 150, 200, 250, 300, 350, 400, 450, 500, 600, 700, 800, 900, 1000 nucleotides or any number of nucleotides between these values) forming a unique identifier (UID ). In some embodiments, interposed between the first portion and the second portion of the first and second primer is a third portion (for example, from 2 to 4,000 nucleotides, for example, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 150, 200, 250, 300, 350, 400, 450, 500, 600, 700, 800, 900, 1000 nucleotides or any number of nucleotides between those values), each of which forms a unique identifier (UID). In some embodiments, the third portion of the first initiator is the same length as the third portion of the second initiator. In some embodiments, the third portion of the first initiator is a different length than the third portion of the second initiator. In some modalities, unique identifiers in the population have at least 4, at least 16, at least 64, at least 256, at least 1,024, at least 4,096, at least 16,384, at least 65,536, at least 262,144, at least

1,048,576, at least 4,194,304 at least 16,777,216 or at least

67,108,864 different sequences. In some embodiments, the first and second primers are complementary to the opposite strands of the gene or portion of the gene. A kit can be made containing both primers to attach exogenous UIDs, as well as amplification primers, that is, the third and fourth primers complementary to the second portions of each of the first and second primers. The third and fourth primers can optionally contain additional grafting or indexing sequences. The UID can include randomly selected sequences, predefined nucleotide sequences or both randomly selected sequences and predefined nucleotides. If both, they can be joined in blocks or interleaved.

[445] In some modalities of Safe-SeqS approaches provided in this document, methods of analysis can be used to quantify and also to determine a sequence. For example, the relative abundance of two analyte DNA fragments can be compared using methods described herein.

[446] The results described here demonstrate that the Safe-SeqS approach can substantially improve the accuracy of massively parallel sequencing (Tables 52 and 53). Safe-SeqS can be implemented through endogenous UIDs or introduced exogenously (or both) and can be applied to virtually any sample preparation workflow or sequencing platform. As demonstrated here, Safe-SeqS can be easily used to identify rare mutants in a population of DNA models, to measure polymerase error rates and to judge the reliability of oligonucleotide syntheses. One of the advantages of the strategy is that it generates the number of models analyzed, as well as the fraction of models containing variant bases. In vitro methods previously described for detecting a small number of model molecules (eg Dressman D, Yan H, Traverso G, Kinzler KW, & Vogelstein B (2003) Transforming single DNA molecules into fluorescent magnetic particles for detection and enumeration of genetic variations.

Natl Acad Sci USA 100: 8817-8822; Li J, et al. (2008) Replacing PCR with COLD-PCR enriches variant DNA sequences and redefines the sensitivity of genetic testing. Nat Med 14: 579-584) allow to determine the fraction of mutant models, but cannot determine the number of normal and mutant models in the original sample.

[447] It is interesting to compare Safe-SeqS with other approaches to reduce errors in next generation sequencing. Sophisticated rhythms to increase the accuracy of the base call have been developed (for example, (Erlich Y, Mitra P, hersBastide M, McCombie WR, & Hannon GJ (2008) Alta-Cyclic: a self-optimizing base caller for next-generation sequencing. Nat Methods 5: 679-682; Rouge-mont J, et al. (2008) Probabilistic base calling of Solexa sequencing data. BMC Bioinformatics 9: 431; Druley TE, et al. (2009) Quantification of rare allelic variants from pooled genomic DNA, Nat Methods 6: 263-265; Vallania FL, et al. (2010) High-throughput discovery of rare insertions and deletions in large cohorts. Genome Res 20: 1711-1718)). This can certainly reduce false positive calls, but their sensitivity is still limited by artifact mutations that occurred during the PCR steps required for library preparation, as well as (a small number of) base call errors. For example, the algorithm used in the present study used very strict criteria for base calls and was applied to short reading lengths, but has not yet managed to reduce the error rate to less than the average of 2.0 × 10−4 errors / bp. This frequency of error is at least as low as those reported with other algorithms. To further improve sensitivity, these base call improvements can be used in conjunction with Safe-SeqS. Travers et al. described another powerful strategy for reducing errors (Eid J, et al. (2009) Real-time DNA sequencing from single polymerase molecules. Science 323: 133-138). With this technology, both strands of each model molecule are sequenced in a redundant way after several preparative enzymatic steps. However, this approach can only be performed on a specific instrument. In addition, for many clinical applications, there are relatively few model molecules in the initial sample and the evaluation of almost all of them is necessary to obtain the necessary sensitivity. In some modalities, approaches described here that use exogenously introduced UIDs (FIG. 63) address this concern by coupling the UID assignment step with a subsequent amplification in which few molecules are lost.

[448] Strong evidence supports the fact that mutations identified by conventional analyzes in the present study represent artifacts, rather than true mutations in the original models, are provided by the observation that the prevalence of mutation in all experiments , except one, was similar - 2.0 × 10−4 to 2.4 × 10−4 mutations / bp (Tables 52 and 53). The exception was the experiment with oligonucleotides synthesized from phosphoramidites, in which the error of the synthetic process was apparently greater than the error rate of the conventional Illumina analysis when used with strict criteria of base call. On the other hand, the prevalence of Safe-SeqS mutations varied much more, from 0.0 to 1.4 × 10−5 mutations / bp, depending on the model and the experiment. In addition, the prevalence of mutation measured by Safe-SeqS in the most controlled experiment, in which the polymerase fidelity was measured (Table 53A), was almost identical to that predicted in previous experiments in which the polymerase fidelity was measured by biological assays. The measurements of the prevalence of mutation in the DNA of normal cells provided here are consistent with some previous experimental data. However, estimates of these prevalences vary widely and may depend on the type and sequence of cells analyzed (see SI text). Therefore, it cannot be said with certainty that the few mutations revealed by Safe-

SeqS represented errors occurring during the sequencing process, instead of true mutations present in the original DNA models. The potential sources of error in the Safe-SeqS process are described in the SI text.

[449] Another potential application of Safe-SeqS is the minimization of PCR contamination, a serious problem for clinical laboratories. With the assignment of endogenous or exogenous UIDs, the UIDs of mutant models can simply be compared to those identified in previous experiences; the probability that the same mutation from two independent samples will have the same UID in different experiments is insignificant when the mutations are infrequent. In addition, with exogenous UIDs, a control experiment with the same model, but without the UID assigning PCR cycles (FIG. 63), can guarantee that no DNA contamination is present in this model preparation; no model should be amplified in the absence of UID assignment cycles and, therefore, no PCR product of the appropriate size should be observed.

[450] It has been shown that the exogenous UID strategy can be used to analyze a single amplicon in depth. This technology may not be applicable to situations where multiple amplicons must be analyzed from a sample containing a limited number of models. Multiplexing UID assignment cycles (FIG. 63) can provide a solution to this challenge. A second potential concern is that clinical samples may contain inhibitors that reduce the efficiency of this step. Presumably, this problem can be overcome by running more than two cycles in the UID assignment PCR step (FIG. 63), although this has the potential to complicate the determination of the number of models analyzed. The specificity of Safe-SeqS is currently limited by the fidelity of the polymerase used in the PCR step of UID assignment, that is, 8.8 ×

10−7 mutations / bp in its current implementation with two cycles. Increasing the number of cycles in the UID assignment PCR step to five would reduce the overall specificity to ~ 2 × 10−6 mutations / bp. However, this specificity can be increased by requiring more than one super-mutant to identify the mutation - the probability of introducing the same mutation artifact two or three times would be extremely low ([2 × 10−6] 2 or [2 × 10−6] 3, respectively). In summary, there are several simple ways to perform Safe-SeqS variations and analysis variations to meet the needs of specific experiments.

[451] Luria and Delbrück, in their classic 1943 article, wrote that their “prediction cannot be verified directly, because what we observe, when we count the number of resistant bacteria in a culture, is not the number of mutations that occurred, but the number of resistant bacteria that emerged by multiplying those that mutated, the amount of multiplication depending on how long the mutation occurred. ” Several Safe-SeqS procedures described here can verify these predictions, because the number and time of occurrence of each mutation can be estimated from the data, as observed in the experiments on the fidelity of polymerase. In addition to the models generated by polymerases in vitro, the same approach can be applied to the DNA of bacteria, viruses and mammalian cells. Therefore, it is expected that this strategy will provide definitive answers to a variety of important biomedical questions.

[452] In some embodiments, a genetic biomarker (for example, one or more genetic biomarkers) is detected using any of the various methods described in U.S. Patent Application Publication 2018/0208999, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include analysis of the count, fragmentation patterns and the size of nucleic acids without cells, for example, plasmatic DNA and serum DNA, including nucleic acids from pathogens, such as viruses.

Various modalities are targeted for applications (eg, classification of biological samples) of count analysis, fragmentation patterns and size of nucleic acids without cells, for example, plasma DNA and serum DNA, including nucleic acids from pathogens, like viruses.

Some application modalities can determine whether an individual has a specific condition.

For example, a method can determine whether an individual has cancer or a tumor, or another pathology.

Modalities from another application can be used to assess the stage of a condition or the progression of a condition over time.

For example, a method can be used to determine a stage of cancer in an individual or the progression of cancer in an individual over time (for example, using samples taken from an individual at different times). According to one embodiment, the sequence readings obtained from a sequence of the mixture of cell-free nucleic acid molecules can be used to determine an amount of the sequence readings aligned to a reference genome corresponding to the virus.

The number of sequence readings aligned with the reference genome can be compared to a cutoff value for screening the pathology.

According to another modality, sizes of viral nucleic acid molecules (for example, those aligned with a reference genome corresponding to the virus) can be used.

A statistical value of a size distribution of the virus nucleic acid molecules can be determined.

A level of pathology in the individual can be determined by processing the statistical value in relation to a cutoff value.

According to another modality, a first quantity of nucleic acid molecules without cells that ends within one or more windows of a reference genome corresponding to the virus is determined. Each first window comprising at least one of a first set of genomic positions in which ends of the cellless nucleic acid molecules are present at a rate above a first threshold in individuals with cancer (or other pathology) associated with the virus. A relative abundance can be calculated by normalizing the first amount using a second amount of cellless nucleic acid molecules, which includes cellless nucleic acid molecules that end in a second set of genomic positions outside one or more first windows , including the first set of genomic positions. A level of cancer in the individual can be determined by processing the relative abundance in relation to a cutoff value. Modalities can combine several techniques. For example, a first run can be based on count, size or fragmentation. A second test can be one of the other techniques. As examples, a majority vote can be used or cutoff values can be determined for both techniques, thus determining a set of data points for the two techniques that correspond to a specific level of pathology.

[453] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in US Patent Application Publication 2018/0203974, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include a computer-implemented method, involving receiving a data set on a computer that comprises a processor and a computer-readable medium, in which the computer-readable medium comprises instructions that, when executed by the processor, they cause the computer, for example, to identify somatic mutations in the biological test sample; and generate a somatic mutational profile that comprises somatic mutations; and to detect the presence of cancer in the patient based on the exposure weights of the mutational signatures.

In addition or alternatively, the detection of a genetic biomarker may include using a non-negative matrix factorization (NMF) approach to construct a signature matrix that can be used to identify latent signatures in a patient.

In other modalities, the methods can use principal component analysis (PCA) or vector quantization (VQ) approaches to build a signature matrix.

In one example, the patient sample is a nucleic acid sample without cells (for example, DNA without cells (cfDNA) and / or RNA without cells (cfRNA)). The construction of a signature matrix using non-negative matrix factorization can be generalized for several relevant resources for the detection and / or classification of cancer.

In some modes, a signature matrix comprises a plurality of signatures, where the probability of the occurrence for each of a plurality of resources is represented.

Examples of relevant characteristics include, but are not limited to, a sequence context upstream of a base substitution mutation, a sequence context downstream of a base substitution mutation, an insertion, an exclusion, a change in the number of somatic copies (SCNA), a translocation, a genomic methylation status, a chromatography state, a depth of sequencing coverage, an early versus late replicator region, a sense versus antisense tape, a distance between mutations, a distance of varied mutation, a variant allele frequency, a fragment start / stop, a fragment length and a gene expression status or any combination thereof.

In one embodiment, the upstream and / or downstream sequence context may comprise a region of a nucleic acid that varies in length from about 2 to about 40 bp, such as from about 3 to about 30 bp, as from about 3 to about 20 bp, or as from about 2 to about 10 bp of the sequence context of a base substitution mutation.

In a mode, the upstream and / or downstream sequence context can be a triple sequence context, a quadruple sequence context, a quintuple sequence context, a sextile sequence context, or a septuplete sequence of base replacement mutations.

In some embodiments, the upstream and / or downstream sequence context may be the triple sequence context of a base substitution mutation.

In one embodiment, the methods are used to identify latent somatic mutational signatures in an individual's cfDNA sample (eg, an asymptomatic individual) for early detection of cancer.

In another modality, the methods are used to infer tissue originating from a patient's cancer based on latent mutational signatures identified in the patient's cfDNA sample.

In yet another modality, the methods are used to identify latent mutational signatures in a patient's cfDNA sample that can be used to classify the patient for different types of therapies.

In yet another modality, non-negative matrix factorization is applied to learn error modes in a so-called variant (mutation) assay. For example, systematic errors (for example, errors contributed during library preparation, PCR, hybridization capture and / or sequencing) underlying the assay can be identified and assigned unique signatures that can be used to distinguish between con - distribution of true somatic variants and variant artifacts resulting from the technical processes of the test.

In yet another modality, non-negative matrix factorization can be used to identify mutational signatures that are associated with healthy aging.

The mutation processes associated with aging receive mutational signatures that can be used to distinguish between healthy somatic mutations associated with the patient's age and contributed somatic mutations that are indicative of a cancer process in the patient.

In another modality, one or more mutational signatures can be monitored over time and used to diagnose, monitor and / or classify cancer.

For example, the mutational profile observed in the cfDNA of patient samples at two or more points in time can be assessed.

In some embodiments, two or more mutational signature processes can be evaluated as a combination of different mutational signatures.

In yet another modality, one or more mutational signatures can be monitored over time (for example, at various points in time) to monitor the effectiveness of a therapeutic regimen or other cancer treatment.

Somatic mutations (that is, driver and passenger mutations) in a cancer genome are typically the cumulative consequence of one or more mutational DNA damage and repair processes.

Although he does not wish to be limited by theory, it is believed that the strength and duration of exposure to each mutational process (for example, environmental factors and DNA repair processes) results in a unique somatic mutation profile in an individual ( for example, a cancer patient). These unique combinations of types of mutations form a unique "mutational signature" for the cancer patient.

In addition, as is well known in the art, a somatic mutation or mutational profile may depend on the particular sequence context of the mutation.

For example, UV damage usually results in a basic change from C to T, when the basic change occurs within a sequence context of (−T | C | -) C (A | T | C | G). In this example, C is the mutated base and the upstream (T or C) and downstream (A, T, C or G) bases of C affect the likelihood of a mutation under UV radiation.

In another example, spontaneous deamination of 5-methylcytosine typically results in a basic change from C to T, when the basic change occurs within a context of

(A | T | C | G) C (- | - | - | G). Therefore, in one modality, the context of the sequence of identified mutations can be used as a resource to analyze somatic mutations in the detection and / or classification of cancer.

[454] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include any of the various forms described in PCT Publication WO 2018/119399, which is incorporated herein by reference in its entirety . For example, the detection of a genetic biomarker may include methods for preparing a sequencing library from a test sample containing DNA, including methods for rescuing one or more partially linked DNA fragments to improve conversion efficiency. library preparation. The methods can also be used to improve the retrieval of duplex sequence information from double-stranded DNA. Additionally or alternatively, the detection of a genetic biomarker can include a method for preparing a double-stranded DNA sequencing library, the method comprising the following steps: (a) obtaining a test sample comprising a plurality of DNA fragments double-stranded (dsDNA), wherein the dsDNA fragments comprise a forward strand and a reverse strand; (b) attaching double-stranded DNA adapters to both ends of the dsDNA fragments; and (c) extending the 3 'unbound ends of the dsDNA fragments with a DNA polymerase to create dsDNA fragment adapter templates to prepare a sequencing library. In some embodiments, the dsDNA fragment adapter models are still amplified prior to sequencing. In other modalities, one or more steps of the method can be performed in a single reaction step. For example, steps (b) to (c) can be performed in a single reaction tube using a reaction mixture comprising a first set of dsDNA adapters, a ligase,

a polymerase (optionally with tape-shifting activity), a terminal deoxynucleotidyl transferase and a second set of oligonucleotides or ssDNA primers (for example, including sequencing adapters and / or a universal primer). Optionally, the dsDNA molecules can be purified and optionally fragmented from the test sample before the ligation step (b). In addition or alternatively, the detection of a genetic biomarker may include a method for preparing a double-stranded DNA sequencing library, the method comprising the following steps: (a) obtaining a test sample comprising a plurality of fragments double-stranded DNA (dsDNA), fragments of dsDNA comprising a forward strand and a reverse strand; (b) adding double strand adapters to the dsDNA fragments and attaching the double strand adapters to both ends of the dsDNA fragments; (c) extending the 3 'unbound ends of the dsDNA fragments with a polymerase to create dsDNA fragment adapter models, wherein the polymerase further comprises the ribbon displacement activity; (d) adding a poly-adenine tail to the 3 'ends of the dsDNA fragment adapter models; (e) adding a set of ssDNA oligonucleotides (or primers) and hybridizing the ssDNA oligonucleotides to the dsDNA fragment adapter models; and (f) extending the ssDNA oligonucleotide set to create a dsDNA sequencing library.

In some modalities, one or more steps of the method can be performed in a single reaction step.

For example, steps (b) to (f) can be performed in a single reaction tube using a reaction mixture comprising a first set of dsDNA adapters, a ligase, a polymerase (optionally with tape-shifting activity ), a terminal deoxynucleotidyl transferase and a second set of oligonucleotides or ssDNA primers (for example, including sequencing adapters and / or a universal primer). Optionally, the dsDNA molecules can be purified and optionally fragmented from the test sample before the ligation step (b).

[455] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include any of the various forms described in PCT Publication WO 2018/119438, which is incorporated herein by reference in its entirety . For example, the detection of a genetic biomarker may include methods of analyzing sequencing data to detect CNVs in a nucleic acid sample. The detection of CNVs in a nucleic acid sample obtained from a human subject can be informative to determine the presence of cancer in the subject. In one embodiment, the detection of CNVs in a nucleic acid sample obtained from a human subject can be used for early detection of cancer in the subject. In various modalities, the methods determine coverage on individual nucleotide bases determined from targeted sequencing readings. Sources of variation in coverage can be corrected at the base level. For each gene in a targeted gene panel, the base level coverage determined between the bases of the gene can be considered to more effectively detect CNVs for each gene. Generally, baseline coverage biases that exist in each baseline position can be modeled using training data collected from healthy individuals. Therefore, when analyzing a test sample obtained from an individual, coverage at the base level can be determined for each base position, in view of the expected coverage biases obtained through modeling. Specifically, if the coverage bias at a base position for a test sample obtained from the individual differs from the expected coverage bias obtained through modeling, the coverage deviations can be normalized and removed. For a gene in a targeted gene panel, base level coverage at the base positions of the gene is analyzed to determine whether the coverage for the gene differs from an expected level of coverage for the gene, as previously determined using data from training collected from healthy individuals. In that case, a CNV can be called. The call for a CNV may indicate the presence of cancer in the individual or that the individual is susceptible to a greater probability of developing cancer.

[456] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include any of the various forms described in PCT Publication WO 2018/111872, which is incorporated herein by reference in its entirety . For example, the detection of a genetic biomarker may include preparing sequencing libraries based on a plurality of RNA molecules that are labeled and amplified by labeling the molecule with an oligonucleotide that hybridizes to a polyC tail introduced by the transferase activity terminal of the transcriptase, for example, MMLV RT and ligating the oligonucleotide to the RNA molecule using a ligase, for example, T4 RNA ligase, and producing mRNA-based cDNA molecules and reverse displacement transcriptases strand, for example, MMLV RT, and producing a second cDNA strand, in order to produce a dsDNA library for sequencing. In some embodiments, the methods comprise sequencing at least a portion of a sequencing library to obtain sequencing data or sequence readings from a test sample (for example, a biological sample from an individual). In one embodiment, the method for preparing a sequencing library from a test sample comprising RNA comprises the steps: (a) obtaining a test sample comprising RNA sequences and purifying the RNA sequences from the test sample; (b) synthesizing the first complementary DNA strands (cDNA) based on the RNA sequences and the 3 'tail ends of the cDNA strands; (c) annealing a complementary oligonucleotide exchange model to the C tail of the cDNA and attaching the complementary oligonucleotide exchange model to the 5 'ends of the RNA sequences to produce RNA models; and (d) synthesizing a plurality of cDNA strands from the RNA models using a stripe-shift reverse transcriptase.

In some modalities, one or more steps of the method can be performed in a single reaction step.

For example, steps (b) to (d) can be performed in a single reaction tube using a reaction mixture comprising RNA primers (for example, random hexamer RNA primers, polyT primers or a a combination thereof), a stripe reverse transcriptase (for example, MMLV reverse transcriptase), an RNA ligase (for example, T4 RNA ligase) and, optionally, a polynucleotide kinase (for example, polynucleotide kinase from T4). In some embodiments, the method for preparing a sequencing library from a test sample comprising RNA comprises the steps: (a) obtaining a test sample comprising one or more RNA sequences and purifying the one or more RNA sequences the test sample; (b) annealing a first RNA primer to one or more RNA sequences; (c) extending the first RNA primer in a first nucleic acid extension reaction using reverse transcriptase, in which the reverse transcriptase comprises reverse transcription activities and terminal transferase, to generate a plurality of sequences DNA complementary to one or more RNA models, and wherein the complementary DNA sequences (cDNA) further comprise a plurality of bases not modeled at the 3 'end of the cDNA sequences; (d) annealing a complementary nucleic acid sequence to the non-modeled bases at the 3 'end of the cDNA sequence, wherein the complementary nucleic acid sequence further comprises a unique molecular identifier (UMI) or a unique sequence tag ; (e) attaching the complementary nucleic acid sequence to the 5 'end of one or more RNA sequences to generate one or more RNA models, wherein the one or more RNA models comprise one or more original RNA sequences linked covalently to the complementary nucleic acid sequence comprising the UMI or single sequence tag; (f) annealing one or more second RNA primers to one or more RNA models; and (g) extending the one or more second RNA primers in a second nucleic acid extension reaction using a stranded reverse transcriptase to generate a plurality of complementary DNA sequence to one or more RNA models , wherein the plurality of complementary DNA sequences (cDNA) each comprises the complementary DNA sequence and a UMI or a unique sequence marker.

In some modalities, one or more stages of the method can be performed in a single reaction stage.

For example, in some embodiments, steps (b) to (g) can be performed in a single reaction tube using a reaction mixture comprising RNA primers (eg, hexamer RNA primers, polyT primers) or a combination thereof), a tape-displacement reverse transcriptase (for example, MMLV reverse transcriptase), an RNA ligase (for example, T4 RNA ligase) and, optionally, a polynucleotide kinase (for example, poly - T4 nucleotide kinase). In one embodiment, the method involves preparing a sequencing library from a test sample comprising RNA molecules, the method comprising the steps: (a) obtaining a test sample comprising one or more RNA sequences and purifying the one or more more RNA sequences from the sample test; (b) annealing a first RNA primer to one or more RNA sequences; (c) extending the first RNA primer in a first nucleic acid extension reaction using a reverse transcriptase, where the reverse transcriptase comprises reverse transcription and terminal transferase activities, to generate a plurality of complementary DNA sequences to one or more RNA sequences, in which the terminal transferase activity adds a cytosine tail (C) to the 3 'end of the complementary DNA sequences (cDNA); (d) annealing a template switching oligonucleotide with the 3'-cytosine tail of the cDNA sequence, wherein the template switching oligonucleotide comprises a unique molecular identifier (UMI) or a single sequence marker; (e) attaching the model change oligonucleotide to the 5 'end of one or more RNA sequences with the T4 RNA ligase to generate one or more RNA models, wherein the one or more RNA models comprise the one or more original RNA sequences covalently linked to the model change oligonucleotide and the UMI or single sequence marker; (f) annealing a plurality of second RNA primers to one or more models of RNA; and (g) extending the plurality of second RNA initiators in a second nucleic acid extension reaction using a stripe-shift reverse transcriptase to generate a plurality of complementary DNA sequence to one or more RNA models, wherein the plurality of complementary DNA (cDNA) each comprises the complementary DNA sequence and a UMI or unique sequence marker.

In some embodiments, one or more steps in a method can be performed in a single reaction step.

For example, steps (b) to (g) can be performed in a single reaction tube using a reaction mixture comprising RNA primers (for example, random hexamer RNA primers, polyT primers or a combination of same), a ribbon-shift reverse transcriptase (eg MMLV reverse transcriptase), an RNA ligase (eg T4 RNA ligase) and, optionally, a polynucleotide kinase (eg, polynucleotide kinase from T4).

[457] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include any of the various forms described in PCT Publication WO 2018/085862, which is incorporated herein by reference in its entirety . For example, the detection of a genetic biomarker may include methods and systems for identifying somatic signatures of mutations to detect, diagnose, monitor and / or classify cancer in a patient known to have or suspected to have cancer. In many ways, the methods use a non-negative matrix factorization (NMF) approach to build a signature matrix that can be used to identify latent signatures in a patient sample for cancer detection and classification. In other modalities, the methods can use principal component analysis (PCA) or vector quantization (VQ) approaches to build a signature matrix. In one example, the patient sample is a sample of nucleic acid without cells (for example, DNA without cells (cfDNA) and / or RNA without cells (cfRNA)). The construction of a signature matrix using non-negative matrix factorization can be generalized to several relevant resources for the detection and / or classification of cancer. In some modalities, a signature matrix comprises a plurality of signatures, where the probability of the occurrence for each of a plurality of resources is represented. Examples of relevant features include, but are not limited to, a sequence context upstream of a base substitution mutation, a sequence context downstream of a base substitution mutation, an insertion, an exclusion, an alteration of somatic copy number (SCNA), a translocation, a genomic methylation status, a chromatin state, a depth of sequencing coverage, an early versus late replicator region, a sense versus antisense tape, a distance between mutations,

a varying mutation distance, a variant allele frequency, a fragment start / stop, a fragment length and a gene expression status or any combination thereof.

In one embodiment, the upstream and / or downstream sequence context may comprise a region of a nucleic acid that varies in length from about 2 to about 40 bp, such as from about 3 to about 30 bp, as from about 3 to about 20 bp, or as from about 2 to about 10 bp of the sequence context of a base substitution mutation.

In one embodiment, the upstream and / or downstream sequence context can be a triple sequence context, a quadruple sequence context, a quintuple sequence context, a sixth sequence context or a sequence sequence context. septuplet of base substitution mutations.

In some modes, the upstream and / or downstream sequence context may be the triple sequence context of a base substitution mutation.

In one embodiment, the methods are used to identify latent somatic mutational signatures in an individual's cfDNA sample (for example, an asymptomatic individual) for early cancer detection.

In another fashion, methods are used to infer tissue originating from a patient's cancer based on latent mutational signatures identified in the patient's cfDNA sample.

In yet another modality, the methods are used to identify latent mutational signatures in a patient's cfDNA sample that can be used to classify the patient for different types of therapies.

In yet another modality, non-negative matrix factorization is applied to learn error modes in a so-called variant (mutation) assay. For example, systematic errors (eg errors contributed during library preparation, PCR, hybridization capture and / or sequencing) underlying the assay can be identified and assigned unique signatures that can be used to distinguish between contribution of true somatic variants and variant artifacts arising from the technical processes of the test. In yet another modality, non-negative matrix factorization can be used to identify mutational signatures that are associated with healthy aging. The mutation processes associated with aging receive mutational signatures that can be used to distinguish between healthy somatic mutations associated with the patient's age and contributed somatic mutations that are indicative of a cancer process in the patient. In another modality, one or more mutational signatures can be monitored over time and used to diagnose, monitor and / or classify cancer. For example, the mutational profile observed in the cfDNA of patient samples at two or more points in time can be assessed. In some embodiments, two or more mutational signature processes can be evaluated as a combination of different mutational signatures. In yet another modality, one or more mutational signatures can be monitored over time (for example, at various points in time) to monitor the effectiveness of a therapeutic regimen or other cancer treatment. In one modality, the context of the sequence of identified mutations can be used as a resource to analyze somatic changes in the detection and / or classification of cancer.

[458] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in US Patent Application Publication 2018/0163201, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker can include methods for preparing sequencing libraries comprising a plurality of RNA molecules. In one embodiment, the method for preparing a sequencing library from a test sample comprising RNA comprises the steps: (a) obtaining a test sample comprising sequences from

RNA and purify the RNA sequences from the test sample; (b) synthesizing the first complementary DNA strands (cDNA) based on the RNA sequences and the 3 'tail ends C of the cDNA strands; (c) annealing a complementary oligonucleotide exchange model to the C tail of the cDNA and attaching the complementary oligonucleotide exchange model to the 5 'ends of the RNA sequences to produce RNA models; and (d) synthesizing a plurality of cDNA strands from the RNA models using a reverse stripe transcriptase.

In some modalities, one or more steps of the method can be performed in a single reaction step.

For example, steps (b) to (d) can be performed in a single reaction tube using a reaction mixture comprising RNA primers (for example, random hexamer RNA primers, polyT initiators or a combination of stripe reverse transcriptase (for example, MMLV reverse transcriptase), an RNA ligase (for example, T4 RNA ligase) and, optionally, a polynucleotide kinase (for example, kinase polynucleotide) T4). In one embodiment, the method comprises the steps: (a) obtaining a test sample comprising one or more RNA sequences and purifying the one or more RNA sequences from the test sample; (b) annealing a first RNA primer to one or more RNA sequences; (c) extending the first RNA primer in a first nucleic acid extension reaction using reverse transcriptase, in which the reverse transcriptase comprises reverse transcription activities and terminal transferase, to generate a plurality of sequences DNA complementary to one or more RNA models, and wherein the complementary DNA sequences (cDNA) further comprise a plurality of bases not modeled at the 3 'end of the cDNA sequences; (d) annealing a complementary nucleic acid sequence to the non-modeled bases at the 3 'end of the cDNA sequence, wherein the complementary nucleic acid sequence further comprises a unique molecular identifier (UMI) or a unique sequence tag ; (e) attaching the complementary nucleic acid sequence to the 5 'end of one or more RNA sequences to generate one or more RNA models, wherein the one or more RNA models comprise one or more original RNA sequences linked covalently to the complementary nucleic acid sequence comprising the UMI or single sequence tag; (f) annealing one or more second RNA primers to one or more RNA models; and (g) extending the one or more second RNA primers in a second nucleic acid extension reaction using a stranded reverse transcriptase to generate a plurality of complementary DNA sequence to one or more RNA models , wherein the plurality of complementary DNA sequences (cDNA) each comprises the complementary DNA sequence and a UMI or a unique sequence marker.

In some modalities, one or more stages of the method can be performed in a single reaction stage.

For example, in some embodiments, steps (b) to (g) can be performed in a single reaction tube using a reaction mixture comprising RNA primers (eg, hexamer RNA primers, polyT primers) or a combination thereof), a tape-displacement reverse transcriptase (for example, MMLV reverse transcriptase), an RNA ligase (for example, T4 RNA ligase) and, optionally, a polynucleotide kinase (for example, poly - T4 nucleotide kinase). In one embodiment, the method involves preparing a sequencing library from a test sample comprising RNA molecules, the method comprising the steps: (a) obtaining a test sample comprising one or more RNA sequences and purifying the one or more more RNA sequences from the sample test; (b) annealing a first RNA primer to one or more RNA sequences; (c) extending the first RNA primer in a first nucleic acid extension reaction using a reverse transcriptase, where the reverse transcriptase comprises reverse transcription and terminal transferase activities, to generate a plurality of complementary DNA sequences to one or more RNA sequences, in which the terminal transferase activity adds a cytosine tail (C) to the 3 'end of the complementary DNA sequences (cDNA); (d) annealing a template switching oligonucleotide with the 3'-cytosine tail of the cDNA sequence, wherein the template switching oligonucleotide comprises a unique molecular identifier (UMI) or a single sequence marker; (e) attaching the model change oligonucleotide to the 5 'end of one or more RNA sequences with the T4 RNA ligase to generate one or more RNA models, wherein the one or more RNA models comprise the one or more original RNA sequences covalently linked to the model change oligonucleotide and the UMI or single sequence marker; (f) annealing a plurality of second RNA primers to one or more models of RNA; and (g) extending the plurality of second RNA initiators in a second nucleic acid extension reaction using a stripe-shift reverse transcriptase to generate a plurality of complementary DNA sequence to one or more RNA models, wherein the plurality of complementary DNA (cDNA) each comprises the complementary DNA sequence and a UMI or unique sequence marker.

In some embodiments, one or more steps in a method can be performed in a single reaction step.

For example, steps (b) to (g) can be performed in a single reaction tube using a reaction mixture comprising RNA primers (for example, random hexamer RNA primers, polyT primers or a combination of themselves), a tape-shift reverse transcriptase (for example,

MMLV reverse transcriptase), an RNA ligase (for example, T4 RNA ligase) and, optionally, a polynucleotide kinase (for example, T4 polynucleotide kinase).

[459] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include any of the various forms described in PCT Publication WO 2018/081130, which is incorporated herein by reference in its entirety . For example, the detection of a genetic biomarker may include a method that comprises obtaining a first biological sample from the individual, wherein the first biological sample comprises nucleic acid without cells from the individual and nucleic acid potentially without cells from a pathogen. In some embodiments, the method comprises conducting a first assay that comprises measuring a number of copies of the nucleic acid without cells of the pathogen in the first biological sample. In some embodiments, the method comprises obtaining a second biological sample from the individual, in which the second biological sample comprises nucleic acid without cells from the individual and nucleic acid potentially without cells from a pathogen. In some ways, the method comprises performing a second assay that comprises massively parallel sequencing of the nucleic acid without cells in the second biological sample to generate sequence readings. In some embodiments, the method comprises determining a sequence reading amount that aligns with a pathogen reference genome. In some embodiments, the method comprises determining an amount of cell-free nucleic acid molecules that have a size within a certain range and align with a pathogen reference genome based on massively parallel sequencing. In some modalities, the method comprises screening the tumor based on the performance of a first assay, comprising measuring a number of copies of the nucleic acid without cells of the pathogen in the first biological sample, and carrying out a second test comprising massively parallel sequencing of the cellless nucleic acid in the second biological sample to generate sequence readings.

In some embodiments, the first biological sample and the second biological sample are the same.

In some modalities, the method also comprises determining a percentage of the sequence reading that aligns with a pathogen reference genome.

In some modalities, the method also includes comparing the percentage of sequence readings that align with a pathogen reference genome with a cutoff value.

In some modalities, the method further comprises determining a size ratio of a first proportion of the cellless nucleic acid molecules of the second biological sample that align with the pathogen reference genome with a size within the given range and a sequence. a second proportion of nucleic acid molecules without cells from the second biological sample that align with a reference genome of the individual with a size within the determined range.

In some modalities, the method also comprises determining a size index, in which the size index is an inverse of the size ratio and comparing the size index with a second cutoff value.

In some modalities, the tumor is nasopharyngeal cancer.

In some embodiments, the pathogen is the Epstein-Barr virus (EBV). In some modes, measuring a number of copies of the nucleic acid without cells of the pathogen in the first biological sample comprises amplification.

In some modalities, amplification comprises polymerase chain reaction (PCR). In some modalities, PCR comprises quantitative PCR (qPCR). In some embodiments, the first biological sample and the second biological sample are plasma.

In some embodiments, the method comprises obtaining a first biological sample from the individual, wherein the first biological sample comprises nucleic acid without cells from the individual and nucleic acid potentially without cells from a pathogen.

In some embodiments, the method comprises conducting a first assay that comprises measuring a number of copies of the nucleic acid without cells of the pathogen in the first biological sample, where the first assay has a positive predictive value for the presence of the tumor in the individual.

In some ways, the method comprises performing a second assay on a second biological sample from the individual, in which the second biological sample comprises nucleic acid without cells from the individual and nucleic acid potentially without cells from the pathogen and in that a positive predictive value for the presence of the tumor in the individual of the first test and the second test is at least 5 times greater than the positive predictive value of the first test.

In some modalities, the positive predictive value for a tumor presence in the individual of the first test and the second test is at least 7.5 times greater than the positive predictive value of the first test.

In some modalities, the positive predictive value for a tumor presence in the subject of the first test and the second test is at least 15%. In some modalities, the positive predictive value for a tumor presence in the individual from the first trial and the second trial is at least 25%. In some modalities, the first biological sample and the second biological sample are the same.

In some embodiments, the first biological sample and the second biological sample are plasma.

In some modalities, the tumor is nasopharyngeal cancer.

In some modalities, the pathogen is the Epstein-Barr virus (EBV). In some modalities, measuring a number of copies of the pathogen-free nucleic acid in the first biological sample comprises amplification.

In some modalities, amplification comprises polymerase chain reaction (PCR). In some embodiments, PCR comprises quantitative PCR (qPCR). In some embodiments, the second assay comprises massively parallel sequencing of the nucleic acid without cells in the second biological sample to generate sequence readings.

In some embodiments, the second assay comprises determining a sequence reading amount that aligns with a pathogen reference genome.

In some embodiments, the second assay comprises determining the number of nucleic acid molecules without cells in the second biological sample that have a size within a certain range and align with a pathogen reference genome.

In some embodiments, the method comprises obtaining a first biological sample from the individual, wherein the first biological sample comprises nucleic acid without cells from the individual and nucleic acid potentially without cells from a pathogen.

In some modalities, the method comprises conducting a first assay that comprises measuring a number of copies of the nucleic acid without cells of the pathogen in the first biological sample, where the first assay has a false positive rate for the presence of the tumor in the individual .

In some embodiments, the method comprises performing a second assay on a second biological sample from the subject, in which the second biological sample comprises nucleic acid without cells from the individual and potentially nucleic acid without cells from the pathogen, in which a false positive rate for the presence of the tumor in the subject of the first trial and the second trial it is at least 5 times lower than the false positive rate of the first trial.

In some embodiments, the false positive rate for a tumor presence in the subject of the first trial and the second trial is at least 10 times lower than the false positive rate of the first trial.

In some modalities, the rate of false positives for the presence of the tumor in the individual in the first trial and the second trial is less than 1%. In some embodiments, the first biological sample and the second biological sample are the same.

In some modalities, the first biological sample and the second biological sample are plasma.

In some modalities, the tumor is nasopharyngeal cancer.

In some embodiments, the pathogen is the Epstein-Barr virus (EBV). In some embodiments, measuring a number of copies of the nucleic acid without cells of the pathogen in the first biological sample comprises amplification.

In some embodiments, the amplification comprises polymerase chain reaction (PCR). In some modalities, PCR comprises quantitative PCR (qPCR). In some embodiments, the second assay comprises massively parallel sequencing of the cellless nucleic acid in the second biological sample to generate sequence readings.

In some embodiments, the second assay comprises determining a sequence reading amount that aligns with a pathogen reference genome.

In some modalities, the second assay comprises determining the amount of nucleic acid molecules without cells in the second biological sample that have a size within a certain range and align with a pathogen reference genome.

In some embodiments, the method comprises analyzing a biological sample, including a mixture of nucleic acid molecules without cells, to determine a level of pathology in an individual from which the biological sample is obtained, the mixture including acid molecules. nucleic acid of the individual and potentially nucleic acid molecules of a pathogen.

In some modes, the method comprises analyzing a first plurality of nucleic acid molecules without cells from a biological sample of the individual, in which the analysis comprises determining a genomic position in a reference genome corresponding to at at least one end of the first plurality of cells free of nucleic acid molecules, the reference genome corresponding to the pathogen.

In some embodiments, the method comprises determining a first quantity of the first plurality of nucleic acid molecules without cells that terminate within one of the first windows, each first window comprising at least one of a first set of genomic positions in which the ends of nucleic acid molecules without cells are present at a rate above a first threshold in individuals with a pathology associated with the pathogen.

In some embodiments, the method comprises calculating a relative abundance of the first plurality of nucleic acid molecules without cells that end within one of the first windows by normalizing the first quantity using a second quantity of the first plurality of nucleic acid molecules without cells from the biological sample. , where the second quantity of the first plurality of nucleic acid molecules without cells includes nucleic acid molecules without cells that end in a second set of genomic positions outside the first windows, including the first set of genomic positions.

In some modalities, the method comprises determining the level of pathology in the individual by processing the relative abundance against one or more cutoff values.

In some embodiments, the relative abundance in relation to one or more cut-off values includes determining whether the relative abundance is greater than one or more cut-off values.

In some modalities, the method further comprises determining the second quantity of the first plurality of nucleic acid molecules without cells that terminate within one of the second windows, each second window comprising at least one of the second set of genomic positions in which extreme. mities of nucleic acid molecules without cells are present at a rate above a second threshold in individuals without a pathology resulting from pathogen, where normalization of the first quantity includes calculating the relative abundance using the first quantity and the second quantity.

In some modalities, the method also comprises identifying the second set of genomic positions.

In some modalities, identification involves analyzing, using a computer system, the nucleic acid molecules without cells in a reference sample of a reference individual who does not have the pathology.

In some modalities, the analysis of each of the plurality of nucleic acid molecules without cells comprises determining a genomic position in the reference genome corresponding to at least one end of the nucleic acid molecule without cells.

In some modalities, the reference person is healthy.

In some embodiments, the relative abundance comprises a ratio between the first quantity and the second quantity.

In some embodiments, the method further comprises identifying the first set of genomic positions in which the ends of the cellless nucleic acid molecules occur at a rate above a first threshold.

In some embodiments, identifying the first set of genomic positions comprises analyzing, by a computer system, a second plurality of nucleic acid molecules without cells from at least one additional first sample to identify the end positions of the second plurali - number of nucleic acid molecules without cells, in which at least one first additional sample is known to have the pathology associated with the pathogen and is of the same type of sample as the biological sample.

In some modalities, the method also comprises, for each genomic window of a plurality of genomic windows, calculating a corresponding number of the second plurality of nucleic acid molecules without cells ending in the genomic window and comparing the number corresponding to a value of reference to determine whether the rate of nucleic acid molecules without cells ending in one or more genomic positions within the genomic window is above the first threshold.

In some modalities, a first genomic window of the plurality of genomic windows has a width of at least one genomic position and in which each of the genomic positions within the first genomic window is identified as having the rate of molecules of nucleic acid without cells ending in the geometrical position above the first threshold when the corresponding number exceeds the reference value. In some modalities, the first set of genomic positions has the highest values of N for the corresponding numbers, where N is at least 100.

[460] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in US Patent Application Publication 20180119216, which is incorporated herein by reference. in its entirety. For example, the detection of a genetic biomarker may include methods for preparing and analyzing a single-stranded sequencing library from a double-stranded DNA sample (for example, double-stranded cfDNA). In some embodiments, the sample includes double-stranded DNA molecules (dsDNA) and damaged dsDNA molecules (for example, cut dsDNA). In some embodiments, the sample includes single-stranded DNA (ssDNA) molecules. The methods facilitate the collection of information, including pairing and connectivity information from dsDNA, ssDNA and damaged DNA strands (for example, cut DNA) in a sample, providing improved diagnostic information compared to sequencing libraries prepared using conventional methods. In addition or alternatively, the detection of a genetic biomarker may include preparing a single-stranded DNA (ssDNA) library for sequencing. For example, the detection of a genetic biomarker may include the use of a ssDNA library preparation, in which the forward (sense) and reverse (antisense) strands of a double-stranded DNA fragment are marked with a identical or substantially identical single sequence (for example, a partition-specific barcode or UMI) that allows the complementary strands of a dsDNA molecule to be identified and analyzed. In one embodiment, the method comprises preparing a single-stranded DNA library for sequencing, the method comprising the following steps: (a) obtaining a test sample comprising double-stranded DNA (dsDNA) and isolating the dsDNA from the test sample; (b) partition the dsDNA sample into a plurality of individual reaction compartments; (c) adding a reaction mixture to each of said individual reaction compartments, said reaction mixture including a plurality of oligonucleotides comprising a unique sequence marker; (d) denaturing dsDNA to produce single-stranded DNA fragments (ssDNA); and (e) binding of unique sequence markers to ssDNA fragments.

In another embodiment, a method is provided for the preparation of a cell-free DNA library for sequencing, the method comprising the following steps: (a) obtaining a test sample comprising double-stranded DNA without cells (dsDNA) and isolate the dsDNA from the test sample; (b) partition the dsDNA sample into a plurality of individual reaction drops; (c) adding a reaction mixture to each of said individual drops, said reaction mixture including a plurality of DNA capture spheres, wherein each of said DNA capture spheres includes a plurality of attached oligonucleotides comprising a unique sequence marker; (d) heating the drops to denature the dsDNA or chemically denaturing the dsDNA to produce single-stranded DNA fragments (ssDNA) and release the unique sequence markers from the spheres; and (e) attaching the unique sequence markers to the 3 'ends of the ssDNA fragments.

In some modalities, these spheres are selected from the group comprising spheres coated with streptavidin, reversible solid phase immobilization sphere (SPRI) and magnetic spheres.

In another embodiment, a method is provided for preparing a single-stranded DNA library for sequencing, the method comprising the following steps: (a) providing a plurality of partitions, wherein the individual partitions of the plurality comprise: ( i) a portion of a test sample comprising, for example, damaged and / or undamaged double-stranded DNA (dsDNA) isolated from one or more individuals; and (ii) a plurality of oligonucleotides, wherein the plurality of oligonucleotides comprises a partition-specific bar code; (b) incubating the partitions under conditions suitable for denaturing double-stranded DNA into single-stranded DNA; and (c) linking single-stranded DNA to oligonucleotides, where the bond covalently links the partition-specific barcode to single-stranded DNA and produces single-stranded DNA with partition-specific barcode. In some modalities, the method also comprises combining the plurality of partitions. In some embodiments, the method further comprises hybridizing the oligonucleotide primer to single-stranded DNA with partition-specific barcode and extending the primer, thereby producing double-stranded DNA with partition-specific barcode. In some embodiments, the method comprises amplifying single-stranded DNA with partition-specific barcode and / or double-stranded DNA with partition-specific barcode. In some modalities, the method also comprises dephosphorylating double-stranded DNA isolated from one or more individuals. In some embodiments, the method comprises dephosphorylating the double-stranded DNA isolated from one or more individuals and then partitioning the double-stranded DNA isolated from one or more individuals, thus providing the plurality of partitions.

[461] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in US Patent Application Publication 2018/0087105, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include methods for preparing and analyzing a sequencing library from a mixed sample of DNA without cells

(cfDNA), where the mixed sample includes double-stranded DNA (dsDNA), damaged dsDNA (for example, cut dsDNA) and single-stranded DNA molecules (ssDNA). The methods in question facilitate the collection of information from dsDNA, ssDNA and damaged DNA molecules (for example, cut DNA) in a sample, thus providing improved diagnostic information compared to the sequencing libraries prepared only with dsDNA.

In some ways, the method comprises preparing a combined cell-free DNA sequencing library (cfDNA) from a mixed sample of cfDNA: connecting a universal adapter comprising a single sequence tag to at least one molecule of Single-stranded DNA (ssDNA) in the mixed sample of cfDNA; extending the universal adapter to generate a ssDNA-derived double-stranded DNA molecule (dsDNA); and generating a combined cfDNA sequencing library from the ssDNA-derived dsDNA molecule.

In some embodiments, a method further comprises attaching a sequencing Y adapter to the ssDNA-derived dsDNA molecule before generating the combined cfDNA sequencing library.

In some embodiments, the sequencing Y adapter comprises a unique sequence marker.

In some embodiments, the first and second unique sequence tags are different.

In some embodiments, the method further comprises: extending the second sequencing Y adapter to generate a second cut-off dsDNA molecule; attaching a third sequencing Y adapter to the second cut-off dsDNA molecule; and generating a combined cfDNA sequencing library from the first and second cut-derived dsDNA molecules.

In some embodiments, the method for preparing a combined cfDNA sequencing library from a mixed cfDNA sample comprises: attaching a first sequencing Y adapter to a first end of a cut dsDNA molecule in the mixed cfDNA sample, wherein the cut dsDNA molecule comprises a cut ribbon and an uncut ribbon; attaching a second sequencing Y adapter to a second end of the cut dsDNA molecule in the mixed cfDNA sample; denaturing the dsDNA molecule attached to the sequenced Y adapter to generate a first ssDNA molecule derived from the uncut ribbon, a second ssDNA molecule derived from the cut ribbon, and a third ssDNA molecule derived from the cut ribbon; extending the second sequencing Y adapter to generate a first cut-derived dsDNA molecule; attaching a third sequencing Y adapter to the first cut-derived dsDNA molecule; and generating a combined cfDNA sequencing library from the first cut-derived dsDNA molecule. In some embodiments, the first sequencing Y adapter comprises a first unique sequence tag, the second sequencing Y adapter comprises a second unique sequence tag and the third sequencing Y adapter comprises a third exclusive sequence tag siva. In some modalities, the first, second and third unique sequence tags are the same. In some embodiments, the first, second and third unique sequence labels are different. In some embodiments, the first and second single sequence labels are the same and the third single sequence label is different. In some embodiments, the first and third unique sequence tags are the same and the second unique sequence tag is different. In some embodiments, the second and third single sequence labels are the same and the first single sequence label is different.

[462] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in US Patent Application Publication 2018/0002749, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include methods, compositions, reaction mixtures, kits and systems for sequencing RNA and DNA from a single source sample.

In some embodiments, RNA is treated in order to differentiate RNA sequences from DNA sequences derived from the same sample.

In some modalities, RNA and DNA are polynucleotides without cells.

In some modalities, the methods improve the sensitivity and / or baseline accuracy of the sequencing methodologies in identifying mutations (for example, rare sequence variants). In some embodiments, the method comprises: (a) obtaining a sample comprising RNA and DNA; (b) reverse RNA transcription to produce hybrid cDNA / RNA molecules; (c) degrading the RNA of the hybrid molecules to produce single-stranded cDNA; (d) preferably splicing a tag oligonucleotide comprising a tag sequence to the single-stranded cDNA in a reaction comprising a single-stranded DNA ligase to produce labeled cDNA; and (e) sequencing the DNA and the labeled cDNA; in which reverse transcription, preferred splicing and reverse sequencing are performed in the presence of DNA.

In some modalities, RNA and DNA are nucleic acids without cells.

Nucleic acids (including nucleic acids without cells) can be isolated from any of several sources, such as blood, a blood fraction (for example, serum or plasma), urine and other body fluids.

In some embodiments, reverse transcription comprises the extension of primers comprising a random sequence (for example, one or more nucleotides selected at random from a set of two or more different nucleotides in one or more positions, with each of the different nucleotides selected in one or more positions represented in a set of oligonucleotides comprising the random sequence). In some embodiments, reverse transcription comprises the extension of the hybrid cDNA over a model-switching oligonucleotide (TSO), which may comprise a universal switcher primer sequence.

In some embodiments, the tag oligonucleotide is attached to a 3 'end of the single stranded cDNA.

In some embodiments, the tag oligonucleotide comprises a primer binding sequence.

In some modes, sequencing comprises amplifying the labeled cDNA to produce double-stranded cDNA.

In some embodiments, amplification of the labeled cDNA comprises extending a primer hybridized to the primer binding sequence.

In some modes, sequencing comprises the joining of sequencing adapters to the labeled cDNA and DNA.

In some modalities, the tag oligonucleotide comprises a unique molecular identifier (UMI), where each of a plurality of labeled cDNA molecules is distinguishable from others in the plurality of UMD-labeled cDNA molecules (for example, example, as determined by the UMI sequence, optionally in combination with the cDNA sequence). In some modalities, the sample is blood, a blood fraction, plasma, serum, saliva, sputum, urine, semen, transvaginal fluid, cerebrospinal fluid or feces.

In some modes, the sample is blood or a blood fraction (for example, serum or plasma). In some embodiments, the method further comprises using a processor to group sequences derived from RNA separately from sequences derived from DNA based on the presence or absence of the tag sequence, or a complement to the tag sequence.

In some modalities, the method also comprises identifying the presence or absence of an individual's condition (for example, cancer) based on sequences derived from RNA and sequences derived from DNA.

In some embodiments, the method further comprises treating the individual based on sequences derived from RNA and sequences derived from DNA.

In some modalities, the method comprises: (a) obtaining a sample comprising RNA and DNA; (b) attaching a tag oligonucleotide comprising a tag sequence to the RNA in a reaction comprising a RNA ligase to produce labeled RNA; (c) reverse transcription of the labeled RNA to produce labeled cDNA; and (d) sequencing the DNA and the labeled cDNA; in which the union, reverse transcription and sequencing are performed in the presence of DNA.

In some ways, RNA and DNA are nucleic acids without cells.

In some embodiments, the method further comprises fragmenting the RNA to produce fragmented RNA before joining the tag sequence.

In some embodiments, the fragmented RNA has an average size within a predefined range (for example, an average or average length of about 10 to about 1,000 nucleotides in length, such as between 10-800, 10-500 , 50-500, 90 -200 or 50-150 nucleotides; or an average or median length of less than 1500, 1000, 750, 500, 400, 300, 250 or less nucleotides in length). In some modalities, fragmenting the RNA involves subjecting the RNA and DNA to conditions that preferentially fragment the RNA.

In some modalities, fragmenting the RNA comprises sonication, chemical fragmentation or heating.

In some embodiments, the method further comprises dephosphorylating the 3 'ends of the fragmented RNA.

In some embodiments, the tag oligonucleotide is attached to a 3 'end of the RNA.

In some embodiments, the tag oligonucleotide comprises a primer binding sequence.

In some embodiments, reverse transcription comprises the extension of a primer hybridized to the primer binding sequence.

In some embodiments, reverse transcription comprises the extension of the labeled cDNA over a model exchange oligonucleotide (TSO), which may comprise a universal exchange primer sequence.

In some embodiments, sequencing comprises amplifying the labeled cDNA to produce double-stranded cDNA. In some embodiments, sequencing comprises the joining of sequencing adapters to the labeled cDNA and DNA. In some embodiments, the tag oligonucleotide comprises a unique molecular identifier (UMI), where each of a plurality of labeled cDNA molecules is distinguishable from others in the plurality of UMD-labeled molecules based on (for example, as determined by the UMI sequence, optionally in combination with the cDNA sequence). In some modalities, the sample is blood, a blood fraction, plasma, serum, saliva, sputum, urine, semen, transvaginal fluid, cerebrospinal fluid or feces. In some embodiments, the sample is blood or a blood fraction (for example, serum or plasma). In some embodiments, reverse transcription comprises the extension of primers comprising a random sequence. In some embodiments, the method further comprises using a processor to group sequences derived from RNA separately from sequences derived from DNA based on the presence or absence of the tag sequence, or a complement to the tag sequence. In some modalities, the method also comprises identifying the presence or absence of an individual's condition (for example, cancer) based on sequences derived from RNA and sequences derived from DNA. In some embodiments, the method further comprises treating the individual based on sequences derived from RNA and sequences derived from DNA.

[463] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include any of the various forms described in PCT Publication WO 2017/218512, which is incorporated herein by reference in its entirety . For example, the detection of a genetic biomarker may include methods for enriching a plurality of target nucleic acids in a sample, methods comprising providing an endonuclease system, wherein each of the plurality of target nucleic acids comprises a first variant and a second variant, in which the endonuclease system comprises a plurality of regularly interspersed short palindromic repeat RNAs (CRISPR) (crRNAs) or derivatives thereof, each crRNA comprising a targeting sequence and a plurality of proteins (Cas) associated with CRISPR, or variants, each Cas protein capable of binding to a motif site adjacent to the protospacer (PAM) in a target nucleic acid, where the first variant of each nu acid - target cell comprises a PAM site adjacent to a region complementary to a crRNA targeting sequence and in which the second variant does not comprise the PAM site or does not understand the complementary region to the crRNA targeting sequence adjacent to the PAM site and contact the sample with the endonuclease system, thus extending the first variant and enriching the second variant of each of the plurality of target nucleic acids in the sample.

In some embodiments, the first variant of each target nucleic acid comprises a PAM site adjacent to a region complementary to a crRNA targeting sequence, and the second variant does not comprise the PAM site.

In some embodiments, the first variant of each target nucleic acid comprises a PAM site adjacent to a region complementary to a crRNA targeting sequence, and the second variant does not comprise a region complementary to the crRNA targeting sequence adjacent to the PAM site. .

In some embodiments, the first variant of each target nucleic acid comprises a PAM site adjacent to a region complementary to a crRNA targeting sequence, and the second variant does not comprise the region complementary to the crRNA targeting sequence .

In some embodiments, the methods comprise amplifying the second enriched variants of the plurality of target nucleic acids to produce an enriched sequencing library. In some embodiments, the methods comprise sequencing the enriched sequencing library to detect rearrangements or structural mutations in the target nucleic acids in the sample.

[464] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include any of the various forms described in PCT Publication WO 2017/127741, which is incorporated herein by reference in its entirety . For example, the detection of a genetic biomarker may include methods and systems for high-fidelity sequencing and identification of rare nucleic acid variants. The systems and methods can be used to identify rare variants in cell-free nucleic acid samples, such as tumor-specific mutations among a sample comprising a normal majority of genomic nucleic acid. The systems and methods allow for the reliable identification of changes that occur at frequencies below 1: 10,000 in a sample. The identification of such rare variants results from the optimization of several steps in the sequencing process, followed by the analysis of sequencing readings based on aligned reading pairs, called sets. Systems and methods can find applications outside the identification of rare variants, such as optimization of sequencing to a desired level of performance or sensitivity. Methods include sequencing nucleic acid. The steps of the method may include taking sequential readings for a nucleic acid, identifying a set comprising two or more sequencing readings with shared start coordinates and reading lengths, determining a number of sequenced molecules comprised by the set, identifying a variant candidate in the set, and determine a probability that the candidate variant is a true variant using a probability estimation model and the determined number of sequenced molecules.

In certain modes, the step of obtaining sequencing readings may also comprise preparing a sequencing library from the nucleic acid, amplifying the sequencing library, and sequencing the sequencing library using next generation sequencing. (NGS). In certain embodiments, adapters can be attached to the nucleic acid under conditions configured to allow the adapter to be stacked.

The preparation of the sequencing library can comprise nucleic acid binding adapters at a temperature of about 16 degrees Celsius using a reaction time of about 16 hours.

The amplification step may comprise PCR amplification and the methods may further comprise selecting a super-amplification factor and a PCR cycle number necessary to detect variants at a specified concentration in a sample using an embedded model. silico.

In various modalities, the methods include designing a hybrid capture panel to target a genomic region based on factors comprising guanine-cytosine (GC) content, frequency of mutation in a target population and exclusivity of the sequence and capture of the amplified nucleic acid using the hybrid capture panel before the sequencing step.

The capture step may include using a first hybrid capture panel targeting a sense tape from a target loci and a second hybrid capture panel targeting an antisense tape from the target loci.

In certain embodiments, a synthetic nucleic acid control, also known as a control sequence, peak control or positive control, can be added to the nucleic acid before the amplification of the sequencing library and the error rate can be determined using sequencing readings for synthetic nucleic acid control.

The control of synthetic nucleic acid can comprise a known sequence with low diversity through a species from which the nucleic acid is derived and with a plurality of incompatibilities that occur unnaturally with the known sequence and, in certain embodiments, the plurality of incompatibilities of unnatural occurrences can be 4. The control of synthetic nucleic acid can include a distribution of guanine-cytosine (GC) content that is representative of the target loci of the hybrid capture panel or can include a plurality of acids nucleics comprising variable overlays with a probe to pull down the hybrid capture panel. The error rate or frequency of the candidate variant can be determined using sequencing readings from the synthetic nucleic acid control.

[465] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in U.S. Patent 9,902,992, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include a method for detecting copy number variation comprising: a) sequencing extracellular polynucleotides from a body sample from an individual, in which each of the extracellular polynucleotides is optionally linked to codes single bars; b) filter the readings that do not reach a defined threshold; c) map sequence readings obtained from step (a) to a reference sequence; d) quantify / count readings mapped in two or more predefined regions of the reference sequence; e) determine a variation in the number of copies in one or more of the predefined regions (i) normalize the number of readings in the regions predefined among themselves and / or the number of unique bar codes in the regions predefined among themselves; and (ii) compare the normalized numbers obtained in step (i) with the standardized numbers obtained from a control sample. In addition or alternatively, the detection of a genetic biomarker may include a method for detecting a rare mutation in a cellless or substantially cellless sample obtained from an individual comprising: a) sequencing extracellular polynucleotides from an individual's body sample, wherein each of the extracellular polynucleotides generates a plurality of sequencing readings; b) sequencing extracellular polynucleotides from a body sample of an individual, in which each of the extracellular polynucleotides generates a plurality of sequencing readings; sequencing extracellular polynucleotides from a body sample from an individual, where each of the extracellular polynucleotides generates a plurality of sequencing readings; c) filter the readings that do not reach a defined threshold; d) map sequence readings derived from sequencing in a reference sequence; e) identify a subset of mapped sequence readings that align with a variant of the reference sequence at each base mapped position; f) for each mappable base position, calculate a ratio of (a) a number of mapped sequence readings that include a variant compared to the reference sequence, for (b) a total number of sequence readings for each mappable base position; g) normalize the ratios or frequencies of variation for each base mappable position and determine possible variants or rare mutations; h) and compare the resulting number for each of the regions with possible variants or rare changes with numbers similarly derived from a reference sample.

Additionally or alternatively, the detection of a genetic biomarker may include a method for characterizing the heterogeneity of an abnormal condition in an individual, the method comprising generating a genetic profile of extracellular polynucleotides in the individual, wherein the genetic profile comprises a plurality of data resulting from analysis of copy number variation and / or other rare mutation analyzes (eg, genetic alteration). In some modalities, the prevalence / concentration of each rare variant identified in the individual is reported and quantified simultaneously.

In other modalities, a confidence score, in relation to the prevalence / concentrations of rare variants in the individual, is reported.

In some modalities, extracellular polynucleotides comprise DNA.

In other embodiments, extracellular polynucleotides comprise RNA.

Polynucleotides can be fragmented or fragmented after isolation.

In addition or alternatively, the detection of a genetic biomarker may include a method for circulating and isolating nucleic acid.

In some modalities, extracellular polynucleotides are isolated from a body sample that can be selected from a group consisting of blood, plasma, serum, urine, saliva, mucous excretion, sputum, feces and tears.

In some modalities, the methods also comprise a step of determining the percentage of sequences with variation in the number of copies or other rare genetic alteration (for example, sequence variants) in the referred body sample.

In some modalities, the percentage of sequences that vary in the number of copies in that body sample is determined by calculating the percentage of predefined regions with an amount of polynucleotides above or below a predetermined threshold.

In addition or alternatively, the detection of a genetic biomarker may include a method for detecting a rare mutation in a cellless or substantially cellless sample obtained from an individual comprising: a) sequencing extracellular polynucleotides from an individual's body sample, wherein each of the extracellular polynucleotides generates a plurality of sequencing readings; b) filter the readings that do not reach a defined threshold; c) map sequence readings derived from sequencing to a reference sequence; d) identify a subset of mapped sequence readings that align with a variant of the reference sequence at each mapped base position; e) for each base mappable position, calculate a ratio of (a) a number of mapped sequence readings that include a variant compared to the reference sequence, to (b) a total number of sequence readings for each mappable base position; f) normalize the variation ratios or frequencies for each base mappable position and determine possible rare variants or other genetic changes; and g) compare the resulting number for each of the regions. In addition or alternatively, the detection of a genetic biomarker may include a method comprising: a. provide at least one set of marked parental polynucleotides and for each set of marked parental polynucleotides; B. amplifying the labeled parental polynucleotides in the set to produce a corresponding set of amplified parental polynucleotides; ç. sequencing a subset (including an appropriate subset) of the set of amplified parental polynucleotides to produce a set of sequencing readings; and d. collect the set of sequencing readings to generate a set of consensus sequences, each consensus sequence corresponding to a single polynucleotide among the set of marked parental polynucleotides. In certain embodiments, the method further comprises: e. analyze the set of consensus sequences for each set of labeled parental molecules.

[466] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include any of the various forms described in PCT Publication WO 2018/064629, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include a bait set panel comprising one or more sets of baits that selectively enrich for one or more nucleosome-associated regions of a genome, said regions associated with nucleosomes comprising genomic regions with one or more genomically based positions with differential nucleosome occupation, in which the differential nucleosome occupation is characteristic of a cell or type of tissue origin or disease state. In some embodiments, each of the one or more regions associated with nucleosomes in a bait set panel comprises at least one of: (i) significant structural variation, comprising a variation in nucleosome positioning, the said structural variation selected from the group which consists of: insertion, exclusion, translocation, genetic rearrangement, methylation status, microsatellites, variation in the number of copies, structural variation related to the number of copies or any other variation that indicates differentiation; and (ii) instability, comprising one or more fluctuations or significant peaks in a genome partition map, indicating one or more nucleosomal map interruption sites in a genome.

In some embodiments, the one or more sets of baits in a set of bait panels are configured to capture regions of the genome associated with nucleosomes based on a function of a plurality of reference nucleosome occupation profiles (i) associated with one or more disease states and one or more disease-free states; (ii) associated with a known somatic mutation, such as SNV, CNV, indel or rearrangement; and / or (iii) associated with differential expression patterns.

In one embodiment, the one or more sets of baits from a set of bait panels selectively enrich for one or more regions associated with nucleosomes in a sample of cellless deoxyribonucleic acid (cfDNA). Additionally or alternatively, the detection of a genetic biomarker may include a method for enriching a nucleic acid sample for nucleosome-associated regions of a genome comprising (a) bringing a nucleic acid sample into contact with a bait array panel, the said bait set panel comprising one or more sets of baits that selectively enrich for one or more regions associated with nucleosomes of a genome; and (b) enriching the nucleic acid sample for one or more regions associated with a nucleosome of a genome.

Additionally or alternatively, the detection of a genetic biomarker may include a method for generating a set of baits that comprises (a) identifying one or more regions of a genome, the said regions associated with a nucleosome profile and (b) select a set of bait to selectively capture said regions.

In one embodiment, a set of baits in a bait set panel selectively enriches for one or more regions associated with a nucleosome in a cell-free deoxyribonucleic acid sample.

In addition or alternatively, the detection of a genetic biomarker may include a method for enriching for various genomic regions, comprising placing a predetermined amount of a nucleic acid sample in contact with a bait panel comprising (i) a first set baits that selectively hybridize to a first set of genomic regions in the nucleic acid sample, provided in a first concentration ratio that is less than a saturation point of the first set of baits and (ii) a second set of baits that selectively hybridizes with a second set of genomic regions of the sample nucleic acid, supplied in a second concentration ratio that is associated with a saturation point of the second set of baits; and enrich the nucleic acid sample for the first set of genomic regions and the second set of genomic regions.

Additionally or alternatively, the detection of a genetic biomarker may include a method to improve the accuracy of detecting an insertion or (indelible) exclusion of a plurality of sequence readings derived from cell-free deoxyribonucleic acid (cfDNA) molecules in a sample of an individual, whose plurality of sequence readings is generated by the sequencing of nucleic acid, comprising (a) for each of the pluralities of sequence readings associated with DNA molecules without cells, provide: an expectation - predetermined amount of an indel being detected in one or more sequence readings of the plurality of sequence readings; a predetermined expectation that a detected indel is a true indel present in a given cellless DNA molecule of cellless DNA molecules, since an indel was detected in one or more sequence readings; and a predetermined expectation that a detected indel will be introduced by non-biological error, since an indel was detected in one or more sequence readings; (b) provide quantitative measurements of one or more model parameters characteristic of the sequence readings generated by the nucleic acid sequencing; (c) detecting one or more indelible candidates in the plurality of sequence readings associated with DNA molecules without cells; and (d) for each indel candidate, perform a hypothesis test using one or more of the model parameters to classify the indel candidate as a true indel or an introduced indel, thereby improving the accuracy of detecting an indel.

Additionally or alternatively, the detection of a genetic biomarker may include a kit comprising (a) a sample comprising a predetermined amount of DNA; and (b) a bait set panel comprising (i) a first set of baits that selectively hybridizes to a first set of genomic regions of a nucleic acid sample comprising a predetermined amount of DNA, supplied at a first concentration ratio that is less than a saturation point of the first set of baits and (ii) a second set of baits that selectively hybridizes to a second set of genomic regions of the nucleic acid sample, provided in a second concentration ratio that is associated with a saturation point of the second set of baits.

In addition or alternatively, the detection of a genetic biomarker may include a method for enriching for various genetic regions, comprising: (a) bringing a predetermined amount of nucleic acid from a sample into contact with a bait mixture comprising (i ) a first set of baits that selectively hybridize

with a first set of genomic regions of the sample's nucleic acid, provided in a first concentration ratio that is less than a saturation point of the first set of baits and (ii) a second set of baits that selectively hybridizes with a second set of genomic regions of the sample nucleic acid, whose second set of bait is supplied in a second concentration that is associated with a saturation point of the second set of baits; and (b) enrich the nucleic acid sample for the first set of genomic regions and the second set of genomic regions. In addition or alternatively, the detection of a genetic biomarker may include a method for enriching several genetic regions, comprising: (a) bringing a predetermined amount of nucleic acid from a sample into contact with a bait mixture comprising: (i ) a first set of baits that selectively hybridizes to a first set of genomic regions of the sample's nucleic acid, whose first set of baits is supplied in a first concentration that is less than a saturation point of the first set of baits and (ii) a second set of baits that selectively hybridizes to a second set of genomic regions of the sample nucleic acid, whose second set of baits is supplied in a second concentration that is at or above a saturation point of the second set of baits; and (b) enrich the sample nucleic acid for the first set of genomic regions and the second set of genomic regions, thereby producing an enriched nucleic acid.

[467] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the U.S. Patent

9,790,559, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include a method for detecting variation in the number of copies comprising: a) sequencing extracellular polynucleotides from an individual's body sample, where each of the extracellular polynucleotides is optionally linked to codes single bars; b) filter the readings that do not reach a defined threshold; c) map sequence readings obtained from step (a) to a reference sequence; d) quantify / count mapped readings in two or more predefined regions of the reference sequence; e) determine a variation in the number of copies in one or more of the predefined regions (i) normalize the number of readings in the regions predefined among themselves and / or the number of unique bar codes in the regions predefined among themselves; and (ii) compare the normalized numbers obtained in step (i) with the standardized numbers obtained from a control sample.

In addition or alternatively, the detection of a genetic biomarker may include a method for detecting a rare mutation in a cellless or substantially cellless sample obtained from an individual comprising: a) sequencing extracellular polynucleotides from a body sample from a individual, in which each of the extracellular polynucleotides generates a plurality of sequencing readings; b) sequencing extracellular polynucleotides from a body sample of an individual, in which each of the extracellular polynucleotides generates a plurality of sequencing readings; sequencing extracellular polynucleotides from a body sample from an individual, where each of the extracellular polynucleotides generates a plurality of sequencing readings; c) filter the readings that do not reach a defined threshold; d) map sequence readings derived from sequencing to a reference sequence; e) identify a subset of mapped sequence readings that align with a variant of the reference sequence in each mapped base position; f) for each mappable base position, calculate a ratio of (a) a number of mapped sequence readings that include a variant compared to the reference sequence, for (b) a total number of sequence readings for each mappable base position; g) normalize the ratios or frequencies of variation for each base mappable position and determine possible variants or rare mutations; h) and compare the resulting number for each of the regions with possible variants or rare changes with numbers similarly derived from a reference sample.

Additionally or alternatively, the detection of a genetic biomarker may include a method to characterize the heterogeneity of an abnormal condition in an individual, the method comprising generating a genetic profile of extracellular polynucleotides in the individual, wherein the genetic profile comprises a plurality of data resulting from analysis of copy number variation and / or other rare mutation analyzes (eg, genetic alteration). In addition or alternatively, the detection of a genetic biomarker may include a system comprising a computer-readable medium for performing the following steps: selection of predefined regions in a genome; enumerate the number of sequence readings in the predefined regions; normalize the number of sequence readings in the predefined regions and determine the percentage of variation in the number of copies in the predefined regions.

In some modalities, the entire genome or at least 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80% or 90% of the genome is analyzed.

In some embodiments, the computer-readable medium provides data as a percentage of cancer DNA or RNA in plasma or serum to the end user.

In addition or alternatively, the detection of a genetic biomarker may include a method for detecting a rare mutation in a cellless or substantially cellless sample obtained from an individual comprising: a) sequencing extracellular polynucleotides from a body sample of an individual, in which each of the extracellular polynucleotides generates a plurality of sequencing readings; b) filter the readings that do not reach a defined threshold; c) map sequence readings derived from sequencing to a reference sequence; d) identify a subset of mapped sequence readings that align with a variant of the reference sequence at each mapped base position; e) for each mappable base position, calculate a ratio of (a) a number of mapped sequence readings that include a variant compared to the reference sequence, to (b) a total number of sequence readings for each position of mappable base; f) normalize the variation ratios or frequencies for each base mappable position and determine possible rare variants or other genetic changes; and g) compare the resulting number for each of the regions.

In addition or alternatively, the detection of a genetic biomarker may include a method comprising: a. provide at least one set of marked parental polynucleotides and for each set of marked parental polynucleotides; B. amplifying the labeled parental polynucleotides in the pool to produce a corresponding set of amplified parental polynucleotides; ç. sequencing a subset (including an appropriate subset) of the set of amplified parental nucleotides to produce a set of sequencing readings; and d. collect the set of sequencing readings to generate a set of consensus sequences, each consensus sequence corresponding to a single polynucleotide among the set of marked parent polynucleotides.

In certain embodiments, the method further comprises: e. analyze the set of consensus sequences for each set of labeled parent molecules.

In addition or alternatively, the detection of a genetic biomarker may include a method comprising: a. providing at least one set of labeled parental polynucleotides and for each set of labeled parental polynucleotides; B. amplifying the labeled parental polynucleotides in the pool to produce a corresponding set of amplified parental polynucleotides; ç. sequencing a subset (including an appropriate subset) of the set of amplified parental polynucleotides to produce a set of sequencing readings; d. collecting the set of sequencing readings to generate a set of consensus sequences, each consensus sequence corresponding to a single polynucleotide among the set of marked parental polynucleotides; and is. filter among consensus strings those that do not reach a quality threshold.

In one embodiment, the quality threshold considers a number of amplified progeny polynucleotide sequence readings collapsed into a consensus sequence.

In another embodiment, the quality threshold considers a number of sequence readings of amplified progeny polynucleotides collapsed in a consensus sequence.

In addition or alternatively, the detection of a genetic biomarker may include a method comprising: a. provide at least one set of labeled parental polynucleotides, where each set is mapped to a different reference sequence in one or more genomes and, for each set of labeled parental polynucleotides; i. amplify the first polynucleotides to produce a set of amplified polynucleotides; ii. sequencing a subset of the amplified polynucleotide set, to produce a set of sequencing readings; and iii. collect the sequence reading by: 1. group sequenced sequence readings of amplified progeny polynucleotides into families, each family amplified from the same marked parental polynucleotide.

In one modality, the breakdown also comprises: 2. determining a quantitative measure of sequence readings in each family.

In another embodiment, the method further comprises (including a) including a): b. determine a quantitative measure of single families; and C. based on (1) the quantitative measure of single families and (2) the quantitative measure of the sequence reading in each group, inferring a measure of single marked parental polynucleotides in the set.

In another modality, the deduction is performed using statistical or probabilistic models.

In another embodiment, the method also comprises using a control or control sample set to correct amplification or representation biases between the two sets.

In another modality, the method also comprises determining the variation in the number of copies between the sets.

In another embodiment, the method further comprises (including a, b, c): d. determine a quantitative measure of polymorphic forms among families; and is. based on the quantitative measure of polymorphic forms, infer a quantitative measure of polymorphic forms in the number of single marked parental polynucleotides inferred.

In another modality, in which polymorphic forms include, but are not limited to: substitutions, insertions, deletions, inversions, changes in microsatellites, transversions, translocations, fusions, methylation, hypermethylation, hydroxymethylation, acetylation, epigenetic variants, associated regulatory variants or protein-binding sites.

In another modality, in which the sets are derived from a common sample, the method further comprising: a. to infer variation in the number of copies for the plurality of sets based on a comparison of the inferred number of marked parental polynucleotides in each set mapped for each of a plurality of reference sequences.

In another embodiment, the original number of polynucleotides in each set is further inferred.

In addition or alternatively, the detection of a genetic biomarker may include a system comprising a computer-readable medium for carrying out the methods mentioned above.

In addition or alternatively, the detection of a genetic biomarker may include a method of communicating sequence information about at least one individual polynucleotide molecule comprising: a. providing at least one individual polynucleotide molecule; B. encode sequence information on at least one individual polynucleotide molecule to produce a signal; ç. passing at least part of the signal through a channel to produce a received signal comprising nucleotide sequence information about at least one individual polynucleotide molecule, wherein the received signal comprises noise and / or distortion; d. decoding the received signal to produce a message comprising sequence information about at least one individual polynucleotide molecule, in which decoding reduces noise and / or distortion in the message; and is. provide the message to a recipient.

In one embodiment, the noise comprises incorrect nucleotide cells.

In another mode, the distortion comprises uneven amplification of the individual polynucleotide molecule compared to other individual polynucleotide molecules.

In another modality, the distortion results from amplification or sequencing bias.

Additionally or alternatively, the detection of a genetic biomarker may include a method for detecting a rare mutation in a cellless or substantially cellless sample obtained from an individual comprising: a) sequencing extracellular polynucleotides from a body sample from a individual, in which each of the extracellular polynucleotides generates a plurality of sequencing readings; b) perform multiplex sequencing in regions or entire genome sequencing if enrichment is not performed; c) filter the readings that do not reach a defined threshold; d) map sequence readings derived from sequencing in a reference sequence; e) identify a subset of mapped sequence readings that align with a variant of the reference sequence at each mapped base position; f) for each mappable base position, calculate a ratio of (a) a number of mapped sequence readings that include a variant compared to the reference sequence, to (b) a total number of sequence readings for each position of mappable base; g) normalize the ratios or frequencies of variation for each base mappable position and determine possible variants or rare mutations; and h) and compare the resulting number for each of the regions with possible variants or rare mutations or mutations with numbers similarly derived from a reference sample. In addition or alternatively, the detection of a genetic biomarker may include a method for characterizing the heterogeneity of an abnormal condition in an individual, the method comprising generating a genetic profile of extracellular polynucleotides in the individual, wherein the genetic profile comprises a plurality of data resulting from copy number variation and analysis of rare mutations. In addition or alternatively, the detection of a genetic biomarker may include a method that comprises determining the variation in the number of copies or performing a rare mutation analysis on a cellless or substantially cellless sample obtained from an individual using multiplex sequencing. In addition or alternatively, the detection of a genetic biomarker may include a method comprising: a) providing at least one set of labeled parental polynucleotides and for each set of labeled parental polynucleotides; b) amplify the parental polynucleotides marked together to produce a corresponding set of amplified progeny polynucleotides; c) sequencing a subset (including an appropriate subset) of the amplified progeny polynucleotide set, to produce a set of sequencing readings; d) collect the set of sequencing readings to generate a set of consensus sequences, each consensus sequence corresponding to a single polynucleotide among the set of marked parental polynucleotides; and e) filter among the consensus strings those that do not reach a quality threshold.

[468] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the U.S. Patent

7,700,286, which is incorporated herein by reference in its entirety.

For example, the detection of a genetic biomarker may include a cancer detection test in the measurement of plasma / serum, adding and comparing the amount of DNA and RNA of certain genes in the plasma / serum of cancer patients that are the reflection of a gene amplification and gene overexpression.

Thus, gene amplification (seen by more DNA) and gene overexpression (more RNA) are linked.

Additionally or alternatively, the detection of a genetic biomarker may include a method for diagnosing or monitoring the evolution of cancers, which comprises measuring together gene expression (RNA) and genetic amplification (DNA) in the body fluids of patients suspected of harboring cancer in any gene that is amplified and overexpressed in cancer cells and compared to healthy controls.

More particularly, RNA and DNA are extracted from a body fluid, such as plasma, serum, sputum, saliva, etc., purified and amplified, and the expressed RNA and amplified DNA are analyzed and compared with a single gene from house maintenance.

In some embodiments, nucleic acids are amplified by reverse transcriptase chain reaction (RT-PCR) and analyzed by gel staining, by radioactive immunological technique (RIA), by enzyme linked immunosorbent test (ELISA) or by a microchip test (genetic arrangement) and possibly quantified by any method for quantifying nucleic acids.

In some modalities, the quantification of RNA and DNA is performed by PCR in real time, such as "TAQMAN ™", or in capillaries "LIGHTCYCLER ™" or PCR and RT PCR in real time from any company.

In some modalities, the analyzed genes can be compared to a reference nucleic acid extract (DNA and RNA) corresponding to the expression (RNA) and quantity (DNA) of a single housekeeping gene or to an RNA reference code corresponding to the expression of a house maintenance coding gene, or a reference DNA corresponding to a single gene, or can be estimated in reference to a standard curve obtained with nucleic acids from a cell line.

[469] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in US Patent Application Publication 2018/0195131, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include methods for detecting fusion genes, which can be used to detect a disease, such as cancer. In addition or alternatively, the detection of a genetic biomarker may include methods for enriching breakpoint fragments, such as detecting and characterizing fusion genes, which may be associated with a disease, such as cancer. Additionally or alternatively, the detection of a genetic biomarker may include a method for providing a diagnostic or therapeutic intervention to an individual having or suspected of cancer, comprising (a) providing a biological sample comprising nucleic acid molecules without an individual's cells; (b) contacting the nucleic acid molecules without cells in the biological sample with a probe configured under sufficient hybridization conditions to produce polynucleotides captured by the probe, whose probe set comprises a plurality of polynucleotide probes, each of which plurality of polynucleotide probes have (i) sequence complementarity with a fusion gene and (ii) affinity for the fusion gene that is greater than a polynucleotide having complementary sequence with the fusion gene and containing only unmodified nucleotides; (c) isolating probe-captured polylucleotides from the mixture to produce a sample enriched with isolated polynucleotides comprising breakpoints of the fusion gene; (d) sequencing the isolated polynucleotides to produce sequences; (e) detecting polynucleotides comprising breakpoints of fusion genes based on the sequences; and (f) provide diagnostic or therapeutic intervention based on the detection of breakpoint fragments.

In addition or alternatively, the detection of a genetic biomarker may include a method for capturing a breakpoint fragment from a fusion gene, comprising (a) providing a biological sample containing or suspected to contain a nucleic acid molecule without cells comprising the breakpoint fragment of the fusion gene; and (b) contacting the biological sample with a polynucleotide probe in sufficient conditions to (i) allow hybridization between the polynucleotide probe and the breakpoint fragment to provide a probe-captured polynucleotide in a mixture, whose polynucleotide probe has sequence complementarity with the breakpoint fragment and has affinity for the fusion gene that is larger than a polynucleotide having a sequence complementary to the fusion gene and containing only unmodified nucleotides; and (ii) enrichment or isolation of the polynucleotide captured by the mixture probe, in which the polynucleotide probe has sequence complementarity with the interruption point fragment.

Additionally or alternatively, the detection of a genetic biomarker may include a set of probes comprising a plurality of polynucleotide probes, wherein each of the polynucleotide probes has (i) sequence complementarity with a fusion gene as part of a molecule of nucleic acid without cells and (ii) affinity for the fusion gene that is greater than a polynucleotide with a sequence complementary to the fusion gene and con-

having only unmodified nucleotides.

In addition or alternatively, the detection of a genetic biomarker can include a high affinity polynucleotide, comprising a sequence that is configured to specifically hybridize to a nucleic acid sequence associated with a fusion gene in a cellless nucleic acid molecule.

In addition or alternatively, the detection of a genetic biomarker can include a high affinity polynucleotide configured to specifically hybridize to a fusion gene.

In one embodiment, the high affinity polynucleotide comprises one or more blocked nucleic acid nucleotides.

In another embodiment, the high-affinity polynucleotide has a melting temperature that is at least 1 ° C, 2 ° C, 3 ° C, 4 ° C, 5 ° C, 10 ° C, 15 ° C or 20 ° C superior to a polynucleotide with the same sequence comprising only natural nucleotides.

In another embodiment, the high affinity polynucleotide has a melting temperature that is at least 2%, 4%, 6%, 8% or 10% higher than a polynucleotide with the same sequence comprising only natural nucleotides.

In another embodiment, the high-affinity polynucleotide is configured to specifically hybridize to a cancer fusion gene.

In addition or alternatively, the detection of a genetic biomarker may include a high affinity polynucleotide probe comprising a high affinity polynucleotide configured to specifically hybridize to a fusion gene.

In one embodiment, the high-affinity polynucleotide comprises one or more blocked nucleic acid nucleotides.

In another embodiment, the probe comprises a feature selected from a detectable marker, a bond fraction or a solid support.

In another embodiment, the probe is configured to hybridize to a breakpoint fragment from a fusion gene.

In another embodiment, the breakpoint fragment has a length between about 140 nucleotides and about

180 nucleotides.

In another embodiment, the fragment is deoxyribonucleic acid (DNA) or genomic DNA without cells.

In another embodiment, the high-affinity polynucleotide is attached to a solid support.

In addition or alternatively, the detection of a genetic biomarker may include a method for capturing a breakpoint fragment from a fusion gene comprising contacting the breakpoint fragment with a high affinity polynucleotide probe under stringent hybridization conditions and allowing hybridization, where the polynucleotide probe is attached to a solid support and where the polynucleotide probe has a nucleotide sequence that is substantially or perfectly complementary to a nucleotide sequence of the breakpoint fragment .

In one embodiment, the high affinity polylucleotide comprises one or more blocked nucleic acid nucleotides.

In addition or alternatively, the detection of a genetic biomarker may include a method for enriching a sample for polynucleotides comprising a breakpoint of a fusion gene, comprising: a) contacting a set of probes of claim 20 with a mixture polynucleotides in hybridization conditions to produce probe-captured polynucleotides; and b) isolating the probe captured polynucleotides from the mixture to produce a sample enriched with polynucleotides comprising breakpoint fragments of the fusion gene.

In one embodiment, the high-affinity polynucleotide comprises one or more blocked nucleic acid nucleotides.

In another embodiment, polynucleotides comprise DNA without cells or fragmented genomic DNA.

In another modality, the method also includes isolating polynucleotides captured from the probes.

In another mode, the method further comprises sequencing isolated polynucleotides.

In addition or alternatively, the detection of a genetic biomarker may include a method for diagnosing cancer in an individual comprising: a) providing a sample comprising an individual's polynucleotides; b) contacting the cellless DNA (cfDNA) of the sample with a set of probes of claim 20 under hybridization conditions to produce probe-captured polynucleotides; c) isolate the polynucleotides captured by the mixture probe, to produce a sample enriched with polynucleotides comprising fragments of the breakpoint of the fusion gene; d) sequencing the isolated polynucleotides to produce sequences; e) detecting polynucleotides comprising fusion gene breakpoints based on the sequences; and f) diagnose cancer based on the detection of breakpoint fragments. In one mode, the high affinity polynucleotide comprises one or more blocked nucleic acid nucleotides.

[470] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in US Patent Application Publication 2018/0120291, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include a method for analyzing an individual's disease state, comprising (a) the use of a genetic analyzer to generate genetic data from nucleic acid molecules in biological samples from the individual obtained at (i) two or more points in time or (ii) substantially the same point in time, where the genetic data refer to the individual's genetic information and where the biological samples include a biological sample without cells; (b) receiving genetic data from the genetic analyzer; (c) with one or more computer processors programmed, using genetic data to produce a test result adjusted to characterize the individual's genetic information; and (d) send the adjusted test result to the computer's memory. In some modes, genetic data comprise current sequence readings and previous sequence readings, and where (c) it comprises comparing the current sequence reading with the previous sequence reading and updating a diagnostic confidence indication of according to the characterization of the individual's genetic information, whose indication of diagnostic confidence is indicative of a probability of identifying one or more genetic variations in a biological sample of the individual.

In some modalities, the method also comprises obtaining a subsequent characterization and leaving it as an indication of diagnostic confidence in the subsequent characterization for information again.

In some modalities, the method also comprises determining a frequency of one or more genetic variants detected in a collection of sequence readings included in the genetic data and producing the test result adjusted at least in part, comparing the frequency of a or more genetic variants at two or more points in time.

In some modalities, the method further comprises determining an amount of variation in the number of copies in one or more genetic loci detected in a collection of sequence readings included in the genetic data and producing the test result adjusted at least in part, comparing the quantity at two or more points in time.

In some modalities, the method also includes using the adjusted test result to provide (i) a therapeutic intervention or (ii) a diagnosis of health or illness to the individual.

In some embodiments, genetic data comprises a first set of genetic data and a second set of genetic data, where the first set of genetic data is at or below a detection threshold and the second set of genetic data is above the detection threshold.

In some embodiments, the detection threshold is a noise threshold.

In some modalities, the method further comprises, in (c), adjusting a diagnosis of the individual from negative or uncertain to positive when the same genetic variants are detected in the first set of genetic data and in the second set of genetic data in a plurality sampling instances or time points.

In some modalities, the method further comprises, in (c), adjusting a diagnosis of the individual from negative or uncertain to positive in a characterization of a point in the previous time, when the same genetic variants are detected in the first set of genetic data at an earlier time point and the second set of genetic data at a later time point.

In addition or alternatively, the detection of a genetic biomarker may include a method for detecting a trend in the amount of cancer polynucleotides in a biological sample from an individual over time, comprising determining, using or more programmed computer processors. , a frequency of cancer polynucleotides at each of a plurality of time points; determining a range of errors for the frequency at each of the various time points to provide at least a first range of errors at the first time point and a second range of errors at the second time point subsequent to the first time point; and determine whether (1) the first range of errors overlaps the second range of errors, the overlap of which is indicative of the stability of the frequency of cancer polynucleotides over a plurality of time points, (2) the second range of errors. errors is greater than the first range of errors, thus indicating an increase in the frequency of cancer polynucleotides at a plurality of time points, or (3) the second range of errors is less than the first range of errors, thus indicating a decrease in the frequency of cancer polynucleotides at a plurality of time points.

In addition or alternatively, the detection of a genetic biomarker may include a method for detecting one or more genetic variations and / or amount of genetic variation in an individual, comprising sequencing nucleic acid molecules in a nucleic acid sample no individual cells with a genetic analyzer to generate a first set of sequence readings at a first point in time; comparing the first set of sequence readings with at least a second set of sequence readings taken at least at a second time point before the first time point to produce a comparison of the first set of sequence readings and at least the second set sequence readings; use the comparison to update a diagnostic confidence indication accordingly, whose diagnostic confidence indication is indicative of a probability of identifying one or more genetic variations in an individual's cellless nucleic acid sample; and detecting a presence or absence of one or more genetic variations and / or amount of genetic variation in nucleic acid molecules in a sample of nucleic acid without cells from the individual based on the diagnostic confidence indication.

In some embodiments, the method further comprises obtaining nucleic acid without cells.

In addition or alternatively, the detection of a genetic biomarker may include a method for detecting a mutation in an individual's cellless nucleic acid sample, comprising: (a) determining consensus sequences by comparing the readings of the current sequence obtained of a genetic analyzer with the readings of the previous sequence from a previous period to produce a comparison and update a diagnostic confidence indication based on the comparison, where each consensus sequence corresponds to a single polynucleotide among a set of derived marked parental polynucleotides of the nucleic acid sample without cells and (b) based on the diagnostic confidence, generate a genetic profile of extracellular polynucleotides in the individual, in which the genetic profile comprises data resulting from analyzes of variation or mutation in the number of copies .

In addition or alternatively, the detection of a genetic biomarker may include a method for detecting abnormal cell activity, comprising: providing at least one set of tagged parental polynucleotides derived from a biological sample from an individual; amplifying the labeled parental polynucleotides in the set to produce a corresponding set of amplified parental polynucleotides; use a genetic analyzer to sequence a subset of the set of amplified parental polynucleotides to produce a set of sequencing readings; and collect the set of sequencing readings to generate a set of consensus strings by comparing current sequence readings with previous sequence readings from at least a previous period of time and updating a diagnostic confidence indication accordingly, whose diagnostic confidence indication is indicative of a probability of identifying one or more genetic variations in a biological sample of the individual, in which each consensus sequence corresponds to a single polynucleotide among the set of marked parental polynucleotides.

Additionally or alternatively, the detection of a genetic biomarker may include a method for detecting a mutation in a sample without cells or substantially without cells from an individual, comprising: (a) sequencing extracellular polynucleotides from a body sample of the individual with a genetic analyzer; (b) for each of the extracellular polynucleotides, generate a plurality of sequencing readings; (c) filter the readings that do not reach a defined threshold; (d) map the sequence readings derived from the sequencing into a reference sequence; (e) identify a subset of mapped sequence readings that align with a variant of the reference sequence at each mapped base position; (f) for each mappable base position, calculate a ratio of (i) a number of mapped sequence readings that include a variant in comparison to the reference sequence, for (ii) a total number of sequence readings for each mappable base position; and (g) use one or more programmed computer processors to compare the sequence readings with other sequence readings from at least a previous time point and update a diagnostic confidence indication accordingly, whose diagnostic confidence indication - nostico is indicative of a probability of identification of the variant.

In addition or alternatively, the detection of a genetic biomarker may include a method for operating genetic testing equipment, comprising: providing initial initial genetic material obtained from a body sample obtained from an individual; converting double-stranded polynucleotide molecules from the initial genetic material into at least one set of unmarked parental polynucleotides, in which each polynucleotide in a set is mapped to a reference sequence; and for each set of labeled parental polynucleotides: (i) amplify the labeled parent polynucleotides in the set to produce a corresponding set of amplified progeny polynucleotides; (ii) sequencing the set of amplified progeny polynucleotides to produce a set of sequencing readings; (iii) collect the set of sequencing readings to generate a set of consensus sequences, in which the collection uses sequence information from a tag and at least one of: (1) sequence information in the initial region of a read sequence, (2) an end region of the read sequence, and (3) length of the read sequence, where each consensus sequence in the set of consensus sequences corresponds to a polynucleotide molecule among the set of marked parental polynucleotides ; and (iv) analyze the set of consensus sequences for each set of labeled parental molecules; (v) comparing current sequence readings with previous sequence readings from at least one other point in time; and (vi) update an indication of diagnostic confidence in agreement, whose indication of diagnostic confidence is indicative of a probability of identifying one or more genetic variations in an individual's body sample.

Additionally or alternatively, the detection of a genetic biomarker may include a method for detecting one or more genetic variants in an individual, comprising: (a) obtaining nucleic acid molecules from one or more biological samples without cells from that individual ; (b) testing said nucleic acid molecules to produce a first set of genetic data and a second set of genetic data, wherein said first set of genetic data and / or said second set of genetic data is within a detection threshold; (c) comparing said first set of genetic data with said second set of genetic data to identify said one or more genetic variants in said first set of genetic data or in said second set of genetic data; and (d) based on said one or more genetic variants identified in (c), use one or more programmed computer processors to update a diagnostic confidence indication to identify said one or more genetic variants in a biological sample without cells from said individual.

Additionally or alternatively, the detection of a genetic biomarker may include a method for calling a genetic variant in cellless deoxyribose nucleic acids (cfDNA) from an individual comprising: (a) using a DNA sequencing system to sequence the cfDNA a sample taken at an individual's first time point; (b) detecting a genetic variant in the sequenced cfDNA from the first time point, where the genetic variant is detected at a level below a diagnostic threshold; (c) using the DNA sequencing system to sequence the cfDNA of a sample taken from the individual at one or more subsequent time points; (d) detecting the genetic variant in the sequenced cfDNA from one or more subsequent time points, in which the genetic variant is detected below the diagnostic limit; (e) classify the samples as positive for the genetic variant based on the detection of the genetic variant below the diagnostic limit in samples taken at different time points. Additionally or alternatively, the detection of a genetic biomarker may include a method for calling a genetic variant in cellless deoxyribose nucleic acids (cfDNA) from an individual comprising: (a) using a deoxyribonucleic acid (DNA) sequencing system to sequencing the cfDNA of a sample from an individual; (b) detecting a genetic variant in the sequenced cfDNA, where the genetic variant is detected at a level below a diagnostic limit; (c) use the DNA sequencing system to sequence cfDNA from the sample taken from the individual, where the sample is sequenced again one or more times; (d) detecting the genetic variant in the sequenced cfDNA from one or more resected samples, where the genetic variant is detected below the diagnostic limit; and (e) classify the samples as positive for the genetic variant based on the detection of the genetic variant below the diagnostic limit in resected samples.

[471] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in US Patent Application Publication 2017/0240972, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include systems and methods for determining gene fusion, determining a fused reading containing sequence data from at least a portion of a fused chromosomal DNA molecule; determining a predetermined point in the genome with at least a mapped portion of the fused reading cut at the predetermined point (a breakpoint); identify two portions of reading mapped from two breakpoints (pair of breakpoints) as a potential candidate for fusion; create one or more fusion sets based on pairs of breakpoints and agglomerate the fusion sets in one or more fusion clusters; and identifying each fusion cluster that meets a predetermined criterion as a fusion of genes.

In addition or alternatively, the detection of a genetic biomarker may include a method for processing genetic sequence reading data from a sample, the method comprising: determining a fused reading containing sequencing data from at least one portion of a fused chromosomal DNA molecule; determining a predetermined point in the genome with at least a mapped portion of the fused reading cut at the predetermined point (a breakpoint); identify two mapped reading portions of two breakpoints (pair of breakpoints) as a potential candidate for merger; create one or more fusion sets based on pairs of breakpoints and agglomerate the fusion sets into one or more fusion clusters; and identify each fusion cluster that meets a predetermined criterion as a fusion of genes.

In some modalities, the method comprises assigning a unique molecule or reading identifier (reading ID) to each reading.

In some modalities, the method comprises cutting each mapped portion of the readings on one or both sides.

In some modalities, the breakpoints are independent of the readings on the identity and are identified by a sign, a chromosome and a position.

In some modalities, breakpoints maintain statistics, including a number of readings and molecules that are cut or divided at the breakpoint, and a number of wild-type readings and molecules that pass over the breakpoint.

In some embodiments, the method comprises selecting every two reading portions mapped with common reading IDs that belong to two breakpoints with appropriate signs as a potential candidate for fusion.

In some modalities, the potential location of the candidate for fusion in the original reading before mapping shows the reading portion as originally located next to each other.

In some modalities, the method comprises verifying if the portions of reading are mapped on a tape for differences in the signals of the interruption points.

In some modalities, the method comprises tracking statistics of fusion sets.

In addition or alternatively, the detection of a genetic biomarker may include a system for analyzing genetic information, including a DNA sequencer; a processor coupled to the DNA sequencer, the processor executing the computer code to process genetic sequence reading data from a sample, the computer code comprising instructions for: determining a fused reading containing a portion's sequencing data a fused chromosomal DNA molecule; determine at least one predetermined point in the genome with at least a mapped portion of the fused reading cut at the predetermined point (a breakpoint); identify two mapped reading portions of two breakpoints (pair of breakpoints) as a potential candidate for merger; create one or more fusion sets based on pairs of breakpoints and cluster the fusion sets into one or more fusion sets; and identifying each fusion cluster that meets a predetermined criterion as a fusion of genes.

In addition or alternatively, the detection of a genetic biomarker may include a method comprising: sequencing DNA molecules with a DNA sequencer to generate a collection of sequences; map the sequence collection to a reference genome; identify fused readings from the mapped collection, where a fused reading contains subsequences, where a first subsequence maps to a first genetic locus and a second subsequence maps to a second distinct genetic locus; for each fused reading, identify a first breakpoint at the first genetic locus and a second breakpoint at the second genetic locus, where a breakpoint is a point in the reference genome where a sequence of a fused reading is cut and wherein the first and second breakpoints form a pair of breakpoints; generate sets of fused readings, each set comprising fused readings having the same breakpoint pair; agglomerate sets of fused readings, where each cluster is formed from sets of fused readings with first breakpoints within a first predetermined distance of nucleotides and second breakpoints within a second predetermined distance of nucleotides; and determining a gene fusion for one or more clusters, where a gene fusion for a cluster has, as a first breakpoint of the fusion gene, a breakpoint selected from the first breakpoints in the cluster and , as a second breakpoint of the fusion gene, a breakpoint selected from the second breakpoints in the cluster and where the first and second breakpoints of the fusion gene are selected based on criteria of selection.

In addition or alternatively, the detection of a genetic biomarker may include a method comprising: sequencing a plurality of DNA molecules with a DNA sequencer; tag each of the plurality of sequence molecules with an identifier; map each tagged sequence to a reference genome; identify cut readings from the mapped marked sequences, where a cut reading is a marked sequence that contains a mapped portion and a cut portion, where the mapped portion is mapped to a genetic locus and the cut portion is not mapped to the genetic locus; determining a breakpoint for each cut reading, where a breakpoint is a point in the reference genome where a cut reading sequence is cut; create sets of breakpoints, each set of breakpoints comprising identifiers of readings having the same breakpoint; create sets of breakpoint pairs by comparing pairs of breakpoint sets, each set of breakpoint pairs, including identifiers present in the two members of a compared pair of breakpoint sets; agglomerate sets of breakpoint pairs, where each cluster includes sets of breakpoint pairs with a first breakpoint of the pair within a predetermined first genetic distance and a second breakpoint of the pair within a second predetermined genetic distance; and determining a gene fusion for one or more of the clusters, where a gene fusion for a cluster has, as a first breakpoint in the fusion gene, a breakpoint selected from the first breakpoints in the cluster and, as a second breakpoint of the fusion gene, a breakpoint selected from the second breakpoints in the cluster and where the first and second breakpoints of the fusion gene are selected based on a selection criterion.

In some modalities, the selection criteria include the breakpoint with the most merged readings in the clusters.

Additionally or alternatively, the detection of a genetic biomarker may include a method for identifying a fusion gene breakpoint, the method comprising: determining a fused reading containing sequencing data from at least a portion of a fused chromosomal DNA molecule; determining a predetermined point in the genome with at least a mapped portion of the fused reading cut at the predetermined point (a breakpoint); identify two portions of reading mapped from two breakpoints (pair of breakpoints) as a potential candidate for fusion; create one or more fusion sets based on pairs of breakpoints and agglomerate the fusion sets into one or more fusion clusters; identify each fusion cluster that meets a predetermined criterion as a gene fusion, and identify a gene fusion breakpoint as the fusion gene breakpoint. Additionally or alternatively, the detection of a genetic biomarker may include a method for diagnosing a condition in an individual, the method comprising: determining a fused reading containing sequencing data from at least a portion of a DNA molecule fused chromosome; determining a predetermined point in the genome with at least a mapped portion of the fused reading cut at the predetermined point (a breakpoint); identify two portions of reading mapped from two breakpoints (pair of breakpoints) as a potential candidate for fusion; create one or more fusion sets based on pairs of breakpoints and agglomerate the fusion sets in one or more fusion clusters; and identifying each fusion cluster that meets a predetermined criterion as a gene fusion, where said genetic fusion is indicative of the condition.

[472] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any one of several methods described in US Patent Application Publication 2017/0240973, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include a method comprising: (a) obtaining sequencing readings of deoxyribonucleic acid (DNA) molecules from a sample of body fluid without cells from an individual; (b) generate from the sequence readings a first set of data comprising for each genetic locus in a plurality of genetic loci a quantitative measure related to the sequencing reading coverage ("reading coverage"); (c) correct the first set of data by executing the saturation balance correction and the probe efficiency correction; (d) determining a baseline reading coverage for the first data set, where the baseline reading coverage refers to the saturation balance and probe efficiency; and (e) determining a copy number status for each genetic locus in the plurality of genetic loci in relation to the baseline reading coverage.

In some modalities, the first data set comprises, for each genetic locus in a plurality of genetic loci, a quantitative measure related to (i) guanine-cytosine content ("GC content") of the genetic locus.

In some modalities, the method comprises, before (c), removing from the first set of genetic loci data that are high-variance genetic loci, in which the removal comprises: (i) adjusting a model that relates the measurements quantitative measures related to guanine-cytosine content and quantitative measures for sequencing the reading coverage of genetic loci; and (ii) removing from the genetic loci at least 10% of the genetic loci, in which the removal of the genetic loci comprises removing the genetic loci that most differ from the model, thus providing the first set of genetic line loci data. base.

In some embodiments, the method comprises removing at least 45% of the genetic loci.

In some modalities, determining a copy number status involves comparing the reading coverage of the genetic loci with the reading coverage of the baseline.

In some modalities, the body fluid without cells is selected from the group consisting of serum, plasma, urine and cerebrospinal fluid.

In some modalities, reading coverage is determined by mapping sequencing readings to a reference genome.

In some modalities, obtaining the sequence readings comprises adapters of binding to the DNA molecules from the body fluid without cells of the individual.

In some modalities, the DNA molecules are duplex DNA molecules and the adapters are linked to the duplex DNA molecules, so that each adapter differently identifies complementary strands of the DNA molecule to provide labeled strands.

In some modalities, determining the quantitative measure related to the probability that a DNA strand derived from the genetic locus will be represented in the sequencing readings comprises classifying sequencing readings into paired readings and unpaired readings, where (i) each paired reading corresponds to sequence readings generated from a first marked strand and a second differently marked complementary strand derived from a double stranded polynucleotide molecule in that set, and (ii) each reading does not match Relief represents a first marked strand that does not have a differently marked second complementary strand derived from a double stranded polynucleotide molecule represented between said sequence readings in said set of sequence readings.

In some modalities, the method also comprises determining quantitative measures of (i) said paired readings and (ii) said unpaired readings that map each of one or more genetic loci to determine a quantitative measure related to the total number of DNA molecules double-stranded in said samples that maps for each of said one or more genetic loci based on said quantitative measure related to paired readings and mapping of unpaired readings for each locus.

In some embodiments, the adapters comprise barcode strings.

In some embodiments, determining the reading coverage comprises collating the sequencing readings based on the mapping position of the sequencing readings to the reference genome and barcode sequences.

In some embodiments, the genetic loci comprise one or more oncogenes.

In some modalities, a method comprises determining that at least a subset of the baseline genetic loci has changed in the number of copies in the individual's tumor cells, determining relative quantities of variants in the baseline genetic loci for which the individual's germline genome is heterozygous.

In some embodiments, the relative quantities of the variants are not nearly the same.

In some embodiments, genetic baseline loci for which the relative quantities of the variants are not approximately equal are removed from the baseline genetic loci, thereby providing baseline genetic loci corrected with allelic frequency.

In some embodiments, the genetic baseline loci corrected for the allele frequency are used as the baseline loci in the methods of any of the preceding claims.

In addition or alternatively, the detection of a genetic biomarker may include a method comprising: receiving in readings sequencing readings of deoxyribonucleic acid (DNA) molecules from a sample of body fluid without cells from an individual; execute code with a computer processor to perform the following steps: generate from the sequence readings a first set of data comprising for each genetic locus in a plurality of genetic loci a quantitative measure related to the coverage of the sequencing reading ("reading coverage"); correct the first data set by correcting the saturation balance and correcting the probe efficiency; determining a baseline reading coverage for the first data set, where the baseline reading coverage refers to the saturation balance and probe efficiency; and determining a copy number status for each genetic locus in the plurality of genetic loci in relation to the baseline reading coverage.

In addition or alternatively, the detection of a genetic biomarker can include a system comprising: a network; a database that comprises computer memory configured to store nucleic acid sequence data (for example, DNA) that are connected to the network; a bioinformatics computer comprising a computer memory and one or more computer processors, a computer connected to the network; where the computer also comprises machine executable code that, when executed by one or more computer processors, copies nucleic acid sequence data (for example, DNA) stored in the database, writes the copied data to the memory bioinformatics computer and performs steps including: generating from the nucleic acid sequence data (for example, DNA) a first data set comprising for each genetic locus in a plurality of genetic loci a quantitative measure related to coverage of reading sequencing ("reading coverage"); correct the first data set by correcting the saturation balance and correcting the probe efficiency; determine a baseline reading coverage for the first data set, where the baseline reading coverage refers to the saturation balance and probe efficiency; and determining a copy number status for each genetic locus in the plurality of genetic loci in relation to the baseline reading coverage. In some modalities, the database is connected to a DNA sequencer.

[473] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in US Patent Application Publication 2017/0260590, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include a method comprising: (a) sequencing cancer cell polynucleotides from a biological sample from an individual; (b) identify and quantify somatic mutations in polynucleotides; (c) develop a profile of tumor heterogeneity in the individual, indicating the presence and the relative quantity of a plurality of somatic mutations in the polynucleotides, in which different relative amounts indicate tumor heterogeneity; and (d) to determine a therapeutic intervention for a cancer that exhibits the heterogeneity of the tumor, in which the therapeutic intervention is effective against a cancer that has the heterogeneity profile of the tumor determined.

In some embodiments, cancer cells are spatially distinct.

In some modalities, therapeutic intervention is more effective against a cancer that presents with the plurality of somatic mutations than against a cancer that presents with any, but not all, of the somatic mutations.

In some modalities, the method also comprises: (e) monitoring changes in the tumor's heterogeneity over time and determining different therapeutic interventions over time based on the changes.

In some modalities, the method also includes: (e) showing the therapeutic intervention.

In some modalities, the method also includes: (e) implementing the therapeutic intervention.

In some modalities, the method also comprises: (e) generating a phylogeny of the tumor's evolution based on the tumor's profile; in which the determination of therapeutic intervention takes phylogeny into account.

In some modalities, the determination is made with the aid of the algorithm performed by a computer.

In some modalities, the sequence readings generated by the sequencing are subject to noise reduction before identifying and quantifying.

In some modalities, noise reduction comprises molecular tracking of sequences generated from a single polynucleotide in the sample.

In some modalities, the determination of a therapeutic intervention takes into account the relative frequencies of genetic changes related to the tumor.

In some modalities, therapeutic intervention comprises administering, in combination or in series, a plurality of drugs, in which each drug is relatively more effective against a cancer that presents with a different somatic mutation that occurs at different relative frequencies.

In some modalities, a drug that is relatively more effective against cancer that presents with a somatic mutation that occurs with greater relative frequency is administered in greater quantities.

In some modalities, drugs are distributed in stratified doses to reflect the relative quantities of variants in the DNA.

In some modalities, cancers that have at least one of the genetic variants are resistant to at least one of the drugs.

In some modalities, the determination of a therapeutic intervention takes into account the tissue of origin of the cancer.

In some modalities, the therapeutic intervention is determined based on a database of interventions that prove to be therapeutic for cancers with tumor heterogeneity characterized by each of the somatic mutations.

In addition or alternatively, the detection of a genetic biomarker may include a method that comprises providing a therapeutic intervention for an individual with cancer with a tumor profile from which the heterogeneity of the tumor can be inferred, in which the therapeutic intervention is effective against cancers with the tumor profile.

In some modalities, the tumor profile indicates the relative frequency of a plurality of more somatic mutations.

In some modalities, the method also includes monitoring changes in the relative frequencies in the individual over time and determining different therapeutic interventions over time based on the changes.

In some modalities, therapeutic intervention is more effective against a cancer that presents with each somatic mutation than against a cancer that presents with any, but not all, of the somatic mutations.

In some modalities, therapeutic intervention comprises administering, in combination or in series, a plurality of drugs, in which each drug is relatively more effective against a cancer that presents with a different somatic mutation that occurs at different frequencies relative.

In some modalities, a drug that is relatively more effective against cancer that presents with a somatic mutation that occurs with greater relative frequency is administered in greater quantities.

In some modalities, the drugs are distributed in stratified doses to reflect the relative amounts of the variant in the DNA.

In some modalities, cancers that have at least one of the genetic variants are resistant to at least one of the drugs.

In addition or alternatively, the detection of a genetic biomarker may include a system comprising a computer-readable medium comprising machine-executable code which, after execution by a computer processor, implements a method comprising: (a) receiving readings in memory sequence of polynucleotides mapped to a genetic locus; (b) determining, among said sequence readings, the identity of the bases that are different from a base of a reference sequence at the locus of the total number of mapping sequence readings to a locus; (c) report the identity and relative quantity of the determined bases and their location in the genome; and (d) infer the heterogeneity of a given sample based on the information in (c). In some modes, the implemented method also comprises receiving readings derived from samples in a plurality of different times and calculating a difference in the relative quantity and identity of a plurality of bases between the two samples.

In addition or alternatively, the detection of a genetic biomarker may include a method comprising: (a) performing biomolecular analysis of biomolecular polymers from disease cells (for example, spatially distinct disease cells) from an individual;

(b) identify and quantify biomolecular variants in biomolecular macromolecules; (c) develop a profile of disease cell heterogeneity in the individual, indicating the presence and relative quantity of a plurality of variants in the biomolecular macromolecules, in which different relative quantities indicate the disease cell heterogeneity; and (d) determining a therapeutic intervention for a disease that exhibits the cellular heterogeneity of the disease, where the therapeutic intervention is effective against a disease that has the determined cell heterogeneity profile of the disease.

In some modalities, the disease cells are spatially distinct disease cells.

In some modalities, therapeutic intervention is determined based on a database of interventions that prove to be therapeutic for cancers with tumoral heterogeneity characterized by each of the somatic mutations.

Additionally or alternatively, the detection of a genetic biomarker may include a method for detecting the heterogeneity of disease cells in an individual comprising: a) quantifying polynucleotides that have a sequence variant in each of a plurality of genetic loci in polynucleotides a sample from the individual, in which the sample comprises polynucleotides from somatic cells and pathological cells; b) determine for each locus a measure of the variation in the number of copies (CNV) for polynucleotides carrying the sequence variant; c) determine for each locus a weighted measure of the amount of polynucleotides carrying a sequence variant at the locus as a function of the CNV at the locus; and d) compare the weighted measures in each of the plurality of loci, in which different weighted measures indicate heterogeneity of the disease cells.

In some embodiments, the disease cells are tumor cells.

In some modalities, polynucleotides comprise cfDNA.

In addition or alternatively, the detection of a genetic biomarker may include a method to infer a measure of the DNA load of cells subjected to cell division in a sample that comprises measuring the change in copy number induced by the proximity of one or more genomic loci to the origins of cell replication, in which the increase in CNV indicates cells subjected to cell division.

In some modalities, the charge is measured in DNA without cells.

In some modalities, the load measurement refers to the fraction of tumor cells or equivalent to the DNA genome of the tumor cells in the sample.

In some modalities, CNV due to its proximity to the origins of replication is inferred from a set of control samples or cell lines.

In some modalities, a hidden markov model, regression model, model based on principal component analysis or model modified by genotype is used to approximate variations due to the origins of replications.

In some modalities, the measure of the load is the presence or absence of cells subjected to cell division.

In some embodiments, the proximity is within 1 kb of an origin of replication.

In addition or alternatively, the detection of a genetic biomarker may include a method to increase the sensitivity and / or specificity of determining variations in the number of copies related to the gene, improving the effect of the variations due to the proximity of the origins of the replications.

In some modalities, the method comprises measuring CNV at a site, determining the amount of CNV due to the proximity of the site to an origin of replication and correcting the measured CNV to reflect the genomic CNV, for example, subtracting the amount of CNV attributable to cell division.

In some modalities, genomic data is obtained from DNA without cells.

In some modalities, the load measurement refers to the fraction of tumor cells or equivalent to the DNA genome in a sample.

In some modalities, variations due to the origins of the replication are inferred from a set of control samples or cell lines.

In some modalities, a hidden markov model, regression model, model based on principal component analysis or model modified by genotype is used to approximate variations due to the origins of replications.

[474] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in US Patent Application Publication 2017/0061072, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include methods and systems for detecting single nucleotide variations (SNVs) from somatic sources in a biological sample without cells from an individual, such as a mixture of nucleic acid molecules from sources somatic and germ line. In some modalities, the systems and methods detect single nucleotide variations (SNVs) from somatic sources in an individual's biological sample without cells, generating training data with class markers; form a machine learning unit with an output for each so-called adenine base (A), cytosine (C), guanine (G) and thymine (T), respectively; train the machine learning unit with a biological sample training set; and apply the machine learning unit to detect SNVs from somatic sources in the biological sample without cells, where the biological sample without cells can comprise a mixture of nucleic acid molecules (for example, deoxyribonucleic acid (DNA)) from somatic sources and germinative, for example, cells comprising somatic mutations and germline DNA.

[475] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in US Patent Application Publication 2017/0058332, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include a method for detecting a somatic or germline variant, comprising providing a predetermined genomic DNA (gDNA) to an assay mixture, capturing a sample of an individual's genetic information using a gene analyzer and detect genetic variants of genetic information; and classifying a variant from a germline source, if present in molecules derived from gDNA with lengths greater than molecules derived from DNA without cells (cfDNA). In some embodiments, the gDNA has a fragment length greater than about 200 bases.

In some embodiments, the gDNA has a fragment length of at least 400 bases or at least 500 bases.

In some embodiments, the length of the gDNA fragment is greater than the distribution of the length of the cfDNA fragment.

In some embodiments, gDNA is added to the test mixture.

In some embodiments, the gDNA is left in the test mixture after a filtering operation.

In some embodiments, the gDNA is left in the test mixture after a centrifugation operation.

In some embodiments, approximately 1% to 5% of gDNA is added to the test mixture.

In some embodiments, at least 1% gDNA is added to the test mixture.

In addition or alternatively, the detection of a genetic biomarker may include a method comprising providing a sample comprising genomic DNA (gDNA) and cellless DNA (cfDNA) from an individual; determine the germline genotype of the individual with at least one genetic locus of the gDNA; determine a quantitative measure of at least one genetic variant at each genetic locus in cfDNA; determine whether or not the quantitative measure of the genetic variant is consistent with the germline genotype; and classify the genetic variant as a germline variant if the quantitative measure is consistent with the germline genotype or as a somatic mutant if the quantitative measure is not consistent with the germline genotype.

In addition or alternatively, the detection of a genetic biomarker may include a method comprising determining a quantitative measure of a genetic variant detected in an individual's cellless DNA (cfDNA); determine that the measure is consistent with a heterozygous genotype in the individual; determine a probable genotype of the individual in the locus from genomic DNA (gDNA); compare the genotype at the gDNA locus with the variant detected in cfDNA; and classify the variant as a somatic mutation if the variant detected in the cfDNA is not consistent with the genotype in the gDNA locus.

In some modalities, classify the variant as a somatic mutation if the genotype at the gDNA locus is determined to be homozygous.

In some modalities, classify the variant as a somatic mutation if the genotype at the gDNA locus is determined to be heterozygous with a selected confidence in the group consisting of: at least 70%, at least 80%, at least 90% at least 95% or at least 99%. In some modalities, determining a quantitative measure of a genetic variant involves sequencing the cfDNA.

In some modalities, determining the likely genotype of the individual comprises sequencing the individual's genomic DNA.

In some modalities, sequencing is selected from the group consisting of: targeted sequencing, real-time sequencing of single molecule, sequencing by exon, sequencing based on electron microscopy, panel sequencing, transistor-mediated sequencing, direct sequencing, sequencing random spin, Sanger didesoxy termination sequencing, entire genome sequencing, hybridization sequencing, pyrosquencing, capillary electrophoresis, gel electrophoresis, duplex sequencing, cycle sequencing, base extension sequencing single, solid phase sequencing, high throughput sequencing, massively parallel signature sequencing, emulsion PCR, lower denaturation temperature-PCR (COLD-PCR), multiplex PCR, sequencing by reversible dye terminator, sequencing endpoint, short-term sequencing, sequencing exonucleate sequencing, linkage sequencing, short reading sequencing, single molecule sequencing, synthesis sequencing, real-time sequencing, reverse terminator sequencing, nanoporous sequencing, 454, sequencing analyzer Solexa, SOLiD ™, EM-PET and a combination of them.

[476] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include any of the various forms described in PCT Publication WO 2018/119452, which is incorporated herein by reference in its entirety . For example, the detection of a genetic biomarker may include methods, compositions and systems for analyzing a nucleic acid population comprising at least two forms of nucleic acid selected from double-stranded DNA, single-stranded DNA and stranded RNA simple. In some embodiments, the method comprises (a) linking at least one of the nucleic acid forms with at least one labeled nucleic acid to distinguish the forms from one another, (b) amplifying the nucleic acid forms at least one of the which is linked to at least one nucleic acid tag, wherein the nucleic acids and the linked nucleic acid tag, if present, are amplified, to produce amplified nucleic acids, of which those which are amplified from at least one shape is marked; (c) analyzing amplified nucleic acid sequence data, at least some of which are labeled; and (d) decoding labeled nucleic acid molecules from the amplified nucleic acids to reveal the forms of nucleic acids in the population, providing an original model for the amplified nucleic acids attached to the labeled nucleic acid molecules for which the data of sequence were analyzed.

In some embodiments, the method further comprises enriching at least one of the forms in relation to one or more of the other forms.

In some embodiments, at least 70% of the molecules of each form of nucleic acid in the population are amplified in step (b). In some embodiments, at least three forms of nucleic acid are present in the population and at least two of the forms are linked to different forms of labeled nucleic acid that distinguish each of the three forms from one another.

In some embodiments, each of the at least three forms of nucleic acid in the population is linked to a different label.

In some embodiments, each molecule is similarly linked to a tag that comprises the same identification information tag (for example, a tag with the same or comprising the same sequence). In some modalities, molecules are similarly linked to different types of tags.

In some modalities, step (a) comprises: subjecting the population to reverse transcription with a labeled primer, in which the labeled primer is incorporated into the cDNA generated from the RNA in the population.

In some embodiments, reverse transcription is sequence specific.

In some modalities, reverse transcription is random.

In some modalities, the method also includes degrading RNA duplex to cDNA.

In some modalities, the method further comprises separating single-stranded DNA from double-stranded DNA and attaching nucleic acid tags to double-stranded DNA.

In some embodiments, single-stranded DNA is separated by hybridization with one or more capture probes.

In some embodiments, the method further comprises differential labeling of single-stranded DNA with a single-stranded label using a ligase that works on single-stranded nucleic acids and double-stranded DNA with double-stranded adapters using ligase that works on double-stranded nucleic acids.

In some embodiments, the method further comprises, prior to analysis, assembling labeled nucleic acids comprising different forms of nucleic acid.

In some modalities, the method also comprises analyzing sets of DNA partitioned separately in individual assays.

The tests can be the same, substantially similar, equivalent or different.

In any of the above methods, the sequence data may indicate the presence of a somatic variant or germline, or a copy number variation or a single nucleotide variation, or an indelible or genetic fusion.

In addition or alternatively, the detection of a genetic biomarker may include a method for analyzing a population of nucleic acids comprising nucleic acids with different extensions of modification.

In addition or alternatively, the detection of a genetic biomarker may include methods for screening for characteristics (for example, 5 'methylcytosine) associated with a disease.

The method comprises contacting the nucleic acid population with an agent (such as a methyl-binding domain or protein) that binds preferentially to nucleic acids carrying the modification; separating a first set of nucleic acids bound to the agent from a second set of nucleic acids not bound to the agent, where the first set of nucleic acids is over-represented for the modification and the nucleic acids in the second set are over-represented for modification; linking the nucleic acids in the first set and / or the second set to one or more nucleic acid markers that distinguish the nucleic acids in the first set and the second set to produce a population of labeled nucleic acids; amplifying the labeled nucleic acids, wherein the nucleic acids and the attached tags are amplified; analyzing sequence data for amplified nucleic acids and linked tags; decode the tags to reveal whether the nucleic acids for which the sequence data was analyzed were amplified from models in the first or second set.

In addition or alternatively, the detection of a genetic biomarker may include a method for analyzing a population of nucleic acids in which at least some of the nucleic acids include one or more modified cytosine residues.

The method comprises binding capture fractions, for example, biotin, to nucleic acids in the population to serve as models for amplification; perform an amplification reaction to produce amplification products from the models; separate connected models to capture fractions of amplification products; analyzing sequence data from linked models to capture portions by bisulfite sequencing; and testing sequence data for amplification products.

In addition or alternatively, the detection of a genetic biomarker may include a method for analyzing a population of nucleic acids comprising nucleic acids with different extensions of 5-methylcytosine.

The method comprises (a) contacting the nucleic acid population with an agent that binds preferentially to 5-methylated nucleic acids; (b) separating a first set of nucleic acids bound to the agent from a second set of nucleic acids not bound to the agent, wherein the first set of nucleic acids is over-represented for 5-methylcytosine, and the nucleic acids in the second set they are underrepresented for 5-methylation; (c) attaching the nucleic acids in the first set and / or the second set to one or more nucleic acid markers that distinguish the nucleic acids in the first set and the second set, where the nucleic acid tags attached to the nucleic acids in the first set comprises a capture fraction (eg biotin); (d) amplifying the labeled nucleic acids, wherein the nucleic acids and the attached tags are amplified; (e) separating amplified nucleic acids carrying the capture fraction from amplified nucleic acids that do not support the capture fraction; and (f) analyzing the sequence data of the separate amplified nucleic acids.

In addition or alternatively, the detection of a genetic biomarker may include a method for analyzing a population of nucleic acids comprising at least two forms of nucleic acids selected from double-stranded DNA, single-stranded DNA and single-stranded RNA, the method , wherein each of the at least two forms comprises a plurality of molecules, comprising: attaching at least one of the nucleic acid forms to at least one nucleic acid tag to distinguish the forms from each other, amplifying the forms nucleic acid, at least one of which is linked to at least one nucleic acid tag, wherein the nucleic acids and the linked nucleic acid tag are amplified, to produce amplified nucleic acids, of which those are amplified to from at least one shape are marked; analyzing amplified nucleic acid sequence data, at least some of which are labeled; where the assay obtains sufficient sequence information to decode the nucleic acid molecules from the amplified nucleic acid to reveal the nucleic acid forms in the population, providing an original model for the amplified nucleic acids attached to the nucleic acid molecules of label for which the sequence data was tested.

In one embodiment, the method further comprises the step of decoding the labeled nucleic acid molecules from the amplified nucleic acids to reveal the forms of nucleic acids in the population, providing an original model for the amplified nucleic acids attached to the nucleic acid molecules label for which the sequence data was analyzed.

In another embodiment, the method further comprises enriching at least one of the forms in relation to one or more of the other forms.

In another embodiment, at least 70% of the molecules of each form of nucleic acid in the population are amplified.

In another mode, at least three forms of nucleic acid are present in the population and at least two of the forms are linked to different forms of label nucleic acid that distinguish each of the three forms from one another.

In another embodiment, each of the at least three forms of nucleic acid in the population is linked to a different label.

In another embodiment, each molecule is similarly linked to a tag comprising the same information as the tag.

In another embodiment, molecules are similarly linked to different types of tags.

In another embodiment, the method further comprises subjecting the population to reverse transcription with a labeled primer, in which the labeled primer is incorporated into the cDNA generated from the RNA in the population.

In another embodiment, reverse transcription is sequence specific.

In another mode, in which reverse transcription is random.

In another mode, the method also comprises the degrading RNA duplex to the cDNA.

In another embodiment, the method further comprises separating single-stranded DNA from double-stranded DNA and attaching nucleic acid tags to double-stranded DNA.

In another embodiment, single-stranded DNA is separated by hybridization with one or more capture probes.

In another embodiment, the method further comprises circulating single-stranded DNA with a circligase and attaching nucleic acid tags to the double-stranded DNA.

In another embodiment, the method comprises, prior to analysis, assembling labeled nucleic acids comprising different forms of nucleic acid.

In another embodiment, the nucleic acid population is from a sample of body fluid.

In another embodiment, the body fluid sample is blood, serum or plasma.

In another embodiment, the nucleic acid population is a nucleic acid population without cells.

In another embodiment, the body fluid sample is from an individual suspected of having cancer.

In another embodiment, the sequence data indicates the presence of a somatic variant or germline.

In another mode, the sequence data indicates the presence of a variation in the number of copies.

In another embodiment, the sequence data indicates the presence of a single nucleotide variation (SNV), indelible or gene fusion.

In another embodiment, the sequence data indicates the presence of a single nucleotide variation (SNV), indelible or gene fusion.

In addition or alternatively, the detection of a genetic biomarker may include a method, comprising: providing a population of nucleic acid molecules obtained from a body sample from an individual; fractionating the population of nucleic acid molecules based on one or more characteristics to generate plurality of groups of nucleic acid molecules, wherein the nucleic acid molecules of each of the plurality of groups comprise distinct identifiers; bringing together the plurality of groups of nucleic acid molecules; sequencing the assembled plurality of groups of nucleic acid molecules to generate plurality of sets of sequence readings; and split the sequence readings based on the identifiers.

In addition or alternatively, the detection of a genetic biomarker may include a method to analyze the fragmentation pattern of DNA without cells, comprising: providing a population of DNA without cells from a biological sample; fractionate the DNA population without cells, generate subpopulations of DNA without cells; sequencing at least one subpopulation of DNA without cells, thus generating sequence readings; align the sequence liters to a reference genome; and, determine the DNA fragmentation pattern without cells in each subpopulation by analyzing any number of: length of each sequence read in the mapping for each base position in the reference genome; number of mappings of sequence readings to the base position in the reference genome as a function of the length of the sequence readings; number of sequence readings starting at each base position in the reference genome; or, number of sequence readings that end at each base position in the reference genome. In another modality, the one or more characteristics comprise a chemical modification selected from the group consisting of: methylation, hydroxymethylation, formylation, acetylation and glycosylation.

[477] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include any of the various forms described in PCT Publication WO 2018/009723, which is incorporated herein by reference in its entirety . For example, the detection of a genetic biomarker may include methods, systems and compositions for performing nucleosome profiling using cellless nucleic acids (for example, cfDNA). This can be used to identify new trigger genes, determine copy number variation (CNV), identify somatic mutations and structural variations, such as fusions and indels, as well as identify regions that can be used in an assay multiplexed to detect any of the above variations. In addition or alternatively, the detection of a genetic biomarker can include various uses of nucleic acids without cells (for example, DNA or RNA). Such uses include detecting, monitoring and determining treatment for an individual with or suspected of having a health condition, such as a disease (for example, cancer). The methods provided can use sequence information on a macro and global scale, with or without information on somatic variants, to evaluate a fragment fragment profile that may be representative of a tissue of origin, disease, progression, etc. In addition or alternatively, the detection of a genetic biomarker may include a computer-implemented method to determine the presence or absence of a genetic aberration in deoxyribonucleic acid (DNA) fragments from cellless DNA obtained from an individual, the method comprising: (a) building, by a computer, a multiparametric distribution of the DNA fragments over a plurality of base positions in a genome; and (b) without taking into account a basic identity of each base position in a first locus, use the multiparametric distribution to determine the presence or absence of the genetic aberration in the first locus in the individual.

In some modalities, the genetic aberration comprises a sequence aberration.

In some embodiments, the sequence aberration comprises a single nucleotide (SNV) variant. In some embodiments, the sequence aberration comprises an insertion or exclusion (indelible), or a fusion of genes.

In some embodiments, the sequence aberration comprises two or more different members selected from the group consisting of (i) a single nucleotide variant (SNV), (ii) an insertion or exclusion (indel) and (iii) a gene fusion .

In some modalities, genetic aberration comprises a variation in the number of copies (CNV). In some embodiments, the multiparametric distribution comprises a parameter indicating the length of the DNA fragments that align with each of the plurality of base positions in the genome.

In some embodiments, the multiparametric distribution comprises a parameter indicative of a number of DNA fragments that align with each of the plurality of base positions in the genome.

In some embodiments, the multiparametric distribution comprises a parameter indicating a number of DNA fragments that begin or end at each of the plurality of base positions in the genome.

In some modalities, the multiparametric distribution comprises parameters indicative of two or more of: (i) a length of DNA fragments that align with each of the plurality of base positions in the genome, (ii) a number of DNA fragments that align with each of the plurality of base positions in the genome and (iii) a number of fragments of

DNAs that start or end at each of the plurality of base positions in the genome.

In some modalities, the multi-type distribution comprises parameters indicative of (i) a length of the DNA fragments that align with each of the plurality of base positions in the genome, (ii) a number of DNA fragments that align with each of the plurality of base positions in the genome and (iii) a number of DNA fragments that begin or end at each of the plurality of base positions in the genome.

In addition or alternatively, the detection of a genetic biomarker may include a method for generating a classifier to determine the probability that an individual belongs to one or more classes of clinical significance, the method comprising: a) providing a training set comprising, for each of the one or more classes of clinical significance, cellless DNA populations of each of a plurality of individuals of a species belonging to the class of clinical significance and of each of a plurality of individuals of the species not belonging to the class of clinical significance; b) sequencing cell-free DNA fragments from cell-free DNA populations to produce a plurality of DNA sequences; c) for each population of DNA without cells, map the plurality of DNA sequences for each of one or more genomic regions in a reference genome of the species, each genomic region comprising a plurality of genetic loci; d) prepare, for each population of DNA without cells, a data set comprising, for each of a plurality of genetic loci, values indicating a quantitative measure of at least one characteristic selected from: (i) DNA sequences mapped to the genetic locus, (ii) DNA sequences starting at the site and (iii) DNA sequences ending at the genetic site, to produce a training set; and e) to train a computer-based machine learning system in the training set, thus generating a classifier to determine the probability that the individual belongs to one or more classes of clinical significance.

In addition or alternatively, the detection of a genetic biomarker may include a method for determining an abnormal biological state in an individual, the method comprising: a) sequencing DNA fragments without cells from the individual's DNA without cells to produce DNA sequences; b) map the DNA sequences for each of one or more genomic regions in a reference genome of an individual species, each genomic region comprising a plurality of genetic loci; c) prepare a set of data comprising, for each of a plurality of genetic loci, values indicating a quantitative measure of at least one resource selected from: (i) DNA sequences mapped to the genetic locus, (ii ) DNA sequences starting at the locus and (iii) DNA sequences ending at the genetic locus; and d) based on the data set, determine a probability of the abnormal biological state.

In addition or alternatively, the detection of a genetic biomarker may include a method implemented by computer to generate an output indicative of the presence or absence of a genetic aberration in fragments of deoxyribonucleic acid (DNA) from DNA without cells obtained for an individual, the method comprising: (a) building, by a computer, a distribution of DNA fragments from cellless DNA over a plurality of base positions in a genome; and (b) for each one or more genetic loci, calculate, by computer, a quantitative measure indicative of a ratio of (1) a number of DNA fragments with dinucleosomal protection associated with a genetic locus of one or more genetic loci and (2) a number of DNA fragments with mononucleosomal protection associated with the genetic locus, or vice versa; and (c) determining, using the quantitative measure for each one or more genetic loci, said result indicative of a presence or absence of the genetic aberration in one or more genetic loci in the individual.

In some embodiments, the distribution comprises one or more multiparametric distributions.

Additionally or alternatively, the detection of a genetic biomarker may include a computer-implemented method to deconstruct a distribution of deoxyribonucleic acid (DNA) fragments from cellless DNA obtained from an individual, the method comprising: (a ) build, by a computer, a distribution of a covering of DNA fragments from DNA without cells through a plurality of base positions in a genome; and (b) for each or more genetic loci, deconstruct, by a computer, the coverage distribution, generating fractional contributions associated with one or more selected members of the group consisting of a copy number component (CN), a cell clearance component and a gene expression component.

In addition or alternatively, the detection of a genetic biomarker may include a computer-implemented classifier to determine genetic aberrations in a test subject using deoxyribonucleic acid (DNA) fragments from cellless DNA obtained from the test subject, comprising comprising: (a) an entry of a set of distribution scores for each of one or more populations of DNA without cells obtained from each of a plurality of individuals, where each distribution score is generated based on at least one or more of the following items: (i) a length of the DNA fragments that align with each of a plurality of base positions in a genome, (ii) a number of DNA fragments that align with each one of a plurality of base positions in a genome and (iii) a number of DNA fragments that start or end in each of a plurality of base positions in a genome; and (b) an exit from classifications of one or more genetic aberrations in the test subject.

In addition or alternatively, the detection of a genetic biomarker may include a method implemented by a computer to create a trained classifier, comprising: (a) providing a plurality of different classes, where each class represents a set of individuals with a shared characteristic; (b) for each of a plurality of cell-free DNA populations obtained from each of the classes, provide a representative multiparametric model of cell-free deoxyribonucleic acid (DNA) fragments from cell-free DNA populations, thus providing a data set training; and (c) train, by computer, a learning algorithm in the training data set to create one or more trained classifiers, in which each trained classifier is configured to classify an individual's cell-free DNA test population in one or more of the plurality of different classes.

In addition or alternatively, the detection of a genetic biomarker may include a method for classifying a test sample from an individual, comprising: (a) providing a multiparametric model representative of deoxyribonucleic acid (DNA) fragments without cells from a DNA test population without cells of the individual; and (b) classifying the cell-free DNA test population using a trained classifier.

In addition or alternatively, the detection of a genetic biomarker may include a computer-implemented method that comprises: (a) generating, by a computer, sequence information from DNA fragments without cells from an individual; duo; (b) map, by computer, the DNA fragments without cells to a reference genome based on the sequence information; and (c) analyzing, by computer, the DNA fragments without cells mapped to determine, in each of a plurality of base positions in the reference genome, a plurality of measures selected from the group consisting of: (i ) number of cells - DNA fragments without cells mapping to the base position, (ii) length of each DNA fragment without cells mapped to the base position, (iii) number of DNA fragments without cells mapped to the base position the base position as a function of the length of the DNA fragment without cells; (iv) number of DNA fragments without cells that start at the base position; (v) number of DNA fragments without cells that end in the base position; (vi) number of DNA fragments without cells starting at the base position as a function of length and (vii) number of DNA fragments without cells ending at the base position as a function of length. Additionally or alternatively, the detection of a genetic biomarker may include a computer-implemented method to deconstruct a distribution of deoxyribonucleic acid (DNA) fragments from cellless DNA obtained from an individual, the method comprising: (a ) build, by a computer, a distribution of a covering of DNA fragments from DNA without cells through a plurality of base positions in a genome; and (b) for each or more genetic loci, deconstruct, by a computer, the coverage distribution, generating fractional contributions associated with one or more selected members of the group consisting of a copy number component (CN), a cell clearance component and a gene expression component. In some modalities, the method also comprises generating an output indicative of the presence or absence of a genetic aberration based on at least a portion of the fractional contributions.

[478] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include any of the various forms described in PCT Publication WO 2017/181146, which is incorporated herein by reference in its entirety . For example, the detection of a genetic biomarker may include methods and systems that can be used for the early detection of cancer.

In some modalities, the method comprises (a) providing a sample comprising an individual's cfDNA, in which the individual does not detect cancer in a detectable way; (b) capturing the sample of cfDNA molecules covered by a sequencing panel, where the sequencing panel comprises one or more regions of each of a plurality of different genes, where: (i) the sequencing panel is not greater than 50,000 nucleotides; (ii) the presence of a tumor marker in any of the different genes indicates that the individual has cancer; and (iii) at least 80% of individuals with cancer have a tumor marker present in at least one of the plurality of different genes; and (c) sequencing the captured cfDNA molecules at a reading depth sufficient to detect tumor markers with a frequency in the sample as low as 0.01%. In some modalities, a tumor marker is selected from the group consisting of a single base substitution, a variation in the number of copies, an indel, a fusion of genes, a transversion, a translocation, an inversion, an exclusion, aneuploidy, partial aneuploidy, polyploidy, chromosomal instability, changes in chromosomal structure, chromosomal fusions, gene truncation, gene amplification, gene duplication, chromosomal damage, DNA damage, abnormal changes in chemical modifications of nucleic acids, abnormal changes in epigenetic patterns and abnormal changes in nucleic acid methylation.

In some modalities, at least 85%, at least 90%, at least 93%, at least 95%, at least 97%), at least 98% or at least 99% of individuals with cancer have a tumor marker present in at least least one among the plurality of different genes.

Some modalities include sequencing captured cfDNA molecules at a reading depth sufficient to detect tumor markers with a frequency in the sample as low as 0.005%, 0.001% or 0.0005%. In addition or alternatively, the detection of a genetic biomarker may include a method comprising: a. providing a sample comprising cell-free nucleic acid (cfNA) molecules from an individual, in which the individual does not detect cancer in a detectable way; B. capturing the sample of cfNA molecules covered by a sequencing panel, wherein the sequencing panel comprises one or more regions of each of a plurality of different genes, where: i. the sequencing panel is not larger than 50,000 nucleotides; ii. a presence of a tumor marker in any of the different genes indicates that the individual has cancer; and iii. at least 80% of individuals with cancer have a tumor marker present in at least one of the plurality of different genes; and c. sequence the captured cfNA molecules at a reading depth sufficient to detect tumor markers with a frequency in the sample as low as 1.0%, 0.75%, 0.5%, 0.25%, 0.1 %, 0.075%, 0.05%, 0.025%, 0.01% or 0.005%. In addition or alternatively, the detection of a genetic biomarker may include a method for detecting cancer in an individual comprising: sequencing DNA without circulating cells (cfDNA) from the individual to a depth of at least 50,000 readings per base to detect one or more genetic variants associated with cancer.

In some modalities, the sequencing is at a depth of at least 100,000 readings per base.

In some modalities, sequencing is at a depth of around 120,000 readings per base.

In some modalities, the sequencing is at a depth of around 150,000 readings per base.

In some modalities, sequencing is at a depth of about 200,000 readings per base.

In some embodiments, the base readings represent at least 5,000 molecules of original nucleic acid, at least 10,000 molecules of original nucleic acid, at least 20,000 molecules of original nucleic acid, at least 30,000 molecules of original nucleic acid, at least less than 40,000 molecules of original nucleic acid or at least 50,000 molecules of original nucleic acid.

In some modalities, the method also comprises comparing sequence information from cfDNA with sequence information obtained from a cohort of healthy individuals, a cohort of cancer patients or DNA from the individual's germline.

In some embodiments, the method further comprises amplifying cfDNA prior to sequencing and determining a consensus sequence from the sequence readings obtained from the sequence to reduce amplification or sequencing errors.

In some embodiments, the determination of the consensus sequence is performed on a molecule-by-molecule basis.

In some modalities, the determination of the consensus sequence is carried out on a base-by-base basis.

In some modalities, the detection of the consensus sequence is based on the evaluation of the probabilities of each of the potential nucleotides based on the observed sequencing output, as well as on the characteristics of the sequencing and amplification error profile of an individual sample, a batch of samples or a sample reference set.

In some embodiments, the determination of the consensus sequence is performed using molecular bar codes that mark individual cfDNA molecules derived from the individual.

In some modalities, a set of molecules with a consensus sequence deviated from human reference is compared to those observed in other samples processed in the laboratory to determine and exclude any potential contamination event.

In some modalities, the determination of the consensus sequence is optimized by comparing the consensus sequence with those obtained in the cohort of healthy individuals, in the cohort of cancer patients or in the individual's germline DNA.

In some modalities, the method also comprises marking the cfDNA molecules with a barcode, so that at least 20% of the cfDNA in a sample derived from the individual is marked.

In some embodiments, marking is performed by attaching adapters comprising a bar code.

In some embodiments, adapters comprise any or all of the blunt-ended adapters, restriction enzyme overhang adapters or adapters with a single nucleotide overhang.

In some modalities, adapters with a single nucleotide protrusion comprise tail adapters C, tail adapters A, tail adapters T and / or tail adapters G.

In some modalities, the marking is performed by PCR amplification using barcode primers.

In some embodiments, the barcode is single-stranded.

In some embodiments, the barcode is double-stripe.

In some modalities, the method also comprises dividing the cfDNA into partitions.

In some modalities, the cfDNA in each partition is marked exclusively in relation to another partition.

In some modalities, the cfDNA in each partition is marked in a non-exclusive way in relation to another partition.

In some modalities, the cfDNA in each partition is not marked.

Additionally or alternatively, the detection of a genetic biomarker may include a method for detecting a tumor in an individual suspected of having cancer or having cancer, comprising: (a) sequencing DNA molecules without cells (cfDNA) derived from a sample cell-free DNA (cfDNA) obtained from the individual; (b) analyzing sequence readings derived from the sequencing to identify (i) circulating tumor DNA (ctDNA) between cfDNA molecules and (ii) one or more driver mutations in cfDNA; and (c) use information about the presence, absence or quantity of one or more driver mutations in the ctDNA molecules to identify (i) the tumor in the individual and (ii) actions for the treatment of the tumor to be performed by the individual, in that the method detects the tumor in the individual with a sensitivity of at least 85%, a specificity

at least 99% and a diagnostic accuracy of at least 99%.

[479] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include any of the various forms described in PCT Publication WO 2017/136603, which is incorporated herein by reference in its entirety . For example, the detection of a genetic biomarker may include methods and systems for detecting or monitoring the evolution of cancer. Additionally or alternatively, the detection of a genetic biomarker may include a method implemented by computer, comprising: (a) obtaining information about a plurality of individuals with cancer at a first point in time, in which the information with - comprise for each individual of the plurality of individuals at least, a genetic profile of a tumor obtained by the genotyping of nucleic acids from a body fluid without cells and any treatment provided to the individual before the first point in time, and determining a first state for each plurality of individuals based on the information at the first time point to produce a set of first states; (b) obtaining information about the plurality of individuals at one or more second time points following the first time point and determining a second state of each individual's plurality at each one or more second time points based on the information in a given point of one or more seconds, to produce a set of subsequent states; and (c) use the set of first states of (a) and the set of subsequent states of (b) to generate a predictive algorithm configured to determine the probability that a given first state will result in a second state among a set of states at a time point subsequent to the first specified state.

In some embodiments, the method further comprises (d) for the first state given between a set of states at a previous time point, determining the probability that the first determined state will result in the second state among the set of states at the later point of time; and (e) generate an electronic output indicative of the probability determined in (d). Additionally or alternatively, the detection of a genetic biomarker may include a computer-implemented method, comprising: (a) obtaining information about a plurality of individuals with cancer in a first point of time, in which the information comprises, for each individual the plurality of individuals, at least one genetic profile of a tumor obtained by genotyping at least 50 genes and any treatment provided to the individual before the first point in time, and determining a first state of each of the plurality of individuals based on the information at the first point of time, to produce a set of first states; (b) obtaining information about the plurality of individuals at one or more second time points following the first time point and determining a second state of each individual's plurality at each one or more second time points based on the information in a given point of one or more second time points, to produce a set of subsequent states; and (c) use the set of first states of (a) and the set of subsequent states of (b) to generate a predictive algorithm configured to determine the likelihood that a given first state will result in a second state between a set of states at a later point in time subsequent to the first specified state.

In some embodiments, the method further comprises (d) for the first state given between a set of states at a previous time point, determining the probability that the first determined state will result in the second state among the set of states at the point later time; and (e) generate an electronic output indicative of the probability determined in (d). In some modes, obtaining the information comprises sequencing the cellless deoxyribonucleic acid (cfDNA) of the plurality of individuals and, optionally, conducting a medical interview of each of the plurality of individuals.

In some modalities, treatment was provided to the individual before the first point in time.

In some modes, the methods comprise generating one or more decision trees, each decision tree comprising a root node, one or more decision branches, one or more decision nodes and one or more terminal nodes, where a state at the root node represents the first time point, the one or more decision branches represent alternative treatments, and the one or more decision nodes and the one or more terminal nodes represent subsequent states.

In some modalities, the one or more decision branches comprise a plurality of decision branches.

In some modalities, the subsequent states comprise a viability status (s) of the individuals indicating that the individuals are alive or deceased.

In some embodiments, subsequent states comprise an individual's survival rate.

In some embodiments, each of the first states comprises a common set of one or more somatic mutations.

In some modalities, the information also includes an individual profile.

Additionally or alternatively, the detection of a genetic biomarker may include a method, comprising: (a) obtaining information about an individual with cancer in a first point of time, in which the information comprises at least one characteristic of the individual from a patient profile, a tumor profile or a treatment; (b) determining an individual's initial state based on the information at the first point of time; (ç)

determining a probability for each of a plurality of subsequent states at each or more subsequent time points based on the individual's initial state, thus providing a set of probabilities in relation to the state's results; (d) generate a recommendation for a treatment for cancer based, at least in part, on the set of probabilities with respect to state results that optimize for a probability that the individual will obtain a specific result; and (e) generate an electronic output indicating the recommendation generated in (d). In some embodiments, the probability is at least in part a function of a treatment choice from a plurality of treatment options.

In some embodiments, the subsequent one or more time points comprise a plurality of subsequent time points.

In some modalities, the method further comprises determining the probability at a plurality of subsequent time points.

In some modalities, time points comprise at least three time points.

In some embodiments, time points comprise at least four time points.

In some modes, the first time point is prior to the individual receiving treatment and the subsequent time point is after the individual receiving treatment.

In some embodiments, a second treatment is administered after the subsequent time point based on the subsequent state at the subsequent time point.

In some modalities, at least one characteristic of the individual is from the patient's profile and is selected from the group consisting of: age, gender, genetic profile, enzyme levels, organ function, quality of life, frequency of medical interventions, remission status and patient outcome.

In addition or alternatively, the detection of a genetic biomarker may include a method, comprising: (a) establishing one or more communication links in a communication network with one or more medical service providers; (b) receive, through the communications network from one or more medical service providers, medical information about one or more individuals; (c) receiving from the medical service provider one or more samples comprising cellless deoxyribonucleic acid (cfDNA) from each or more individuals; (d) sequencing the cfDNA and identifying one or more genetic variants present in the cfDNA; (e) create or supplement a database with information for each or more individuals, comprising both the identified genetic variants and the medical information received; and (f) use the database and a computer-implemented algorithm, generating at least one predictive model that predicts, based on an individual's initial state, the likelihood of a subsequent state for each of a plurality of therapeutic interventions. different medicines.

In addition or alternatively, the detection of a genetic biomarker may include a non-transient computer-readable medium comprising machine-executable code that, after being executed by one or more computer processors, implements a method comprising: (a) obtaining information about a plurality of individuals with cancer in a first point in time, in which the information comprises for each individual of the plurality of individuals in at least one genetic profile of a tumor obtained by the genotyping of nucleic acids from a fluid body without cells and any treatment provided to the individual before the first point in time, and determining a first state of each plurality of individuals based on the information in the first point of time to produce a set of first states; (b) obtain information about the plurality of individuals at one or more second time points subsequent to the first time point and determine a second state of each plurality of individuals at each one or more second time points based on information at a given point of one or more seconds, to produce a set of subsequent states; and (c) use the set of first states of (a) and the set of subsequent states of (b) to generate a predictive algorithm configured to determine the probability that a given first state will result in a second state among a set of states at a time point subsequent to the first specified state.

In addition or alternatively, the detection of a genetic biomarker may include a method comprising: (a) obtaining information about an individual comprising at least a genetic profile of a tumor and a treatment previously or currently provided to the individual, if any, and determine an individual's initial state based on the information; (b) providing a decision tree, in which a root node represents an individual's initial state, decision branches represent alternative treatments available to the individual, chance nodes represent points of uncertainty and decision nodes or terminal nodes represent states subsequent ones; (c) providing a course of treatment for the individual that maximizes a person's likelihood of reaching a state of life at a terminal node; and (d) administering the course of treatment to the individual.

In some modalities, the method further comprises: (e) at a second point in time subsequent to the initial state, obtaining information about an individual comprising at least a genetic profile of a tumor and a treatment previously or currently provided to the individual, if any, and to determine a second state of the individual among a plurality of subsequent states based on the information; (f) based on the second state, providing a subsequent course of treatment for the individual that maximizes the individual's likelihood of reaching a living state at a terminal node; and (g) administer the subsequent course of treatment to the individual.

In some modalities, the method further comprises: (e) at a second point in time subsequent to the initial state, obtaining information about an individual comprising at least a genetic profile of a tumor and a treatment previously or currently provided to the individual , if there is and determines a second state of the individual among a plurality of subsequent states based on the information; (f) based on the second state, providing a subsequent course of treatment for the individual that maximizes the individual's likelihood of reaching a living state at a terminal node; and (g) administer the subsequent course of treatment to the individual.

[480] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the U.S. Patent

10,017,759, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include methods for preparing a library of nucleic acid fragments and, more specifically, methods for preparing a library of nucleic acid fragments in a single tube using proteases for a variety of applications, including, for example, next-generation DNA sequencing. In addition, a method for preparing a library of labeled nucleic acid fragments, including (a) contacting a cell population directly with a lysis reagent to generate a cell lysate, wherein the lysis reagent has one or more proteases and wherein the cell lysate contains a target nucleic acid; (b) inactivating one or more proteases to form an inactivated cell lysate and (c) directly applying at least one transposase composition and at least one transposon terminal composition containing a tape transferred to the inactivated cell lysate under conditions where the acid target nucleic acid and the final composition of the transposon undergoes a transposition reaction to generate a mixture,

wherein (i) the target nucleic acid is fragmented to generate a plurality of target nucleic acid fragments and (ii) the strip transferred from the final transposon composition is joined to the 5 'ends of each of a plurality of fragments of target nucleic acid to generate a plurality of 5 'labeled target nucleic acid fragments. In some embodiments, steps (a), (b) and (c) are performed in a single reaction mixture, for example, in a tube.

In some modalities, the cell population is a minimum cell population.

In some embodiments, the minimum cell population contains one, two, three, four or five cells.

In some embodiments, the target nucleic acid is double-stranded DNA, and the target nucleic acid remains double-stranded DNA prior to the application of a final transposase and transposon composition in step (c). In some embodiments, the target nucleic acid is genomic DNA.

In some embodiments, the target nucleic acid contains chromosomal DNA or a fragment thereof.

In some embodiments, the target nucleic acid includes a genome or a partial genome.

In some embodiments, the method further includes (d) incubating the mixture from step (c) directly with at least one nucleic acid modifying enzyme under conditions in which a 3 'tag is attached to the 5' target nucleic acid fragments for generating a plurality of di-tagged target nucleic acid fragments.

In some embodiments, steps (a), (b), (c) and (d) are performed in a single reaction tube.

In some embodiments, the method further includes (e) amplifying one or more double-labeled target nucleic acid fragments to generate a library of nucleic acid fragments labeled with an additional sequence at the 5 'and / or 3' end of the acids nucleic with di tag.

In some modalities, steps (a), (b), (c), (d) and (e) are performed in a single reaction tube.

In some modalities, the expansion

fication includes the use of one or more of a polymerase chain reaction (PCR), a ribbon displacement amplification reaction, a rotating circle amplification reaction, a ligase chain reaction, a mediated amplification reaction by transcription or a loop-mediated amplification reaction.

In some embodiments, the amplification includes a PCR using a single primer that is complementary to the 3 'tag of the target DNA fragments with the di tag.

In some embodiments, the amplification includes a PCR using a first and a second primer, wherein at least a portion of the 3 'end of the first primer is complementary to at least a portion of the 3' tag of the nucleic acid fragments target with di tag, and wherein at least a 3 'end portion of the second primer displays the sequence of at least a 5' tag portion of the target di-fragments of target nucleic acid.

In some embodiments, a 5 'end portion of the first primer is not complementary to the 3' tag of target nucleic acid fragments with a di tag and a 5 'end portion of the second primer does not exhibit the sequence of at least a portion of the 5 'tag of the di-tagged target nucleic acid fragments.

In some modes, the first primer includes a first universal sequence and / or in which the second primer includes a second universal sequence.

In some embodiments, the method also includes sequencing the labeled nucleic acid fragments.

In some embodiments, sequencing of labeled nucleic acid fragments includes using one or more sequences by synthesis, bridged PCR, chain termination sequencing, hybridization sequencing, nanopore sequencing and ligation sequencing.

In some embodiments, sequencing the tagged nucleic acid fragments includes using next generation sequencing.

In some modalities, the method also includes analyzing the variation in the number of copies. In some modalities, the method also includes analyzing the variation of a single nucleotide.

[481] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the U.S. Patent

9,944,924, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include using application-specific capture initiators in next-generation sequencing. A method for modifying an immobilized capture initiator may include: a) providing solid support with an immobilized application specific capture initiator, an application specific capture initiator including: i) a 3 'portion including a region of application specific capture and ii) a 5 'portion including a universal capture region; b) contacting an application-specific polynucleotide with the application-specific capture primer under conditions sufficient for hybridization to produce an immobilized application-specific polynucleotide and c) removing the application-specific capture region from a application-specific capture not hybridized with an application-specific polynucleotide to convert the non-hybridized application-specific capture primer into a universal capture primer. In some embodiments, a portion of the application-specific capture region is removed. In some embodiments, the application specific capture initiator comprises a plurality of different immobilized application specific capture initiators. In some embodiments, the application-specific polynucleotide comprises a plurality of different application-specific polynucleotides. In some modes, the application-specific capture region includes a target-specific capture region and the application-specific polynucleotide includes a target polynucleotide. In some embodiments, the application-specific capture region includes a transposon end (TE) region and the application-specific polynucleotide includes a TE oligonucleotide. In some embodiments, the method also includes applying an oligonucleotide before performing step c) under conditions sufficient for hybridization of oligonucleotides to the universal capture region of an application specific capture primer to produce a double stranded DNA region. . In certain embodiments, the oligonucleotide is a P5 or P7 oligonucleotide. In some modalities, the method also includes applying an oligonucleotide prior to the execution of step c) under conditions sufficient for hybridization of oligo-nucleotides with the application-specific capture region of an application-specific capture primer to produce an double-stranded DNA region. In some embodiments, the method also includes contacting the application-specific capture initiator with a nucleus, in which the application-specific capture region of an application-specific capture initiator is not hybridized with a specific polynucleotide from the application is removed by the nuclease. In some modalities, the nuclease is an exonuclease. In some modalities, the exonuclease is exonuclease I. In some modalities, the exonuclease is exonuclease III. In some embodiments, the nuclease is an endonuclease.

[482] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the U.S. Patent

9,992,598, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include a method for preparing amplicons. The method includes: (a) contacting a nucleic acid sample including a plurality of target polynucleotides with at least one primer under conditions sufficient for hybridization, the at least one primer containing an adapter; (b) amplifying by polymerase chain reaction (PCR) the plurality of target polynucleotides to produce a plurality of amplicons; (c) directly contacting a plurality of target specific capture primers immobilized on a solid support with the plurality of amplicons under conditions sufficient for hybridization to produce a first plurality of immobilized amplicons, the solid support further including a plurality of primers of immobilization universal capture; (d) extending the plurality of target specific capture primers to produce a plurality of immobilized extension products complementary to the target polynucleotides; (e) pair the plurality of universal capture initiators to the plurality of immobilized extension products and (f) PCR amplify the plurality of immobilized extension products to produce a second plurality of immobilized amplicons, in which the population of immobilized amplicons include a uniformity of 85% or more.

The method can be used with 10 ng or less of incoming nucleic acid and can further include sequencing the second plurality of immobilized amplicons.

The method can also be used to determine the presence of a gene associated with a disorder or disease, including a gene associated with cancer.

Cellless DNA can also be used in the method.

In addition, the detection of a genetic biomarker may include a method to increase the sensitivity of detection of a nucleic acid sequence variant, which includes: (a) contacting a nucleic acid sample including a plurality of target polynucleotides with direct primers and specific reverses of the gene under conditions sufficient for hybridization, each species of the specific direct primer of the gene, including a unique sequence index and an adapter; (b) amplifying by polymerase chain reaction (PCR) the plurality of target polynucleotides to produce a plurality of amplicons;

(c) directly contacting a plurality of target specific capture primers immobilized on a solid support with the plurality of amplicons under conditions sufficient for hybridization to produce a first plurality of immobilized amplicons, the solid support further including a plurality of primers of immobilization universal capture; (d) extending the plurality of target specific capture primers to produce a plurality of immobilized extension products complementary to the target polynucleotides; (e) pair the plurality of universal capture initiators to the plurality of immobilized extension products; (f) PCR amplifying the plurality of immobilized extension products to produce a second plurality of immobilized amplifiers, wherein the second plurality of immobilized amplicons includes a uniformity of 85% or more; (g) sequencing the second plurality of immobilized amplicons and (h) eliminating random sequence errors for one or more target polynucleotides by comparing three or more nucleotide sequences in a variant position for a target polynucleotide species, in which the target polynucleotide species are identified by the unique sequence index to thereby determine a true nucleotide sequence variant in one or more target polynucleotides. The method can detect a mismatch rate of 0.3% or less for a variant nucleotide position.

[483] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the U.S. Patent

9,879,312, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include methods for the selective enrichment of nucleic acids. Some modalities include the selective enrichment of long nucleic acids comprising a target nucleic acid. Some modalities include the selective enrichment of PCR products.

Some modalities of the methods comprise (a) contacting the nucleic acid population with a nickase, thereby producing a population of cut nucleic acids; (b) contacting the population of nucleic acids cut with an exonuclease, thereby generating a nucleic acid having a single strand portion, wherein the single strand portion comprises at least a portion of the target; (c) contacting a capture probe with at least a portion of the target, wherein the probe hybridizes to the target; and (d) separating a nucleic acid hybridized with the capture probe from a nucleic acid not bound to the capture probe.

In other embodiments, the methods comprise (a) obtaining a population of nucleic acids, in which at least some of the population's nucleic acids comprise a target; (b) contacting the nucleic acid population with a nickase, thereby producing a population of cut nucleic acids; (c) contacting the nucleic acid population cut with an exonuclease, thereby generating a nucleic acid having a single strand portion, wherein the single strand portion comprises at least a portion of the target; (d) contacting a capture probe with at least a portion of the target, wherein the probe hybridizes to the target; and (e) separating a nucleic acid hybridized with the capture probe from a nucleic acid not bound to the capture probe.

Some method modalities also include a step of releasing the hybridized nucleic acid from the capture probe.

Other modalities also include amplifying the target.

Still other modalities additionally include sequencing at least a portion of the target.

In some embodiments, one or more process steps, for example step (a), may also include contacting the double stranded nucleic acid population with a type II restriction endonuclease that includes a nickase isosquizomer; and recirculating double stranded nucleic acids under conditions that favor the intramolecular recirculation of individual nucleic acids.

In such modalities, various type II restriction endonucleases, or combinations of type II restriction endonucleases, can be used.

In some embodiments, for example, the restriction endonuclease includes BbvCI.

In other modalities, nickase includes Nb.BbvCI and Nt.BbvCI.

In some methods, the probe includes a capture fraction.

In some of these modalities, the capture fraction includes biotin or streptavidin.

In some embodiments, the step of separating a nucleic acid hybridized with the capture probe from a nucleic acid not bound to the capture probe also includes contacting the target and the hybridized probe with a binding fraction.

In some embodiments, the binding fraction includes avidin and streptavidin.

In some modalities, the bonding fraction also includes a sphere, microsphere or another particle.

Methods of the methods also include repeating one or more steps in the process.

In certain modalities, all steps of the method are repeated.

In some methods, the target includes a first capture fraction and the probe includes a second capture fraction.

Some of these modalities also include contacting the first capture fraction with a first connection fraction, thus providing the target enrichment and contacting the second capture fraction with a second connection fraction, thus providing the probe enrichment.

In addition to the above, some modalities of the methods also provide the selective enrichment of a nucleic acid that comprises the steps of (a) providing a population of nucleic acids, in which at least some of the population's nucleic acids include a target hybridized with a capture probe; (b) blocking the hybridized probe on the target; and (c) separating a blocked nucleic acid for a probe from a nucleic acid that is not blocked for a probe.

In other embodiments, the methods comprise (a) obtaining a population of nucleic acids, wherein at least some of the population's nucleic acids include a target; (b) hybridize the target with a capture probe; (c) block the probe hybridized to the probe on the target; and (d) separating a blocked nucleic acid for a probe from a nucleic acid that is not blocked for a probe.

In addition to the above, some modalities of the methods also include methods for selective enrichment of a nucleic acid which comprises (a) supplying a population of nucleic acids, in which at least some of the population's nucleic acids include a target comprising a a 5 'end portion of a nucleic acid and a 3' end portion of the nucleic acid, said target being hybridized to a selector probe comprising a first and a second oligonucleotide paired together, wherein the first oligonucleotide is complementary to at least at least a portion of the 5 'end of the nucleic acid and complement at least a portion of the second oligonucleotide and the second oligonucleotide is complementary to at least a portion of the 3' end of the nucleic acid; (b) joining the selector probe to the target; and (c) separating a nucleic acid attached to the selection probe from a nucleic acid not attached to the selection probe.

Other embodiments of the enrichment methods comprise the steps of (a) obtaining a population of nucleic acids, wherein at least some of the population's nucleic acids include a target, the target including a portion of the 5 'end of a nucleic acid kleic and a portion of the 3 'end of the nucleic acid; (b) obtaining a selector probe comprising a first and a second annealed oligonucleotide, wherein the first oligonucleotide is complementary to at least a portion of the 5 'end of the nucleic acid and complementary to at least a portion of the second oligonucleotide, and the second oligonucleotide is complementary to at least a portion of the 3 'end of the nucleic acid; (c) contacting the targeting probe with the target, wherein the probe hybridizes to the target; (d) attach the selector probe to the target; and (e) separating a nucleic acid attached to the selection probe from a nucleic acid not attached to the selection probe.

In addition to the foregoing, some modalities of the methods also include methods for selective enrichment of a nucleic acid comprising (a) providing a population of single stranded nucleic acids, in which at least some of the population's nucleic acids include a target , the target comprising the 5 'end of a nucleic acid and the 3' end of the nucleic acid, said target being hybridized with a selection probe comprising a first and a second oligonucleotide recollected together, wherein the the first oligonucleotide comprises a 5 'portion complementary to the 3' end of the nucleic acid, a spacer portion and a 3 'portion complementary to the 5' end of the nucleic acid, the second oligonucleotide being complementary to the spacer portion; (b) joining the selector probe to the target; and (c) separating a nucleic acid attached to the selection probe from a nucleic acid not attached to the selection probe.

Other modalities of the methods comprise (a) obtaining a single stranded nucleic acid population, wherein at least some of the population nucleic acids include a target, the target comprising the 5 'end of a nucleic acid and the end 3 'nucleic acid; (b) obtaining a selector probe that includes a first and a second annealed oligonucleotide together, wherein the first oligonucleotide comprises a 5 'portion complementary to the 3' end of the nucleic acid, a spacer portion and a complementary 3 'portion at the 5 'end of the nucleic acid, the second oligonucleotide complementary to the spacer portion; (c) contacting the targeting probe with the target, wherein the probe hybridizes to the target; (d) attach the selector probe to the target; and (e) separating a nucleic acid attached to the selection probe from a nucleic acid not attached to the selection probe. Some modalities also include methods to normalize amplified nucleic acids that include selecting a first population of oligonucleotides with an oligonucleotide ratio that includes capture fractions for oligonucleotides that lack capture fractions for a first oligonucleotide population; obtain a second population of oligonucleotides; amplification of target nucleic acids with the first and second oligonucleotide populations; and separating amplified targets having built-in oligonucleotide comprising capture fractions from amplified targets without built-in oligonucleotide capture fractions. In some embodiments, the separation step further comprises contacting the target and probe hybridized with a binding fraction. In some embodiments, the binding fraction includes avidin and streptavidin. In some embodiments, the bonding fraction also includes a sphere, microsphere or another particle.

[484] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the U.S. Patent

9,828,672, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker can include reagents for the preparation of nucleic acid comprising a siderophore. In some embodiments, the siderophore is a bacterial siderophore. In some embodiments, siderophore is a mesylate salt of desferrioxamine B (DFO-B). In some embodiments, the reagents may comprise a DNA polymerase. In some modalities, reagents may comprise dNTPs. In some embodiments, the reagents do not contain EDTA. Some modalities provide methods for preparing a nucleic acid library, which comprises: providing a plurality of nucleic acid molecules from a sample; and manipulating the plurality of nucleic acid molecules in a reagent for the preparation of nucleic acid comprising a syndophore.

In some embodiments, manipulating the plurality of nucleic acid molecules comprises hybridizing the plurality of nucleic acid molecules to a plurality of oligonucleotide probes.

In some embodiments, the plurality of nucleic acid molecules and / or the plurality of oligonucleotide probes are immobilized on a support.

In some embodiments, the plurality of nucleic acid molecules and / or the plurality of oligonucleotide probes are immobilized on the support through a pair of support binding partners.

In some embodiments, the support is a magnetic sphere.

In some embodiments, manipulation of the plurality of nucleic acid molecules comprises the removal of oligonucleotide probes not specifically linked to the plurality of nucleic acid molecules.

In some embodiments, the methods comprise modifying the oligonucleotide probes specifically linked to the plurality of nucleic acid molecules.

In some modes, the methods comprise fragmenting the plurality of nucleic acid molecules.

In some embodiments, the methods comprise adding adapters to the plurality of nucleic acid molecules.

In some embodiments, adapters are added to the plurality of nucleic acid molecules by amplification.

Some modalities provide methods for reducing oxidative damage to a nucleic acid molecule, the methods of which comprise preparing the nucleic acid molecule in the absence of EDTA.

In some embodiments, the preparation of the nucleic acid molecule comprises the preparation of the nucleic acid molecule in the presence of a siderophore.

In some embodiments, the siderophore is a bacterial siderophore.

In some embodiments, siderophore is a mesylate salt of desferrioxamine B (DFO-B). In some embodiments, the preparation of the nucleic acid molecule comprises exposing the nucleic acid molecule to the

Fe (III). In some embodiments, the preparation of the nucleic acid molecule comprises exposing the nucleic acid molecule to a magnetic sphere. In some embodiments, oxidative damage comprises a point mutation in the nucleic acid molecule. In some modalities, the point mutation is a transversion from C to A. Some modalities provide methods to increase the Q (phred) score of a sequencing reaction, whose methods comprise preparing a nucleic acid molecule in the absence of EDTA . In some embodiments, the preparation of the nucleic acid molecule comprises the preparation of the nucleic acid molecule in the presence of a siderophore. In some modalities, the siderophore is a bacterial siderophore. In some embodiments, siderophore is a mesylate salt of desferrioxamine B (DFO-B). In some modalities, the Q score is greater than about 34. In some modalities, the Q score is greater than about 38. In some modalities, the Q score is greater than about 42. In some modalities, the sequence reaction - funding is a deep sequencing application. In some embodiments, the deep sequencing application is a cancer-related deep sequencing application. In some modes, the methods comprise sequencing the nucleic acid molecule in the absence of EDTA. Some embodiments provide kits comprising at least one container medium, wherein at least one container medium comprises a reagent for the preparation of nucleic acid comprising a siderophore. In some modalities, siderophore is a mesylate salt of desferrioxamine B (DFO-B). In some embodiments, the reagent does not contain EDTA.

[485] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the U.S. Patent

9,708,655, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include compositions, systems and methods for detecting events using anchored bonds or adjacent to nanopores.

In some embodiments, a composition includes a nanopore including a first side, a second side and an opening that extends through the first and second sides; and a permanent tie including a head region, a tail region and an elongated body disposed between them.

The head region can be anchored or adjacent to the first side or the second side of the nanopore.

The elongated body including a reporter region can be movable within the opening in response to a first event that occurs adjacent to the first side of the nanopore.

In a non-limiting example, the head region can be anchored to a molecule, like a protein, arranged on the first side or the second side of the nanopore.

In some embodiments, a method includes providing a nanopore including a first side, a second side and an opening that extends through the first and second sides; and providing a permanent tie including a head region, a tail region and an elongated body disposed between them.

The head region can be anchored or adjacent to the first or second side of the nanopore and the elongated body can include a reporter region.

The method may include moving the reporter into the opening in response to a first event that occurs adjacent to the first side of the nanopore.

In some modalities, the reporter region is mobile for translation within the responsive opening to the first event.

Additionally, or alternatively, the reporter region can be mobile in rotation within the responsive opening to the first event.

Additionally, or alternatively, the reporter region can be conformationally mobile within the responsive opening to the first event.

In some embodiments, the head region is anchored either adjacent to the first side or the second side of the nanopore through a covalent bond.

The head region can be anchored to the first side of the nanopore. The tail region can extend freely towards the second side of the nanopore. In some modalities, the reporter region is translationally mobile towards the first side of the nanopore responsive to the first event. The reporter region can be translationally mobile towards the second side after the first event. The reporter region can also be translationally mobile towards the first side responsive to a second event that occurs adjacent to the first side of the nanopore, the second being after the first event. The reporter region can also be translationally mobile towards the second side after the second event. In some embodiments, the first event includes adding a first nucleotide to a polynucleotide. In embodiments that include a second event, the second event may include adding a second nucleotide to the polynucleotide. An electrical blocking or flow characteristic of the reporter region may be different from an electrical blocking or flow characteristic of another region of the elongated body. In some embodiments, a system may include a composition and measurement circuit configured to measure a first current or flow through the aperture or to measure a first optical signal while the reporter region is moved in response to the first event.

[486] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the U.S. Patent

9,670,530, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include methods for determining a haplotype or partial haplotype of a DNA sample containing high molecular weight segments of genomic DNA. Such methods can be characterized by the following operations: (a)

processing the DNA sample to produce a DNA-enriched DNA sample from a high molecular weight first segment with a plurality of alleles from a first haplotype; (b) sequencing DNA in the enriched DNA sample to produce a plurality of sequence readings, which are shorter than the first high molecular weight segment, where some of the sequence readings contain a first allele of the first haplotype and another of the sequence readings contain a second allele of the first haplotype; (c) align the sequence readings to a reference genome to produce aligned readings, where aligned readings from the first high molecular weight segment tend to cluster into islands in the reference genome; (d) determine the distances that separate the adjacent readings aligned in the reference genome, where the separation distances between the adjacent aligned readings fall into at least two groups distinguishable by the magnitude of their separation distances; (e) select a first group of readings aligned with separation distances for adjacent aligned readings less than a cutoff value, thus excluding readings aligned with greater separation distances, in which at least a portion of the first group of readings aligned belongs to the same island in the reference genome; and (f) use alleles from the first group of aligned readings to define a first haplotype or first partial haplotype.

In some modalities, the methods have an additional operation to determine a complete haplotype from the first partial haplotype and other partial haplotypes.

In some embodiments, selecting a first group of aligned readings includes determining the cutoff value.

As an example, the determination of the cut-off value includes: (i) generating a mixture model from the separation distances between adjacent aligned readings, in which the mixture model adjusts two distributions to the separation distances; and (ii) determining the cut-off value of a property of at least one of the two distributions. In some cases, each of the two distributions comprises its own central tendency (for example, the mean of a Gaussian distribution). In certain embodiments, selecting a first group of aligned readings includes determining the cutoff value. As an example, the determination of the cut-off value includes: (i) generating a mixture model from the separation distances between adjacent aligned readings, in which the mixture model adjusts two distributions to the separation distances; and (ii) determining the cut-off value of a property of at least one of the two distributions. In some cases, each of the two distributions comprises its own central tendency (for example, the average of a Gaussian distribution). In certain modalities, generating a mixing model involves applying an expectation maximization procedure to the separation distances between adjacent aligned readings. In some implementations, the cut value determination includes an operation to identify a fraction of the probability mass of the distribution that contains the shortest separation distances. For example, the fraction of the probability mass of the distribution that contains the shortest separation distances can be about 80% or more.

[487] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the U.S. Patent

9,587,273, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include a method of selecting a representational sample of nucleic acid sequences from a complex mixture. The method includes: (a) contacting a complex mixture of nucleic acids under conditions sufficient for hybridization with a population of capture probes complementary to one or more nucleic acids comprising a predetermined portion of the sequence present collectively in the mixture complex to form hybridization complexes of the one or more nucleic acids to the probe population, the capture probe population being connected to a solid support and (b) removing unhybridized nucleic acids to select a representational sample of nucleic acids with a complexity less than 10%, but more than 0.001% of the complex mixture, where the representational sample comprises a copy of nucleic acid having a ratio of each sequence in the copy to all other sequences in the copy substantially the same as the proportions of the sequences in the predetermined portion of one or more nucleic acids within the complex mixture. In addition or alternatively, the detection of a genetic biomarker may include a method for selecting a representational sample of genomic sequences from a complete genome. In some embodiments, the method still provides a nucleic acid population that includes a representational sample with a complexity of less than 10%, but more than 0.001% of a complex mixture, the representational sample comprising a copy of nucleic acid having a ratio of each sequence in the copy relative to all other sequences in the copy is substantially equal to the proportions of sequences in a predetermined portion of a sequence present collectively in one or more nucleic acids within the complex mixture.

[488] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the U.S. Patent

9,574,234, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include methods and compositions for analyzing nucleic acid sequences. In some modalities, the methods use clonal objects, such as balls of

DNA, which were captured in spheres.

Some modalities provide compositions that have a sphere and a clonal object, in which the clonal object is attached by affinity or hybridized to the sphere, for example, through an adhesive on the surface of the sphere, as a bonding adhesive by affinity or a hybridization sticker.

In some aspects, the adhesive includes a plurality of polynucleotides linked to a single region on the surface of the sphere.

Some modalities also provide a population of spheres that have affinity linked or hybridized clonal objects.

In particular modalities, each of the clonal objects is connected by affinity or hybridized with the spheres through an adhesive on the surface of each sphere, like an affinity bonding adhesive or a hybridization plaster.

The ratio of spheres to cloned or hybridized objects in the population can be 1: 1. In particular modalities, no more than one clonal object is linked or hybridized to any sphere in the population.

Using these methods, the compositions can be manufactured in which a sphere and a clonal object are connected by affinity or hybridized to each other through fixation to a plaster on the surface of the sphere.

Some modalities may provide a method for making an affinity bonding plaster on a sphere, providing a sphere having a plurality of capture fractions; providing a solid surface with a plurality of capture complement fractions, wherein the capture complement fractions further comprise a cleavable fraction and an affinity ligand; specifically connect the capture fractions to the capture complement fractions, thus forming a sphere immobilized on the solid surface; and cleaving the cleavable fraction so as to retain the affinity ligand on the sphere, thereby manufacturing an affinity binding plaster on the sphere.

In particular modalities, catch fractions or catch complement fractions or both

include polynucleotide capture sequences.

Accordingly, some modalities may provide a method for making an affinity binding plasmid in a sphere, providing a sphere with a plurality of first polynucleotides attached to the surface of the sphere, in which the first polynucleotides have a sequence capture; providing a solid surface with a plurality of second polynucleotides attached to the solid surface, wherein the second polynucleotides each have a complementary capture sequence, a cleavable fraction and an affinity ligand; hybridize the capture sequences of the first polynucleotides with the capture-complement sequences of the second polynucleotides, thus forming a sphere immobilized on the solid surface; and cleaving the second polynucleotides in the cleavable fraction, so as to retain the affinity ligand in the second plurality of polynucleotides, thereby manufacturing an affinity binding plaster on the sphere.

In some aspects, the method further includes making a clonal object attached to the affinity binding envelope, contacting the affinity ligand with a binding agent, where the binding agent has two or more binding sites and linking a clonal object to the binding agent via a second affinity binder on the clonal object, where the single clonal object has a single target nucleic acid molecule repeatedly in tandem, thereby manufacturing a clonal object attached to the binding plaster of affinity.

Some modalities may provide a method for making a sphere with a clonal object, providing a sphere with a plurality of first portions of capture; provide a solid surface with a plurality of second capture fractions standardized on patches on the surface, where the second capture fractions have a cleavable portion, where a clonal object is attached to a patch on the surface through one or more of the second fractions capture, in which the single clonal object has a single target nucleic acid molecule repeatedly in tan; specifically connect the first capture fraction to the clonal object, thus forming a sphere immobilized on the solid surface and cleaving the cleavable fraction in order to retain the clonal object, thus manufacturing a sphere with a clonal object.

In particular modalities, the capture fractions comprise polynucleotides.

Therefore, some modalities may provide a method for making a sphere with a clonal object, providing an account with a plurality of first polynucleotides; provide a solid surface with a plurality of second polynucleotides standardized in patches on the surface, where the second polynucleotides each have a cleavable fraction, in which a clonal object is hybridized with a polynucleotide patch on the surface, in which the single clonal object has a single target nucleic acid molecule repeatedly in tandem; hybridizing the first polynucleotides with the clonal object, thus forming a sphere immobilized on the solid surface and cleaving the second polynucleotides in the cleavable fraction, in order to retain the clonal object, thus manufacturing a sphere with a clonal object.

Some modalities may provide a method to manufacture a hybridization plaster on a sphere, provide a sphere with a plurality of first polynucleotides attached to the surface of the sphere, where the first polynucleotides each have a first capture sequence, provide a solid surface having a plurality of second polynucleotides attached to the solid surface, where the second polynucleotides have a first complement-capture sequence and a second complement-capture sequence, hybridizing the first capture sequences of the first polynucleotides to the first capture-complement sequence of the second polynucleotides, thus forming a sphere immobilized on the solid surface and extending the first

first immobilized sphere polynucleotides using the second capture complement sequence as a model, thereby manufacturing a hybridization patch of first polynucleotides extended on the sphere, the first extended polynucleotides having a second capture sequence. In some respects, the method even includes making a clonal object attached to the plaster on the sphere, providing a clonal object with the second capture-complement sequence and hybridizing the second capture complement sequence of the clonal object on the second capture sequences of the sphere, thus manufacturing a clonal object attached to the plaster on the sphere. In some aspects of the method, the extension of the first polynucleotides includes adding one or more nucleoside triphosphates with an affinity ligand, thereby making an affinity binding patch on the sphere. Some modalities include methods of amplifying a target nucleic acid molecule. Some modalities provide a method of amplifying a target nucleic acid molecule, placing compositions or populations of spheres that have affinity linked or hybridized clonal objects on a solid surface with microwells, where only one sphere can fit spatially in one microwell and amplify the target nucleic acid molecules in the microwells, forming amplicons. In some respects, the method also includes sequencing amplified target nucleic acid molecules using methods such as sequencing by synthesis, sequencing by ligation or sequencing by hybridization, thereby determining the nucleic acid sequence of the target nucleic acid molecule.

[489] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the U.S. Patent

9,453,258, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include determining a sequence, for example, a nucleic acid sequence, using a minimal set of dyes, minimal excitation light sources, and minimal optical emission filters, while still it allows the differentiation of the incorporation of all four nucleotides in a sequencing reaction.

In addition or alternatively, the detection of a genetic biomarker may include methods for determining the sequence of a polynucleotide comprising detecting in a sequencing reaction the incorporation of three different types of detectable nucleotide conjugates into a polynucleotide and determining incorporation of a fourth type of nucleotide based on the detection pattern of the three different types of nucleotides detectable in the polynucleotide, thus determining the sequence of a polynucleotide, in which the incorporation of three different types of detectable nucleotide conjugates is detected from a signal state and wherein the incorporation of the fourth type of nucleotide is determined from a dark state.

In addition or alternatively, the detection of a genetic biomarker may include methods for determining the sequence of a polynucleotide comprising applying a sample of polynucleotide to sequence a solution comprising four types of modified nucleotides, in which three types of modified nucleotides are conjugated to one or more detection fractions and one or more ligands positioned between the nucleotide and one or more detection portions, and in which a fourth type of nucleotide does not have a detection portion, to detect an incor- porting said modified nucleotides in a sequencing reaction thereby capturing a first detectable pattern, applying one or more compositions to the sequencing reaction thereby changing the first detectable pattern, detecting a second detectable pattern and determining the sample sequence polynucleotide based on detectable standards.

In some embodiments, the polynucleotide for sequencing comprises one or more deoxyribonucleic acids, modified deoxyribonucleic acids, ribonucleic acids and modified ribonucleic acids.

In some modalities, the polynucleotide for sequencing is a genomic DNA library preparation.

In some embodiments, the nucleotide conjugate comprises nucleotide types selected from the group consisting of dATP, dTTP, dUTP, dCTP, dGTP or unnatural nucleotide analogs thereof.

In some embodiments, the unnatural nucleotide analog comprises a reversible terminating fraction and is selected from the group consisting of rbATP, rbTTP, rbCTP, rbUTP and rbGTP.

In some embodiments, the incorporation of nucleotides is sequence by synthesis, sequence by ligation and sequence by hybridization or a combination thereof.

In some modalities, the three nucleotide-type conjugates are detected by detecting a fluorescent fraction.

In some modalities, the fluorescent fraction is the same for the three nucleotide conjugates, while in other modalities the fluorescent fraction is one or more different fluorescent fractions.

In some embodiments, one or more different fluorescent fractions are detected by the same emission filter.

In some embodiments, the fluorescent fraction comprises a fraction of the fluorescent resonance energy transfer system.

In some modalities, the incorporation of the fourth nucleotide is determined by the lack of detection.

In some modalities, detectable nucleic acid conjugates are detected by fluorescence.

In some modalities, fluorescence is detected by a first and a second imaging event, in other modalities the first and second imaging events are separated

time.

In some modalities, the first imaging event detects a fluorescence pattern that is different from the fluorescence pattern detected by the second imaging event.

In some modalities, the incorporation of one or more nucleotides is determined by the difference in the fluorescence pattern between the first and the second imaging events.

In some embodiments, the one or more nucleotide-type conjugates further comprise one or more ligation sequences, in other embodiments the one or more ligation sequences comprise one or more of a cleavable linker and a spacer linker.

In some embodiments, the cleavable linker comprises one or more cleavable linker groups selected from the group consisting of a disulfide, a diol, a diazo, an ester, a sulfone, an azide, an ally and a silyl ether, while in the preferred embodiments the cleavable link group is a disulfide.

In some modalities, the spacer binder is one or more polyethylene glycol or concatamers thereof and 2- {2- [3- (2-amino-ethylcarbornyl) -phenoxy] -1- azido-ethoxy} ethoxy-acetic acid .

In some embodiments, the one or more linker spacers further comprise one or more cleavable linker groups, wherein the cleavable linker group is selected from the group consisting of a disulfide, a diol, a diazo, an ester, a south - phono, an azide, an ally and a silyl ether.

In some embodiments, the spacer ligand is polyethylene glycol or concatamers thereof, while in other embodiments the spacer ligand is 2- {2- [3- (2-amino-ethylcarbornyl) -phenoxy] -1-azido-ethoxy} - ethoxy-acetic.

In some embodiments, the one or more nucleotide conjugates comprise a polyethylene glycol ligand and a 2- {2- [3- (2-amino-ethylcarbonyl) -phenoxy] -1-azido acid ligand -ethoxy} -ethoxy-acetic which may or may not yet comprise a hapten and a fluorescent fraction.

In some modalities, hapten is selected from the group consisting of biotin, digoxigenin and dinitrophenol.

In some embodiments, the one or more nucleotide conjugates comprise a structavidin fluorescent fraction conjugate, while in other embodiments, the one or more nucleotide conjugates comprise a fluorescent fraction of anti-hapten antibody selected from the group consisting of antidigoxigenin and antidinitrophenol. In some embodiments, the nucleotide conjugate comprising a polyethylene glycol linker and a 2- {2- [3- (2-amino-ethylcarbornyl) -phenoxy] -1- azido-ethoxy} -ethoxy-acetic acid linker still two fluorescent fractions. In some embodiments, the two fluorescent fractions constitute a resonance energy transfer system for fluorescence.

[490] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the U.S. Patent

9,441,267, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include methods for determining the identity of a nucleotide at a detection position in a target sequence. The methods comprise providing a hybridization complex comprising the target sequence and a capture probe covalently attached to a microsphere on the surface of a substrate. The methods comprise determining the nucleotide at the detection position. The hybridization complex can comprise the capture probe, a capture extension probe and the target sequence. In addition, the target sequence may comprise exogenous adapter sequences. In a further aspect, the method comprises contacting microspheres with a plurality of detection probes, each comprising a single nucleotide in the reading position and a unique detectable label. The signal from at least one of the detectable markers is detected to identify the nucleotide at the detection position. In a further aspect, the detection probe does not contain a detection tag, but is identified based on its characteristic mass, for example, via mass spectrometry.

In addition, the detection probe comprises a unique marker that is detected based on its characteristic mass.

In addition or alternatively, the detection of a genetic biomarker may include methods in which the target sequence comprises a first target domain directly 5 'adjacent to the detection position.

The hybridization complex comprises the target sequence, a capture probe and an extension primer hybridized to the first target domain of the target sequence.

The determination step comprises contacting the microspheres with a polymerase enzyme and a plurality of NTPs, each comprising a covalently linked detectable marker, under conditions where if one of the bases of the NTPs matches the base at the detection position, the extension primer is extended by the enzyme to incorporate the label.

As is known in the art, dNTPs and ddNTPs are the preferred substrates for DNA polymerases.

NTPs are the preferred substrates for RNA polymerases.

The base at the detection position is then identified.

In addition or alternatively, the detection of a genetic biomarker may include methods in which the target sequence comprises a first target domain directly 5 'adjacent to the detection position, where the capture probe serves as an extension primer and is hybridized to the first target domain of the target sequence.

The determination step comprises contacting the microspheres with a polymerase enzyme and a plurality of NTPs, each comprising a covalently linked detectable marker, under conditions where one of the bases of the NTPs matches the base at the detection position, the extension primer is extended by the enzyme to incorporate the label.

The base at the detection position is then identified.

In addition or alternatively, the detection of a genetic biomarker may include methods in which the target sequence comprises (5 'to 3'), a first target domain comprising an overlap domain comprising at least one nucleotide in the detection position and a second target domain adjacent to the detection position. The hybridization complex comprises a first probe hybridized to the first target domain and a second probe hybridized to the second target domain. The second probe comprises a detection sequence that does not hybridize with the target sequence and a detectable label. If the second probe comprises a base that is perfectly complementary to the detection position, a cleavage structure is formed. The method further comprises contacting the hybridization complex with a cleavage enzyme that moves to the signal probe detection sequence and then forms a test complex with the detection sequence, a covalently linked capture probe. to a microsphere on the surface of a substrate and at least one marker. The base at the detection position is then identified.

[491] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the U.S. Patent

7,060,431, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker can include array compositions comprising a substrate with a surface comprising distinct sites. The composition may further comprise a population of microspheres comprising at least one first and a second subpopulation; each subpopulation comprises a bioactive agent; and an identifier linker that will link a decoder linker so that the identity of the bioactive agent can be elucidated. The microspheres are distributed on the surface. Some embodiments provide arrangement compositions comprising a substrate with a surface comprising distinct sites and a population of microspheres comprising at least one first and a second subpopulation.

Each subpopulation comprises a bioactive agent and does not include an optical signature.

Some modalities provide methods for making an arrangement composition, as described above.

The methods comprise forming a surface comprising individual sites on a substrate and distributing microspheres on said surface, so that said individual sites contain microspheres.

The microspheres comprise at least one first and a second subpopulation, each comprising a bioactive agent and do not comprise an optical signature.

Some modalities provide methods for making a composition comprising forming a surface that comprises individual sites on a substrate and distributing microspheres on the surface, so that the individual sites contain microspheres.

The microspheres comprise at least one first and a second subpopulation, each comprising a bioactive agent and an identifier linker that will link a decoder linker so that the identification of the bioactive agent can be elucidated.

In addition or alternatively, the detection of a genetic biomarker may include methods of decoding an array composition comprising providing an array composition as described above and adding a plurality of decoding linkers to the array composition to identify the location of at least least a plurality of bioactive agents.

In addition or alternatively, the detection of a genetic biomarker may include methods to determine the presence of a target analyte in a sample.

The methods comprise contacting the sample with an arrangement composition, as described above, and determining the presence or absence of the target analyte.

Additionally or alternatively, the detection of a genetic biomarker may include a method which comprises providing an array composition comprising a population of microspheres comprising at least a first and a second subpopulation, wherein each subpopulation comprises a bioactive agent and at least a first and a second decoding attribute and detecting each of said first and second decoding attributes to identify each of said bioactive agents.

In addition or alternatively, the detection of a genetic biomarker may include a method for increasing the information obtained in a decoding step.

The method includes using degenerate probes as DBL-IBL combinations.

In addition or alternatively, the detection of a genetic biomarker may include using various decoding attributes in a sphere.

In addition or alternatively, the detection of a genetic biomarker may include a method to increase decoding reliability.

The method includes using decoding as a quality control measure.

In addition or alternatively, the detection of a genetic biomarker may include a method for decoding an array composition comprising providing an array composition comprising a population of microspheres comprising at least 50 subpopulations, each subpopulation comprising an agent bioactive by adding a plurality of decoding linkers to said microsphere population to identify at least 50 of the bioactive agents.

In addition or alternatively, the detection of a genetic biomarker may include a method for determining the presence of a target analyte in a sample comprising contacting said sample with a composition comprising a population of microspheres comprising at least 50 subpopulations, each subpopulation in which it comprises a bioactive agent adding a plurality of decoding linkers to said microsphere population to identify at least 50 of the biactive agents and to determine the presence or absence of said target analyte.

[492] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the U.S. Patent

7,060,431, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include microfluidic devices for the detection of a target analyte in a sample. The devices comprise a solid support having any number of modules, including a sample inlet port and at least one sample handling well, comprising a well inlet port and a well outlet port. The device generally also comprises a first microchannel to allow fluid contact between the sample inlet port and the sample handling well. The device also comprises a detection module comprising a substrate with a surface comprising distinct sites and a population of microspheres comprising at least a first and a second subpopulation, each subpopulation comprising a bioactive agent. The microspheres are distributed on said surface. The detection module also comprises a detection input port for receiving the sample. The device also comprises a second microchannel to allow fluid contact between the sample handling well and the detection inlet port. In addition or alternatively, the detection of a genetic biomarker may include a method of mounting a detector in a microfluidic device. The method includes providing a microfluidic device comprising a first microchannel to allow fluid contact between a sample inlet port and a sample handling well, a second microchannel to allow fluid contact between said sample handling cavity and a detection gateway and a detection module comprising a substrate with a surface comprising distinct sites. The method further includes flowing a fluid through the substrate. The fluid comprises a population of microspheres comprising at least a first and a second subpopulation, each subpopulation comprising a bioactive agent, whereby the spheres flow through the different sites are deposited randomly at the different sites. The method additionally includes reversing the flow of the fluid. In addition or alternatively, the detection of a genetic biomarker may include a method of mounting a detector on a microfluidic device. The method includes providing a microfluidic device comprising a plurality of first microchannels and a population of microspheres in microchannels. The device also includes a receiver chamber connected to said microchannels. The method further includes flowing said microspheres through said micro-channels to said receiving chamber.

[493] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the U.S. Patent

9,222,134, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker can include methods, systems and compositions for detecting molecules. In particular, methods, systems and compositions for detecting various types of molecules on a solid support. Some modalities refer to methods for detecting molecules. In some embodiments, these methods comprise the steps of (a) providing a solid support comprising molecules associated with a site on the solid support, so that the molecules are detected in aggregate during a detection step, where the site comprises at least two different types of molecules; (b) detecting a signal corresponding to the aggregate of molecules at the site; (c) estimate the fraction of different types of molecules at the site or estimate the amount of signal corresponding to different types of molecules at the site; (d) calculate the amount of signal corresponding to different types of molecules at the site using the fraction estimate, thus obtaining a signal estimate or calculating the fraction of different types of molecules at the site using the signal estimate, thus obtaining a fraction estimate; and (e) iteratively update the fraction estimate and the signal estimate until the estimates converge, thus detecting the molecules associated with the site.

In some modalities of the methods described above, the supply step further comprises providing a mixture of molecules to the solid support.

In other modalities, the supply stage also involves associating the molecules to the site.

In yet other modalities, the delivery stage also comprises attaching the molecules to the site.

In some modalities of the methods described above, the estimation step is performed by guessing the fraction of different types of molecules at the site or by guessing the amount of signal corresponding to different types of molecules at the site.

In other modalities, the estimation stage comprises performing a principal component analysis (PCA). In some modalities of the methods described above, the update step comprises executing a numerical optimization algorithm.

In some of these methods, the numerical optimization algorithm is based on an iterative map search.

In some of these modalities, the numerical optimization algorithm is based on Fienup's iteration map.

In some embodiments of the methods described above, sequence data is obtained for one or more molecules.

In some of these methods, the sequence data is obtained by a synthesis sequencing process.

In certain embodiments, the synthesis sequencing process comprises a pyrosquencing process.

In some embodiments of the methods described above, the solid support comprises a sphere.

In some other embodiments, the solid support comprises a flow cell.

In preferred modes of the methods described above, the molecules comprise nucleic acids.

In some of these methods, nucleic acids are bound at the site.

In some embodiments, nucleic acids comprise a first subpopulation of nucleic acids and a second subpopulation of nucleic acids, where the nucleic acids of the first subpopulation have an identical target region and the nucleic acids of the second subpopulation have an identical region which is a variant of the target region.

In some embodiments, the nucleotide sequence of the target region of the nucleic acids of the first subpopulation has at least 1 nucleotide which is different compared to the nucleotide sequence of the variant of the target region of the nucleic acids of the second subpopulation.

In some embodiments, the nucleotide sequence of the target region of the nucleic acids of the first subpopulation has at least 3 nucleotides that are different compared to the nucleotide sequence of the variant of the target region of the nucleic acids of the second subpopulation.

In some embodiments, a difference in nucleotide sequence between the target region in the nucleic acids of the first subpopulation and the variant of the target region in the nucleic acids of the second subpopulation comprises at least one difference selected from the group consisting of a mutation, a polypeptide. limorphism, an insertion, an exclusion, a substitution, a simple tandem repeat polymorphism and a single nucleotide polymorphism (SNP). In some embodiments, nucleic acids comprise alleles of a genetic locus in a polyploid organism.

In some other embodiments, the nucleic acids comprise alternative splices of a nucleic acid.

In still other modalities, nucleic acids comprise alleles of a genetic locus in a diploid organism.

Molecular detection systems are also described.

Molecule detection systems can comprise a solid support comprising molecules associated with a site on the solid support, so that the molecules are detected in aggregate, where the molecules comprise at least two different types of molecules and a detector configured for detect molecules associated with the site.

In some embodiments, the molecules are connected at the site.

In a preferred embodiment, the molecules comprise nucleic acids.

In some embodiments of molecule detection systems, a site comprises about 2 to about 1011 molecules, about 2 to about 1010 molecules, about 2 to about 109 molecules, about 2 to about 108 molecules, about from 2 to about 107 molecules, about 2 to about 106 molecules, about 2 to about 105 molecules, about 2 to about 104 molecules.

In some embodiments, the molecules are associated with the site.

In other embodiments, the molecules are connected at the site.

In certain embodiments, the molecules comprise nucleic acids.

Some embodiments of the molecule detection systems described above may further comprise a fluid handling system configured to apply fluid to the site.

Other modalities of the molecule detection systems described above may further comprise a light source configured to provide an excitation beam to the site.

Some modalities of the molecule detection systems described above may also comprise a first data processing module configured to estimate the fraction of different types of molecules at the site or the amount of signal corresponding to different types of molecules at the site.

In some modalities, the first data processing module is also used to determine the variation associated with the estimate.

In other embodiments, the determination step is performed using a separate data processing module.

In some modalities of such systems, the systems may also comprise a second data processing module configured to calculate the amount of signal corresponding to different types of molecules at the site using the fraction estimate or to calculate the fraction of different types of molecules at the site using signal estimation.

In other modalities of such systems, the systems may also comprise a third data processing module configured to iteratively update the fraction estimate and signal estimate.

In some embodiments of the molecule detection systems described above, the systems are configured to identify the nucleotide sequence of a target region of a nucleic acid.

In addition or alternatively, the detection of a genetic biomarker can include methods for identifying a target region of a nucleic acid.

The methods may comprise (a) associating a first subpopulation of nucleic acids with a site on a solid support, wherein the nucleic acids of the first subpopulation comprise an identical target region; (b) associating a second subpopulation of nucleic acids to the site on the solid support, wherein the nucleic acids of the second subpopulation comprise an identical target region which is a variant of the target region of the nucleic acids of the first subpopulation; (c) detecting a signal corresponding to one or more nucleotides of the target region of the first nucleic acids of the subpopulation and one or more nucleotides of the variant of the target region of the second nucleic acids of the subpopulation; (d) estimate the fraction of nucleic acids of the first subpopulation and nucleic acids of the second subpopulation associated with the site or estimate the amount of signal corresponding to the nucleic acids of the first subpopulation and nucleic acids of the second subpopulation associated with the site; (e) calculate the amount of signal corresponding to the nucleic acids of the first subpopulation and nucleic acids of the second subpopulation associated with the site using the fraction estimate or calculate the fraction of the nucleic acids of the first subpopulation and nucleic acids of the second subpopulation associated with the site using signal estimation; and (f) iteratively update the fraction estimate and the signal estimate until the estimates converge, thus identifying a target region for a nucleic acid.

In some embodiments of the methods described above, step (a) comprises attaching nucleic acids from the first subpopulation and nucleic acids from the second subpopulation to the solid support.

In some modalities of the methods described above, step (d) comprises performing a major component analysis (PCA). In some modalities of the methods described above, step (f) comprises executing a numerical optimization algorithm.

In some of these modalities, the numerical optimization algorithm is based on iterative map research.

In some other modalities, the numerical optimization algorithm is based on Fienup's iteration map.

In some embodiments of the methods described above, the sequence data is obtained from the first and second nucleic acids of the subpopulation.

In some of these modalities, the sequence data is obtained by a synthesis sequencing process.

In some embodiments, the synthesis sequencing process comprises a pyrosquencing process.

In addition or alternatively, the detection of a genetic biomarker may include methods to identify a bioassignment.

The methods may comprise the steps of (a) providing samples obtained from a plurality of individuals, in which the samples comprise molecules; (b) marking the sample molecules, in order to identify the individual from whom each sample originated; (c) associating sample molecules to a location on a solid support, so that the molecules are detected aggregated during a detection step, in which the site comprises at least two different types of molecules; (d) obtaining a bio-signature for molecules associated with the site: i) detecting a signal corresponding to the aggregate of the molecules at the site; ii) estimate the fraction of different types of molecules at the site or the amount of signal corresponding to different types of molecules at the site, iii) calculate the amount of signal corresponding to different types of molecules at the site using the fraction estimate or calculate the fraction different types of molecules at the site using the signal estimate and iv) iteratively update the fraction estimate and the signal estimate until the estimates converge, thus obtaining a bio signature for molecules at the site; and (e) compare the bio-signature obtained in step (d) with a reference bio-signature, thus identifying the bio-signature.

In a preferred embodiment, the molecules are linked at the site.

In a preferred embodiment of the methods described above, the molecules comprise nucleic acids.

In some of these modalities, nucleic acids comprise a marker of a pathogen.

In certain embodiments, the pathogen comprises a pathogen selected from the group consisting of a virus, a bacterium and a eukaryotic cell.

In some embodiments, the eukaryotic cell can be a cancer cell.

In some embodiments of the methods described above, the sample comprises an abnormal cell type.

In some modalities of the methods described above, the sample is obtained from a cancer patient.

A solid support is also described, including a population of nucleic acids associated with a site on the solid support, so that the nucleic acids of the nucleic acid population are detected in aggregate, the population of nucleic acids comprising a first subpopulation and a second subpopulation, in which the nucleic acids of the first subpopulation comprise an identical target region and the nucleic acids of the second subpopulation comprise an identical region which is a variant of the target region. Additionally or alternatively, the detection of a genetic biomarker may include beads comprising a first subpopulation of capture nucleic acids that have a competing molecule hybridized to it and a second subpopulation of capture nucleic acids comprising a region that allows hybridization of a complementary molecule . In addition or alternatively, the detection of a genetic biomarker may include spheres comprising capture nucleic acids hybridized to an amplified nucleic acid comprising a degenerate tag, the degenerate tag being hybridized to a capture nucleic acid. In some embodiments, the sphere is present in a channel on a substrate. In other modalities, the sphere is present in a well of a substrate of multiple wells. In a preferred embodiment, the well is configured to retain a single sphere with the amplified nucleic acids hybridized to it.

[494] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the U.S. Patent

9,163,283, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include compositions comprising a plurality of nucleic acids, each nucleic acid comprising an invariant sequence, a variable sequence and a marker. Additionally or alternatively, the detection of a genetic biomarker may include a method for decoding an array composition. The method includes providing an arrangement composition comprising a substrate with a surface comprising distinct sites and a population of microspheres comprising

having first and second subpopulations, each subpopulation comprising an identifying nucleic acid sequence comprising a primer sequence and a decoder sequence. The method further comprises adding to the array a first set of combinatorial decoding probes comprising an initiation sequence, at least one decoding nucleotide and a marker, and detecting the presence of the marker.

[495] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the U.S. Patent

9,045,796, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include a method for detecting one or more typifiable loci contained in a given genome, where the method includes the steps of providing an amplified representative population of genome fragments with those loci typifiable, contact the genome fragments with a plurality of nucleic acid probes with sequences corresponding to the typifiable loci under conditions in which probe fragment hybrids are formed; and detection of typeable loci of probe-fragment hybrids. In particular embodiments, these nucleic acid probes are at most 125 nucleotides in length. However, probes with varying lengths or sequences can be used as set out in more detail below. In addition or alternatively, the detection of a genetic biomarker may include a method for detecting typifiable loci of a genome, including the steps of providing an amplified representative population of genome fragments that have these typifiable loci, contacting the genome fragments with a plurality of nucleic acid probes having sequences corresponding to the typable loci under conditions in which hybrids and probe fragment are formed; and direct detection of typeable loci of probe fragment hybrids.

In addition or alternatively, the detection of a genetic biomarker may include a method for detecting typifiable loci of a genome, including the steps of providing a representative amplified population of genome fragments with typifiable loci; contacting the genome fragments with a plurality of immobilized nucleic acid probes having sequences corresponding to the typeable sites under conditions in which immobilized probe fragment hybrids are formed; modify the immobilized probe fragment hybrids; and detecting a probe or fragment that has been modified, thereby detecting the typeable loci of the genome.

In addition or alternatively, the detection of a genetic biomarker may include a method, including the steps of (a) providing a plurality of genome fragments, wherein the plurality of genome fragments has at least 100 µg of DNA with a complexity of at least 1 Gigabases; (b) contacting the plurality of genome fragments with a plurality of different immobilized nucleic acid probes, in which at least 500 of the different nucleic acid probes hybridize with genome fragments to form probe fragment hybrids ; and (c) detecting typeable loci of probe fragment hybrids.

In addition or alternatively, the detection of a genetic biomarker may include a method may also include the steps of (a) providing a plurality of genome fragments, wherein the plurality of genome fragments has a concentration of at least 1 ug / ul of DNA having a complexity of at least 1 Gigabases; (b) contacting the plurality of genome fragments with a plurality of different immobilized nucleic acid probes, wherein at least 500 of the different nucleic acid probes hybridize with genome fragments to form probe fragment hybrids; and (c) detecting typeable loci of probe fragment hybrids.

In addition or alternatively, the detection of a genetic biomarker may include a method of amplifying genomic DNA, including the steps of providing isolated double-stranded genomic DNA, producing DNA cut by contact with double-stranded genomic DNA with a cutting agent, contact this DNA cut with a ribbon-displacement polymerase and a plurality of primers, in order to amplify the genomic DNA.

In addition or alternatively, the detection of a genetic biomarker can include a method for detecting typeable loci of a genome.

The method includes the steps of (a) in vitro transcription of a plurality of amplified gDNA fragments, thereby obtaining fragments of genomic RNA (gRNA); (b) hybridizing the gRNA fragments to a plurality of nucleic acid probes having sequences corresponding to the typeable loci; and (c) detecting typeable loci of the gRNA fragments that hybridize to the probes.

Additionally or alternatively, the detection of a genetic biomarker may include a method to produce an amplified representative population of reduced complexity, specific to locus, of genome fragments.

The method includes the steps of (a) replicating a native genome with a plurality of random primers, thus producing an amplified representative population of genome fragments; (b) replicating a subpopulation of the representative amplified population of genome fragments with a plurality of different locus-specific primers, thereby producing a representative amplified and locus-specific population of genome fragments; and (c) isolating the subpopulation, thus producing an amplified representative population of reduced complexity, specific to locus, of genome fragments.

In addition or alternatively, the detection of a genetic biomarker may include a method to inhibit ectopic probe extension in a primer extension assay.

The method includes the steps of (a)

contacting a plurality of probe nucleic acids with a plurality of target nucleic acids under conditions in which probe hybrids are formed; (b) contacting the plurality of probe nucleic acids with an ectopic extension inhibitor under conditions in which hybrid ectopic extension inhibitors are formed; and (c) selectively modify probes in the probe's target hybrids compared to probes in the probe's ectopic extension inhibitor hybrids. Additionally or alternatively, the detection of a genetic biomarker may include a method that includes the steps of (a) contacting a plurality of genome fragments with a plurality of different nucleic acid probes immobilized under conditions in which hybrids are formed probe fragment immobilized; (b) modify the immobilized probes while hybridizing with the fragments of the genome, thus forming modified immobilized probes; (c) removing said genome fragments from said probe fragment hybrids; and (d) detecting the modified immobilized probes after removing the genome fragments, thereby detecting the typifiable sites of the genome fragments. In addition or alternatively, the detection of a genetic biomarker may include a method that includes the steps of (a) representative amplification of a native genome, in which a representative amplified population of genome fragments having the typifiable loci is produced under isothermal conditions; (b) contacting the genome fragments with a plurality of nucleic acid probes with sequences corresponding to the typeable loci under conditions in which probe fragment hybrids are formed; and (c) detecting typifiable loci of those of probe fragment hybrids.

[496] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the U.S. Patent

8,765,419, which is incorporated herein by reference in its entirety.

For example, the detection of a genetic biomarker may include methods and systems for detecting pyrophosphate, which can be used alone or in connection with other technologies, such as pyrosequencing.

In some modalities, these methods and systems allow the detection of pyrophosphate with a reduced background.

In addition or alternatively, the detection of a genetic biomarker may include methods and systems for nucleose acids with reduced background nucleic acids.

In addition or alternatively, the detection of a genetic biomarker may include methods for delaying the detection of pyrophosphate.

Methods may include the steps of providing a pyrophosphate sequestering agent, generating pyrophosphate in the presence of the sequestering agent, wherein the pyrophosphate is reversibly sequestered, releasing the pyrophosphate from the sequestering agent and detecting the pyrophosphate.

In addition or alternatively, the detection of a genetic biomarker may include methods for sequencing a nucleic acid.

The method may include the steps of providing nucleotides or nucleotide analogs in the presence of a pyrophosphate sequestering agent and a pyrophosphate detection agent, incorporating one or more nucleotides or nucleotide analogs into a polynucleotide in order to extend the polynucleotide in the presence of the pyrophosphate sequestering agent, thereby generating sequestered pyrophosphate, removing unincorporated nucleotides or nucleotide analogs from the presence of the pyrophosphate detection agent, releasing the pyrophosphate from the sequestering agent in the presence of the pyrophosphate detection agent and detecting pyrophosphate - released fact, in which the released pyrophosphate indicates that one or more nucleotides or nucleotide analogues were incorporated into the polynucleotide.

In addition or alternatively, the detection of a genetic biomarker may include methods of modulating the availability of free pyrophosphate during the sequencing of a nucleic acid molecule.

Such methods may include the steps of combining nucleotides or nucleotide analogs with a nucleic acid model; incubate the nucleic acid model and the nucleotides or nucleotide analogs together with a polymerase and a pyrophosphate sequestering agent under conditions sufficient to form a polynucleotide complementary to all or a portion of the nucleic acid model, in that the pyrophosphate generated during incubation is reversibly sequestered by the sequestering agent, remove from the nucleic acid model the nucleotides or nucleotide analogues that were not incorporated into the polynucleotide and release the pyrophosphate from the pyrophosphate sequestering agent, providing a release reagent .

Additionally or alternatively, the detection of a genetic biomarker may include arrangements that comprise a solid support with a plurality of sites distributed therein, wherein at least a portion of the sites comprises a model nucleic acid and a pyrophosphate sequestering agent capable of to hijack the pyrophosphate reversibly.

In some respects, the sites comprise wells.

In certain respects, the nucleic acid model is attached to a particle or sphere within the wells.

In certain aspects, wells also comprise spheres with a pyrophosphate detection agent attached to them.

In certain aspects, the pyrophosphate sequestering agent is disposed between the standard nucleic acid and the pyrophosphate detection agent.

In certain aspects, the pyrophosphate detection agent comprises ATP sulfurylase and luciferase.

In some respects, wells also comprise packaging spheres.

In some embodiments, pyrophosphate is reversibly sequestered by adsorption with the sequestering agent.

In some respects, the sequestering agent comprises a cationic agent capable of sequestering pyrophosphate through chelation, complexation or adsorption.

In certain respects, the cationic agent comprises an agent selected from the group consisting of a metal, metal salt, metal oxide or other agent set out below.

In certain aspects, the metal or metal oxide comprises Ti or TiO2. In other respects, the pyrophosphate sequestering agent comprises hydroxyapatite.

In other respects, the sequestering agent comprises an ammonium or substituted ammonium salt or a resin or sphere that contains these groups.

In certain aspects of the above embodiments, the pyrophosphate sequestering agent comprises particles or spheres.

In addition to the above, in some modalities of methods and arrangements, pyrophosphates can be released from the sequestering agent, providing a release reagent to the sequestering agent.

In certain aspects, the releasing reagent comprises an anion capable of displacing the pyrophosphate from the sequestering agent, for example, preferentially complexing or chelating the cation of the sequestering agent.

In certain respects, the release reagent comprises an agent selected from the group consisting of an acid or salt of an acid such as oxalic acid, oxalate salt, sulfamic acid, sulfamate salt, ethylene diamine tetraacetic acid (EDTA) , ethylene glycol-bis-β-amino-ethyl ether N, N, N ′, N′-tetraacetic acid (EGTA), citric acid, tartaric acid, acetic acid or other carboxylic acids or their salts.

In other respects, the release reagent comprises phosphate.

In other respects, the release reagent comprises a bisphosphonate.

In certain aspects, the release reagent is the ATP sulfurylase enzyme.

In this particular aspect, the ATP sulfurylase is in solution, instead of being attached to a sphere or other surface.

ATP sulfurylase can release the pyrophosphate from the sequestering agent, transforming the pyrophosphate into ATP in the presence of adenisine phosphosulfate (APS). Typically, ATP will have a lower binding affinity for the sequestering agent than pyrophosphate.

In some respects, the arrangements may include sites that further comprise a polymerase and nucleotides or nucleotide analogs.

In some modalities, the arrangements may also comprise at least one electrode capable of producing an electric field in the presence of the sites.

Additionally or alternatively, the detection of a genetic biomarker may include methods for making an arrangement.

The methods may include the steps of providing a solid support with a plurality of sites distributed over it and providing a model nucleic acid and a pyrophosphate sequestering agent capable of reversibly sequestering pyrophosphate to at least a portion of the sites.

In certain aspects, the step of providing the template nucleic acid for the plurality of sites occurs before providing the pyrophosphate sequestering agent.

In certain aspects, the step of providing the template nucleic acid for the plurality of sites occurs after delivery of the pyrophosphate sequestering agent.

In certain aspects, the step of providing the template nucleic acid for the plurality of sites occurs while providing the pyrophosphate sequestering agent.

In some embodiments, arrangements manufactured according to the above methods can be used in the sequencing and / or sequestering and pyrophosphate release processes.

For example, in some modalities, pyrophosphate is reversibly sequestered by adsorption with the sequestering agent.

In some respects, the sequestering agent comprises a cationic agent capable of sequestering pyrophosphate through chelation, complexation or adsorption.

In certain aspects, the cationic agent comprises an agent selected from the group consisting of a metal, metal salt, a metal oxide or other agent set out below.

In certain aspects, the metal or metal oxide comprises Ti or TiO2. In other respects, the pyrophosphate sequestering agent comprises hydroxyapatite.

In other respects, the sequestering agent comprises an ammonium or substituted ammonium salt or a resin or sphere that contains these groups.

In certain aspects of the above embodiments, the pyrophosphate sequestering agent comprises particles or spheres. In addition to the above, in some modalities, arrangements manufactured according to the above methods can be used in processes in which pyrophosphate can be released from the sequestering agent, providing a release reagent to the sequestering agent. In certain aspects, the release reagent comprises an anion capable of displacing the pyrophosphate from the sequestering agent, for example, preferentially complexing or chelating the cation of the sequestering agent. In certain aspects, the release reagent comprises an agent selected from the group consisting of an acid or salt of an acid such as oxalic acid, oxalate salt, sulfamic acid, sulfamate salt, ethylene diamine tetraacetic acid (EDTA), acid ethylene glycol-bis-β-amino-ethyl ether N, N, N ′, N′-tetraacetic (EGTA), citric acid, tartaric acid, acetic acid or other carboxylic acids or their salts. In other respects, the release reagent comprises phosphate. In other respects, the release reagent comprises a bisphosphonate. In certain aspects, the release reagent is the ATP sulfurylase enzyme. In this particular aspect, ATP sulfurylase is in solution, rather than being attached to a sphere or other surface. ATP sulfurylase can release the pyrophosphate from the sequestering agent, transforming the pyrophosphate into ATP in the presence of adenisine phosphosulfate (APS). Typically, ATP will have a lower binding affinity for the sequestering agent than pyrophosphate.

[497] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the U.S. Patent

8,741,630, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include methods for detecting a target analyte in a biological sample, comprising providing a composite arrangement comprising a substrate having a surface; a first and a second test location on said surface, wherein said test locations comprise a population of microspheres and wherein said microspheres comprise bioactive agents; a physical partition separating said first test location from said second test location; adding said biological sample to said first test site under conditions sufficient to allow said target analyte to bind to said bioactive agents; and detecting the binding of said bioactive agents to said target analyte.

In more specific modalities, the binding of bioactive agents to the target analyte can be detected by a change in the optical signature of the microspheres.

The target analytes and bioactive agents can include a nucleic acid, for example.

In some embodiments, the methods may further comprise detecting a target analyte in a second biological sample, adding said second biological sample to said second test site and, subsequently, detecting the binding of said bioactive agents to said target analyte.

In certain aspects, the substrate may include a microscope slide.

Additional methods include detecting a target nucleic acid in a biological sample, wherein said target nucleic acid includes one or more single nucleotide polymorphisms (SNPs) in one or more predetermined positions, comprising providing a composite arrangement comprising - giving a substrate having a surface; a population of microspheres, wherein said microspheres are attached to capture probes configured to bind to said target nucleic acid at said one or more predetermined positions; a first and second test location on said surface, wherein said test locations comprise said population of microspheres; a physical partition separating said first test location from said second test location; adding said biological sample to said first assay location under conditions sufficient to allow said target nucleic acid to bind to said capture probes; and detecting the binding of said target nucleic acid to said capture probes. In addition or alternatively, the detection of a genetic biomarker may include arrangement compositions comprising a rigid support; a molded layer with at least one first test location comprising different sites, where the molded layer is adhered to the rigid support; a layer of bonding agent that adheres to the rigid support in the molded layer; and a population of microspheres comprising at least a first and a second subpopulation, wherein the first subpopulation comprises a first bioactive agent and the second subpopulation comprises a second bioactive agent in which the microspheres are randomly distributed at the sites. In addition or alternatively, the detection of a genetic biomarker may include a method for making an array composition containing at least one first test site with distinct sites that comprise the steps of contacting a surface of a model structure, a surface comprising one or more sets of projections, with a moldable material; removing the moldable material from the surface of the model structure, whereby the removed moldable material forms a molded layer with at least one first test location comprising distinct sites; adhere the molded layer to a rigid support; and randomly distribute microspheres in the molded layer, so that individual distinct sites comprise microspheres, where the microspheres comprise at least one first and a second subpopulation, where the first subpopulation comprises a first bioactive agent and the second subpopulation comprises comprises a second bioactive agent.

[498] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the U.S. Patent

7,901,897, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include composite array compositions comprising a first substrate with a surface comprising a plurality of test sites, each test site comprising a plurality of distinct sites. The substrate further comprises a population of microspheres comprising at least a first and a second subpopulation, each subpopulation comprising a bioactive agent. The microspheres are distributed in each of the test sites. In addition or alternatively, the detection of a genetic biomarker may include compositions of composite arrangement comprising a first substrate with a surface comprising a plurality of test sites and a second substrate comprising a plurality of arrangement sites, each location arrangement comprising distinct sites. The compositions further comprise a population of microspheres comprising at least a first and a second subpopulation, each subpopulation comprising a bi-active agent. The microspheres are distributed in each of the arrangement locations. Additionally or alternatively, the detection of a genetic biomarker can include methods of decoding an array composition comprising providing an array composition as described above and adding a plurality of decoding linkers to the composite array composition for identify the location of at least a plurality of bioactive agents. Additionally or alternatively, the detection of a genetic biomarker may include methods for determining the presence of one or more target analytes in one or more samples comprising contacting the sample with the composition and determining the presence or absence of said target analyte.

In addition or alternatively, the detection of a genetic biomarker can include a hybridization chamber.

The hybridization chamber includes a base plate and a cover.

A seal is located between the cover and the base plate to provide an airtight seal.

When a two-component arrangement system is used, the chamber also includes component ports on the cover to immobilize the arrangement's components.

That is, the arrangement components are inserted through the door on the cover.

Doors can include seals to maintain an airtight seal.

The chamber can also include staples and alignment pins.

In addition or alternatively, the detection of a genetic biomarker may include a hybridization chamber in which the base plate contains holes.

The holes can be in a microplate arrangement format.

In one embodiment, at least two holes are joined by a channel.

In one embodiment, a flexible membrane is placed on the base plate.

When pressure, that is, a vacuum, is applied to the membrane, wells form in the membrane at the location of the holes in the base plate.

The apparatus also includes a pneumatic device to provide a vacuum or positive pressure to the membrane.

In addition or alternatively, the detection of a genetic biomarker may include a method of mixing samples in a formal arrangement.

The method includes providing a vacuum for the membrane, so that wells are formed.

A solution is then applied to the membrane so that at least one of the wells is filled with liquid.

Subsequently, the vacuum is applied intermittently to the membrane, which results in mixing of the liquid.

In addition or alternatively, the detection of a genetic biomarker can include an apparatus comprising a hybridization chamber and any of the composite arrangement compositions.

Additionally or alternatively, the detection of a genetic biomarker may include performing methods of decoding an array composition in a hybridization chamber. In addition or alternatively, the detection of a genetic biomarker may include performing methods to determine the presence of one or more target analytes in one or more samples in a hybridization chamber.

[499] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the U.S. Patent

8,288,103, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include a method for detecting target sequences in a sample which comprises providing a first solid support comprising at least a first and a second target sequence, contacting the first and second target sequences with the first and second probes, respectively, wherein each of the first and second probes comprises a first universal initiation site, a specific target domain substantially complementary to at least a portion of the target sequence, to form the first and the second hybridization complexes, respectively, remove unhybridized probes, contact the first and second hybridization complexes with a first enzyme to form the first and second modified probes, respectively, contact the first and second modified probes with at least a first primer that hybridizes to the NTPs of the universal initiation site and an extension enzyme, where the first area The second modified probes are amplified to form the first and second amplicons, respectively, and detect the amplicons. Additionally or alternatively, the detection of a genetic biomarker may include a method for detecting target sequences in a sample comprising providing a first solid support comprising at least a first and a second target sequence, contacting the first and second target sequences with the first and second probes, respectively, in which each of the first and second probes comprises a first universal initiation site, a specific target domain substantially complementary to at least a portion of the target sequence, to form the first and second hybridization complexes, respectively, remove unhybridized probes, contact the first and second probes with at least one first universal primer that hybridizes to the universal initiation site, NTPs and an enzyme extension, in which the first and second probes are extended to form the first and second modified probes, respectively, contact the first and the second probes modified with at least the third and fourth probes, respectively, where the first and second modified probes comprise a detection position, the third and fourth probes comprise an interrogation position and a second enzyme, in which the second enzyme modifies only the third and fourth probes if there is perfect complementarity between the bases in the interrogation position and detection position, forming the modified third and fourth probes and detecting the modified third and fourth probes .

Additionally or alternatively, the detection of a genetic biomarker may include a method comprising providing a plurality of target nucleic acid sequences, each comprising from 3 'to 5' a first, second and third target domain, the first domain target comprising a detection position, the second target domain being at least one nucleotide in contact with the target nucleic acid sequences with sets of probes for each target sequence, each set comprising a first probe comprising from 5 'to 3' a first domain comprising a first universal initiation sequence and a second domain comprising a sequence substantially complementary to the first target domain of a target sequence and a query position within the four terminal bases 3 ', a second probe comprising a first domain comprising giving a sequence substantially complementary to the third target domain of a target sequence, for f form a set of first hybridization complexes, contact the first hybridization complexes with an extension enzyme and dNTPs, under conditions where if the base in the interrogation positions is perfectly complementary with the bases in the detection positions, the extension of the first probes occur through the second target domains to form second hybridization complexes, contact the second hybridization complexes with a ligase to connect the first extended probes to the second probes to form amplification models.

In addition or alternatively, the detection of a genetic biomarker may include a multiplex reaction method that comprises providing a sample comprising at least the first and second targets hybridizing the first and second targets with the first and second probes. form the first and second hybridization complexes respectively, immobilize the first and second hybridization complexes respectively, wash to remove unhybridized nucleic acids, contact the first and second hybridization complexes with an en - zyme, in which the first and second probes are modified to form the first and second modified probes, respectively, whereby the first and second modified probes are modified to contain the first and second interrogation nucleotides which are complementary to the first and second detection nucleotides in the first and second targets, respectively, contact the first and second modified probes with the first and second allele-specific primers, respectively, so the first and second allele-specific primers hybridize to the first and second modified probes, respectively, 5 ′ to the first and second interrogation nucleotides, dNTPs, polymerase, where the first and second allele-specific primers are modified when a target domain of the allele-specific primers is perfectly complementary to the modified target probes to form specific first and second modified allele probes, amplify the first and second specific probes alleles modified to form first and second amplicons and detect the first and second amplicons.

In addition or alternatively, the detection of a genetic biomarker may include a method comprising providing a plurality of target nucleic acid sequences, each comprising from 3 'to 5' a first, second and third target domain, the first domain target comprising a detection position, the second target domain being at least one nucleotide, contacting the target nucleic acid sequences with sets of probes for each target sequence, each set comprising a first probe comprising from 5 'to 3' a first domain comprising a first universal initiation sequence and a second domain comprising a sequence substantially complementary to the first target domain of a target sequence and an interrogation position within the four terminal bases 3 ', a second probe comprising a first domain comprising a sequence substantially complementary to the third target domain of a target sequence, to form a set of first hybridization complexes, contact the first hybridization complexes with at least one first universal primer that hybridizes to the first universal initiation sequence, an extension enzyme and dNTPs, under conditions where the base is in the interro positions - gating is perfectly complementary with the bases at the detection positions, the extension of the first probes occurs through the second target domains to form second hybridization complexes, contact the second hybridization complexes with a ligase to connect the first extended probes second probes to form amplification models.

[500] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the U.S. Patent

7,899,626, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include a method for measuring the level of DNA methylation. The method can include the steps of providing data representing the standard deviation of DNA methylation measurements, determining the methylation level of at least one locus in the sample DNA, and comparing the methylation level of at least one locus with the data to determine the standard deviation of the measurement. In addition or alternatively, the detection of a genetic biomarker may include a method for comparing the methylation level of DNA samples. The method can include the steps of providing data that represent the standard deviation of DNA methylation measurements; determine the methylation level of at least one locus in a first DNA sample; determine the methylation level of at least one locus in a second DNA sample; identify the standard deviations of the methylation level of at least one locus in the first DNA of the sample and in the second DNA of the data sample; and determining whether the methylation level of at least one locus in the DNA of the first sample and in the DNA of the second sample is the same or different based on the standard deviations. In addition or alternatively, the detection of a genetic biomarker may include a DNA methylation level detection system, including a scanner to read the methylation levels of a plurality of loci in a sample DNA and a first module configured to compare methylation levels with data representing the standard deviation of DNA methylation measurements.

Some modalities refer to a method of measuring the DNA methylation level, including providing data representing the standard deviation of DNA methylation measurements; determine the methylation level of at least one locus in a DNA sample; and comparing the methylation level of said at least one locus with said data to determine the standard deviation of said measurement.

In some embodiments, at least one locus comprises a plurality of loci.

In some embodiments, methylation levels are determined using an arrangement.

In some embodiments, the plurality of loci comprises at least 100 loci measured simultaneously in said arrangement.

In some modalities, the data correlate the standard deviation of the methylation level as a function of the methylation level.

In some embodiments, the data comprise different standard deviation values for different methylation levels.

In some modes, the data comprise the said different standard deviation values that occur throughout a parabola when correlated with the said different levels of methylation.

In some modalities, data are produced through the creation of a training set comprising mixtures of DNA with varying levels of methylation, in which the said training set comprises replicas of said mixtures; determine the methylation level of at least one locus in said mixtures of said training set; determine standard deviation values for said methylation levels determined for said replicas of said training set; and correlate the referred standard deviation values and the referred methylation levels determined for the referred training set.

In some modalities, mixtures of the training set comprise different ratios of genomic DNA from a population of cells with highly methylated DNA and a population of cells with minimally methylated DNA.

In some modalities, the methylation levels for the mixtures of the referred training set vary from 0 to 1. Some modalities also include identifying at least three regions from 0 to 1, determining the median of the methylation levels for each of the regions and adjust a parameter to the said median for each of the said regions.

In some modalities, the standard deviation values comprise the standard deviation values of the 95th percentile.

Some modalities also include the steps of determining the methylation level of the referred at least one locus in a second sample DNA; identifying the standard deviations of said methylation level of said at least one locus in said sample DNA and in said second sample DNA of said data; and determining whether said methylation level of said at least one locus in said first sample DNA and said second sample DNA is the same or different based on said standard deviations.

Some modalities refer to a DNA methylation level detection system, including a scanner to read the methylation levels of a plurality of loci in a sample DNA and a first module configured to compare said methylation levels. with data that represent the standard deviation of DNA methylation measurements.

In some embodiments, methylation levels are determined using an arrangement.

In some modalities, the plurality of loci comprises at least 100 loci measured simultaneously in the referred arrangement.

In some modalities, the data correlate the standard deviation of the methylation level as a function of the methylation level.

In some modalities, the data comprise different standard deviation values for different levels of methylation.

In some embodiments, the data comprise said different standard deviation values that occur throughout a parabola when correlated with said different methylation levels.

In some modalities, data are produced by creating a training set comprising mixtures of DNA with varying levels of methylation, in which said training set comprises replicas of said mixtures; determine the level of methylation of at least one locus in said mixtures of the said training set; determine standard deviation values for the said methylation levels determined for the said replicates of the referred training set; and correlating said standard deviation values and said methylation levels determined for said training set. Some modalities refer to a method for comparing the methylation level of DNA samples, including providing data that represent the standard deviation of DNA methylation measurements; determine the methylation level of at least one locus in a first sample DNA; determining the methylation level of said at least one locus in a second sample DNA; identifying the standard deviations of said methylation level of at least one locus in said first sample DNA and in said second sample DNA of said data; and determining whether said methylation level of said at least one locus in said DNA from the first sample and in said DNA from the second sample is the same or different based on said standard deviations.

[501] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the U.S. Patent

7,776,531, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include a probe composition, including (a) a substrate; (b) a probe molecule bound to the substrate; and (c) a stabilizing polymer layer on the substrate, wherein said stabilizing polymer layer covers the probe molecule. In addition or alternatively, the detection of a genetic biomarker may include a method for making a probe composition. The method includes the steps of (a) providing a substrate with a biopolymer probe attached; and (b) contacting the substrate with a stabilizing polymer. In addition or alternatively, the detection of a genetic biomarker may include a method of sending a solid phase probe. The method includes the steps of (a) providing a substrate with an attached probe molecule and a stabilizing polymer layer; (b) placing the substrate in a package; and (c) send the package to a remote location.

[502] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the U.S. Patent

7,499,806, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include array compositions comprising a substrate with a surface comprising distinct sites, at least one fiducial, and a population of microspheres comprising at least a first and a second subpopulation. Each subpopulation comprises a bi-active agent, and the microspheres are distributed on said surface. Each subpopulation can optionally comprise a unique optical signature, an identifier linker that will link a decoder linker so that the identification of the bioactive agent can be elucidated, or both. In addition or alternatively, the detection of a genetic biomarker may include compositions comprising a computer-readable memory for directing a computer to function in a specified manner. The computer-readable memory comprises an acquisition module for receiving an image of data from a random arrangement comprising a plurality of different sites, a registration module for registering a data image and a comparison module for comparing images of data. recorded data.

Each module comprises computer code to perform its function.

The registration module can use any number of fiducials, including a fiducial fiber when the substrate comprises an optical fiber bundle, a fiducial microsphere or a fiducial model generated from the random arrangement.

In addition or alternatively, the detection of a genetic biomarker may include methods for making the arrangement compositions comprising forming a surface comprising individual sites on a substrate, distributing microspheres on the surface so that the individual sites contain microspheres and incorporating at least a man on the surface.

When the arrangement has complete freedom of rotation, at least two fiducials are preferred in the arrangement to allow for the correction of the rotation.

In addition or alternatively, the detection of a genetic biomarker may include methods for comparing images of data separated from a random array.

The methods comprise using a computer system to record a first data image of the random arrangement to produce a first recorded data image, using the computer system to register a second data image of the random arrangement to produce a second recorded data image and compare the first and second recorded data images to determine the differences between them.

Additionally or alternatively, the detection of a genetic biomarker may include methods for decoding a random arrangement composition comprising providing a random arrangement composition.

A first plurality of decoding linkers is added to the arrangement composition and a first data image is created.

A fiducial is used to generate a first image of recorded data.

A second plurality of decoding linkers is added to the array composition and a second data image is created.

The fiducial is used to generate a second image of recorded data.

A computer system is used to compare the first and second images of recorded data to identify the location of at least two bioactive agents.

In addition or alternatively, the detection of a genetic biomarker may include methods for determining the presence of a target analyte in a sample.

The methods comprise acquiring a first image of data from a random arrangement composition and registering the first image of data to create a first image of recorded data.

The sample is then added to the random array and a second image of data is acquired from the array.

The second data image is registered to create a second registered data image.

Then, the first and second recorded data images are compared to determine the presence or absence of the target analyte.

Optionally, data acquisition can be at different wavelengths.

In addition or alternatively, the detection of a genetic biomarker can include methods for pre-processing or pre-filtering signal data comprising acquiring an image of an array data and determining the similarity of a first signal from at least one site in the array with a reference sign to determine if the site comprises a candidate sphere.

Additionally or alternatively, the detection of a genetic biomarker may include methods for recording an analytical image of a microsphere array comprising providing an image of hybridization intensity.

After the array of microspheres is decoded, a registration grid is calculated based on known locations of bioactive agents in the microspheres obtained from the decoding step.

The sample is added to the microsphere array and an image of hybridization intensity is acquired from the array.

The types of shiny spheres are distributed throughout the arrangement to serve as fiducials.

The registration grid is superimposed on the image and then the registration grid is aligned so that the identity of the signal strength at each location in the grid is identified for each type of sphere in the arrangement. Once the correct grid position is obtained, each nucleus is assigned a number so that the correct grid positioning can be done for other sequential images.

[503] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the U.S. Patent

6,942,968, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include a composition that includes a substrate with a surface comprising distinct sites, a reflective coating on the surface and a population of microspheres distributed on the substrate. Microspheres comprise at least one first and a second subpopulation. Generally, at least one subpopulation comprises a bioactive agent. Additionally or alternatively, the detection of a genetic biomarker may include a composition in which the substrate comprises a first and a second surface, in which the first surface comprises distinct sites and the reflective coating is on the second surface. The population of microspheres is distributed on the first surface. In addition or alternatively, the detection of a genetic biomarker may include a method for making a probe composition. The method includes providing a substrate with a surface comprising different sites, applying a coating of reflective material to the surface and distributing microspheres on the surface. In addition or alternatively, the detection of a genetic biomarker may include a method, in which the substrate comprises a first and a second surface, in which the first surface comprises distinct sites, the reflective material is on the second surface and the microspheres are distributed on the first surface.

Additionally or alternatively, the detection of a genetic biomarker may include a method comprising providing a preformed optical fiber unit bundle comprising a proximal and a distal end, the distal end comprising the plurality of distinct sites comprising a population of microspheres , the population comprising at least the first and second subpopulations and imaging the fiber optic bundle from the distal end.

A reflective coating can be applied to the distal end or the proximal end of the optical fiber bundle.

In addition or alternatively, the detection of a genetic biomarker may include an array composition comprising a substrate with a surface comprising distinct sites comprising wells in an alternative manner.

The wells can contain a cross section in the shape of a square, a hexagon, a star, a triangle, a pentagon or an octagon.

Additionally or alternatively, the detection of a genetic biomarker may include a method which comprises providing a substrate with a plurality of distinct sites, sites comprising wells of alternative shape and a population of microspheres, the population comprising at least the first and the second subpopulations and the substrate image.

In addition or alternatively, the detection of a genetic biomarker may include an array composition comprising a substrate with a surface comprising distinct sites and a population of microspheres distributed on the substrate, where the microspheres comprise a bioactive agent and a signal transducer element.

In addition or alternatively, the detection of a genetic biomarker may include a method for detecting an unlabeled target analyte in a sample that comprises providing a substrate with a plurality of distinct sites, distributing a population of microspheres comprising a bioactive agent and a signal transducer element, contacting the substrate with the sample, by connecting the target analyte to the bioactive agent, a signal from the signal transducer element is changed as an indication of the presence of the target analyte.

Additionally or alternatively, the detection of a genetic biomarker may include a method for detecting a chiral molecule in a sample comprising providing a substrate with a surface comprising at least first and second distinct sites, at least first and second bioactive agents linked to the first and second distinct sites, respectively, contact the substrate with the sample, illuminate the substrate with polarized light and detect light rotation in at least one of the first and second distinct sites as an indication of the presence of the chiral molecule.

In addition or alternatively, the detection of a genetic biomarker may include a method for determining the location of a microsphere in an arrangement that comprises providing a substrate with a first surface comprising at least one first and second distinct sites, where the first distinct site comprises a microsphere, but the second distinct site does not comprise a microsphere, illuminate the substrate and detect substrate illumination, so the reduced illumination at the first distinct site in relation to the second distinct site provides an indication the presence of the first microsphere in the first distinct site.

In addition or alternatively, the detection of a genetic biomarker may include a method for increasing the signal output of an array comprising providing a substrate with a surface comprising at least first and second distinct sites and at least first and second attached markers at the first and second distinct sites respectively, cool the substrate to at least below room temperature and detect a signal from the first and second markers, in which the signal is increased in relation to a signal obtained from a substrate that does not it is cold.

In addition or alternatively, the detection of a genetic biomarker may include a method for subtracting the background signal in an arrangement comprising providing a substrate with a surface comprising at least the first and second distinct sites and at least the first and the second markers attached to the first and second distinct sites respectively, to detect the signal from the first and second distinct sites in a plurality of different emissions and subtract the lowest signal from each of the first and second distinct sites of the signals remaining from the first and second distinct sites, respectively.

In addition or alternatively, the detection of a genetic biomarker may include a method for correcting the non-uniformity of the image, comprising providing a substrate with a surface comprising at least the first and second distinct sites, at least the first and the second. second markers attached to the first and second distinct sites, respectively and at least one first internal reference point of known signal strength, detecting a first and second signal from the first and second markers, respectively, detecting a signal from the reference point internal and determine the variation between the signal of the internal reference point and the intensity of the known signal of the internal reference point as an indication of the non-uniformity of the said image.

Additionally or alternatively, the detection of a genetic biomarker may include a method for detecting a target analyte in a sample comprising providing an array comprising a substrate with a surface comprising distinct sites, a reflective coating on that surface and a population of microspheres distributed on the substrate.

The microspheres comprise at least one first and a second subpopulation, each comprising a different bioactive agent.

The method also includes contacting the arrangement with the sample, so that the target analyte binds to at least one of the bioactive agents and detects the presence of the target analyte. In a preferred embodiment, the target analyte is marked. In addition or alternatively, the detection of a genetic biomarker may include a method for detecting a target analyte in a sample comprising providing an array comprising a substrate with a surface comprising distinct sites, comprising wells of alternative shape and a population of microspheres distributed on the substrate. The microspheres comprise at least one first and a second subpopulation, each comprising a different biactive agent. The method also includes contacting the arrangement with the sample, so that the target analyte binds to at least one of the bioactive agents and detecting the presence of the target analyte. Additionally or alternatively, the detection of a genetic biomarker may include a method for detecting a target analyte in a sample comprising providing a substrate with a surface comprising at least first and second distinct sites and a population of microspheres distributed on the substrate, where the microspheres comprise at least a first and a second subpopulation, each comprising a different bioactive agent, contacting the substrate with the sample, so that the target analyte binds to at least one of the bioactive agents. In some embodiments, the method includes cooling the substrate to at least below room temperature and detecting a signal, by which the signal is increased over a signal obtained from a substrate that is not cooled.

[504] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the U.S. Patent

6,890,764, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include compositions that comprise a substrate with a surface that comprises different sites and a population of microspheres distributed at the sites.

At least one of the microspheres comprises a nanocrystal.

The nanocrystals can be incorporated into the microsphere, for example, using the sol-gel polymerization process, or can be attached to the microsphere.

The microspheres optionally comprise bi-active agents and / or identifier binding binders.

In an additional aspect, the microsphere population comprises at least a first and a second subpopulation comprising a first and a second bioactive agent, respectively, and a first and a second optical signature, respectively, capable of identifying each bioactive agent.

At least one of the optical signatures comprises a nanocrystal.

In addition or alternatively, the detection of a genetic biomarker may include methods for making a composition comprising forming a surface comprising individual sites on a substrate and distributing microspheres on the surface, so that the individual sites contain microspheres.

The microspheres comprise an optical signature and at least one optical signature comprises at least one nanocrystal.

In addition or alternatively, the detection of a genetic biomarker may include a method for determining the presence of a target analyte in a sample that comprises contacting the sample with a composition.

The composition comprises a substrate with a surface comprising distinct sites and a population of microspheres comprising at least a first and a second subpopulation, each comprising a bioactive agent and an optical signature capable of identifying the bioactive agent .

The microspheres are distributed on the surface in such a way that the different sites contain microspheres and in which at least

in one of the optical signatures it comprises at least one nanocrystal. The presence or absence of the target analyte is then determined. In addition or alternatively, the detection of a genetic biomarker can include methods for making a composition comprising adhering nanocrystals to porous silica and sealing the pores of the silica using the sol-gel polymerization process.

[505] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in US Patent Application Publication 2018/0201992, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include methods, devices, systems and computer program products to determine nucleic acid fragment sequences using unique molecular indices (UMIs). In some implementations, UMIs include non-random UMIs (NRUMIs) or unique non-random molecular indices of variable length (vNRUMIs). In addition or alternatively, the detection of a genetic biomarker may include methods for sequencing nucleic acid molecules from a sample. The method includes: (a) applying adapters to DNA fragments in the sample to obtain DNA adapting products, where each adapter includes a unique non-random molecular index and where unique non-random molecular indexes of the adapters have at least two different molecular lengths and form a set of unique non-random molecular indices of varying length (vNRUMIs); (b) amplifying the DNA adapter products to obtain a plurality of amplified polynucleotides; (c) sequencing the plurality of amplified polynucleotides, thus obtaining a plurality of readings associated with the set of vNRUMIs; (d) identify, among the plurality of readings, readings associated with the same single non-random molecular index of variable length

(vNRUMI); and (e) determining a sequence of a DNA fragment in the sample using the readings associated with the same vNRUMI.

In some implementations, identifying the readings associated with the same vNRUMI includes obtaining, for each reading of the plurality of readings, alignment scores in relation to the set of vNRUMIs, each alignment score indicating similarity between a subsequent reading and a vNRUMI, where the subsequence is in a reading region in which the nucleotides derived from vNRUMI are likely to be located.

In some implementations, alignment scores are based on nucleotide matches and nucleotide edits between the reading sequence and vNRUMI.

In some implementations, nucleotide editions include nucleotide substitutions, additions and deletions.

In some implementations, each alignment score penalizes incompatibilities at the beginning of a sequence, but does not penalize incompatibilities at the end of the sequence.

In some implementations, obtaining an alignment score between a reading and a vNRUMI includes: (a) calculating an alignment score between vNRUMI and each of all possible prefix sequences for the reading sequence; (b) calculate an alignment score between the subsequent reading and each of all possible vNRUMI prefix sequences; and (c) obtain a higher alignment score between the alignment scores calculated in (a) and (b) as the alignment score between reading and vNRUMI.

In some implementations, the substring has a length equal to the length of the longest vNRUMI in the set of vNRUMIs.

In some implementations, identifying the readings associated with the same vNRUMI in (d) also includes: selecting, for each reading of the plurality of readings, at least one vNRUMI from the vNRUMI set based on the alignment scores; and associate each reading of the plurality of readings with at least one vNRUMI selected for the reading.

In some implementations, selecting at least one vNRUMI from the set of vNRUMIs includes selecting a vNRUMI with a higher alignment score among the set of vNRUMIs.

In some implementations, at least one vNRUMI includes two or more vNRUMIs.

In some implementations, the method also includes selecting one of the two or more vNRUMI as the same vNRUMI of (d) and (e). In some implementations, the adapters applied in (a) are obtained by: (i) providing a set of oligonucleotide sequences with at least two different molecular lengths; (ii) select a subset of oligonucleotide sequences from the set of oligonucleotide sequences, all edit distances between oligonucleotide sequences in the subset of oligonucleotide sequences that meet a threshold value, the subset of oligonucleotide sequences forming the set ofN; and (iii) synthesizing the adapters, each including a hybridized double-stranded region, a single-stranded 5 'arm, a single-stranded 3' arm and at least one vNRUMI from the vNRUMI set.

In some implementations, the threshold value is 3. In some implementations, the set of vNRUMIs includes 6-nucleotide vNRUMIs and 7-nucleotide vNRUMIs.

In some implementations, the determination of (e) includes collapsed readings associated with the same vNRUMI in a group to obtain a consensus nucleotide sequence for the sequence of the DNA fragment in the sample.

In some implementations, the consensus nucleotide sequence is obtained based partially on the quality indexes of the readings.

In some implementations, the determination of (e) includes: identifying, among the readings associated with the same vNRUMI, readings with the same reading position or similar reading positions in a reference sequence and determining the sequence of the DNA fragment using readings that (i) are associated with the same vNRUMI and (ii) have the same reading position or similar reading positions in the reference sequence.

In some implementations, the set of vNRUMIs includes no more than 10,000 different vNRUMIs.

In some implementations, the set of vNRUMIs includes no more than about 1,000 different vNRUMIs.

In some implementations, the set of vNRUMIs includes no more than 200 different vNRUMIs.

In some implementations, applying adapters to the DNA fragments in the sample includes applying adapters to both ends of the DNA fragments in the sample.

In addition or alternatively, the detection of a genetic biomarker may include methods for preparing sequencing adapters, including methods: (a) providing a set of oligonucleotide sequences with at least two different molecular lengths; (b) select a subset of oligonucleotide sequences from the set of oligonucleotide sequences, edit all distances between oligonucleotide sequences in the subset of oligonucleotide sequences that meet a threshold value, the subset of oligonucleotide sequences forming a set of oligonucleotide sequences forming a set of oligonucleotide sequences forming a set of indices single non-random molecules of variable length (vNRUMIs); and (c) synthesizing a plurality of sequencing adapters, each sequencing adapter including a hybridized double-stranded region, a single-stranded 5 'arm, a single-stranded 3' arm and at least one vNRUMI from the array of vNRUMIs.

In some implementations, (b) includes: (i) selecting an oligonucleotide sequence from the set of oligonucleotide sequences; (ii) adding the selected oligonucleotide to a set of expanding oligonucleotides and removing the selected oligonucleotide from the set of oligonucleotide sequences to obtain a reduced set of oligonucleotide sequences; (iii) selecting an instantaneous oligonucleotide sequence from the reduced set that maximizes a distance function, where the distance function is a minimum editing distance between the instant oligonucleotide sequence and any oligonucleotide sequences in the expandable set, and where the distance function meets the threshold value; (iv) adding the instant oligonucleotide to the expansion set and removing the instant oligonucleotide from the reduced set; (v) repeat (iii) and (iv) one or more times; and (vi) providing the expansion set as the subset of oligonucleotide sequences that form the set of vNRUMIs.

In addition or alternatively, the detection of a genetic biomarker may include a method for sequencing nucleic acid molecules in a sample, including (a) applying adapters to DNA fragments in the sample to obtain DNA adapter products, where each adapter tator includes a single non-random molecular index, and in which unique non-random molecular indexes of the adapters have at least two different molecular lengths and form a set of unique non-random molecular indices of varying length (vNRUMIs); (b) amplifying DNA adapter products to obtain a plurality of amplified polynucleotides; (c) sequencing the plurality of amplified polynucleotides, thus obtaining a plurality of readings associated with the set of vNRUMIs; and (d) to identify, among the plurality of readings, readings associated with the same single non-random molecular index of variable length (vNRUMI). In addition or alternatively, the detection of a genetic biomarker may include a method for sequencing nucleic acid molecules in a sample, including (a) applying adapters to DNA fragments in the sample to obtain DNA adapter products, where each adapter includes a single molecular index (UMI), and where the adapter's unique molecular indexes (UMIs) have at least two different molecular lengths and form a set of molecular indices

unique lecular cells of variable length (vUMIs); (b) amplifying DNA adapter products to obtain a plurality of amplified polynucleotides; (c) sequencing the plurality of amplified polynucleotides, thus obtaining a plurality of readings associated with the set of vUMIs; and (d) identify, among the plurality of readings, readings associated with the same single molecular index of variable length (vUMI). In addition or alternatively, the detection of a genetic biomarker may include a method for sequencing nucleic acid molecules in a sample, including (a) applying adapters to DNA fragments in the sample to obtain DNA adapter products, where each adapter includes a single molecular index (UMI) in a set of unique molecular indexes (UMIs); (b) amplifying the DNA adapting products to obtain a plurality of amplified polynucleotides; (c) sequencing the plurality of amplified polynucleotides, thus obtaining a plurality of readings associated with the set of UMIs; (d) obtain, for each reading of the plurality of readings, alignment scores in relation to the set of UMIs, each alignment score indicating similarity between a subsequent reading and a UMI; (e) identify, among the plurality of readings, readings associated with the same UMI using the alignment scores; and (e) determining a sequence of a DNA fragment in the sample using the readings associated with the same UMI.

In addition or alternatively, the detection of a genetic biomarker may include a system, apparatus and computer program products to determine sequences of DNA fragments that implement the methods.

Additionally or alternatively, the detection of a genetic biomarker may include a computer program product, including a non-transitory, machine-readable medium storage program code that, when run by one or more processors in a computer system, makes with the computer system to implement a method to determine sequence information of a sequence of interest in a sample using unique molecular indices (UMIs). The program code includes instructions for performing the above methods.

[506] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in US Patent Application Publication 2018/0201974, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker can include methods and compositions for targeted DNA amplification and sample identification. In addition or alternatively, the detection of a genetic biomarker may include methods for obtaining nucleic acid sequence information from a biological sample comprising: (a) providing a biological sample comprising different target nucleic acids; (b) contacting the biological sample with a plurality of different probe sets to form hybridization complexes with the different target nucleic acids; (c) amplifying nucleic acid from biological samples to produce amplicons; in which there is no purification of the nucleic acid from the biological sample before the contact step (b); and (d) obtaining nucleic acid sequence information for a plurality of portions of the amplified sample. In addition or alternatively, the detection of a genetic biomarker may include a method for obtaining nucleic acid sequence information from an FFPE sample comprising: (a) providing an FFPE sample comprising different target nucleic acids incorporated within a preserved fabric; (b) contacting the FFPE sample with a plurality of different probe sets to form hybridization complexes with the different target nucleic acids; (c) amplifying nucleic acid from the FFPE samples to produce amplicons; where there is no purification of the nucleic acid from the FFPE sample before the contact step (b); and (d) obtaining nucleic acid sequence information for a plurality of amplicons.

In particular modalities, there is no purification of the nucleic acid from the FFPE sample before amplification in step (c). In addition or alternatively, the detection of a genetic biomarker may include methods for amplifying nucleic acid from an FFPE sample comprising: (a) providing an FFPE sample comprising nucleic acid incorporated within a preserved tissue , having the nucleic acid from 3 ′ to 5 ′: first, second and third contiguous target domains; (b) contacting the FFPE sample with a plurality of different probe sets to form hybridization complexes with the different target nucleic acids, each probe set comprising: (i) a first probe comprising, from 5 'to 3 ': a first initiation sequence and a sequence that is substantially complementary to the first target domain; and (ii) a second probe comprising 5 'to 3': a sequence substantially complementary to the third target domain and a second initiation sequence; (c) contacting the hybridization complexes with an extension enzyme and nucleotides, in which the first probes are extended over the second target domains of the hybridization complexes formed in (b); (d) connect the first extended probes to the second probes to form amplification models; and (e) amplify the amplification models with the first and the second initiators complementary to the first initiation sequence and the second initiation sequence to produce amplicons and obtain nucleic acid sequence information for a plurality of ampli - cons.

In addition or alternatively, the detection of a genetic biomarker may include a method for identifying samples of nucleic acids comprising: (a) providing a cell sample containing nucleic acid; (b) lyse the sample cells with a lysis reagent to release nucleic acid from within the cells of the cell sample, thus forming a lysate; (c) amplifying the nucleic acid from the lysed samples; in which there is no purification of the nucleic acid in the lysate before starting the amplification step (c); and (d) obtaining nucleic acid sequence information for a plurality of portions of the amplified sample and comparing the sequence information with a second set of sequence information.

In some ways, the nucleic acid is DNA.

In some ways, the sample is a blood sample.

In certain respects, the sample comprises dried blood.

In certain respects, the sample comprises a tissue sample of FFPE.

In some respects, the second set of sequence information comprises an entire sequence of the genome.

In certain aspects, the second set of sequence information comprises exome sequence information.

In certain respects, amplification comprises a targeted amplification reaction.

In certain aspects, the targeted amplification reaction comprises the extension and connection of two probes.

In certain respects, the targeted amplification reaction comprises the polymerase chain reaction using at least two amplification primers that are specific to a portion of the sample genome.

In addition or alternatively, the detection of a genetic biomarker may include a method for tracking the identity of a biological sample during different stages of sample processing, comprising: (a) providing a cell sample containing nucleic acid; (b) separating a portion of the sample into a first portion and a second portion and obtaining a first set of nucleic acid sequence information from the first portion of the biological sample according to the above modalities, wherein the first set of sequence information nucleic acid comprises informational information on identity sequences; (c) purifying the second portion nucleic acid and obtaining a second set of sequence information; and (d) use computer-assisted logic, compare informational information from the identity sequence of the first set of information in the sequence of information from the nucleic acid sequence with the second set of information from the sequence to confirm that the first and second sets of information in the sequence were obtained from the same source. In addition or alternatively, the detection of a genetic biomarker may include a method for confirming the source of two different biological samples, comprising: (a) providing a first cell sample containing nucleic acid; (b) obtaining a first set of nucleic acid sequence information from the first portion of the biological sample according to some of the above modalities, wherein the first set of nucleic acid sequence information comprises sequence information identity information; (c) providing a second nucleic acid sample comprising purified nucleic acid and obtaining a second set of sequence information; and (d) use computer assisted logic, compare informational information from the identity sequence of the first set of information from the sequence of information from the nucleic acid sequence with the second set of information from the sequence to confirm that the first and second sets of information in the sequence were obtained from the same individual.

[507] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in US Patent Application Publication 2018/0155774, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include compositions, systems and methods for sequencing polynucleotides using straps anchored in polymerases adjacent to nanopores.

In one aspect, a composition includes a nanopore including a first side, a second side and an opening that extends through the first and second sides.

The composition can also include a plurality of nucleotides, where each nucleotide includes an elongated tag.

The composition can also include the first and the second polynucleotides, the first polynucleotide being complementary to the second polynucleotide.

The composition can also include a polymerase arranged adjacent to the first side of the nanopore, the polymerase configured to add nucleotides from the plurality of nucleotides to the first polynucleotide based on a sequence from the second polynucleotide.

The composition can also include a permanent chain including a head region, a tail region and an elongated body arranged between them, the head region being anchored to the polymerase, in which the elongated body occurs at the opening of the nanopore.

The composition can also include a first portion disposed on the elongated body, where the first portion is configured to attach to the elongated label of a first nucleotide on which the polymerase is acting, as well as to a reporter region disposed on the body elongated, in which the reporter region is configured to indicate when the first nucleotide is complementary or is not complementary to a next nucleotide in the sequence of the second polynucleotide.

In another aspect, a method may include providing a nanopore including a first side, a second side and an opening that extends through the first and second sides.

The method may further include providing a plurality of nucleotides, wherein each nucleotide includes an elongated tag.

The method may also include providing the first and second polynucleotides,

the first polynucleotide being complementary to the second polynucleotide. The method may also include providing a polymerase arranged adjacent to the first side of the nanopore, the polymerase configured to add nucleotides from the plurality of nucleotides to the first polynucleotide based on a sequence from the second polynucleotide, in which the polymerase is anchored to a permanent tie including a region of the head, a region of the tail and an elongated body arranged between them, the elongated body occurring at the opening of the naporepore. The method may also include determining that a first nucleotide is being triggered by the polymerase based on the attachment of the elongated tag to a first fraction disposed on the elongated body. The method can also include, with a reporter region arranged in the elongated body, indicating when the first nucleotide is complementary or is not complementary to a next nucleotide in the sequence of the second polynucleotide.

[508] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in US Patent Application Publication 2018/0141020, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include substrates and methods useful for placing a single molecule in a target area. In a first aspect, there is a substrate that includes a plurality of first and second capture initiators immobilized on a resource on the substrate. At least one target polynucleotide, one end attached to one of the capture primers and the other end attached to a target molecule, wherein the target polynucleotide includes a target region flanked by the first and second capture primer binding regions complementary to the first and second capture primers, the second capture primer binding region includes a base pair mismatch with the second capture primer and a plurality of clonal amplicons complementary to the target polynucleotide immobilized on the resource.

In some embodiments, the base pair mismatch is a 1, 2, 3, 4, 5, 6, 7, 8, 9 or 10 base pair mismatch.

In some embodiments, the base pair mismatch is a three base pair mismatch.

In some embodiments, the substrate also includes a plurality of characteristics.

In some embodiments, the resource includes a single target molecule.

In some modalities, the resource is filled to capacity with the plurality of clonal amplicons.

In some embodiments, the plurality of resources includes a single target molecule.

In some embodiments, two or more features include different single target molecules.

In some modalities, resources are filled to capacity with the plurality of clonal amplifications.

In addition or alternatively, the detection of a genetic biomarker may include methods for placing a single target molecule in a substrate resource.

In one aspect, the method is a method of placing a single target molecule on a substrate resource, hybridizing a plurality of first and second capture primers immobilized on a resource on a substrate with at least one target polynucleotide, in which the target polynucleotide includes a target region flanked by the first and second capture primer binding regions complementary to the first and second capture primers and the second capture primer binding region includes a base pair mismatch with the second capture primer and is linked to a target molecule.

The method further includes amplifying at least one target polynucleotide at an average rate of amplification that exceeds an average rate of transport from a target polynucleotide to a resource to produce a plurality of amplification.

clonal cons to the target polynucleotide.

In some methods, the base pair incompatibility is a 1, 2, 3, 4, 5, 6, 7, 8, 9 or 10 base pair incompatibility.

In some methods, the base pair mismatch is a three base pair mismatch.

In some methods of the methods, the substrate comprises a plurality of resources.

In some methods, the resource includes a single target molecule.

In some methods, the resource is filled to capacity with the plurality of clonal amplicons.

In some methods, the plurality of resources includes a single target molecule.

In some methods of the methods, the two or more resources include different single target molecules.

In some methods, the resources are filled to capacity with the plurality of clonal amplicons.

In some method modalities, the average amplification rate of the subsequent amplicons produced in the resource exceeds the average amplification rate of a first amplicon.

In some embodiments, the target polynucleotide includes one or more polynucleotides selected from the group consisting of RNA, DNA and PNA.

In some embodiments, target polynucleotides include double-stranded DNA (dsDNA). In some modalities, the target polynucleotide comprises less than 1,000 nucleotides.

In some embodiments, the target polynucleotide comprises between 10 to 25, 26 to 50, 51 to 100, 101 to 200, 201 to 300, 301 to 400, 401 to 500, 501 to 600, 601 to 700, 701 to 800 , 801 to 900 or 901 to 1000 base pairs in length.

In some embodiments, the target molecule includes a polypeptide, polynucleotide, carbohydrate, amino acid, nucleotide, monosaccharide, hapten, ligand, antigen, analyte, small molecule organic compound or inorganic compound.

In some embodiments, the target molecule includes a polypeptide.

In some modalities, the polypeptide is selected from the group consisting of a nanopore, binding polypeptide and enzyme. In some embodiments, the nanoporous pore is selected from the group consisting of MspA, OmpF, OmpG, NalP, WZA, ClyA toxin, α-hemolysin, anthrax toxin, leukocidins and DNA origami nanopores. In some embodiments, the binding polypeptide is selected from the group consisting of an antibody, a Fab, a Fab ', an F (ab') 2, a scFV, a diabody, a tri-body, a mini-body and a single domain antibody (sdAB), T cell receptor, microcins, neuropeptides, G protein coupled receptors, antibody, epidermal growth factor receptor and HER2.

[509] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in US Patent Application Publication 2018/0095969, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include methods, systems and devices for capturing, integrating, organizing, navigating and consulting large-scale data from high-throughput biological and chemical assay platforms. Some modalities provide methods, systems and interfaces for associating experimental data, resources and groups of data related by structure and / or function to chemical, medical and / or biological terms in an ontology or taxonomy. Some modalities also provide methods, systems and interfaces to filter data by information from the data source, allowing dynamic navigation through large amounts of data to find the most relevant results for a specific query. A system of one or more computers can be configured to perform specific operations or actions by virtue of having software, firmware, hardware or a combination of them installed in the system that in operation causes or causes the system to perform the actions. One or more computer programs can be configured to perform specific operations or actions due to the inclusion of instructions that, when executed by a data processing device, make the device perform the operations, including: (a) select, by one or more processors, a plurality of gene sets from a database, where each gene set of the plurality of gene sets includes a plurality of genes and a plurality of experimental values associated with the plurality of genes, and in which the plurality of experimental values is correlated with the biological, chemical or medical concept of interest in at least one experiment; (b) determining, for each set of genes and by one or more processors, one or more experimental gene scores for one or more genes among the plurality of genes using one or more experimental values of the first or more genes; (c) determine, for each set of genes and by one or more processors, one or more in silico gene scores for the second or more genes among the plurality of genes based, at least in part, on the first correlations of one or more more genes with the second or more genes, where the correlations of the first or more genes with the second or more genes are indicated in other sets of genes in the database, alongside the plurality of sets of genes; (d) obtain, at least one or more summary scores of processors for the first and second one or more genes based, at least in part, on one or more experimental gene scores for the first or more determined genes in (b) and the one or more gene scores in silico for the second or more genes determined in (c), where each summary score is aggregated in the plurality of gene sets; and (e) identify, by one or more processors, the genes potentially associated with the biological, chemical or medical concept of interest, using the summary scores of the first and second one or more genes.

Implementations

tions may include one or more of the following characteristics.

In some implementations, (c) includes, for each set of genes in the plurality of sets of genes: (i) identifying a second plurality of sets of genes from the database, each set of genes of the second plurality of sets of genes, including a second plurality of genes and a second plurality of experimental values associated with the second plurality of genes, and where the second plurality of experimental values is correlated with a first gene between the first or more genes.

The method can also include (ii) aggregating the experimental values through the second plurality of sets of genes to obtain a vector of aggregated values for the first gene among the first or more genes.

The method can also include (iii) applying (i) and (ii) to one or more other genes among the first or more genes, thereby obtaining one or more vectors of experimental values for the one or more other genes among the first or more genes.

The method can also include (iv) aggregated vectors of aggregated values for the first gene and one or more other genes between the first or more genes, thus obtaining a compressed vector including one or more in silico gene scores for the second or more genes.

Additionally or alternatively, the detection of a genetic biomarker may include a method in which each of the vectors aggregated from (iv) for a given gene among the first or more genes is weighted in proportion to an experimental value of the gene in question. private.

The method in which each of the aggregated vectors of (iv) for a specific gene among the first or more genes is weighted in proportion to a number of sets of genes in the second plurality of sets of genes identified for the particular gene.

Some implementations even provide the method, including, determining, before (d), one or more scores of groups of genes for one or more genes.

Some implementations provide the method in which each score of a group of genes for a specific gene is determined using (i) genetic associations of one or more groups of genes that include a group of genes related to a group marker, where the group of genes includes the particular gene (ii) at least some of the one or more experimental values of the first or more genes.

Some implementations provide the method in which (d) includes obtaining summary scores for the first and second one or more genes, based, at least in part, on the gene group scores of at least some of the one or more genes, as well as one or more experimental scores for the first or more genes determined in (b) and one or more in silico scores for the second or more genes determined in (c). Some implementations provide the method in which determining one or more gene group scores for the third or more genes includes: identifying, for a particular gene among the third or more genes, the one or more groups of genes that include each particular gene.

The method may also include determining, for each group of genes, a percentage of members of the group of genes that are among the first or more genes.

The method can also include aggregating, for each group of genes, one or more experimental values of at least some of the first or more genes that are members of the gene group, thus obtaining an experimental sum value for the group of genes.

The method may also include determining, for the particular gene among the third or more genes, a score from the gene group using the percentage of members of the gene group that are among the first or more genes and the sum of the experimental value of the gene group.

Some implementations provide the method for determining the score of the gene group using the percentage of members of the gene group that are among the first or more genes and the sum of the experimental value for the group of genes includes: obtain, for each group of genes, a product of the percentage of members and the sum of the experimental value, thus obtaining one or more products for the one or more groups of genes. The method can also include adding, through one or more groups of genes, the one or more products, thus obtaining an added product. The method may also include determining, for the particular gene among the third or more genes, a score of the group of genes based on the added product. In some implementations, the method also includes, before (d), determining the scores of the interactome respectively for the fourth or more genes. In some implementations, each interactome score for a particular gene is determined using (i) connections between the particular gene and other genes connected to the gene in a gene network and (ii) at least some of the one or more values experimental results of the first or more genes. In some implementations, (d) includes obtaining summary scores for at least the first or more genes and the second or more genes based, at least in part, on the inter-system scores of at least some of the rooms one or more genes, as well as one or more experimental gene scores for the first one or more genes determined in (b) and the scores of one or more in silico genes for the second or more genes determined in (c). In some implementations, the gene network is based on interactions and relationships between genes, proteins and / or phospholipids. In some implementations, calculating the score of the interactome includes calculating the score of the interactome as Ni ′:

[510] Ni ′ = Ni + Σ ((Ni + Nn) * edge_weightn)

[511] where Ni is the summary score of the specific gene i, Nn is a summary score of the gene n connected to the specific gene and edge_weightn is the weight of the edge connecting the specific gene i and the gene n. In some implementations, calculating the interaction score

intake also includes: saving Ni ′ below a second threshold in a first-pass dictionary; and repeat the calculation for all genes in the first pass dictionary, updating the inter- ratoma scores. In some implementations, the method also includes training the model, optimizing an objective function. In some implementations, model training includes applying a bootstrap technique to the bootstrap samples. In some implementations, the objective function is related to at least one summary score distribution after the bootstrap. In some implementations, optimizing the objective function includes minimizing the differences in summary scores between a training set and a validation set. In some implementations, optimizing the objective function includes maximizing a distance between a summary score distribution obtained from the plurality of gene sets and a summary score distribution obtained from random gene sets. In some implementations, the summary scores are classified and stored in intervals of defined size, where the penalty scores are assigned to the intervals, the penalty scores favoring higher summary scores. In some implementations, the objective function is based only on the highest rated summary scores. In some implementations, model training includes using the objective function in an unsupervised machine learning approach to learn the model's parameters. In some implementations, the model has the form:

[512] F (θ) = k1 * c1 + k2 * c2 + ... + kn * cn

[513] where θ are model parameters, ci are model components and ki are weight factors for the components. In some implementations, the method also includes partitioning one or more of the model's components into subcomponents based on the sample weights of the experimental data types.

In some implementations, summary scores for the first and second or more genes are penalized based on the probability that the experimental values of the first and second or more genes in one or more random sets of genes are correlated with the biological concept. , chemist or doctor of interest.

In some implementations, each summary score of a particular gene is penalized by a penalty value inversely proportional to the p value of a classified product, where the classified product includes a product of classifications of the specific gene in one or more random sets of genes.

A general aspect includes a computer program product, including a non-transitory machine-readable medium storage program code that, when executed by one or more processors in a computer system, causes the system to implement - a method for identifying genes potentially associated with a biological, chemical or medical concept of interest, said program code including: (a) code for selecting a plurality of sets of genes from a database, where each set of genes of the plurality of sets of genes includes a plurality of genes and a plurality of experimental values associated with the plurality of genes and where the plurality of experimental values is correlated with the biological, chemical or medical concept of interest in at least one experiment.

The program code also includes (b) code to determine, for each set of genes, one or more experimental gene scores for the first or more genes among the plurality of genes, using one or more experimental values of the first or more genes.

The program code also includes (c) code to determine, for each set of genes, one or more in silico gene scores for the second or more genes between

of genes based, at least in part, on the first correlations of one or more genes with the second or more genes, where the correlations of the first or more genes with the second or more genes are indicated in other sets of genes in the database, next to the plurality of sets of genes. The program code also includes (d) code for obtaining summary scores for the first and second one or more genes based, at least in part, on one or more experimental gene scores for the first or more genes determined in (b) and one or more gene scores in silico for the second or more genes determined in (c), where each summary score is aggregated in the plurality of gene sets. The program code also includes (e) code to identify the genes that are potentially associated with the biological, chemical or medical concept of interest, using summary scores from the first and second or more genes.

[514] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in US Patent Application Publication 2018/0023119, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include methods for preparing a sequencing library that includes nucleic acids from a plurality of single cells. In one embodiment, the method includes providing nuclei isolated from a plurality of cells; subject the isolated nuclei to a chemical treatment to generate nucleosome without nucleosome, maintaining the integrity of the isolated nuclei; distribute subsets of nucleosides devoid of nucleosomes in a first plurality of compartments and contact each subset with a transposome complex, in which the transposome complex in each compartment includes a transposase and a first index sequence that is different from the first sequences of index in the other compartments; splitting nucleic acids into the deprecated nucleosome nucleus subsets into a plurality of nucleic acid fragments and incorporating the first index sequences into at least one strand of the nucleic acid fragments to generate indexed nuclei that include indexed nucleic acid fragments , in which the indexed nucleic acid fragments remain linked to the transposes; combining indexed cores to generate clustered indexed cores; distribute subsets of the indexed cores assembled in a second plurality of compartments; incorporating into the nucleic acid fragments indexed in each compartment a second index sequence to generate double index fragments, in which the second index sequence in each compartment is different from the second index sequences in the other compartments; and combining the double index fragments, thereby producing a sequencing library that includes nucleic acids from the entire genome from the plurality of single cells.

In one embodiment, chemical treatment includes treatment with a chaotropic agent capable of interrupting acid-nucleic-protein interactions, such as lithium 3,5-diiodosalicylic acid.

In one embodiment, chemical treatment includes treatment with a detergent capable of interrupting acid-nucleic-protein interactions, such as sodium dodecyl sulfate (SDS). In a fashion, the cores are treated with a crosslinking agent before subjecting the isolated cores to chemical treatment, such as formaldehyde.

The crosslinking agent can be in a concentration of about 0.2% to about 2%, and in one embodiment it is about 1.5%. In one embodiment, the crosslinking by formaldehyde is reversed after the distribution of subsets of the assembled indexed nuclei and before the incorporation into the nucleic acid fragments indexed in each compartment a second index sequence.

In one mode,

reversal of crosslinking includes incubation at about 55 ° C to about 72 ° C.

In one embodiment, transposases are disassociated from the indexed nucleic acid fragments before the crosslinking is reversed.

In one embodiment, transposases are disassociated from the indexed nucleic acid fragments using sodium dodecyl sulfate (SDS). In one embodiment, the nuclei are treated with a restriction enzyme before fragmenting the nucleic acids into the nucleosome-depleting nucleus subsets into a plurality of nucleic acid fragments and incorporating the first index sequences.

In one embodiment, the nuclei are treated with a ligase after treatment with the restriction enzyme.

In one embodiment, the distribution subsets of the nucleos lacking the nucleosome, the distribution subsets of the grouped indexed nuclei, or their combination, are performed by classification of nuclei activated by fluorescence.

In one embodiment, the subsets of the nucleosome-free nuclei include approximately equal numbers of nuclei, and in one embodiment, the subsets of the nucleosome-free nuclei include from 1 to about 2000 nuclei.

In one embodiment, the subsets of the grouped indexed cores include approximately equal numbers of cores, and in one mode, the subsets of the grouped indexed cores include from 1 to about 25 cores.

In one embodiment, the subsets of the clustered indexed nuclei include at least 10 times less nuclei than the nucleosome-free nucleus subsets, or at least 100 times less nuclei than the nucleosome-free nucleus subsets.

In one embodiment, the first plurality of compartments, the second plurality of compartments, or the combination thereof, is a multi-well plate, such as a 96-well plate or a 384-well plate.

In one embodiment, the transposome complex is added to the compartments after the distribution of the subsets of nuclei devoid of nucleosomes in the compartments. In one embodiment, each of the transposome complexes includes a transposon, and each of the transposons includes a transferred tape. In one embodiment, the transferred tape includes the first index sequence and a first universal sequence. In one embodiment, incorporating the second index sequence into the indexed nucleic acid fragments includes contacting indexed nucleic acid fragments in each compartment with a first universal primer and a second universal primer, each including an index sequence and each including a sequence identical to or complementary to a portion of the first universal sequence and performing an exponential amplification reaction. In one embodiment, the exponential amplification reaction can be a polymerase chain reaction (PCR) and, in one embodiment, the PCR can include 15 to 30 cycles.

[515] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in US Patent Application Publication 2018/0037950, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include microarrays and methods for modifying immobilized capture initiators. In one aspect, it is a microarray including: a) a substrate including at least one well, a surface surrounding the well and an internal surface of the well; b) a first layer covering the internal surface of the well and including at least a first pair of capture initiators; and c) a second layer covering the first layer and the surface surrounding the well. In another aspect, it is a microarray including: a) a substrate including at least one well, a surface surrounding the well and an internal surface of the well; and b) a layer that covers the inner surface of the well and includes at least a first pair of capture initiators and at least a second pair of capture initiators.

In another aspect, there is a method for amplifying a nucleic acid, including: a) producing a first layer on a substrate, where the substrate includes at least one well, a surface surrounding the well and an internal surface of the well, wherein the first layer covers the inner surface of the well; b) depositing at least one first pair of capture initiators on the first layer; c) producing a second layer on the substrate covering the first layer and the surface surrounding the well; d) contacting a sample including a plurality of target polynucleotides with the substrate under conditions sufficient for a target polynucleotide to hybridize with a capture primer from at least one first pair of capture primers and e) perform a first kinetic exclusion assay (KEA) to produce a clonal population of amplicons from the target polynucleotide inside the well, thereby amplifying the target polynucleotide.

In addition or alternatively, the detection of a genetic biomarker may include a method for amplifying a nucleic acid, including: a) producing a first layer on a substrate, where the substrate includes at least one well, a surface which surrounds the well and an internal surface of the well, where the first layer covers at least partially the internal surface of the well; b) depositing at least a first pair of capture primers on the first layer, wherein the first pair of capture primers includes a plurality of first capture primers, including a 3 'portion, including a nucleotide sequence of the Illumina® P5 primer and a plurality of second capture primers, including a 3 'portion including an Illumina® P7 primer nucleotide sequence; c) producing a second layer on the substrate covering the first layer and the surface surrounding the well; d) depositing at least a second pair of capture primers on the second layer, wherein the second pair of capture primers is terminated with a 3 'phosphate and includes a plurality of first capture primers, including a 3' portion , including an Illumina® P5 primer nucleotide sequence and a plurality of second capture primers, including a 3 'portion, including an Illumina® P7 primer nucleotide sequence; e) contacting a sample including a plurality of target polynucleotides with the substrate under conditions sufficient for a single target polynucleotide per well to hybridize with an initiator from at least one first capture primer pair, where the target polynucleotides are flanked by complementary universal primary regions each including an Illumina® P5 'complementary primer nucleotide sequence or an Illumina® P7' complementary primer nucleotide sequence; f) performing a first KEA to produce a monoclonal population of amplicons from the single target polynucleotide within at least one well, thereby amplifying the target polynucleotide; g) contacting the substrate with a T4-kinase to unblock the primers of the second pair of primers, and h) performing bridge amplification or a second KEA to increase the monoclonal population of amplicons of the single target polynucleotide beyond the well.

Additionally or alternatively, the detection of a genetic biomarker may include a method for amplifying a nucleic acid, including: a) producing a first layer on a substrate, where the substrate includes at least one well, a surface surrounding the well and an internal surface of the well, where the first layer covers at least partially the internal surface of the well; b) depositing at least a first pair of capture primers on the first layer, wherein the first pair of capture primers includes a plurality of at least one first capture primer including a 3 'portion

including an Illumina® P5 primer nucleotide sequence and an Illumina® SBS3 primer nucleotide sequence and a plurality of at least one second capture primer including a 3 'portion including an Illumina® P7 primer nucleotide sequence and an Illumina primer nucleotide sequence ® SBS8; c) produce a second layer on the substrate that covers the first layer and the surface that surrounds the well; d) depositing at least a second pair of capture primers on the second layer, wherein the at least one second pair of capture primers includes a plurality of first capture primers, including a 3 'portion, including a sequence - Illumin® P5 primer nucleotide and a plurality of second capture primers, including a 3 'portion, including an Illumina® P7 nucleotide sequence; e) contacting a sample including a plurality of target polynucleotides with the substrate under conditions sufficient for a single target polynucleotide per well to hybridize to a primer from at least a first pair of capture primers, wherein the plurality of target polynucleotides is flanked by a complementary SBS each including a complementary nucleotide sequence from Illumina® SBS3 'or a complementary nucleotide sequence from Illumina® SBS8', and f) contacting a sample including a plurality of targets performing a KEA for an extended time to produce a monoclonal population of amplicons from the single target polynucleotide inside and outside of at least one well, thereby amplifying the single target polynucleotide within the well and increasing the monoclonal population of target polynucleotides beyond at least one well.

In addition or alternatively, the detection of a genetic biomarker may include a method for amplifying a nucleic acid, including: a) producing a first layer on a substrate, where the substrate includes at least one well, a surface around the well and an internal surface well, in which the first layer covers at least partially the internal surface of the well; b) depositing at least a first pair of capture primers on the first layer, wherein the first pair of primers includes a plurality of first capture primers, including a 3 'portion, including an Illumina® primer nucleotide sequence P5 and a plurality of second capture initiators, including a 3 'portion including an Illumina® P7 primer nucleotide sequence; c) producing a second layer on the substrate covering the first layer and the surface surrounding the well; d) contacting a sample including a plurality of target polynucleotides with the substrate under conditions sufficient for a single target polynucleotide per well to hybridize with an initiator from at least one first capture primer pair, wherein the plurality of polynucleotides is flanked complementary universal primer regions each including a complementary Illumina® P5 'primer nucleotide sequence or a complementary Illumina® P7' primer nucleotide sequence; e) performing a first KEA to produce a monoclonal population of amplicons from the single target polynucleotide within at least one well, thereby amplifying the target polynucleotide; f) depositing at least a second pair of capture primers on the second layer, wherein the at least one second pair of capture primers includes a plurality of first capture primers, including a 3 'portion, including a Illumina® P5 primer nucleotide sequence and a plurality of second capture primers, including a 3 'portion, including an Illumina® P7 primer nucleotide sequence, eg) perform bridged amplification or a second KEA for increase the monoclonal population of amplicons of the single target polynucleotide.

In addition or alternatively, the detection of a genetic biomarker may include a method for amplifying a nucleic acid, including: a) producing a layer on a substrate, where the substrate includes at least one well, a surface surrounding the well and a internal surface of the well, where the well has a diameter of about 1 µm or more and where the layer covers at least partially the internal surface of the well; b) depositing at least a first pair of capture initiators and at least a second pair of capture initiators in the layer, where the density of the initiator of at least a first pair of capture initiators is greater than the density the initiator of at least the second pair of initiators; c) contacting a sample including a plurality of target polynucleotides with the substrate under conditions sufficient for a single target polynucleotide per well to hybridize to the second primer; d) performing a KEA to produce a monoclonal population of amplicons from the single target polynucleotide hybridized to the second primer within the well, thereby amplifying the single target polynucleotide.

In addition or alternatively, the detection of a genetic biomarker may include a method for modifying an immobilized capture primer, including: a) contacting a substrate including a plurality of immobilized capture primers with a plurality of model nucleic acids under conditions sufficient to that hybridization produces one or more immobilized model nucleic acids, wherein the plurality of immobilized capture primers includes a first plurality of primers, including a universal 5 'Y-capture region and a second plurality of primers including a 3 'Z terminal universal capture region, and where each model nucleic acid is flanked by the 5' and 3 'terminal universal capture Y or Z regions and includes one or more restriction sites and the specific target capture between the universal capture region of terminal 5 'and the one or more restriction sites or between the universal capture region from the 3 'terminal and one or more restriction sites, and b) extending one or more immobilized capture primers to produce one or more immobilized extension products complementary to one or more model nucleic acids.

In addition or alternatively, the detection of a genetic biomarker may include a method for modifying an immobilized capture primer, including: a) contacting a substrate including a plurality of immobilized capture primers with a plurality of model nucleic acids under conditions sufficient for hybridization to produce one or more immobilized model nucleic acids, wherein the plurality of immobilized capture primers includes a first plurality of primers, including a 3'-terminal Illumina® P5 primer nucleotide sequence and a second plurality of primers, including a 3'-terminal Illumina® P7 primer nucleotide sequence, and where each model nucleic acid is flanked by a complementary 3'-terminal Illumina® P5 'primer nucleotide sequence and a nucleic sequence - Illumina® P7 'primer complementary to the 5'-terminal and includes two SapI restriction sites, and includes restriction sites SapI ion, a spacer region between the SapI restriction sites and a target specific capture region between the 3'-terminal complementary Illumina® P5 'primer nucleotide sequence and the SapI restriction sites; and b) extending one or more immobilized capture primers to produce one or more immobilized extension products complementary to one or more model nucleic acids. c) amplifying the one or more immobilized extension products by bridge amplification or KEA to produce one or more monoclonal clusters of immobilized double-stranded model nucleic acids; d) contacting one or more monoclonal clusters of immobilized double-stranded model nucleic acids with SapI to cut the two restriction sites into a plurality of immobilized double-stranded model nucleic acids to produce a plurality of chimeric capture primers from immobilized double strand, including the Illumina® P5 primer nucleotide sequence and the target specific capture region and a plurality of immobilized double-stranded regenerated universal capture primers, including the Illumina® P7 primer nucleotide sequence, and e) optionally contacting the plurality of immobilized double-stranded chimeric capture primers and regenerated universal double-stranded capture primers with a 5'-3 'dsDNA exonuclease to produce a plurality of chimeric capture primers from single strand immobilized and a plurality of regenerated single strand universal capture initiators fixed assets.

[516] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in US Patent Application Publication 2017/0356030, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include compositions, systems and methods for detecting the presence of polymer subunits using chemiluminescence. In one aspect, a composition includes a substrate; a first polynucleotide coupled to the substrate; a second polynucleotide hybridized to the first polynucleotide; and a catalyst coupled to a first nucleotide of the second polynucleotide, the catalyst being operable to cause a chemiluminogenic molecule to emit a photon. In some embodiments, the composition also includes a plurality of chemiluminogenic molecules. The catalyst can cause each of the chemiluminogenic molecules to emit a corresponding photon. The composition can also include a plurality of reagent molecules, the catalyst causing each of the chemiluminogenic molecules to emit a corresponding photon oxidizing that chemiluminogenic molecule using a reagent molecule. The oxidized chemiluminogenic molecule may have an ex-

mentioned that decays emitting the corresponding photon.

A system can include any of the previous compositions and circuits configured to detect the photon emitted by the chemiluminogenic molecule.

In some modalities, the circuit is still configured to detect the presence of the first nucleotide based on the detection of the photon.

In another respect, a method may include providing a substrate; providing a first polynucleotide coupled to the substrate; hybridizing a second polynucleotide to the first polynucleotide; coupling a first catalyst to a first nucleotide of the second polynucleotide; and cause, by the first catalyst, a first chemiluminogenic molecule to emit a photon.

In another aspect, a method for sequencing a first polynucleotide includes providing the first polynucleotide to be sequenced and coupled to a substrate; b) hybridizing a second polynucleotide to the first polynucleotide; and contacting the second poly-nucleotide with a polymerase and a plurality of nucleotides.

A first subset of the plurality of nucleotides includes a first portion, a second subset of the plurality of nucleotides includes a second portion, a third subset of the plurality of nucleotides includes a third portion and a fourth subset of the plurality of nucleotides includes a fourth portion or no portion.

The method may further include adding a nucleotide from the plurality of nucleotides to the second polynucleotide based on a sequence from the first polynucleotide.

The method may further include exposing the nucleotide to a catalyst coupled to a fifth portion; exposing the nucleotide to chemiluminogenic molecules; and detecting photon emission or absence of photons from the chemiluminogenic molecules.

The method may further include exposing nucleotide to a catalyst coupled to a sixth portion; exposing the nucleotide to chemiluminogenic molecules; and to detect photon emission or absence of photons from the chemiluminogenic molecules.

The method may further include exposing the nucleotide to a catalyst coupled to a cut molecule; exposing the nucleotide to chemiluminogenic molecules; and detecting photon emission or absence of photons from the chemiluminogenic molecules.

The method may also include detecting the added nucleotide based on the detection of photon emission or the absence of photons from the chemo-luminogenic molecules in one or more of the detection steps or a combination thereof.

In another aspect, a composition includes a catalyst operable to cause a chemiluminogenic molecule to emit a photon; a substrate; a first polynucleotide coupled to the substrate; a second polynucleotide hybridized to the first polynucleotide; and an inhibitor coupled to a first nucleotide of the second polynucleotide, the inhibitor operable to inhibit the emission of photons by the chemiluminogenic molecule.

In another respect, one method includes providing an operable catalyst to cause a first chemiluminogenic molecule to emit a photon; provide a substrate; providing a first polynucleotide coupled to the substrate; hybridizing a second polynucleotide to the first polynucleotide; coupling a first extinguisher to a first nucleotide of the second polynucleotide; and inhibit, by the first extinguisher, the emission of photons by the first chemiluminogenic molecule.

In another respect, a method for sequencing a first polynucleotide includes providing the first polynucleotide to be sequenced and coupled to a substrate; hybridizing a second polynucleotide to the first polynucleotide; and providing a catalyst coupled close enough to the second polynucleotide so that an inhibitor coupled to the second polynucleotide can inhibit the emission of photons from chemiluminescent molecules that interact with the catalyst.

The method may also include contacting the second polynucleotide with a polymerase and a plurality of nucleotides.

A first subset of the plurality of nucleotides includes a first portion, a second subset of the plurality of nucleotides includes a second portion, a third subset of the plurality of nucleotides includes a third portion and a fourth subset of the plurality of nucleotides includes a fourth portion or no portion.

[517] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in US Patent Application Publication 2017/0137876, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include a method that can be used to substantially reduce or eliminate high quality errors that can be generated during the first extension. In another modality, the methods can also be used to reduce errors caused by the incorrect incorporation of nucleotides during the first amplification cycles. In addition or alternatively, the detection of a genetic biomarker may include a more accurately sequencing method comprising; provide a nucleic acid model; producing, by linear amplification directly from the standard nucleic acid, a population comprising a plurality of complementary strands retained close to each other or identifiable as being obtained from the same standard nucleic acid; and conducting a sequencing reaction on said oligonucleotides retained by proximity (for example, bound to the surface). In some modalities, the method also includes the step of performing additional (exponential) amplification of the population of complementary strips after the linear amplification cycles and before executing the sequencing reaction. Optionally, linear amplification (directly from the nucleic acid model) includes the steps of; hybridizing said nucleic acid template to a first primer; extend the first initiator to produce a chain complementary to the model; denature to release the complementary chain that remains close (for example, it remains attached to the surface and, therefore, does not move or diffuse much before re-hybridizing nearby); and repeating the hybridization and amplification steps to produce a population of complementary strands attached to the surface obtained directly from the standard nucleic acid.

[518] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in US Patent Application Publication 2017/0101676, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include techniques for enriching target sequences in a nucleic acid library and reducing the capture of off-target sequences by a set of target hybridization probes. Since the target hybridization probes have imperfect specificity for their nucleic acid targets, a sequencing run using a set of target hybridization probes can also include a certain percentage of readings that represent off-target sequences. For example, in an exome sequencing reaction, certain hybridization probes can pull intronic or intergenic sequences from a library of nucleic acids along with target sequences. Those off-target fragments, once removed, are present in the set of nucleic acid fragments that are sequenced. Although sequencing information representative of off-target readings is normally discarded, the present techniques use the sequencing information acquired from those off-target readings to design hybridization probes specific to off-target sequences and used to separate and / or remove fragments that include these sequences from the set of fragments captured by the target specific hybridization probes.

The off-target hybridization probes are designed based on the analysis of off-target readings of a hybrid capture sequencing run that is performed with a set of target hybridization probes.

In certain embodiments, the probe design on the target can also be based on systematic out-of-target analyzes between samples to improve the specificity of the target hybridization probes to their desired targets.

In addition or alternatively, the detection of a genetic biomarker may include a method to reduce off-target capture in a targeted sequencing reaction.

The method includes the steps of providing a set of off-target hybridization probes that specifically bind to a plurality of off-target sequences present in a nucleic acid library generated from a sample, the acid library nucleic acids comprising a plurality of nucleic acid fragments and providing a set of target specific hybridization probes that specifically bind to a plurality of target sequences present in the nucleic acid library.

The method also includes the steps of contacting off-target hybridization probes with the nucleic acid library under conditions where off-target hybridization probes hybridize to off-target sequences and contacting target-specific hybridization probes with the nucleic acid library under conditions whereby the target specific hybridization probes hybridize to the target sequences.

The method also includes the steps of selecting a group of nucleic acid fragments from the nucleic acid library attached to the target specific hybridization probes; and sequencing the group of nucleic acid fragments linked to the target specific hybridization probes.

In addition or alternatively, the detection of a genetic biomarker may include a method for providing probes for capturing off-target sequence in a targeted sequencing reaction. The method includes the steps of receiving a request for a set of target specific hybridization analyzes. The method also includes the steps of contacting the target specific hybridization probes with a reference nucleic acid library generated from a reference sample, the nucleic acid library comprising a plurality of nucleic acid fragments to generate a reference group of target specific and nonspecific nucleic acid fragments linked to the target specific hybridization probes and separate the reference group of nucleic acid fragments linked to the target specific hybridization probes from unbound nucleic acid fragments . The method also includes the steps of sequencing the nucleic acid fragment reference group to generate reference sequencing data; identify off-target sequences in the reference sequence data; and providing a set of off-target hybridization probes based on the identified off-target sequences. In addition or alternatively, the detection of a genetic biomarker may include a sequencing kit to reduce off-target capture in a targeted sequencing reaction that includes a set of off-target hybridization probes that specifically link to a plurality of off-target sequences present in a nucleic acid library generated from a sample, the nucleic acid library comprising a plurality of nucleic acid fragments and a set of target specific hybridization probes that specifically bind to a plurality of target sequences present in the nucleic acid library.

[519] In some modalities, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the several methods described in the Publication of the

U.S. Patent Application 2016/0319345, which is incorporated herein by reference in its entirety.

For example, the detection of a genetic biomarker may include methods, devices, systems and computer program products to determine nucleic acid fragment sequences using unique molecular indices (UMIs). In various implementations, sequencing methods determine the sequence of nucleic acid fragments from both strands of the nucleic acid fragments.

In some implementations, the methods employ physical UMIs located on one or both strands of sequencing adapters.

In some implementations, the methods also employ virtual UMIs located on both strands of the nucleic acid fragments.

One aspect concerns a method for sequencing nucleic acid molecules in a sample using unique molecular indexes (UMIs). Each unique molecular index (UMI) is an oligonucleotide sequence that can be used to identify an individual molecule of a double-stranded DNA fragment in the sample.

The method includes: (a) applying adapters to both ends of the double-stranded DNA fragments in the sample, where each of the adapters includes a hybridized double-stranded region, a simple 5 'strand arm, a single 3 'strand arm, and a physical UMI on one strand or each strand of the adapters, thereby obtaining DNA adapter products; (b) amplifying both strands of the DNA adapter products to obtain a plurality of amplified polynucleotides; (c) sequence the plurality of amplified polynucleotides, thus obtaining a plurality of readings each associated with a physical UMI; (d) identify a plurality of physical UMIs associated with a plurality of readings; (e) identify a plurality of virtual UMIs associated with the plurality of readings, where each virtual UMI is a sequence found in a DNA fragment in the sample; and (f) determine sequences

copies of the double stranded DNA fragments in the sample using the plurality of readings obtained in (c), the plurality of physical UMIs identified in (d) and the plurality of virtual UMIs identified in (e). In some implementations, the method includes operation (f) includes: (i) combining, for each one or more fragments of double-stranded DNA in the sample, (1) readings with a first physical UMI and at least one Virtual UMI in the 5 ′ to 3 ′ direction and (2) readings having a second physical UMI and at least one virtual UMI in the 5 ′ to 3 ′ direction to determine a consensus nucleotide sequence; and (ii) determine, for each or more fragments of double-stranded DNA in the sample, a sequence using the consensus nucleotide sequence.

In some implementations, each of the adapters includes a physical UMI on just one wire of the adapters on the single strand 5 'arm or on the single strand 3' arm.

In some of these implementations, (f) includes: (i) collapsed readings with the same first physical UMI in a first group to obtain a first consensus nucleotide sequence; (ii) collapse readings with the same second physical UMI in a second group to obtain a second consensus nucleotide sequence; and (iii) determining, using the first and second consensus nucleotide sequences, a sequence of one of the double stranded DNA fragments in the sample.

In some implementations, (iii) includes: (1) obtaining, using location information and sequence information from the first and second consensus nucleotide sequences, a third consensus nucleotide sequence and (2) determining, using the third consensus nucleotide sequence, the sequence of one of the double stranded DNA fragments.

In some implementations, operation (e) includes identifying the plurality of virtual UMIs, while adapters include physical UMI in only one strand of the adapters in the 5 'single strand arm region or the 3' single strand arm region. .

In some implementations, (f) includes: (i) combining readings with a first physical UMI and at least one virtual UMI in the 5 ′ to 3 ′ direction and readings with a second physical UMI and at least one virtual UMI in the 5 direction ′ To 3 ′ to determine a consensus nucleotide sequence; and (ii) determining a sequence for one of the double stranded DNA fragments in the sample using the consensus nucleotide sequence.

In some implementations of the above methods, obtaining the plurality of readings in operation (c) includes: obtaining two pair end readings from each of the amplified polynucleotides, in which the pair pair readings include a long reading and a short reading, the long reading is greater than the short reading.

In some of these implementations, operation (f) includes: combination of reading pairs associated with a first physical UMI in a first group and combination of reading pairs associated with a second physical UMI in a second group, where the first and the second physical UMIs are exclusively associated with a fragment of double tape in the sample; and determining the sequence of the double strand fragment in the sample using long reading sequence information in the first group and long reading sequence information in the second group.

Another aspect adapts to both ends of the double-stranded DNA fragments in the sample, where the adapters include a hybridized double-stranded region, a single 5 'strand arm, a single 3' strand arm and an index single physical molecular (UMI) in the simple 5 'tape arm or in the simple 3' tape arm; (b) amplifying both strands of ligation products from (a), thereby obtaining a plurality of single-stranded amplified polynucleotides; (c) sequence the plurality of amplified polynucleotides, thus obtaining a plurality of readings each associated with a physical UMI; (d) identify a plurality of physical UMIs associated with a plurality of readings; and (e) determining sequences of the double-stranded DNA fragments in the sample using the plurality of sequences obtained in (c) and the plurality of physical UMIs identified in (d). An additional aspect concerns a method for sequencing nucleic acid molecules from a sample.

The method includes: (a) attaching adapters to both ends of the double-stranded DNA fragments in the sample, where each adapter includes a hybridized double-stranded region, a single 5 'strand arm, an arm single-stranded 3 ', and a single physical molecular index (UMI) of less than 12 nucleotides on one strand or each strand of the adapters; (b) amplifying both strands of ligation products from (a), thereby obtaining a plurality of single-stranded amplified polynucleotides, each including a physical UMI; (c) sequencing the plurality of amplified polynucleotides, thereby obtaining a plurality of readings each associated with a physical UMI; (d) identify a plurality of physical UMIs associated with a plurality of readings; and (e) determining sequences of the double-stranded DNA fragments in the sample using the plurality of readings obtained in (c) and the plurality of physical UMIs identified in (d). Another aspect concerns a method for making a duplex sequencing adapter with a physical UMI on each tape.

The method includes: providing a preliminary sequencing adapter, including a hybrid double-stranded region, two single-stranded arms and a ledge including 5'-CCANNN- NANNNNTGG-3 'at one end of the double-stranded hybrid region, which two simple ribbon arms are farthest from the region; extend a tape from the hybridized region of double tape using the protrusion as a model, thus producing an extension product; and applying the restriction enzyme Xcm1 to digest a double stranded end of the extension product, thereby producing the duplex sequencing adapter with a physical UMI on each strand.

In some implementations, the preliminary sequencing adapter includes a sequence of read primers on each tape.

Another aspect concerns a computer program product, including a storage program code.

non-transient machine-readable average device that, when run by one or more processors in a computer system, causes the computer system to implement a method for determining sequence information of a sequence of interest in a sample using unique molecular indices (UMIs). The program code includes: (a) code to obtain readings from a plurality of amplified polynucleotides, where the plurality of amplified polynucleotides is obtained by amplifying double-stranded DNA fragments in the sample, including the sequence of interest and adaptation from adapters to double-stranded DNA fragments; (b) code to identify a plurality of physical UMIs in the readings of the plurality of amplified polynucleotides, where each physical UMI is found in an adapter connected to one of the double stranded DNA fragments; (c) code to identify a plurality of virtual UMIs in the readings received from the plurality of amplified polynucleotides, where each virtual UMI is found in an individual molecule of one of the double stranded DNA fragments; and (c) code to determine sequences of double-stranded DNA fragments using readings of the plurality of amplified polynucleotides, the plurality of physical UMIs and the plurality of virtual UMIs, thereby reducing errors in the determined sequences of the fragments of Dula strand DNA.

In some implementations, adapters include a hybridized double-stranded region, a single-stranded 5 'arm, a single-stranded 3' arm, and a single physical molecular index (UMI) on one strand or each strand. adapters.

An additional aspect concerns a computer system, including: one or more processors; system memory; and one or more computer-readable storage media.

The medium has stored instructions executable by computer that lead the system to implement a method to determine safety information.

sequence of a sequence of interest in a sample using unique molecular indices (UMIs), which are sequences of oligonucleotides that can be used to identify individual molecules of double-stranded DNA fragments in the sample.

The instructions include: (a) receiving readings from a plurality of amplified polynucleotides, in which the plurality of amplified polynucleotides is obtained by amplifying double-stranded DNA fragments in the sample, including the sequence of interest and adapting adapters double-stranded DNA fragments; (b) identify a plurality of physical UMIs in the readings received from the plurality of amplified polynucleotides, where each physical UMI is found in an adapter connected to one of the double stranded DNA fragments; (c) identifying a plurality of virtual UMIs in the readings received from the plurality of amplified polynucleotides, where each virtual UMI is found in an individual molecule of one of the double stranded DNA fragments; and (d) determining sequences of the double stranded DNA fragments using the sequences of the plurality of amplified polynucleotides, the plurality of physical UMIs and the plurality of virtual UMIs, thus reducing errors in the determined sequences of the DNA strand fragments.

One aspect provides methods for sequencing nucleic acid molecules in a sample using unique non-random molecular indices (UMIs). The methods involve: (a) applying adapters to both ends of the DNA fragments in the sample, where each adapter includes a hybridized double-stranded region, a single stranded 5 'arm, a 5' stranded arm simple and a 3 'arm of simple strand and a single non-random molecular index (UMI) on one strand or each strand of the adapters, thus obtaining DNA adapter products; (b) amplifying DNA adapter products to obtain a plurality of amplified polynucleotides; (c) sequencing the plurality of amplified polynucleotides, thus obtaining a plurality of readings associated with a plurality of non-random UMIs; (d) the plurality of readings, identify readings that share a common non-random UMI; and (e) from the readings identified that share the common non-random UMI, determine the sequence of at least a portion of a sample DNA fragment, having an adapter applied with the common non-random UMI.

Another aspect refers to methods for sequencing nucleic acid molecules in a sample using non-random unique molecular indices (UMIs).

In some implementations, a method involves: (a) applying adapters to both ends of the double stranded DNA fragments in the sample, where the adapters include a hybridized double stranded region, a single stranded 5 'arm and an 3 ′ single strand and a single non-random molecular index (UMI) on one strand or each strand of adapters, thus obtaining DNA adapter products, where non-random UMI can be combined with other information to identify exclusively an individual molecule of the double stranded DNA fragments; (b) amplifying both strands of the DNA adapter products to obtain a plurality of amplified polynucleotides; (c) sequencing the plurality of amplified polynucleotides, thus obtaining a plurality of readings each associated with a non-random UMI; (d) identify a plurality of non-random UMIs associated with the plurality of readings; and (e) use the plurality of readings and the plurality of non-random UMIs to determine sequences of the double stranded DNA fragments in the sample.

In addition or alternatively, the detection of a genetic biomarker may include a system, apparatus and computer program products for determining sequences of DNA fragments that implement the disclosed methods.

One aspect provides a computer program product, including a non-transitory, machine-readable medium storage program code that, when run by one or more processors in a computer system, makes the computer system implement a method to determine sequence information for a sequence of interest in a sample using unique molecular indices (UMIs). The program code includes instructions for performing the above methods.

[520] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in US Patent Application Publication 2015/0360193, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include methods, compositions and kits for the amplification of nucleic acid samples to generate nucleic acid libraries. In addition or alternatively, the detection of a genetic biomarker may include a method for creating a nucleic acid library from a nucleic acid sample, the method comprising: a) providing a set of amplification primers to a sample nucleic acid set, the set of amplification primers comprising a plurality of random primers and a plurality of site specific primers, wherein the site specific primers are configured to amplify a plurality of predetermined regions of the library. nucleic acids and where random primers are more abundant compared to site specific primers; and b) amplify nucleic acid library using the set of amplification primers, thereby creating a nucleic acid library. A kit for amplifying a nucleic acid sample is also established, in which the kit comprises a plurality of random primers and a plurality of site specific primers configured to amplify a plurality of predetermined regions of a nucleic acid library. -

waistband.

In some respects, the kit further comprises a set of instructions for using random primers and site specific primers in a set of amplification reactions, where random primers are in greater abundance compared to specific primers de siege.

In certain respects, the kit further comprises a set of instructions for combining the set of amplification initiators with a nucleic acid library and amplifying the nucleic acid library.

In addition to the above method, a method of creating a nucleic acid library from a nucleic acid sample is also established, the method comprising: a) amplifying a nucleic acid sample with an AT-rich set of primers. random amplification.

In some respects, the AT-rich set of random amplification primers is a mix of primers.

A kit for amplifying a nucleic acid sample is also established, in which the kit comprises an AT-rich set of random amplification primers.

In certain respects, the kit also comprises a set of instructions for combining the set of amplification primers with a nucleic acid library and amplifying the nucleic acid library.

In certain other respects, the kit further comprises a DNA polymerase.

In still other aspects, the AT-rich set of random amplification primers is a mixture of primers.

In addition or alternatively, the detection of a genetic biomarker may include a method for creating a nucleic acid library from a nucleic acid sample, the method comprising: a) amplifying a nucleic acid sample with a set of primers. random amplification, random amplification primers comprising 5 'tails rich in AT.

In some respects, the set of random amplification primers is a mixture of primers.

A method for creating a nucleic acid library from a nucleic acid sample is also established, the method comprising: amplifying a nucleic acid sample with a set of random amplification primers of varying length, each primer of random amplification of variable length comprises a random 3 'portion and a degenerate 5' tail, the degenerate 5 'tail being proportional in length to the A / T content of the random 3' portion of the primer. In some respects, the set of random amplification primers of varying length is a mixture of primers. A method is also established to create a nucleic acid library from a nucleic acid sample, the method comprising: a) amplifying a nucleic acid sample with a set of random amplification primers, in which each primer comprises a random 3 'portion and a 5' constant initiation portion, thereby producing amplification products, wherein each amplification product comprises the 5 'constant initiation portion; b) circulating amplification products; and c) amplify the circularized amplification products using primers that hybridize to the 5 'constant initiation portion. In certain aspects, the amplification in step (c) comprises performing multiple displacement amplification. In some ways, the set of random amplification primers is a mixture of primers.

[521] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in US Patent Application Publication 2015/0176071, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include determining the proximity of sequence fragments to a larger target nucleic acid from which the fragments were derived. For example, the methods can be used to determine the phase and identify haplotypes for a relatively long target nucleic acid sequence when the individual sequence readings are shorter than the length of the target nucleic acid being evaluated.

In addition or alternatively, the detection of a genetic biomarker may include a method for sequencing a target nucleic acid polymer.

The method can include the steps of (a) modifying a target nucleic acid polymer to produce a modified nucleic acid polymer, wherein the modified nucleic acid polymer includes a plurality of sequence regions of the target nucleic acid polymer; (b) producing fragments of the modified nucleic acid polymer in a container with a solid support surface, each fragment comprising one of the sequence regions; (c) capture the fragments randomly at locations in a region of the solid support surface; (d) determining nucleotide sequences from the sequential regions by detecting fragments at the sites; and (e) producing a representation of the nucleotide sequence for the target nucleic acid polymer based on the nucleotide sequences of the fragments and the relative distances between sites on the solid support surface.

Additionally or alternatively, the detection of a genetic biomarker may include a method for sequencing a target nucleic acid polymer that includes the steps of (a) adding inserts in a target nucleic acid polymer to form a modified acid polymer nucleic including a plurality of internal inserts; (b) producing fragments of the modified nucleic acid polymer in a fluid that is in contact with a solid support surface, thereby releasing fragments that each include at least a portion of the inserts; (c) capture fluid fragments randomly at locations on a solid support surface; (d) determining nucleotide sequences of the fragments by detecting the fragments at the sites; and (e) produce a representation of the nucleotide sequence for the target nucleic acid polymer based on the nucleotide sequences of the fragments and the relative distances between sites on the solid support surface.

Additionally or alternatively, the detection of a genetic biomarker may include a method for sequencing a target nucleic acid polymer, which includes the steps of (a) modifying a target nucleic acid polymer to produce a nucleic acid polymer modified nucleic acid, wherein the modified nucleic acid polymer includes a plurality of sequence regions of the target nucleic acid polymer; (b) attaching the modified nucleic acid polymer to a region on a solid support surface; (c) producing fragments of the modified nucleic acid polymer that is attached to the solid support surface, where the fragments are attached to locations in the region of the solid support surface; (d) determining the nucleotide sequences of the fragments by detecting the fragments at the sites; and (e) producing a representation of the nucleotide sequence for the target nucleic acid polymers based on the nucleotide sequences of the fragments and the relative distances between sites on the solid support surface.

In addition or alternatively, the detection of a genetic biomarker may include a method for determining the source of individual sequences in a mixture of sequences from different sources.

The method may include the steps of (a) providing a mixture of target nucleic acid polymers from a plurality of different sources; (b) modifying the mixture of target nucleic acid polymers to produce a mixture of modified nucleic acid polymers, wherein the mixture of modified nucleic acid polymers includes a plurality of sequence regions from different sources; (c) producing fragments of the modified nucleic acid polymers in a container with a solid support surface, each fragment comprising a sequence region from a single of the different sources; (d) capture the fragments randomly at locations on the solid support surface, under conditions where fragments of a common target nucleic acid polymer preferably locate locations close to proximal locations on the solid support surface; (e) determining nucleotide sequences of the fragments at the sites; and (f) identifying the nucleotide sequences that are derived from a common source in the plurality of different sources based on the nucleotide sequences of the fragments and the relative distances between sites on the solid support surface.

[522] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in US Patent Application Publication 2014/0364323, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include methods for determining the presence of a plurality of nucleotide sequences of interest in a plurality of samples, preserving the identity of each sample. The method can be used in many applications, including genotyping, expression analysis and identification of individual species in complex samples. In one embodiment, each sample is contacted with a plurality of probe sets. A first probe has a first identification sequence and a first hybridization sequence complementary to a first portion of the sequence of interest. A second probe has a second hybridization sequence complementary to a second portion of the same sequence of interest and a second identification sequence. If the first hybridization sequence is hybridized to the first portion of the sequence of interest, and the second hybridization sequence is hybridized to the second portion of the same sequence of interest, then the first and second probes are joined. This can also be accomplished using connection and / or extension methods, as in a GoldenGate® assay design.

The presence of the sequence of interest and the identity of the sample containing the sequence of interest are determined, based on the identification sequence codes present in the joined probes.

[523] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in US Patent Application Publication 2013/0059741, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include compositions and methods for assessing the presence of a target analyte in a sample using a solid support. In addition or alternatively, the detection of a genetic biomarker may include a solid support with a binding protein, such as an antibody, antibody fragment or protein receptor, immobilized on the solid support and at least two separate nucleic acid primers immobilized near the binding protein. In addition or alternatively, the detection of a genetic biomarker may include a solid support, in which a binding complex is formed between the binding protein immobilized on the solid support, a target analyte and a second binding protein. In some embodiments, a solid support is provided in which that binding complex further forms a hybridization complex between a nucleic acid primer immobilized on the solid support and an oligonucleotide tag attached to the second binding protein. In addition or alternatively, the detection of a genetic biomarker can include an array that includes a plurality of such solid supports. In some embodiments, solid supports can be used in a method to detect various target analytes. In one embodiment, the method for detecting a target analyte includes providing a solid support with a binding protein immobilized on the solid support and a second binding protein provided in solution, where the first binding protein recognizes and is capable of binding an analyte target in the presence of the second binding protein, which also recognizes and binds to the same target analyte, contact the solid support with the target analyte and the second binding protein under conditions sufficient to allow the formation of a binding complex between the target analyte and the first and second binding proteins, hybridize the oligo-nucleotide tag attached to the second binding protein to a first primer of nucleic acid immobilized on the solid support, extend that first primer by which a complement is generated of the oligo-nucleotide tag, amplify the newly generated complement using a second immobilized nucleic acid primer for the solid support and detect the pre presence of amplicon, in which the presence of amplicon indicates the presence of the target analyte. In addition or alternatively, the detection of a genetic biomarker may include a method for detecting a target analyte, in which the method described above proceeds alternatively after the extension step, hybridizing the complement of the oligonucleotide tag that is generated, with a second nucleic acid primer immobilized on the solid support forming a second hybridization complex, extending the second nucleic acid primer with at least one labeled nucleic acid residue, using methods such as single base extension or synthesis sequencing, where the nucleic acid residue added to the primer is dependent on the nucleic acid sequence of the oligonucleotide tag, followed by detection of the presence of the marked nucleic acid residue on the solid surface, where the presence of the marked nucleic acid residue indicates the presence of the target analyte.

[524] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in US Patent Application Publication 2012/0156753, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include methods for labeling 5 'binding of uncapped RNA in a sample that has a 5' polyphosphate group, comprising: (A) providing: (i) a sample containing Uncapped RNA that has a 5 'polyphosphate group, including where the sample additionally contains RNA that has a 5' monophosphate group and / or capped RNA and / or RNA that has a 5 'hydroxyl group; (ii) 5 'polyphosphatase RNA; (iii) an acceptor oligonucleotide that exhibits a tag; and (iv) RNA ligase; (B) contact the sample with the 5 'polyphosphatase RNA under conditions and for sufficient time when the uncapped RNA that has a 5' polyphosphate group is converted into RNA that has a 5 'monophosphate group; and (C) contacting the sample from step (B) with the acceptor oligonucleotide and the RNA ligase under conditions and long enough that the 3 'end of the acceptor oligonucleotide is linked to the RNA that has a 5' monophosphate group, but not to capped RNA and RNA labeled with 5 'linkage is generated.

In other embodiments, the sample provided in step (A) additionally contains RNA that has a 5 'monophosphate group, but the acceptor oligonucleotide is bound only to RNA that has a 5' monophosphate group that has been converted from the uncapped RNA that has a 5 ′ polyphosphate group in step (B) and is not linked to the RNA that has a 5 'monophosphate group already in the sample provided in step (A), in which the method additionally comprises the substeps of: providing an RNA 5 ′ monophosphatase; and, before step (B), contact the sample with RNA 5 'monophosphatase under conditions and long enough that the RNA in the sample that has a 5' monophosphate group is converted into RNA that has a 5 'hi group - droxyl; and inactivating or removing the 5 'RNA monophosphatase.

In other ways, the method additionally comprises marking the 5 'linkage of the capped RNA in the sample, where the method additionally comprises the substeps of: providing a nucleic acid pyrophosphatase or stripping enzyme; and, before step (C), contact the sample from step (B) with the nucleic acid pyrophosphatase or stripping enzyme under conditions and long enough that the RNA capped in the sample is converted into RNA that has a group 5 'monophosphate, in which the capped RNA contained in the sample provided in step (A) is also marked with the 5' link in step (C).

[525] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in US Patent Application Publication 2012/0010091, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include a method for preparing a cDNA library from a plurality of single cells. In one aspect, the method includes the steps of releasing mRNA from each single cell to provide a plurality of individual mRNA samples, synthesizing a first mRNA cDNA strand in each individual mRNA sample and embedding a tag in the cDNA to provide a plurality of labeled cDNA samples, pool the labeled cDNA samples and amplify the pooled cDNA samples to generate a double-stranded cDNA library. In some embodiments, a cDNA library can be produced by the methods above. In addition or alternatively, the detection of a genetic biomarker may include methods for analyzing gene expression in a plurality of cells, preparing a cDNA library as described above and sequencing the library.

[526] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in US Patent Application Publication 2011/0152111, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include a method for detecting a target nucleic acid sequence in an archived tissue sample, comprising providing a nucleic acid sample prepared from an archived tissue sample, hybridizing a first set of probes binding to said target sequence to form a binding structure, ligating said probes using a ligase to form a bound probe, amplifying said bound probe to form amplicons and detecting said amplicons.

In addition or alternatively, the detection of a genetic biomarker may include a method for detecting a plurality of target nucleic acid sequences in an archived tissue sample, comprising providing a nucleic acid sample prepared from an archived tissue sample, said sample comprising a plurality of target nucleic acid sequences, adding a plurality of detection probes, each substantially complementary to one of said target nucleic acid sequences, providing an enzyme to form modified detection probes, amplifying said detection probes modified to form amplicons and detect said amplicons.

Additionally or alternatively, the detection of a genetic biomarker may include a method for detecting a plurality of target nucleic acid sequences in an archived tissue sample, comprising providing a nucleic acid sample prepared from a tissue sample archived, said sample comprising a plurality of target nucleic acid sequences, hybridizing a plurality of sets of binding probes to said target sequence to form a plurality of binding structures, linking each of said pluralities of binding structures using a ligase to form a plurality of linked probes, amplify said linked probes to form a plurality of amplicons and detect said amplicons as an indication of the presence of said plurality of target nucleic acid sequences.

[527] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include any of the various forms described in PCT Publication WO 2017/019456, which is incorporated herein by reference in its entirety .

For example, the detection of a genetic biomarker may include methods and compositions that facilitate the characterization of transcriptomes and / or genomic variation in tissues, preserving spatial information related to the origin of the target nucleic acids in the tissue.

For example, the methods may make it possible to identify the location of a cell or cell cluster in a tissue biopsy that carries an aberrant mutation.

The methods can therefore be useful for diagnostic purposes, for example, for the diagnosis of cancer and possibly assist in the selection of targeted therapies.

Additionally or alternatively, the detection of a genetic biomarker may include a capture array for spatial detection and analysis of nucleic acids in a tissue sample, comprising a capture site comprising a pair of capture probes immobilized on a surface, in that a first capture probe of the capture probe pair comprises a first connection region to the initiator and a spatial address region, and whereas a second capture probe of the capture probe pair comprises a second region of capture connection to the initiator and a capture region.

Additionally or alternatively, the detection of a genetic biomarker may include a method for spatial detection and analysis of nucleic acids in a tissue sample that includes (a) providing a capture arrangement, comprising a capture site comprising a pair of capture probes immobilized on a surface, where a first capture probe of the capture probe pair comprises a first primer connection region and a spatial address region and where a second capture probe of the capture probe pair compares comprises a second primer binding region and a capture region. In addition or alternatively, the detection of a genetic biomarker may include a method for spatial detection and analysis of nucleic acids in a tissue sample which includes providing a magnetic nanoparticle comprising an immobilized capture probe comprising a capture region. Additionally or alternatively, the detection of a genetic biomarker may include a capture arrangement for spatial detection and analysis of nucleic acids in a tissue sample, comprising a capture site comprising a capture probe comprising a region of contact. spatial address and a transposon (TE) end region.

[528] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include any of the various forms described in PCT Publication WO 2016/130704, which is incorporated herein by reference in its entirety . For example, the detection of a genetic biomarker may include methods and compositions related to the evaluation of components of a single cell preserved or incorporated or contained in contiguity preservation (EC) elements. In one aspect, there are methods for analyzing the plurality of analyte types from a single cell. In some modalities, a plurality of contiguity preservation elements (EC) are provided, each EC comprising a single cell. The cells are lysed within the CE, so that the plurality of analytes within the single cell is released within the CE. In some embodiments, a plurality of types of reporter portions are provided, so that each type of reporter portion is specific to each type of analyte. In some embodiments, the reporter fraction identifies a single cell. The plurality of analytes is modified so that each type of analyte comprises a reporter fraction specific to the type of analyte.

In some modalities, the EC comprising the analytes comprising the said portions is combined.

In some embodiments, the combined EC comprising the analytes comprising the said portions is compartmentalized.

In some embodiments, additional reporter portions are provided and combined with the analytes comprising analytes so that the analytes comprise two or more different reporter portions.

The analytes comprising the reporter portion are analyzed so that the identity of the analyte is detected and the reporter portion identifies the source of the analyte from a single cell.

In some embodiments, the exemplary plurality of analytes includes, but is not limited to, DNA, RNA, cDNA, protein, lipids, carbohydrates, cellular organelles ((eg, nucleus, Golgi apparatus, ribosomes, mitochondria, endoplasmic reticulum, chloroplast, cell membrane, etc.), cell metabolites, tissue sections, cells, single cell, cell or single cell content, nucleic acid isolated from cells or a single cell or nucleic acid isolated from cells or a single cell and subsequently modified, or DNA without cells (for example, from placental fluid or plasma) In some embodiments, the plurality of analytes includes genomic DNA and mRNA.

In some embodiments, the mRNA has a poly A tail.

In some modes, genomic DNA and mRNA are immobilized on a solid support within the EC simultaneously.

In some modalities, the immobilization of genomic DNA is sequential for the immobilization of mRNA to the solid support.

In some embodiments, genomic DNA is combined with transposome complexes and the ends of the transposon are immobilized on a solid support and the mRNA is immobilized on the solid by hybridization of oligo probes (dT) immobilized on a solid support.

In some modalities, genomic DNA is

combined with transposome complexes and, optionally, the transponder ends up hybridized with complementary sequences immobilized on a solid support, so that the mRNA is immobilized on the solid by hybridizing oligo (dT) probes immobilized on a solid support. Other methods can also be used to immobilize the mRNA. In some embodiments, the solid support is a sphere. In some embodiments, the solid support is a flow cell surface. In some embodiments, the solid surface is the wall of a reaction vessel. In some embodiments, the methods include sequencing nucleic acids preserved or incorporated into or contained in the EC. Some modalities are related to the preparation of DNA within the CE to obtain assembly information of phases and sequences of a target nucleic acid and to obtain sequence information of assembly of phases and sequences of such models. Particular modalities refer to the use of integrase, for example transposases, to maintain the physical proximity of associated ends of fragmented nucleic acids; and the use of combinatorial indexing to create individual libraries for each EC. Obtaining information on EC haplotypes includes distinguishing between different alleles (for example, SNPs, genetic abnormalities, etc.) in a target nucleic acid. Such methods are useful for characterizing different alleles in a target nucleic acid and reducing the error rate in the sequence information.

[529] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include any of the various forms described in PCT Publication WO 2002/012897, which is incorporated herein by reference in its entirety . For example, the detection of a genetic biomarker may include array compositions comprising a substrate with a surface comprising distinct sites, at least one fiducial, and a population of microspheres comprising at least a first and a second subpopulation.

Each subpopulation comprises a bi-active agent, and the microspheres are distributed on said surface.

Each subpopulation can optionally comprise a unique optical signature, an identifier linker that will link a decoder linker so that the identification of the bioactive agent can be elucidated, or both.

In a further aspect, compositions are provided comprising a computer-readable memory for directing a computer to function in a specified manner.

The computer-readable memory comprises an acquisition module for receiving a data image from a random arrangement comprising a plurality of distinct sites, a registration module for registering a data image and a comparison module for comparing images from recorded data.

Each module comprises computer code to perform its function.

The registration module can use any number of fiducials, including a fiducial fiber when the substrate comprises an optical fiber bundle, a fiducial microsphere or a fiducial model generated from the random arrangement.

In some embodiments, methods for making the arrangement compositions comprising forming a surface comprising individual sites on a substrate, distributing microspheres on the surface so that the individual sites contain microspheres and incorporating at least one fiducial on the surface are provided.

When the arrangement has complete freedom of rotation, at least two fiduciaries are preferred in the arrangement to allow for the correction of rotation.

In addition or alternatively, the detection of a genetic biomarker may include methods for comparing images of data separated from a random array.

The methods comprise using a computer system to register a first data image of the random arrangement to produce a first registered data image, using the computer system to register a second data image of the random arrangement to produce a second registered data image. and compare the first and second images of recorded data to determine the differences between them.

Some modalities provide methods for decoding a random arrangement composition comprising providing a random arrangement composition.

A first plurality of decoding linkers is added to the arrangement composition and a first data image is created.

A fiducial is used to generate a first image of recorded data.

A second plurality of decoding linkers is added to the array composition and a second data image is created.

The fiducial is used to generate a second image of recorded data.

A computer system is used to compare the first and second images of recorded data to identify the location of at least two bioactive agents.

Some modalities provide methods for determining the presence of a target test in a sample.

The methods comprise acquiring a first image of data from a random arrangement composition and registering the first image of data to create a first image of recorded data.

The sample is then added to the random array and a second image of data is acquired from the array.

The second data image is registered to create a second registered data image.

Then, the first and second recorded data images are compared to determine the presence or absence of the target analyte.

Optionally, data acquisition can be at different wavelengths.

Some modalities provide methods for pre-processing or pre-filtering signal data comprising acquiring an image of data from an array and determining the similarity of a first signal from at least one site in the array with a reference signal to determine whether the site comprises a candidate sphere.

[530] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include any of the various forms described in PCT Publication WO 2018/136416, which is incorporated herein by reference in its entirety .

For example, the detection of a genetic biomarker may include methods and systems for identifying splice variants.

In an implementation, a method comprises: determining one or more sample splice joints from a plurality of RNA sequence readings from a single biological sample; recovering, a set of baseline splice junctions determined from a plurality of healthy RNA samples; compare the one or more sample splice joints with the baseline splice joint set; and identifying one or more filtered sample splice joints, the filtered sample splice joints comprising sample splice joints that do not overlap the baseline splice joints, where the one or more splice joints filtered sample splice are candidate oncogenic events.

Some modalities also include leaving the list of candidate oncogenic events.

In some embodiments, the plurality of healthy RNA samples comprise healthy RNA samples taken from a cross section of one or more of the following: geographic regions, ages, genders, ethnic groups, types of tissue or type of preservation qualities. sample.

In some embodiments, the plurality of healthy RNA samples comprises samples from one or more types of tissues selected from the group consisting of: lung, adrenal gland, bladder, breast, ovary, liver, prostate, skin and spleen.

In some embodiments, the plurality of healthy RNA samples comprise donor samples across an age range.

In some embodiments, the baseline splice joints of the plurality of healthy RNA samples are determined prior to determining the sample joints of the single sample.

In some embodiments, the plurality of healthy RNA samples for the baseline splice junctions are not obtained from the same biological object as the only biological sample.

In some modalities, the baseline junctions are from the same genomic region as the sample junctions.

In some embodiments, the only biological sample is from a tumor sample.

In some embodiments, sample splice joints and baseline splice joints are determined using a common assay.

In some modalities, determining one or more junctions in the sample comprises: determining the plurality of RNA sequence readings from the single biological sample; recover, a DNA reference sequence aligned with the RNA sequence readings of the single biological sample; and determining one or more junctions in the sample as contiguous locations missing from the RNA reading compared to the DNA reference.

In some embodiments, the filtered sample splice joints do not overlap with third party joints, the third party joints determined from a splice graph that captures multiple alternative exon combinations for a given gene.

In some embodiments, the set of splice junctions in the baseline is determined without determining a splice plot that captures various alternative combinations of exons for a given gene.

Some modalities provide a system for identifying splice variants.

The system includes a memory, at least one processor; and at least one non-transitory computer-readable medium containing instructions that, when executed by at least one processor, cause at least one processor to perform operations that comprise determining one or more sample splice joins from a plurality of sequence readings. RNA from a single biological sample; recovering, a set of baseline splice junctions determined from a plurality of healthy RNA samples; compare the one or more sample splice joints with the baseline splice joint set; and identifying one or more filtered sample splice joints, the filtered sample splice joints comprising sample splice joints that do not overlap the baseline splice joint set, where the filtered sample splice joints are candidate oncogenic events.

[531] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include any of the various forms described in PCT Publication WO 2018/093780, which is incorporated herein by reference in its entirety . For example, the detection of a genetic biomarker may include a computer-implemented method to validate variant calls. The method operates under the control of one or more processors executing instructions from the program to receive sequencing data, including a reading sample that has a corresponding sequence of nucleotides along the genomic sequence of interest, receiving a indication of a potential variant call at a designated position within the nucleotide sequence along the genomic sequence of interest and obtaining baseline variant frequencies at the designated position within one or more baseline genomic sequences. The method obtains a sample variant frequency at the position designated for the genomic sequence of interest. The method analyzes the baseline and sample frequencies at the designated position to obtain a quality index; and the so-called potential variant is valid for the genomic sequence of interest based on the quality index. Optionally, the analysis operation includes obtaining a relationship between the frequency of the sample variant and a distribution of the frequencies of the baseline variant, the quality index based on the relationship.

Optionally, the analysis operation comprises indexing the frequency of the sample variant in relation to a distribution of the frequencies of the baseline variant.

The relationship can be based on a non-parametric sum test of the Wilcoxon classification.

Baseline variant frequencies indicate a degree of background noise at corresponding positions along the baseline genomic sequence.

Optionally, validation also includes comparing the quality index with a threshold; and declare that the potential variant call is valid when the quality score exceeds the threshold.

Baseline variant frequencies can be derived from several baseline genomic sequences associated with more than one type of allele.

Optionally, the method further comprises receiving sequencing data that includes a plurality of reference readings from a nucleotide sequence along the baseline genomic sequence and determining the baseline variant frequencies for the baseline readings. reference in designated positions.

The determination of baseline variant frequencies may further comprise receiving sequencing data from the reference readings for a set of positions within a current base pair window; identify a candidate variant frequency for one or more positions in the set of positions in the current base pair window; select one of the candidate variant frequencies as the baseline variant frequency for a designated position in the reference reading; and move the base pair window along the baseline genomic sequence and repeat the operations.

In accordance with the above modalities, systems and methods are described to reduce the call for false-positive variants of systematic errors.

Systematic errors can arise due to several factors, such as FFPE artifacts,

errors in library preparation, PCR errors and the like. Variant calls are statically subjected to a locus-specific background error distribution that can be compiled from a panel of normal FFPE samples with varying DNA quality from various tissues sequenced by the NGS-based assay. The same sequencing data as normal FFPE samples can also be used to normalize the systematic bias in reading coverage caused by PCR, DNA quality, probe extraction efficiency, or GC sequence content to reveal the true changes in the number of copies in a test sample. To further increase the signal to noise ratio in the so-called CNV, additional enhancer probes can be added to the hybrid capture to provide a robust estimate of gene amplification. In addition or alternatively, the detection of a genetic biomarker may include methods and systems that address noise problems and prevent systematic errors from contributing to false positive variant calls. In connection with this, a set of normal samples is used to identify systematic bias so that the system increases the rigor of calling in tumor samples in regions with high background noise. For FFPE samples, normal FFPE samples can be used to build the baseline. For ctDNA samples, normal genomic DNA data can be used to build the baseline.

[532] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include any of the various forms described in PCT Publication WO 2018/068014, which is incorporated herein by reference in its entirety . For example, the detection of a genetic biomarker may include systems and methods for sequencing polynucleotides. In a modality

In fact, the system comprises: a memory comprising a reference nucleotide sequence; a processor configured to execute instructions that execute a method comprising: receiving a first nucleotide substring from a reading of a sequencing system; processing the first nucleotide substring using a first alignment path to determine a first plurality of candidate reading sites in the reference sequence; determine whether the first nucleotide subsequence aligns with the reference sequence based on the determined candidate sites; receiving a second nucleotide substrate from the sequencing system; process the second nucleotide substring to determine a second plurality of candidate reading locations that align with the reference sequence using: a second alignment path if the reading is aligned with the reference sequence and the first alignment path if not, in that the second alignment path is more computationally efficient than the first alignment path for determining the second plurality of candidate reading locations. In one embodiment, the method comprises: receiving a first nucleotide substring from a sequencing system during a sequencing run; and perform a secondary analysis of the first nucleotide subsequence of a reading based on a reference sequence using a first analysis path or a second analysis path, where the second analysis path is more computationally efficient than the first path of processing when performing the secondary analysis.

[533] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include any of the various forms described in PCT Publication WO 2018/057770, which is incorporated herein by reference in its entirety .

For example, the detection of a genetic biomarker may include detecting variations in the number of copies in a biological sample.

In one embodiment, variants of the number of copies may have at least a single gene in size.

In another embodiment, the number of copy variants can be at least 140 bp, 140-280 bp or at least 500 bp.

In one embodiment, a "copy number variant" refers to the nucleic acid sequence in which differences in the number of copies are found by comparing a sequence of interest in the test sample with an expected level of the sequence of interest .

In some embodiments, a reference sample is derived from an unmatched sample sequencing data set to generate normalization information that allows an individual test sample to be normalized, so that deviations from the expected copy numbers can be determined in normalized sequencing data.

The normalization data is generated using the techniques provided and allows the normalization of a more representative hypothetical sample corresponding to the test sample.

When normalizing the test sample, noise introduced by sequencing or other bias is removed.

In certain embodiments, the coverage of raw sequencing data from a targeted sequencing run is normalized to reduce technical and biological noise to improve CNV detection.

In one embodiment, samples of interest (for example, paraffin embedded samples fixed in formalin) are sequenced according to a desired sequencing technique, such as a targeted sequencing technique that uses a probe sequencing panel to reach regions of interest .

After the sequencing data is collected, the sequencing data is normalized to remove noise and the normalized data is subsequently analyzed to detect CNVs.

In some embodiments, a method is provided to normalize the number of copies that includes the steps of receiving a sequencing request from a user to sequence one or more regions of interest in a biological sample; acquiring baseline sequencing data from one or more regions of interest from a plurality of baseline biological samples that do not correspond to the biological sample; determine copy number normalization information using baseline sequence data, where copy number normalization information comprises at least one copy number baseline for a region of interest in one or more more regions of interest; and provide the user with information on normalizing the number of copies.

In another embodiment, a method is provided to detect the variation in the number of copies that includes the steps of acquiring sequencing data from a biological sample, where the sequencing data comprises a plurality of raw sequential reading counts for a respective plurality of regions of interest; and normalize the sequencing data to remove region-dependent coverage.

Normalization comprises: for each region of interest, compare a gross sequencing reading count of one or compartments in a region of interest in the biological sample with a median base sequencing reading count to generate a sequencing reading count corrected by the baseline for one or more compartments in the region of interest, where the average reading count of the baseline sequence for one or more compartments in the region of interest is derived from a plurality of baseline samples that do not correspond to the biological sample and are determined only by the most representative portions of the baseline sequence data for each region of interest; and remove the GC bias from the

sequencing reading gem corrected by the baseline to generate a normalized sequencing reading count for each region of interest. The method also includes determining the variation in the number of copies in each region of interest, based on the normalized sequential reading count of one or more compartments in each region of interest. In another modality, a method is provided to evaluate a targeted sequencing panel that includes the steps of identifying a first plurality of targets in a genome for a targeted sequencing panel, where the first plurality of targets corresponds to portions of a respective plurality of genes; determining a GC content of each of the first plurality of targets; eliminating targets from the first plurality of targets with a GC content outside a predetermined range to produce a second plurality of targets smaller than the first plurality of targets; when, after elimination, the individual gene has less than a predetermined number of targets, corresponding to portions of the individual gene, to identify additional targets in the individual gene; adding additional targets to the second plurality to produce a third plurality of targets; and providing a sequencing panel comprising specific probes for the third plurality of targets.

[534] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include any of the various forms described in PCT Publication WO 2017/197027, which is incorporated herein by reference in its entirety . For example, the detection of a genetic biomarker may include methods for enriching or amplifying polynucleotides and, more specifically, methods for enriching or amplifying a target DNA sequence using endonuclease systems, for example, CRISPR-Cas systems or systems Argonaute, and applications thereof. In addition or alternatively, the detection of a genetic biomarker may include methods for amplifying a target double-stranded nucleic acid using CRISPR-Cas systems.

Additionally or alternatively, the detection of a genetic biomarker may include a method for amplifying a target double-stranded nucleic acid, including: (a) providing a system that has: a regularly interspersed short palindromic repeat RNA (CRISPR) ( crRNA) or a derivative thereof, and a protein (Cas) associated with CRISPR or a variant thereof, wherein the crRNA or derivative thereof contains a specific target nucleotide region complementary to a region of a first strand of the double-stranded target nucleic acid; (b) contacting the target double-stranded nucleic acid with the system to form a complex; (c) hybridizing a primer with a second strand of the double stranded nucleic acid, the primer containing a sequence complementary to a region of the second strand of the double stranded target nucleic acid and (d) extending a complementary nucleic acid to the second strand of the primer target double stranded nucleic acid using a polymerase.

Additionally or alternatively, the detection of a genetic biomarker may include a method for amplifying a target double-stranded nucleic acid, comprising: (a) providing a first system with: a first regularly interspersed short palindromic repeat RNA (CRISPR) (crRNA) or a derivative thereof, and a first protein (Cas) associated with CRISPR or a variant thereof, where the first crRNA or derivative thereof contains a specific target nucleotide region complementary to a region of a first double-stranded target nucleic acid strand; (b) supplying a second system with: a second regularly intermixed short palindromic repeat RNA (CRISPR) (crRNA) or a derivative thereof, and a second CRISPR-associated protein (Cas) or a that the second crRNA or derivative thereof contains a target specific nucleotide region complementary to a second strand region of the double stranded target nucleic acid; (c) contacting the target double-stranded nucleic acid with the first system and the second system; (d) hybridizing a first primer with a second strand of the target double stranded nucleic acid, the first primer containing a sequence complementary to a region of the second strand of the target double stranded nucleic acid and hybridizing a second primer with a first strand of the target double-stranded nucleic acid, the second primer containing a sequence complementary to a region of the first strand of the double-stranded target nucleic acid and (e) extend the 3 'end of the first primer and the second primer with one or more polymerases for generate a first and a second target double-stranded nucleic acid.

In some modes, the method also includes repeating steps (a) and (e) for one or more times, for example, until a desired degree of amplification is achieved.

In addition or alternatively, the detection of a genetic biomarker may include a method for amplifying a target double-stranded nucleic acid, including: (a) providing a system that has: a 5 'phosphorylated single-stranded nucleic acid or a derivative thereof and an Argonaut protein or a variant thereof, wherein the 5 'phosphorylated single-stranded nucleic acid or derivative thereof contains a specific target nucleotide region complementary to a region of a first strand of the nucleic acid double target tape; (b) contacting the target double-stranded nucleic acid with the system to form a complex; (c) hybridizing a primer with a second strand of the double stranded target nucleic acid, the primer containing a sequence complementary to a region of the second strand of the double stranded target nucleic acid and (d) extending a complementary nucleic acid to the second strand of the double-stranded nucleic acid targeting the primer using a polymerase.

In addition or alternatively, the detection of a genetic biomarker may include a method for enriching a target nucleic acid, including: obtaining a population of DNA without cells (cfDNA)

from an individual's plasma or serum, the population of DNA without cells containing the target nucleic acid; provide a system that has: a 5 'phosphorylated single-stranded nucleic acid or a derivative thereof and an Argonaut protein or a variant thereof, wherein the 5' phosphorylated single-stranded nucleic acid or derivative thereof contains a region target specific nucleotide complementary to a target nucleic acid region; contacting the target nucleic acid with the endonuclease system to form a complex and separate the complex and thus enrich for the target nucleic acid.

In addition or alternatively, the detection of a genetic biomarker may include a method for detecting a single nucleotide variant (SNV), including: obtaining a cellless DNA population from an individual's plasma or serum; providing a first system having: a first 5 'phosphorylated single-stranded nucleic acid or a derivative thereof, and a first Argonaut protein or a variant thereof, wherein the first 5' phosphorylated single-stranded nucleic acid or derivative it contains a first target-specific nucleotide region complementary to a region of a first target nucleic acid and in which the first Argonauta protein has nuclease activity; cleave the first target nucleic acid using the first en-donuclease system and amplify a second target nucleic acid using the Polymerase Chain Reaction (PCR), where the second target nucleic acid contains a single variant version of the nucleotide of the first target nucleic acid.

In addition or alternatively, the detection of a genetic biomarker may include a method for labeling a target nucleus, including providing a first system with: a 5 'phosphorylated single-stranded nucleic acid or a derivative thereof, and a first Argonaut protein or a variant thereof, wherein the first 5 'phosphorylated single-stranded nucleic acid or the derivative thereof contains a first target-specific nucleotide region complementary to a first target nucleic acid region and in which the first Argonaut protein is capable to generate a simple tape nick; contacting a double-stranded nucleic acid containing the target nucleic acid with the first nuclease system to generate a first single-stranded nick in the first region of the target nucleic acid and labeling the target nucleic acid.

In some embodiments, the method further includes separating the target nucleic acid by labeling and thereby enriching the target nucleic acid.

In some embodiments, the method further includes amplifying the target nucleic acid.

In some embodiments, the method also includes providing a second system with: a second 5 'phosphorylated single-stranded nucleic acid or a derivative thereof and a second Argonaut protein or a variant thereof, in which the second acid 5 'phosphorylated single-stranded nucleic acid or derivative thereof contains a second target-specific nucleotide region complementary to a second region of the target nucleic acid and where the second Argonaut protein is capable of generating a single-stranded nick and contacting the acid double-stranded nucleic acid that contains the target nucleic acid with the second nuclease system to generate a second single-stranded nick in the second region of the target nucleic acid, where the first region of the target nucleic acid is different from the second region of the acid target nucleic acid.

In addition or alternatively, the detection of a genetic biomarker may include a method for enriching a target nucleic acid, including: providing a population of Argonaut proteins programmed with a set of simple 5 'phosphorylated stranded nucleic acids, where the 5 'phosphorylated single stranded nucleic acid set contains 5' phosphorylated single stranded nucleic acids complementary to a series of different regions of the target nucleic acid; contacting the target nucleic acid with the population of Argonaut proteins programmed with the 5 'phosphorylated single-stranded nucleic acid set to generate a series of nucleic acid fragments and binding adapters to at least one of the nucleic acid fragments, where Argonaut proteins are capable of generating double-stranded DNA breaks. In addition or alternatively, the detection of a genetic biomarker may include a method for sequencing a target nucleic acid, including: providing a population of Argonaute proteins programmed with a set of 5 'phosphorylated single-stranded nucleic acids, where the 5 'phosphorylated single stranded nucleic acid set contains 5' phosphorylated single stranded nucleic acids complementary to a number of different regions via the target nucleic acid; contacting the target nucleic acid with the population of Argonaut proteins programmed with the 5 'phosphorylated single-stranded nucleic acid set to generate a series of nucleic acid fragments and sequence the series of nucleic acid fragments. In addition or alternatively, the detection of a genetic biomarker may include a method for sequencing a target nucleic acid, including: providing a plurality of populations of Argonaut proteins, each population of Argonaut proteins being programmed with a different set of 5 'phosphorylated single stranded nucleic acids, where each set of 5' phosphorylated single stranded nucleic acids contains 5 'phosphorylated single stranded nucleic acids, complementary to a different series of regions via the nucleic acid target, contact the target nucleic acid with each of the plurality of Argonaut protein populations in a separate reaction to generate a different series of nucleic acid fragments and sequence the nucleic acid fragments.

[535] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include any of the various forms described in PCT Publication WO 2015/198074, which is incorporated herein by reference in its entirety .

For example, the detection of a genetic biomarker can include methods, devices, systems and computer program products to present sequence information. In some embodiments, this includes obtaining a first sequence and a second sequence, determining a similarity between the first and the second sequence, in which the similarity is based on the distance between the first and the second sequence, and displaying a block at an intersection point on a matrix graph based on the similarity between the first sequence and the second sequence.

[536] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include any of the various forms described in PCT Publication WO 2002/099982, which is incorporated herein by reference in its entirety . For example, the detection of a genetic biomarker may include a composition that includes a substrate with a surface that comprises different sites, a reflective coating on the surface and a population of microspheres distributed on the substrate. The microspheres comprise at least one first and a second subpopulation. Generally, at least one subpopulation comprises a bi-active agent. In addition or alternatively, the detection of a genetic biomarker may include a composition in which the substrate comprises a first and a second surface, where the first surface comprises different sites and the reflective coating is on the second surface. The population of microspheres is distributed on the first surface. In addition or alternatively, the detection of a genetic biomarker may include a method for making a probe composition. The method includes providing a substrate with a surface comprising different sites, applying a coating of reflective material to the surface and distributing microspheres on the surface. Additionally or alternatively, the detection of a genetic biomarker may include a method for detecting an unlabeled target analyte in a sample that comprises providing a substrate with a plurality of distinct sites, distributing a population of microspheres comprising the sites at the sites a bioactive agent and a transducer element signal, contact the substrate with the sample, by connecting the target analyte to the bioactive agent, a signal from the signal transducer element is changed as an indication of the presence of the target analyte.

[537] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include any of the various forms described in PCT Publication WO 2002/016649, which is incorporated herein by reference in its entirety . For example, the detection of a genetic biomarker may include a method for detecting a target nucleic acid. The method comprises contacting the target nucleic acid with an adapter sequence, so that the target nucleic acid is joined to the adapter sequence to form a modified target nucleic acid. In addition, the method comprises contacting the modified target nucleic acid with an array comprising a substrate with a surface comprising distinct sites and a population of microspheres comprising at least a first subpopulation comprising a first capture probe, so that the first capture probe and the modified target nucleic acid form a complex, in which the microspheres are distributed on the surface and detect the presence of the target nucleic acid. In addition, the method comprises adding at least one decoding linker to the array, so that the identity of the target nucleic acid is determined.

[538] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the U.S. Patent

9,914,973, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include methods, compositions and kits for detecting genetic deregulations, such as those resulting from genetic fusions and chromosomal translocations.

The methods, compositions and kits are useful for detecting mutations that cause the differential expression of a 5 'region of a target gene in relation to the 3' region of the target gene.

In addition or alternatively, the detection of a genetic biomarker may include a method for detecting the presence or absence of deregulation in a target gene in a test sample.

In one embodiment, the method includes: (a) amplifying portions of a 5 'region of a target gene transcript or a cDNA derived therefrom, if present in a test sample, with two or more pairs of 5' target primers different that are targeted to the 5 'portions of the target gene; (b) amplifying portions of a 3 'region of a target gene transcript or a derived cDNA, if present in the test sample, with two or more pairs of different 3' target primers that are directed to the portions of the 3 'region of the target gene; c) detecting the amplification products produced by the two or more pairs of 5 'target primers and by the two or more pairs of 3' target primers; (d) determine the average cycle threshold (Ct) between the two or more pairs of 5 ′ target primers and the average Ct between the two or more pairs of 3 ′ target primers, (e) calculate an IDE Score as the difference between the average cycle threshold between the 5 ′ target primer pairs and the average cycle threshold between the 3 ′ target primer pairs and (f) identifying the test sample as (i) having a deregulation of the target gene if the IDE Score is significantly different from a cutoff value and the difference indicates the presence of a deregulation of the target gene or (ii) not having a deregulation of the target gene if the IDE Score in the test sample does not differ significantly the cutoff value.

In addition or alternatively, the detection of a genetic biomarker may include a method for diagnosing the presence or absence of cancer or a susceptibility to cancer in an individual.

In one embodiment, the method includes: (a) obtaining a test sample comprising the individual's nucleic acid; (b) amplifying portions of a 5 ′ region of a target gene transcript or a derived cDNA, if present in the test sample, with two or more pairs of different 5 ′ target primers targeting portions of the 5 'region of the target gene; (c) amplifying portions of a 3 'region of a transcript of the target gene or a derived cDNA, if present in the test sample, with two or more pairs of different 3' target primers that are directed to portions of the region 3 'of the target gene; (d) detecting the amplification products produced by the two or more pairs of 5 'target primers and the two or more pairs of 3' target primers; (e) determining the average cycle threshold (Ct) between the two or more pairs of 5 'target primers and the average Ct between the two or more pairs of 3' target primers; (f) calculate an IDE score as the difference between the average cycle threshold between the target pairs of 5 ′ primers and the average cycle threshold between the target pairs of 3 ′ and (g) diagnose the individual as (i) having cancer or a susceptibility to cancer when the IDE score is significantly different from a cutoff value and the difference indicates the presence of cancer or a susceptibility to cancer, or (ii) not having cancer or a susceptibility to cancer resulting from deregulation of the target gene, if the IDE score in the test sample does not differ significantly from the cutoff value.

In some embodiments, the level of expression of the 5 'region of a target gene is determined by amplification using two, three, four, five or six pairs of different primers targeting various portions of the 5' region of the target gene.

Likewise, two, three, four, five or six pairs of different primers targeting various portions of the 3 'region of the target gene can be used to determine the level of expression of the 3' region of the target gene. The quantities of amplification products can be normalized to the amount of an endogenous control gene transcript ("Control"), such as, for example, ABL. In some embodiments, the level of expression or relative amount of transcript can be determined using real-time PCR and comparing the threshold cycle (Ct) for each amplicon. The average Ct values for each of the 3 ′ (avgCt3 ′) and 5 ′ (avgCt5 ′) regions of a target gene are used to calculate an IDE Score, which can be calculated as IDE = (avgCt5′ − avgCt3 ′ ) or IDE = (avgCt5 ′) / (Ctcontrol) - (avgCt3 ′) / (Ctcontrol), or IDE = [Ln ((avgCt5 ′) / Ctcontrol)] - [Ln ((avgCt3 ′) / Ctcontrol) ]. In some embodiments, Ct values are normalized for a reference sample.

[539] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the U.S. Patent

9,783,854, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker can include methods and compositions for detecting target nucleic acids at very low levels and in the presence of large amounts of non-target nucleic acids. Generally, a target and non-target nucleic acid is distinguished by the presence or absence of a fragmentation site, such as a restriction enzyme recognition site. By differentiating the target and non-target by a fragmentation site, the methods and compositions can be used with various nucleic acid detection methods known in the art, such as PCR. In addition or alternatively, the detection of a genetic biomarker may include a method for detecting the presence or absence of a target nucleic acid, testing a sample that potentially contains the target nucleic acid in the presence of non-target nucleic acid, the method includes: a) fragmenting the sample nucleic acid under conditions such that subsequent amplification directed to the target nucleic acid results in an increased detection of the target nucleic acid over the non-target nucleic acid compared to amplification without fragmentation; b) amplify the target nucleic acid with a pair of primers, where a first primer is specific for the target nucleic acid; and c) detecting the presence or absence of an amplification product, which indicates the presence or absence of the target nucleic acid in the sample.

Additionally or alternatively, the detection of a genetic biomarker may include a method for diagnosing cancer or detecting the presence of a tumor cell, determining whether an individual has a mutant sequence associated with the type of tumor cell or cancer, the method includes: a) obtaining a sample including the individual's nucleic acid; b) fragmenting the sample nucleic acid under conditions such that subsequent amplification targeting the target nucleic acid results in an increased detection of the target nucleic acid over the non-target nucleic acid compared to amplification without fragmentation; c) amplifying the target nucleic acid with a pair of primers, where a first primer is specific for the target nucleic acid; and d) detecting the presence or absence of an amplification product containing the mutant sequence, in which the diagnosis of cancer is determined by the presence or quantity of amplification product containing the mutant sequence.

In addition or alternatively, the detection of a genetic biomarker may include a method for determining the cancer prognosis, determining whether an individual has a mutant sequence associated with cancer, the method includes: a) obtaining a sample containing nucleic acid from the individual; b) fragmenting the mutant nucleic acid under conditions such that subsequent amplification targeting the mutant nucleic acid results in an increased detection of the mutant nucleic acid over the non-mutant nucleic acid compared to the amplification without fragmentation; c) amplifying the mutant nucleic acid with a pair of primers, wherein a first primer is specific for the mutant nucleic acid; and d) detecting the presence, absence and / or quantity of an amplification product containing the mutant sequence, where the probability of a result in the individual is associated with the presence and / or quantity of the mutant nucleic acid sequence.

In addition or alternatively, the detection of a genetic biomarker may include a method for determining the sensitivity to medication of an individual diagnosed with cancer, the method includes: a) obtaining a sample comprising the individual's nucleic acid; b) fragment the mutant nucleic acid under conditions such that a subsequent amplification directed to the mutant nucleic acid results in an increased detection of the mutant nucleic acid over the non-mutant nucleic acid compared to the amplification without fragmentation; c) amplifying the mutant nucleic acid with a pair of primers, wherein a first primer is specific for the mutant nucleic acid; d) detecting the presence, absence and / or quantity of an amplification product containing the mutant sequence; and e) relate the presence, absence and / or quantity of an amplification product containing the mutant sequence to the sensitivity to the cancer drug.

In some embodiments, the changed nucleic acid sequence is due to exclusion, insertion, substitution and / or translocation or combinations thereof.

In preferred embodiments, the fragmentation of the nucleic acid sequence in which the cleavage of the wild type sequence is with a restriction enzyme.

This pre-amplification digestion treatment allows fragmentation to substantially destroy or decrease the number of wild-type sequences that can be amplified.

In even more preferred embodiments, fragmentation using a restriction enzyme is combined with the use of a specific mutation primer (or mutated sequence primer). In preferred embodiments, a mutated sequence destroys or disrupts a restriction enzyme recognition site present in the corresponding wild-type sequence and that a specific mutation primer can be designed to bind to the mutated version of the sequence and not the its wild type counterpart.

For example, a specific mutation primer can overlap a border region, which is a region that contains parts of a wild type sequence adjacent to a portion of the mutated sequence.

In one approach, a sample is analyzed for the presence or absence of a mutated sequence by amplification and detection of the resulting amplification products.

In a preferred embodiment, the amplification of the target nucleic acids is carried out by polymerase chain reaction (PCR). Single or multiple mutant sequences can be analyzed.

Amplification of multiple mutant sequences can be performed simultaneously in a single reaction vessel, for example, multiplex PCR.

In this case, the probes can be marked differently and / or the amplicons can be distinguished by different size.

Alternatively, the test can be carried out in parallel in separate reaction vessels.

In this later case, the probes could have the same marker.

In some embodiments, the methods further comprise a nucleic acid extraction step.

In some embodiments, at least one initiator from each pair of initiators in the amplification reaction is marked with a detectable fraction.

Thus, after amplification, the various target segments can be identified by size and color.

The detectable fraction is preferably a fluorescent dye.

In some embodiments, different pairs of primers in a multiplex PCR can be marked with different distinguishable detectable fractions.

Thus, for example, the HEX and FAM fluorescent dyes can be present in different primers in the multiplex PCR and associated with the resulting amplicons. In other embodiments, the forward primer is marked with a detectable fraction, while the reverse primer is marked with a different detectable fraction, for example, FAM dye for a forward primer and HEX dye for a reverse primer. The use of different detectable fractions is useful to discriminate between amplified products that are the same or very similar in length. Thus, in certain embodiments, at least two different fluorescent dyes are used to label different primers used in a single amplification. In yet another embodiment, control primers can be labeled with one portion, while patient primers (or test sample) can be labeled with a different portion, to allow mixing of both samples (post-PCR) and the simultaneous detection and comparison of normal and test sample signals. In a modification of this modality, the primers used for control samples and patient samples can be changed to allow further confirmation of the results.

[540] Analysis of products amplified from amplification reactions, such as multiplex PCR, can be performed using an automated DNA analyzer, such as an automated DNA sequencer (eg ABI PRISM 3100 Genetic Analyzer), which can evaluate amplified products based on size (determined by electrophoretic mobility) and / or respective fluorescent marker.

[541] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the U.S. Patent

9,546,404, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include methods, compositions and kits aimed at detecting genetic deregulations, such as those resulting from genetic fusions and chromosomal abnormalities, for example, translocations, insertions, inversions and deletions.

In some embodiments, the methods, compositions and kits are useful for detecting mutations that cause the differential expression of a 5 'region of a target gene in relation to the 3' region of the target gene.

In addition or alternatively, the detection of a genetic biomarker may include a method for detecting deregulation in a target gene.

The method may include: (a) amplifying a 5 'region of the target gene transcript, if present, in a biological sample with one or more pairs of 5' target primers that are complementary to the 5 'region of the target gene; (b) amplifying a 3 'region of the target gene transcript, if present, in the biological sample with one or more pairs of 3' target primers that are complementary to the 3 'region of the target gene; and (c) detecting the amounts of amplification product produced by the one or more pairs of target primers 5 'and by the one or more pairs of target primers 3'. The method can also predict that a difference in the quantities of amplification products produced by steps (a) and (b) indicates that the target gene is unregulated.

In addition or alternatively, the detection of a genetic biomarker may include a method for detecting the presence or absence of deregulation in a target gene in a sample.

The method may include: (a) measuring the amount of transcription of a 5 'region of the target gene and a 3' region of the target gene in the test sample; and (b) comparing the relative expression of the 5 'region with the 3' region of the target gene in the test sample with the relative expression of the 5 'region with the 3' region of the target gene in a reference sample.

The method can also predict that a difference in the relative expression in the test sample compared to the reference sample is indicative of the presence of a deregulation of the gene.

In one embodiment, the relative amount of transcription can be determined using real-time PCR and comparing the threshold cycle value, or Ct, for each amplicon.

The Ct value can be normalized for a reference sample.

In addition or alternatively, the detection of a genetic biomarker may include a method for diagnosing cancer or a susceptibility to cancer in an individual.

The method may include: (a) amplifying a 5 'region of the target gene transcript, if present, in a biological sample with one or more 5' target primer pairs that are complementary to the 5 'region of the target gene; (b) amplifying a 3 'region of the target gene transcript, if present, in the biological sample with one or more pairs of 3' target primers that are complementary to the 3 'region of the target gene; and (c) detecting the amounts of amplification product produced by one or more pairs of target primers 5 'and one or more pairs of target primers 3'. The method can also predict that a difference in the quantities of amplification products produced by steps (a) and (b) indicates that the individual has cancer or is susceptible to cancer resulting from genetic deregulation.

In addition or alternatively, the detection of a genetic biomarker may include a method for diagnosing prostate cancer or a susceptibility to prostate cancer in an individual.

The method may include: (a) amplifying a 5 'region of the target gene transcript, if present, in a biological sample with one or more pairs of 5' target primers that are complementary to the 5 'region of the target gene; (b) amplifying a 3 'region of the target gene transcript, if present, in the biological sample with one or more pairs of 3' target primers that are complementary to the 3 'region of the target gene; and (c) detecting the amounts of amplification product produced by the one or more pairs of target primers 5 'and by the one or more pairs of target primers 3'. The method can also predict that a difference in the quantities of amplification products produced by steps (a) and (b) indicates that the target gene is unregulated.

Optionally, the nucleic acid sample containing the target gene of interest can be subjected to another analysis to determine the nature of the deregulation of the gene.

Suitable analyzes include, for example, comparative hybridization (for example, comparative genomic hybridization). Comparative hybridization techniques, such as comparative genomic hybridization (CGH), are limited by the fact that this technique is able to detect only unbalanced rearrangements (rearrangements that lead to the gain or loss of genetic material). Comparative hybridization cannot adequately detect chromosomal abnormalities, such as balanced translocations.

Thus, either method can be used in combination with a comparative hybridization technique.

The combination of methods with comparative hybridization (for example, CGH) will be able to detect balanced and unbalanced rearrangements and provide a more accurate diagnosis than if the comparative hybridization technique were used alone.

In the case of unbalanced rearrangements, the comparative hybridization technique can be used as a confirmatory test.

In some embodiments, deregulation of target genes can arise from gene fusions and chromosomal abnormalities, including, for example, translocations, deletions, inversions and insertions.

In some embodiments, the biological sample is contacted with the one or more pairs of 5 'target primers and the one or more 3' target primers in a multiplex amplification reaction.

In one embodiment, detection is performed using a labeled oligonucleotide probe complementary to each amplification product.

For example, each oligonucleotide probe can include a different detectable marker, such as a donor fluorophore and an extinguishing portion.

In another embodiment, at least one of the primers for the 5 'region and / or at least one of the primers for the 3' region is detectably labeled, preferably with different detectable labels.

In illustrative modalities, amplification is performed using quantitative RT-PCR, for example, real-time RT-PCR.

In some modalities, the chromosomal abnormality is selected from the group consisting of: a translocation, an exclusion, an inversion and an insertion. In one embodiment, the biological sample is a sample of an individual to be tested for a chromosomal abnormality. In some modalities, the methods even amplify a region of an endogenous control gene transcript present in the biological sample with a pair of primers complementary to the endogenous control gene and the detection of the amplification of the endogenous control gene region . In some modalities, the amount of amplified target gene transcripts (ie, the 5 'and 3' region) can be normalized to the amount of amplified endogenous control gene transcript. In some modalities, the method also includes: (a) measuring the amount of transcription of a 5 'region of a second target gene and a 3' region of the second target gene in the test sample; and (b) comparing the relative expression of the 5 'region with the 3' region of the second target gene in the test sample with the relative expression of the 5 'region with the 3' region of the second target gene in a reference sample. The method can also predict that a difference in the relative expression of the target gene and the second target gene in the test sample compared to the reference sample is indicative of the presence of a target gene: translocation of the second target gene. Suitable biological samples include, for example, whole blood, isolated blood cells, plasma, serum and urine.

[542] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the U.S. Patent

8,911,942, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include a method for performing comparative hybridization by comparing the amount of test and reference nucleic acids hybridized to an array of nucleic acids, the quantities determined by detecting a nucleic acid signal that are tagged with the same detectable tag.

This method is applicable to methods of comparative hybridization in general and to comparative genomic hybridization (CGH) in particular.

Therefore, the reference to CGH, where the test and the reference nucleic acid is the genomic nucleic acid, should be understood as encompassing methods in which the test and reference nucleic acids are different from the genomic nucleic acids.

In a preferred embodiment, CGH is performed using two samples of genomic nucleic acids; a test sample containing genomic nucleic acids and a reference or control sample containing genomic nucleic acids without known chromosomal or genetic anomalies.

The test sample and the reference sample are co-hybridized with a nucleic acid array that contains a plurality of nucleic acids or nucleic acid segments stained on a surface (such as a glass slide) in distinct locations. tos.

The array may contain target nucleic acid markers for certain known genetic mutations or disease states, or may represent (in aggregate) an entire chromosome or the complete chromosomal complement to obtain a genetic profile similar to characterization. .

In these approaches, the detectable marker can be attached to the test and reference nucleic acids before hybridization or after hybridization.

In another approach, the detectable marker can be linked to one of the test or reference nucleic acids before hybridization, while the marker is linked to the other of the test or reference nucleic acid after hybridization.

The detectable marker can be linked covalently or non-covalently, such as by a ligand-receptor interaction or by hybridization between complementary nucleotide sequences.

In some modalities, comparative hybridization can be done using the same detectable marker by another approach that can be referred to as an "additive" approach. In accordance with this approach, the nucleic acids in the test sample comprise a first tag; and the nucleic acids in the reference sample comprise a second label.

After hybridization, the surface is contacted with a first complex containing a detectable tag and a first entity, so that the first complex selectively binds to the first tag.

The next step involves determining the location and quantity of the detectable tag attached to the arrangement surface (that is, to "read" the arrangement). After the array is read to determine the amount of detectable label associated with the nucleic acid comprising the first label, the surface is then contacted with a second complex containing the same detectable marker present in the first complex and containing a second entity, so that the second complex selectively binds to the second tag.

The array is then read a second time to determine the location and amount of the total detectable marker that represents the two nucleic acids hybridized to the surface.

The last step involves using results from the two readings to determine the amount of hybridized nucleic acid that is associated with the second tag.

In a preferred approach, the first reading is subtracted from the second reading to obtain the signal that represents the nucleic acid that is attached to the second tag.

The signal from the two samples thus determined can be used to identify differences between the genomic nucleic acids in the test sample and the genomic nucleic acids in the reference sample, in order to detect any chromosomal or genetic abnormalities associated with the nucleic acid in the test sample. test sample.

In some modalities, the amount of the hybridized nucleic acid that is associated with the second tag can be determined using a duplicate of the hybridized arrangement, but which was not contacted with the first complex.

Thus, the duplicate arrangement is contacted with the second complex and not the first complex.

The signal from this second array directly represents the amount of nucleic acid hybridized to the second tag, which can be compared to the amount of signal from the first array that was contacted only with the first complex and represents the amount of hybridized nucleic acid associated with the first label.

As the two analyzes are independent of each other, each arrangement can be processed in any order or simultaneously.

In some embodiments, the arrangement can first be hybridized to the test and reference nucleic acids, in which one of the test and reference nucleic acids has already been labeled (for example, by random initiation). The array is then read after hybridization to determine the signal corresponding to the particular labeled nucleic acid sample.

The array is then contacted with a complex comprising a detectable marker and an entity, in which the complex reacts selectively with the other of the test or reference nucleic acid through a label attached to said other of the test nucleic acid. or reference.

The assay is read again to measure the total signal for both hybridized nucleic acids.

The next step involves using the results of the two readings to determine the amount of hybridized nucleic acid that is associated with the tag.

In a preferred approach, the first reading is subtracted from the second reading to obtain the signal that represents the nucleic acid that was attached to the tag.

The signal from the two samples thus determined can be used to identify differences between the genomic nucleic acids in the test sample and the genomic nucleic acids in the reference sample, in order to detect any chromosomal or genetic abnormalities associated with the nucleic acid in the test sample.

In some embodiments, the hybridized nucleic acid that is associated with the second tag can be determined using a duplicate of the hybridized array, except that the duplicate is prepared by hybridization to test and reference nucleic acids that do not contain a detectable marker.

In that case, the duplicate array is then contacted with a complex comprising a detectable marker and an entity, in which the complex reacts selectively with the other of the test or reference nucleic acid through a tag attached to said other of the test or reference nucleic acid.

The signal from this second array directly represents the amount of nucleic acid hybridized to the second label, which can be compared to the amount of signal from the first array that was contacted only with the first complex and represents the amount of hybridized nucleic acid associated with the first tag.

As the two analyzes are independent of each other, each arrangement can be processed in any order or simultaneously.

In addition or alternatively, the detection of a genetic biomarker may include a method for comparing the expression of genes in a test sample versus that of the reference sample.

The first step of the method includes contacting under conditions of hybridization of cDNA prepared from mRNA from a test sample and cDNA prepared from mRNA from a reference sample to a surface containing a plurality of nucleic acid segments , each immobilized in different places on the surface.

In this case, the cDNA of the test sample and the cDNA of the reference sample are marked before or after hybridization with the same detectable marker that is linked to the cDNA of the test sample by means of a first binding and the cDNA of the reference sample. through a second call.

The first link or the second link is susceptible to selective removal and the detectable marker linked to nucleic acids hybridized to the determined surface.

The location and amount of detectable marker bound to hybridized nucleic acids on the support surface is determined.

The marker is then selectively removed from the cDNA of the hybridized test sample or the cDNA of the hybridized reference sample. The location and quantity of the detectable marker remaining on the support are determined and represent one of the samples. The difference between the site and the amount remaining after removal compared and the site and amount prior to removal represents the other sample. The relative amount of each nucleic acid sample hybridized to the array reflects the expression of genes in the test sample compared to the reference sample.

[543] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the U.S. Patent

8,871,687, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include a method for determining a sequence of contiguous bases within a polynucleotide, the method based on extending the single base primer using labeled dideoxynucleotide terminators. The starters are immobilized on solid supports (for example, microspheres or two-dimensional arrangements), allowing the identification of the marked terminator incorporated in each initiator. Data on the built-in terminators are used to determine the identity of the base of a contiguous nucleotide sequence in a target nucleic acid. In addition or alternatively, the detection of a genetic biomarker may include a method for determining a contiguous sequence comprising at least four bases of a target nucleic acid comprising: (a) preparing one or more reaction mixtures containing the acid target nucleic acid and four or more primers complementary to a portion of the target nucleic acid, so that each primer has a 3 'end located 5' at each nucleotide position of the sequence to be determined, where each reaction contains from one to all said primers in any combination and is under conditions where the primers match the target nucleic acid; (b) extending the one or more primers from step (a) with a polymerase in the presence of one or more labeled dideoxynucleotides; (c) immobilizing said primers on a solid support; and (d) detecting the didesoxynucleotide marker incorporated in each primer and using this information to determine said contiguous sequence of at least four bases of the target nucleic acid.

The initiators can extend before immobilization on the solid support, that is, step (b) occurs before step (c) or extend after immobilization on the solid support, that is, step (c) occurs before step (a). In one embodiment, at least two differently labeled dideoxynucleotides are provided in the same reaction mixture.

In another embodiment, four differently labeled dideoxynucleotides are supplied in the same reaction mixture.

In addition or alternatively, the detection of a genetic biomarker may include methods for determining a contiguous sequence of four or more bases from a target nucleic acid, performing singleplex single base primer extension reactions or multiplex single base primer extension reactions.

In one embodiment, the four or more primers corresponding to the entire portion of the target nucleic acid to be sequenced are combined into a single reaction mixture.

In another embodiment, two or more initiators are combined in a reaction mixture and two or more initiators are combined in an additional reaction mixture or mixtures.

Alternatively, the four or more initiators are each added to a separate reaction mixture.

In one embodiment, the primers comprise a sequence of markers and are immobilized on the solid support by hybridization with a complementary capture oligonucleotide conjugated to the solid support.

In another mode, the initiators are immobilized on the solid support by means of a covalent accessory.

In one mode,

the solid support is a marked microsphere. For example, microspheres can be made of polystyrene. In one embodiment, the marker for each microsphere is detected optically, based on varying concentrations of at least two dyes. In certain embodiments, the labeled microspheres and the labeled dideoxynucleotide are detected by flow cytometry. In another embodiment, the solid support is a two-dimensional arrangement and the immobilized initiators are defined positionally in the arrangement. The primers can be immobilized in the array via a covalent bond or through a bonding sequence. In certain embodiments, primers extended with labeled dideoxynucleotides are detected by scanning the array.

[544] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the U.S. Patent

8,492,089, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include a method for performing comparative hybridization by comparing the amount of test and reference nucleic acids hybridized to an array of nucleic acids, the quantities determined by detecting a nucleic acid signal that are tagged with the same detectable tag. This method is applicable to comparative hybridization methods in general and to CGH in particular. Therefore, reference to CGH, where the test and the reference nucleic acid is the genomic nucleic acid, should be understood as encompassing methods in which the test and reference nucleic acids are different from the genomic nucleic acids. In a preferred embodiment, CGH is performed using two samples of genomic nucleic acids: a test sample containing genomic nucleic acids and a reference or control sample containing genomic nucleic acids without known chromosomal or genetic anomalies.

The test sample and the reference sample are co-hybridized with an array of nucleic acids that contain a plurality of nucleic acids or segments of nucleic acids stained on a surface (such as a glass slide) in different locations.

The array may contain target nucleic acid markers for certain known genetic mutations or disease states, or may represent (in aggregate) an entire chromosome or the complete chromosome complement to obtain a karyotype-like genetic profile.

In these approaches, the detectable marker can be attached to the test and reference nucleic acids before hybridization or after hybridization.

In another approach, the detectable marker can be linked to one of the test or reference nucleic acids before hybridization, while the marker is linked to the other of the test or reference nucleic acid after hybridization.

The detectable marker can be linked covalently or non-covalently, such as by a ligand-receptor interaction or by hybridization between complementary nucleotide sequences.

In some modalities, the test and reference samples are marked with a detectable marker; preferably, the test and reference samples are marked with the same detectable marker; preferably, the detectable marker is a fluorochrome; preferably the detectable marker is dCTP-Cy3. In certain respects, methods are provided that allow the use of a single marker to determine the relative amount of test and reference nucleic acids hybridized to the array.

Additionally or alternatively, the detection of a genetic biomarker may include a method for determining differences between nucleic acid in a test sample and a reference sample, where the method involves amplifying the nucleic acid sequence from the test sample and the nucleic acid sequence from the reference nucleic acid sample, in which one of the amplification reactions is conducted using dUTP and not dTTP and the other is conducted using dTTP and not dUTP; hybridizing to a nucleic acid array a solution comprising the amplified test sample and the amplified reference sample; and determining the relative amount of hybridization test and reference nucleic acids attached to the array.

In certain embodiments, determining the relative amount of hybridized test and reference nucleic acids includes: a) determining a signal for the detectable marker hybridized to the array representing the total test and reference nucleic acids; b) treating hybridized nucleic acids with an enzyme that selectively degrades DNA with uracil residues; and c) determining a signal for the detectable marker hybridized to the array after step b), a signal that represents one of the test nucleic acids or hybridization reference.

In particularly preferred embodiments, the enzyme that selectively breaks down DNA with uracil residues is uracil-DNA N-glycosylase (UNG). In addition or alternatively, the detection of a genetic biomarker may include a method for determining differences between nucleic acid in a test sample and a reference sample is provided, where the method involves: (a) contacting under hybridization conditions a test sample containing nucleic acids and a reference sample containing nucleic acids to a surface containing a plurality of nucleic acid segments, each immobilized at different sites on the surface, where the test sample and the reference sample are marked before or after hybridization with the same detectable marker; (b) determining the location and amount of the detectable marker bound to nucleic acids hybridized to the surface; (c) selectively removing nucleic acids from the hybridized test sample or nucleic acids from the hybridized reference sample; (d) determining the location and amount of the detectable marker bound to nucleic acids hybridized to the surface after step (c); and (e) comparing the results of step (b) with the results of step (d) to detect differences in the nucleic acids in the test sample and the reference sample.

In some preferred embodiments, the step of selectively removing hybridized test nucleic acids or reference nucleic acids is performed by subjecting the nucleic acids to an enzyme that selectively degrades DNA with certain properties; preferably an enzyme that degrades DNA with uracil residues; more preferably, the enzyme that selectively degrades DNA with uracil residues is uracil-DNA N-glycosylase (UNG). In some embodiments, the step of selectively removing hybridized test nucleic acids or reference nucleic acids by subjecting nucleic acids to an enzyme that selectively degrades DNA with uracil residues is achieved by (1) amplification sequence of a test sample and amplification sequence from a reference nucleic acid sample, where one of the amplification reactions is conducted using dUTP and not dTTP and the other is conducted using dTTP and not dUTP; (2) hybridizing amplified nucleic acids; and (3) treating the hybridized nucleic acids with an enzyme that selectively degrades the DNA with uracil residues.

In some modes, methods can be used to detect any differences between nucleic acids in a test sample and a reference sample, including differences in the amount of nucleic acids that have a specific sequence or differences in nucleic acid sequences.

In particularly preferred embodiments, the methods are used to detect genetic abnormalities in the test sample.

The methods can be applied to CGH using a chromosomal dispersion or arrangement-based CGH.

In some preferred embodiments, the methods provided can be used to compare the expression of genes in a test sample versus that of a reference sample.

In addition or alternatively, the detection of a genetic biomarker may include a method for performing comparative hybridization.

The method includes comparing the amount of hybridized test and reference nucleic acids with an array of nucleic acids, wherein the amount of hybridized test and reference nucleic acids is determined by detecting a signal from the hybridized nucleic acids that are marked with the same detectable marker.

In one embodiment, the test amount of hybridized acid and reference nucleic acid is determined by: a) determining a signal for the detectable marker hybridized to the marker representing the total hybridized acid and nucleic acid test of reference; b) treating the hybridized nucleic acids to selectively remove one of the test or reference nucleic acids; c) determining a signal for the detectable marker hybridized to the array after step b), which represents one of the hybridization tests or reference nucleic acid; and d) determine a signal for the other of the hybridized test or reference using the signal of c) and b). In certain preferred embodiments, the amplification sequence step for a test sample and amplification sequence for a reference sample involves amplifying the genomic DNA in the samples is conducted using random initiation, as is well known in the art.

Alternatively, the step of amplifying the sequence of a test sample and amplifying from a reference sample may involve using RNA to generate cDNA and amplifying the cDNA using random priming and or amplifying specific sequences using specific primers.

In certain preferred modes, the amplification reaction can be carried out using one or more labeled nucleotides as a means to label the amplified nucleic acids with a detectable marker; preferably, the nucleic acids in the test and reference sample are amplified with the same labeled nucleotide; preferably the labeled nucleotide is dCTP-Cy3. In addition or alternatively, the detection of a genetic biomarker may include a method for comparing the expression of genes in a test sample versus that of a reference sample is provided. The method includes comparing the amount of cDNA prepared from mRNA from a test sample and cDNA prepared from mRNA from a hybridized reference sample to a nucleic acid array, the amount of hybridized test and reference cDNA determined by detection of a hybridized cDNA signal that is marked with the same detectable marker. The method involves amplifying the nucleic acid sequence of the cDNA prepared from the RNA of the test sample and amplifying the nucleic acid sequence of the cDNA prepared from the RNA of the reference sample, where one of the amplification reactions is conducted using dUTP and not dTTP and the other is conducted using dTTP and not dUTP; hybridize to the nucleic acid array a solution comprising the amplified test sample and the amplified reference sample; and determining the relative amount of hybridization test and reference nucleic acids attached to the array. In certain embodiments, determining the relative amount of hybridized test and reference nucleic acids includes: a) determining a signal for the detectable marker hybridized to the array representing the total test and reference nucleic acids; b) treating hybridized nucleic acids with an enzyme that selectively degrades DNA with uracil residues; and c) determining a signal for the detectable marker hybridized to the array after step b), a signal that represents one of the test nucleic acids or hybridization reference.

[545] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the U.S. Patent

8,093,063, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker can methods for detecting a genomic nucleic acid of interest in a test sample without amplification and without the need for cells or intact nuclei.

Generally, a genomic nucleic acid is hybridized with a labeled probe and anchored to a solid support by means other than nucleic acid hybridization.

Genomic nucleic acid is detected by detecting the marker in the hybridized complex on the solid support.

The method can be used to detect a genetic abnormality, for example, point mutation, duplication or exclusion of genes and chromosomal translocation.

The method can also be used for the diagnosis or prognosis of a disease.

In addition or alternatively, the detection of a genetic biomarker may include a method for detecting a target sequence in the genomic nucleic acid, by: a. contact a sample of genomic nucleic acid containing the target sequence with a probe specific for the target sequence and form on a solid support a complex consisting of the genomic nucleic acid and the probe hybridized to the target sequence, in which the probe contains a detectable marker, the genomic nucleic acid is anchored to the solid support by means other than nucleic acid hybridization and the target sequence of the genomic nucleic acid has not been amplified; and B. detecting the presence of the target sequence in the genetic nucleic acid, detecting the association of the marker with the solid support.

In addition or alternatively, the detection of a genetic biomarker may include a method for detecting the presence or absence of a genetic abnormality in the genomic nucleic acid, by: a. contact a genomic nucleic acid sample with a probe specific for the genetic abnormality and form on a solid support a complex consisting of the genomic nucleic acid and the probe if the genetic abnormality is present in the genomic nucleic acid, the genomic nucleic acid is anchored to the solid support by means other than nucleic acid hybridization and the target sequence of the genomic nucleic acid has not been amplified; B. detect the presence of the genetic abnormality by detecting the association of the marker with the solid support.

In addition or alternatively, the detection of a genetic biomarker may include a method for detecting genetic abnormalities in a genomic nucleic acid, by: a. contact a sample of genomic nucleic acid containing the genetic abnormality with a first probe specific for the genetic abnormality and form a first complex on a solid support consisting of the genetic nucleic acid and the first probe, on which the probe contains a detectable marker, the genomic nucleic acid is anchored to the solid support by means other than nucleic acid hybridization and the target sequence of the genomic nucleic acid has not been amplified; B. contacting a genomic nucleic acid sample with a second probe specific for the reference nucleic acid and forming a second complex on a solid support consisting of the reference nucleic acid and the second probe, where the second probe contains a marker detectable; and C. measure the amount of the first complex formed by detecting the detectable marker of the first probe associated with the complex and measure the amount of the second complex formed by detecting the detectable marker of the second probe associated with the complex; and d. compare the quantity of the first complex with the quantity of the second complex, in which a difference in the quantity of two complexes is indicative of genetic abnormality.

In some embodiments, the genomic nucleic acid and the reference nucleic acid are from the same sample.

In another modality of any of the previous aspects, the genomic nucleic acid and the reference nucleic acid are from a different sample, which may be from the same or different individuals.

In another embodiment, the quantity of the first complex and the second complex is determined using the same solid support, and the detectable markers of the first probe and the second probe are different.

In addition or alternatively, the detection of a genetic biomarker may include a method for detecting genetic abnormalities in a genomic nucleic acid, by: a. contacting a sample of genomic nucleic acid containing the genetic abnormality with a first probe specific for the genetic abnormality and forming a first complex on a solid support consisting of the genomic nucleic acid and the first probe, where the probe contains a detectable marker , the genomic nucleic acid is anchored to the solid support by means other than nucleic acid hybridization and the target sequence of the genomic nucleic acid has not been amplified; B. contacting a genomic nucleic acid sample with a second probe specific for the reference nucleic acid and forming a second complex on a solid support consisting of the reference nucleic acid and the second probe, where the second probe contains a detectable marker; and C. measure the quantity of the first complex formed by detecting the detectable marker of the first probe associated with the complex and measure the quantity of the second complex formed by detecting the detectable marker of the second probe associated with the complex; and d. obtain a ratio of the quantity of the first and the second complex; and is. compare the ratio obtained with a ratio similarly obtained using genomic nucleic acid from a reference sample, where a difference in the ratios is indicative of genetic abnormality.

In preferred embodiments, the genomic nucleic acid and the reference nucleic acid are anchored to the solid support through the interaction of biotin and avidin.

In another preferred embodiment, the solid support is a sphere.

In another preferred embodiment, the first and second complexes are detected by flow cytometry.

In addition or alternatively, the detection of a genetic biomarker may include a method for diagnosis in an individual by: a. contacting a sample of the individual's genomic nucleic acid with a probe complementary to the disease-specific nucleic acid sequence and forming on a solid support a complex consisting of the genomic nucleic acid and the probe if the genomic nucleic acid contains the nucleic acid sequence disease-specific, where the probe contains a detectable marker, the genomic nucleic acid is anchored to the solid support by means other than nucleic acid hybridization and the target sequence of the genomic nucleic acid has not been amplified; and B. measure the amount of complex formed on the solid support, detecting the amount of detectable marker associated with the support; and C. compare the amount of complex formed with the amount of complex formed using genomic nucleic acid from a reference sample analyzed under similar conditions, in which a difference in the amount of complex formed in the individual compared to the reference sample is diagnostic for the disease.

In one embodiment, the reference sample can be obtained from an individual considered free of the disease.

In another way, the reference sample can be obtained from an individual known to have the disease.

In another modality, the reference sample is obtained from the same individual after obtaining the first sample.

In one embodiment, the method can be used to measure tumor burden in an individual suspected of having cancer.

In another modality, the method can be used for the prognosis of a disease.

The genetic nucleic acid can be anchored covalently or non-covalently to the solid support.

In some embodiments, the genomic nucleic acid may be anchored non-covalently to the solid support by means of a "binding pair", which refers to two molecules that form a complex through a specific interaction.

Thus, the genomic nucleic acid can be captured on the solid support through an interaction between one member of the binding pair attached to the genetic nucleic acid and the other member of the binding pair attached to the solid support.

In a preferred embodiment, the binding pair is biotin and avidin, or variants of avidin, for example, streptavidin and NeutrAvidin ™. In other embodiments, the binding pair can be a ligand receptor, a hormone receptor, an antigen antibody. In some modalities, the genomic nucleic acid can be anchored to the solid support by means of covalent bonding. In one embodiment, the covalent bonding of the genomic nucleic acid to the solid support is achieved through photoactive groups, for example, azido, azidophenacil, 4-nitrophenyl 3-diazopyruvate, psolarens, psolaren derivatives. In another embodiment, the genomic nucleic acid can be cross-linked to a variety of solid surfaces by UV cross-linking. In another embodiment, the genomic nucleic acid can be anchored to the solid support through chemical coupling using chemical ligands. In another preferred mode, the genomic nucleic acid is genomic DNA. In another embodiment, the reference nucleic acid is a housekeeping gene or a single copy sequence on a chromosome. In some modalities. the test sample or reference sample containing genomic nucleic acid and reference nucleic acid, respectively, can be obtained or accessed within cells, tissues, body fluids, plasma, serum, urine, central nervous system fluid, feces , bile duct, tissue embedded in paraffin, cell lysates, tissue lysates and the like. The test and the reference nucleic acid can be obtained from any number of sources and by any method.

[546] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the U.S. Patent

8,076,074, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include methods to detect chromosomal abnormalities, including balanced translocations, where the method involves performing array-based CGH in conjunction with probes to detect translocations.

In one aspect, the methods involve hybridizing to an array of genomic nucleic acid, a genomic nucleic acid test sample, a reference nucleic acid sample and at least one probe to detect balanced translocation; and determining the relative amount of hybridization test and reference nucleic acids hybridized to the array, as well as determining hybridization to the probe or probes array to detect translocation.

In a preferred embodiment, the methods are performed using two samples of genomic nucleic acid; a test sample containing genomic nucleic acid and a reference or control sample containing genomic nucleic acids, the last without known chromosomal or genetic abnormalities.

The reference test samples are co-hybridized with an array of nucleic acids containing a plurality of nucleic acids or segments of nucleic acids stained on a surface (such as a glass slide) in different locations.

The array may contain target nucleic acid markers for certain known genetic mutations or disease states, or may represent (in aggregate) an entire chromosome or the complete chromosome complement to obtain a genetic profile.

In these approaches, the detectable marker can be attached to the test and reference nucleic acids before or after hybridization and in any order.

The detectable marker can be linked covalently or non-covalently, such as by a ligand-receptor interaction or by hybridization between complementary nucleotide sequences.

In addition, a probe to detect translocations is hybridized with genomic DNA.

In one approach, the probe is complementary to a moving segment of the genome that is translated.

The moving segment may be upstream or 5 'from the translocation interruption point or downstream or 3' from the translocation interruption point. If the test sample does not contain balanced translocation, the probe will hybridize to the array where the mobile segment is located in the wild type. If the test sample contains balanced translation, the probe will hybridize again to the array where the mobile segment is located in the wild type and to the area of the array that contains the nucleic acid that now contains the mobile segment. In addition, several probes, all complementary to the mobile segment being translocated, can be used in a single hybridization.

[547] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the U.S. Patent

8,021,888, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include a method for performing a rapid hybridization assay. The method can be used to reduce the time to complete hybridization of nucleic acid between nucleic acids in solution and nucleic acid immobilized on a solid support. The method applies acoustic waves to the surface during any pre-hybridization with non-specific nucleic acid, hybridization to reach the nucleic acid or during the washing steps after hybridization. In one approach, hybridization of nucleic acid to immobilized nucleic acid includes the following steps: a) contacting a solid support comprising one or more immobilized nucleic acid probe molecules under conditions of hybridization with a non-specific blocking nucleic acid wherein one or more immobilized nucleic acid probe molecules are capable of hybridizing to a sequence complementary thereto; b) contacting the solid support under hybridization conditions with a test sample containing target nucleic acid molecules; c) apply acoustic waves on the surface to the hybridization of step a) or step b) or both; and d) determining whether one or more nucleic acid probes from the solid support hybridized to test target sample nucleic acid molecules.

In another approach, hybridization of nucleic acid to immobilized nucleic acid includes the following steps: a) contacting a solid support containing one or more immobilized nucleic acid probe molecules under hybridization conditions with a nucleic acid test sample containing one or more target nucleic acid molecules, to one or more immobilized nucleic acid probe molecules capable of hybridizing to a sequence complementary thereto; b) apply acoustic waves on the surface to the hybridization of step a); and c) determining whether one or more nucleic acid probes from the solid support hybridized to test sample nucleic acid target molecules.

This method can also include a pre-hybridization step with non-specific nucleic acid, as described for the method above.

One or more washing steps can be applied after the pre-hybridization step or the hybridization step.

One or more washing steps may include applying acoustic waves to the surface.

With the application of acoustic waves, the pre-hybridization step can be limited to less than about 7 hours, more preferably less than about 5 hours and even more preferably less than about 3 hours.

The hybridization step to reach the nucleic acid can be less than about 3 hours, more preferably less than about 2 hours and even more preferably less than about 1 hour.

The washing steps are carried out for less than about 1 hour, more preferably less than about 30 minutes.

In a preferred embodiment, the method is carried out in less than about 9 hours, more preferably less than about 7 hours and even more preferably less than about 5 hours.

The method of hybridization to a target nucleic acid test molecule can further include one or more target nucleic acid target molecules. The test target molecules and / or reference nucleic acid can be labeled with a detectable agent. In some embodiments, the solid support may contain an array of immobilized nucleic acid probe molecules. In some embodiments, immobilized nucleic acid probe molecules may include the sequence of a bacterial artificial chromosome.

[548] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in US Patent Application Publication 2018/0142304, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include methods and compositions for detecting mutations that are predictive of the responsiveness of an individual diagnosed with breast cancer, colorectal cancer, melanoma or lung cancer to a specific therapeutic regimen. . In some modalities, the methods allow rapid and sensitive detection of mutations in the target nucleic acid sequences of AKT1, ERBB2, FOXL2, IDH2, NRAS, RET, ALK, ERBB4, GNA11, KIT, PDGFRA, SMO, BRAF, FBXW7, GNAQ , KRAS, PIK3CA, STK11, CTNNB1, FGFR2, GNAS, MAP2K1, PIK3R1, TP53, DDR2, FGFR3, HRAS, MET, PTCH1, EGFR, FGFR4, IDH1, NOTCH1 and PTEN. Additionally or alternatively, the detection of a genetic biomarker may include a method to detect at least one mutation in a plurality of cancer-related genes in an individual comprising (a) extracting genomic DNA from a tumor sample embedded in fixed paraffin in formalin obtained from the individual; (b) generate a library comprising amplicons corresponding to each of the plurality of cancer-related genes, said plurality of cancer-related genes comprising AKT1, ERBB2, FOXL2, IDH2, NRAS, RET, ALK, ERBB4, GNA11 , KIT, PDGFRA, SMO, BRAF, FBXW7, GNAQ, KRAS,

PIK3CA, STK11, CTNNB1, FGFR2, GNAS, MAP2K1, PIK3R1, TP53, DDR2, FGFR3, HRAS, MET, PTCH1, EGFR, FGFR4, IDH1, NOTCH1 and PTEN, where (i) generating said library occurs without the use of a set of baits comprising nucleic acid sequences that are complementary to at least one of the plurality of amplicons; and (ii) the quality of genomic DNA extracted from the tumor sample embedded in formalin-fixed paraffin is not assessed using quantitative PCR before the generation of the library; (c) attaching an adapter sequence to the ends of the plurality of amplicons; and (d) detecting at least one mutation in at least one of the plurality of amplifiers using massive high throughput parallel sequencing.

In some modalities of the method, the at least one mutation detected is a mutation in EGFR, KRAS, BRAF, NRAS, ERBB2 or PIK3CA.

In one embodiment, the at least one mutation detected is selected from the group consisting of BRAF V600E, BRAF V600K, BRAF K483Q, BRAF G466V, BRAF G464V, BRAF E501V, BRAF E501K, EGFR ΔE746_A750, EGFR R680Q, EGFR G598E, KRAS KRAS R68M, KRAS L19F, KRAS G12V, KRAS G12D, KRAS G12C, KRAS G13D, KRAS G13C, KRAS G12A, KRAS G12S, KRAS Q22K, NRAS Q61K, NRAS Q61R, NRAS G12R, NRAS G12D, PIK3CA C420R, PIK3CA C420R, PIK3CA , PIK3CA E453K, PIK3CA H1044R, PIK3CA N1044K, PIK3CA E545K, PIK3CA Q546H, PIK3CA H1047R, PIK3CA H1043L, PIK3CA ER104V, PIK3CA E542K, PIK3CA D42B, PIK3CA E510, PIK3CA T10B, PIK3CA T10B S255R, DDR2 H92Y, DDR2 R31L, DDR2 L34P, DDR2 P381R and DDR2 K392N.

In some modalities of the method, the library comprising amplicons corresponding to each of the plurality of cancer-related genes is generated using no more than 10 ng of genomic DNA extracted from the tumor sample embedded in formalin-fixed paraffin. In some modalities of the method, the library comprising amplicons corresponding to each of the plurality of cancer-related genes is generated using 11-25 ng of genomic DNA extracted from the tumor sample embedded in formalin-fixed paraffin. In certain modalities, massive high-throughput parallel sequencing is performed using pyrosequencing, reversible dye sequencing, SOLiD sequencing, ion semiconductor sequencing, Helioscope single molecule sequencing, synthesis sequencing, link sequencing or SMRT ™ sequencing. In some embodiments of the method, the adapter string is a P5 adapter, P7 adapter, P1 adapter, adapter A or Ion Xpress ™ barcode adapter. Additionally or alternatively, in some embodiments, the plurality of amplicons further comprises a unique index sequence. In some modalities, the tumor sample embedded in paraffin fixed in formalin is a heterogeneous tumor. In certain embodiments, 5% of the cells of the heterogeneous tumor harbor at least one mutation in at least one of the plurality of amplicons.

[549] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in US Patent Application Publication 2018/0051329, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker can methods for determining the presence of a variant in one or more genes in an individual, comprising: (a) providing raw sequencing data generated from a nucleic acid sequencing reaction in a sample of nucleic acid from the subject using a nucleic acid sequencer; (b) removing low quality readings from raw sequencing data that fail a quality filter; (c) trimming adapter strings and / or molecular identification (MID) strings from the filtered raw sequencing data; (d) map the raw filtered sequencing data to a genomic reference sequence to generate mapped readings; (e) classify and index the mapped readings; (f) adding reading groups to a data file to generate a processed sequence file; (g) creating targets for realigners; (h) perform local realignment of the processed sequence file to generate a realigned sequence file; (i) remove duplicate readings from the realigned sequence file; (j) analyze coding regions of interest; and (k) generate a report that identifies whether the variant is present based on the analysis in step (j), in which steps (g) and (j) are performed using a modified genomic alignment utility limited to regions of nucleic acid containing one or more genes of interest.

In some embodiments, the method comprises performing the nucleic acid sequencing reaction on the individual's nucleic acid sample using a nucleic acid sequencer to generate the raw sequencing data from step (a). In some modes, the analysis of coding regions of interest comprises calling variants at all positions in the regions of interest.

In some modalities, the regions of interest are filled by an additional 150 bases.

In some embodiments, the variant call is made with a modified GATK variant caller.

In some modalities, the mapping of readings to a genomic reference sequence is performed with a Burrows Wheeler Aligner (BWA). In some modalities, mapping the readings to a genomic reference sequence does not include smooth clipping.

In some modalities, the genomic reference sequence is the reference of the human genome GRCh37.1. In some embodiments, the sequencing method comprises emulsion PCR (emPCR), rotating circle amplification or solid phase amplification.

In some modes, solid-phase amplification is cloned bridge amplification.

In some embodiments, the nucleic acid for sequence analysis is extracted from a biological sample from an individual.

In some embodiments, the biological sample is a fluid or tissue sample.

In some modalities, the biological sample is a blood sample.

In some embodiments, the nucleic acid is genomic DNA.

In some embodiments, the nucleic acid is reverse cDNA transcribed from mRNA.

In some embodiments, in which the nucleic acid samples are prepared before sequencing, using one or more of the following methods: (a) shear nucleic acid; (b) concentrating the nucleic acid sample; (c) selecting the size of the nucleic acid molecule in a sheared nucleic acid sample; (d) repairing the ends of the nucleic acid molecules in the sample using a DNA polymerase; (e) attach one or more adapter sequences; (f) amplifying nucleic acids to increase the proportion of nucleic acids having an attached adapter sequence; (g) enriching the nucleic acid sample for one or more genes of interest; and / or (h) quantifying the nucleic acid sample primer immediately before sequencing.

In some embodiments, the one or more adapter sequences comprise nucleic acid sequences to initiate the sequencing reaction and / or a nucleic acid amplification reaction.

In some embodiments, the one or more adapter strings comprise a molecular identification tag (MID). In some embodiments, enrichment of the nucleic acid sample for one or more genes of interest comprises exon capture using one or more biotinylated RNA baits.

In some modalities, nucleic acid for analysis of sequences

frequency is obtained from an individual who is a mammal.

In some modes, the individual is a human patient.

In some modalities, the individual is a human suspected of having cancer or suspected of being at risk of developing cancer.

In some embodiments, the methods provided further comprise confirming the presence of one or more variants by sequencing.

In addition or alternatively, the detection of a genetic biomarker may include systems that comprise one or more electronic processors configured to: (a) remove low quality readings from the raw sequencing data that fail a quality filter; (b) trimming adapter strings and / or molecular identification (MID) strings from the filtered raw sequencing data; (c) map the filtered raw sequencing data to a genomic reference sequence to generate mapped readings; (d) classify and index the mapped readings; (e) adding reading groups to a data file to generate a processed sequence file; (f) creating goals for realigners; (g) perform local realignment of the processed sequence file to generate a realigned sequence file; (h) remove duplicate readings from the realigned sequence file; and (i) analyze coding regions of interest.

In some modalities, the analysis of coding regions of interest comprises calling variables in all positions in the regions of interest.

In some modalities, the regions of interest are filled by an additional 150 bases.

In some modalities, the variant call is made with a modified GATK variant caller.

In some models, the mapping of readings to a genomic reference sequence is performed with a Burrows Wheeler Aligner (BWA). In some modalities, mapping the readings to a genomic reference sequence does not include smooth clipping.

In some modalities, the genomic reference sequence is the human genome reference GRCh37.1. In addition or alternatively, the detection of a genetic biomarker may include non-transient, computer-readable media with instructions stored therein, instructions comprising: (a) instructions for removing low quality readings that fail a quality filter; (b) instructions for trimming adapter and MID strings from the filtered raw sequencing data; (c) instructions for mapping the filtered raw sequencing data to a geographic reference sequence to generate mapped readings; (d) instructions for classifying and indexing the mapped readings; (e) instructions for adding reading groups to a data file to generate a processed sequence file; (f) instructions for creating realigner destinations; (g) instructions for performing local realignment of the processed sequence file to generate a realigned sequence file; (h) instructions for removing duplicate readings from the realigned sequence file; and (i) instructions for analyzing coding regions of interest. In some modalities, the analysis of coding regions of interest comprises calling variants in all positions in the regions of interest. In some modalities, the regions of interest are filled by an additional 150 bases. In some embodiments, the variant call is made with a modified GATK variant caller. In some modalities, the mapping of readings to a genomic reference sequence is performed with a Wheeler Aligner Wheeler (BWA). In some modalities, mapping the readings to a genomic reference sequence does not include smooth clipping. In some modalities, the genetic reference sequence is the reference of the human genome GRCh37.1.

[550] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the Publication of the

U.S. Patent Application 2017/0316149, which is incorporated herein by reference in its entirety.

For example, the detection of a genetic biomarker may include devices, systems and methods for classifying genetic variables.

In some modalities, a standardized, rules-based process can provide a variant pathogenicity risk score based on clinical grade information in a CLIA-certified laboratory.

This standardized system can provide reliable pathogenicity scores for DNA variants found in a clinical laboratory setting.

In some modalities, a DNA sample can be obtained from a patient, who may or may not have been diagnosed with a disease or other medical condition.

From the sample, the patient's genome can be sequenced in whole or in part.

The result of the sequencing can then be compared, for example, to one or more reference genomes to identify variants in the patient's genome.

One or more of the variants can be compared to databases of known variants.

The result of this comparison may be the identification of one or more previously unknown variants, one or more known but unclassified variants, or both.

In some modalities, an unclassified variant can be assessed against one or more objective criteria.

For example, in a sport, a sport can be assigned an initial score.

The application of one or more objective criteria can cause additions and subtractions of the score, leading to a final score that can be used to classify the variant.

In some modalities, the classification of one or more previously classified variables can be revised, for example, periodically, to reassess the variants in the light of the new information obtained since the previous assessment.

In addition or alternatively, the detection of a genetic biomarker may include a method for assigning a score to a genetic variant, based on several scoring criteria and reflecting an estimate of the pathogenicity of the variant.

The method comprises identifying the variant in the sequenced DNA obtained from a patient and assigning an initial score to the variant, where the initial score is a unique numerical value that is associated with variants of unknown meaning.

In some modalities, the method also comprises: calculating a first score adjustment that is based on the objective assessment of minor evidence and amendment conditions; calculate a second score adjustment based on objective evidence of the frequency with which the variant occurs in a general population; calculate a third score adjustment based on objective evidence of the frequency with which the variant occurs in clinically characterized patients; calculate a fourth score adjustment based on objective evidence of the frequency with which the variant was co-occurring with one or more variants known to be pathogenic; calculate a fifth score adjustment based on objective evidence of a degree in which the variant exhibits segregation in one or more families; calculate an adjustment of the sixth score based on objective evidence of association between the variant and one or more disease phenotypes in the data that describe one or more families; and calculating an adjustment of the seventh score based on objective evidence about whether the variant affects the functions of one or more proteins that are known to be associated with the disease.

In some modalities, the method also comprises calculating from a variant score based on the initial value, the first score adjustment, the second score adjustment, the third score adjustment, the fourth score adjustment, the fourth adjustment score, the fifth score adjustment, the sixth score adjustment and the seventh score adjustment, the variant score being a single numerical value.

And the method comprises attributing the variant to an assigned classification based only on the variant's score, where the assigned classification is part of a group consisting of a plurality of classifications, with each classification in the plurality associated with a respective different assessment of the pathogenicity of the variant.

[551] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in US Patent Application Publication 2017/0009287, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include apparatus, systems and methods for detecting variations in the number of copies of genetic subsequences in an organism's genome. According to some modalities, samples of genetic material, including DNA, can be collected from several patients. Sections of the patient's DNA can then be sequenced, for example, through a process that includes, for each patient, purification, concentration and fragmentation of the patient's DNA. Each fragment can receive a molecular marker that identifies the patient from whom the DNA was received and the DNA can be modified in preparation for sequencing (for example, through one or more steps that may include one or more of the following items: filtration, amplification and modification, such as attaching primers to fragments). The fragments of several patients can be brought together and the set can include, for example, one or more controls that comprise known genetic material. The fragments in the set can then be sequenced and the results of the sequencing can be stored, for example, as one or more computer files. The results can be processed, for example, by one or more computer systems, to identify possible variations in the number of copies in the patients from whom the samples were collected. For example, in some modes, sequences can be demultiplexed to identify the respective patients whose DNA each sequence represents. The samples from each patient can then be aligned to a reference genome, and the coverage for each base pair in each region of interest in the patient's genome can be determined.

From the base pair coverage, the coverage of one or more subunits of the patient's genome can then be determined.

For example, in some modalities, the coverage of several exons can be determined.

In some modalities, coverage measurements can then go through one or more standardization steps.

For example, the average coverage of one or more amplicons known in autosomes can be compared with the average coverage of one or more amplicons known to be on the X chromosome to provide an approximate estimate of the number of X chromosomes (namely, one or two) in the patient's karyotype.

If the patient is determined to have only one X chromosome, normalization may include double the coverage of all amplicons known to come from the X chromosome.

After standardization, reference values can be calculated for each amplicon and CNV can be detected by comparing the actual coverage values of each patient's amplicons with the calculated reference values.

In addition or alternatively, the detection of a genetic biomarker may include a method for detecting copy number variation (CNV) in the DNA of a plurality of patients.

The method comprises receiving a plurality of samples, each sample containing DNA from a single patient and from each sample, generating a plurality of DNA fragments.

The method also comprises the barcode of each of the fragments with an identifier that uniquely identifies the respective patient from whom the DNA was received, group the plurality of samples in a DNA library and submit the DNA library to one or more more filtering stages to increase the relative concentration of fragments within a plurality of selected regions of interest.

In some modalities,

the method also comprises producing sequencing data for the plurality of patients, sequencing the filtered DNA library, demultiplexing the sequencing data and, for each patient, generating coverage data identifying, for each of the regions of interest, the coverage of each region of interest in the sequencing data.

In some modalities, the method comprises generating normalized coverage data from the coverage data and generating reference coverage, common to all samples, for each region of interest, with the generation of reference coverage based on the data. standard coverage.

In some modalities, the method also comprises the automatic detection of CNV for at least one subsequence of at least one of the regions of interest of at least one of the patients, based on the comparison of the reference coverage with the coverage data normalized and in the output supply that identifies the patient, the subsequence and the CNV.

In some modalities, generating normalized coverage data from the coverage data comprises generating raw coverage data for each patient, generating at least an estimate of the number of the patient's X chromosomes based on the coverage data and dimensioning the coverage of the patient. patient of at least one region of interest who is known to be X-linked and also comprises generating normalized coverage data from the raw coverage data.

In some modalities, the generation of the X chromosome number estimate for each patient occurs without reference to any demographic information about the respective patient and without reference to any information about the respective patient's phenotype.

Alternatively, in some modalities, the method comprises, for each patient, generating a second estimate of the patient's X chromosome number based on the standardized coverage data and also comprises reviewing the standardized coverage data based on the second estimates.

In some modalities, the generation of the second estimate of the number of X chromosomes for each patient occurs without reference to any demographic information about the respective patient and without reference to any information about the phenotype of the respective patient.

In some modalities, the coverage data comprises base coverage for each region of interest in the sequencing data; Generating raw coverage data includes scaling the coverage based on the patient from at least one region of interest that is known to be linked to X; standardized coverage data includes coverage by base; the reference coverage comprises base coverage for each position within each region of interest; automatic CNV detection is based on base reference coverage and standardized coverage data.

In some modalities, each region of interest corresponds to exactly one exon and contains that exon.

In some modes, the method comprises automatically detecting CNV for a plurality of adjacent exons within a patient's gene, each of the adjacent exons having the same CNV and automatically curling the CNV of the adjacent exons.

In addition, in one mode, the method comprises automatic CNV detection for all exons within a patient's gene, each exon within the gene having the same CNV, and automatic CNV aggregation of exons within of the gene into a single CNV for the gene.

In some modalities, the method comprises submitting the DNA library to one or more stages of amplification, so that each region of interest is an amplicon.

In some modalities, the coverage data comprises base coverage for each region of interest in the sequencing data; standardized coverage data includes coverage by base; the reference coverage comprises base coverage for each position within each region of interest; automatic CNV detection is based on base reference coverage and standardized coverage data.

[552] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in US Patent Application Publication 2009/0088328, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include a method for determining the print quality of nucleic acid arrays and providing methods for determining the efficiency of procedures for blocking non-specific binding in nucleic acid arrays. In addition or alternatively, the detection of a genetic biomarker may include the use of fluorescence detection to assess the quality of a printed nucleic acid array without the need to add or otherwise bind a fluorescent compound or dye to the nucleic acid. Suitable nucleic acid arrays for this analysis are those in which the points of the array are formed by printing a solution containing the nucleic acid and one or more ions. Thus, the arrangement is formed from nucleic acid in an ionic solution and the quality of the print is evaluated by the fluorescence associated with each printed dot. The quality of the impression can be assessed by measuring the intensity of fluorescence at the location of each printed sample and / or by measuring the "morphology" (ie shape) of the printed sample. The printed dots can be “imaged” by measuring fluorescence in a speckled sample in two dimensions. The image resulting from a printed dot can be compared with an expected printed reference image for the printing equipment and the solid phase used. The methods can be used to determine the quality of specific points in an arrangement, to determine the quality of specific regions in an array.

to determine the quality of an arrangement as a whole. The punctual quality and / or the quality of the arrangement can be detected immediately after printing the arrangement or after the arrangement is subjected to the processing steps before hybridization. Such steps may include exposing the arrangement to heat, humidity, UV irradiation, a blocking and / or washing procedure. In the event that the quality of a blocking step for non-specific binding is performed, the quality of the blocking can be determined by measuring the fluorescence in each sample loaded before and after a blocking procedure. A decrease in fluorescence after the washing and / or blocking procedure indicates the efficiency of the blocking and / or washing step. In addition or alternatively, the detection of a genetic biomarker may include a method for determining the print quality of a nucleic acid array prior to hybridization, said method comprising: (a) printing an array of nucleic acid samples onto a solid support, each sample comprising acid in an ionic solution; and (b) detecting the fluorescence of the printed samples to determine the print quality.

[553] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in US Patent Application Publication 2006/0292576, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include methods of detecting and analyzing chromosomal abnormalities of interest in a test sample. In preferred embodiments, the nucleic acids in a test sample are hybridized with two probes complementary to different segments of a gene of interest or different segments to a chromosomal fragment of interest. One probe is anchored to the solid support, while the second probe comprises a detectable marker that is used for detection.

This method provides for the capture and detection of target nucleic acids that hybridize with both probes simultaneously.

Hybridization of the first and second probes to the same target nucleic acid indicates the detection of a chromosomal abnormality in the target nucleic acid, whereas hybridization of only one of the probes to the same target nucleic acid indicates the absence of a genetic abnormality in the target nucleic acid.

The anchored probe can be anchored covalently or non-covalently to the support.

If non-covalent bonding is used, a preferred method is through a "bonding pair", which refers to two molecules that form a complex through a specific interaction.

Thus, the nucleic acid probe can be captured on the solid support through an interaction between one member of the binding pair attached to the probe and the other member of the binding pair attached to the solid support.

A binding pair member can also be used to attach the detectable marker to another nucleic acid probe.

In a preferred embodiment, the binding pair is biotin and avidin or streptavidin.

In other embodiments, the binding pair is composed of a ligand receptor, a hormone receptor, an antigen antibody or an oligonucleotide complement.

In some embodiments, the two probes can be hybridized to the target nucleic acid in a liquid, and then the complex can be captured by a solid support.

The probe anchored in this approach is preferably anchored non-covalently and preferably through a connection pair.

In other variants, the solid support may first comprise the anchored probe, which is then counted for hybridization with the target nucleic acid, either alone or in conjunction with the labeled probe.

In addition or alternatively, the detection of a genetic biomarker may include methods for detecting the presence or absence of a genetic abnormality in a target nucleic acid in a test sample.

The method includes forming on a solid support a complex comprising the target nucleic acid, a first nucleic acid probe hybridizing to a first segment of the target nucleic acid, the first nucleic acid probe labeled with a detectable marker and a second nucleic acid probe hybridizing to a second segment of the target nucleic acid, the second nucleic acid probe anchored to the solid support.

The complex is detected by the detection of a built-in detectable marker, in which the hybridization of the first and second probes to the same target nucleic acid indicates the presence of genetic abnormality in the target nucleic acid, while the hybridization of only one of the probes in the same acid target nucleus indicates the absence of a genetic abnormality in the target nucleic acid.

In addition or alternatively, the detection of a genetic biomarker may include methods for detecting a chromosomal translocation in the nucleic acid of a test sample.

The method includes hybridizing two nucleic acid probes, one complementary to a sequence of the donor chromosome segment and the other complementary to a sequence of the recipient chromosome that joins or is close to the inserted donor chromosome segment.

One probe is anchored to the support and the other probe is marked with a detectable marker.

A genomic DNA test sample that hybridizes with both probes will form a complex on the support or that complex will be preformed and then captured on a solid support and detected through the detectable marker.

The amount of labeled complex captured from the test sample represents the test value.

If the test value shows that the marker is associated with the captured hybridization complexes, the test sample is determined to contain the chromosomal translocation.

In one embodiment, you can compare the test value for the test sample with a test value for a reference sample that contains the target gene, but without translocation.

In addition or alternatively, the detection of a genetic biomarker may include methods to detect a duplication or exclusion in a specific target region or chromosomal gene in an individual.

The method includes forming on a solid support a complex comprising the nucleic acid associated with the specific chromosomal region or gene that is obtained from the sample, a labeled nucleic acid probe that hybridizes to a first segment of the region or chromosomal gene specific and a second nucleic acid probe that hybridizes to a second segment of the particular chromosomal region or gene, wherein the second nucleic acid probe is anchored to the solid support.

In a preferred embodiment, the target nucleic acid is the genomic DNA that has been fragmented.

The amount of labeled complex captured from the test sample represents the test value.

The test value can be compared to a control value that can be obtained from the amount of complex obtained from a different target gene or chromosomal region, preferably from the same sample.

A higher test value compared to the control value is indicative of duplication or amplification, while a lower test value compared to the control value is indicative of a chromosomal or genetic exclusion.

In another approach, you can determine a ratio of the test value of the sample to the control value in that sample and compare it with a similar ratio that represents the test value and the control value of a reference sample containing acid nucleic acid that does not contain a deletion, duplication or amplification in the chromosomal region or gene of interest.

In addition or alternatively, the detection of a genetic biomarker may include methods for determining the diagnosis, predicting response to therapy, detecting minimal residual disease or prognosis of a disease in an individual.

In this method, a complex is formed between a target nucleic acid from a test sample, a probe comprising a detectable marker and hybridizing to a segment of a target nucleic acid and a second probe anchored in the support and hybridizing to a second segment of the target nucleic acid.

The amount of complex in the solid support is measured by detecting the detectable marker incorporated from the first probe.

The amount of complex formed is compared with the amount of complex formed in a similar way from a sample obtained from a reference sample.

The reference sample can be obtained from a normal individual, in which a difference between the measurements of the test and reference samples is correlated with the diagnosis or prognosis of a disease.

In addition or alternatively, the detection of a genetic biomarker may include methods for monitoring the treatment or progression of a disease.

In this method, samples are taken from a patient at different points in time (for example, before and after a disease treatment regimen). A complex is formed between a target nucleic acid from the first test sample, a probe comprising a detectable marker and hybridizing to a segment of a target nucleic acid and a second probe anchored in the support and hybridizing to a second segment of a nucleic acid target.

The amount of complex in the support of the first sample is compared with the amount of complex formed using the same probes and target nucleic acid as in the second sample.

A difference in the amount of complex formed can be correlated with the progression of the disease or the success of the treatment regimen.

In addition or alternatively, the detection of a genetic biomarker may include methods for measuring the tumor burden in an individual.

In this method, a complex is formed on a solid support between a target nucleic acid from a test sample, a probe comprising a detectable marker and hybridizing to a segment of a target nucleic acid and a second probe anchored on the support and hybridize to a second segment of the target nucleic acid. The amount of complex in the solid support is measured by detecting the detectable marker incorporated from the first probe. The amount of complex formed is compared to a reference value or set of values of the amount of complex formed in a similar way from a sample obtained from a reference sample, from a patient whose tumor burden is known, to determine the tumor burden of the test sample. In some embodiments, methods for determining tumor burden include the formation of two complexes on solid support. The first complex comprises a first target nucleic acid from a test sample from the subject and two nucleic acid probes; one containing a detectable marker and the other anchored to the support. The second complex comprises a second target or control sample nucleic acid and two different nucleic acid probes, one containing a detectable marker, distinguishable from the first complex marker and the other probe anchored on the solid support . The quantity of each of the two complexes is measured and a test ratio is determined. This ratio is then compared to a reference ratio or set of reasons that correlate the test ratio to the tumor burden.

[554] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in US Patent Application Publication 2006/0127918, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include a substrate containing nucleic acids that includes: (a) a surface pretreated with organosilane; (b) a polymer film cross-linked to the surface pretreated with organosilane; and (c) a nucleic acid molecule attached to one or more polymeric films and to the organosilane pretreated surface.

In preferred embodiments, the polymer film is formed from a polymer comprising reactive groups, and the nucleic acid molecule has not been covalently modified to facilitate covalent bonding to the reactive groups.

The nucleic acid molecule can be associated or linked to one or more polymer films and to the surface pretreated with organosilane through covalent and / or non-covalent interactions.

In preferred embodiments, the nucleic acid molecule is at least about 250 nucleotides in length and more preferably at least about 500 nucleotides in length.

In one embodiment, the nucleic acid molecule is a DNA molecule present in the form of a bacterial artificial chromosome (BAC) or another suitable cloning vector (for example, an E. coli P1-based artificial chromosome, a plasmid, a cosmid and the like). In some embodiments, the bound nucleic acid molecule is present on the substrate surface at a concentration sufficient to detect a target nucleic acid molecule by the nucleic acid hybridization methodology.

For example, the nucleic acid molecule may be present at a concentration of at least about 500 copies / cm2 on the substrate surface.

More suitably, the nucleic molecule is present on the substrate surface at a concentration of at least about 1000 copies / cm2 and / or at least about 5000 copies / cm2. The nucleic acid molecule preferably remains substantially bound to the substrate when subjected to washing under high stringency conditions (for example, when the slide is washed with a low salt buffer, optionally including a non-ionic detergent at a relatively high temperature) high). In some embodiments, organosilane is a modified silane molecule that includes alkyl groups.

In one embodiment, the organosilane includes alkyl groups with six or more carbon atoms and preferably ten or more carbon atoms.

Organosilane can include alkoxy groups.

Organosilane can also include halide groups.

In some modalities, the polymer comprises reactive groups.

Suitable reactive groups include electrophilic groups that react with nucleophilic groups under suitable conditions.

For example, reactive groups can include amino-reactive groups (that is, groups that react with the nitrogen atom of an amino group), thiol-reactive groups (that is, groups that react with the sulfur atom of a thiol group) , groups reactive to hydroxyl (that is, groups that react with the oxygen atom of a hydroxyl group) and combinations thereof.

In some embodiments, the polymer may include activated esters, epoxides, azlactones, activated hydroxides, aldehydes, isocyanates, thioisocyanates, carboxylic acid chlorides, alkyl halides, maleimide, α-iodoacetamides or combinations thereof.

In one embodiment, the reactive group is an activated ester and, in particular, the activated ester can include an N-hydroxylsuccinimide ester.

In some embodiments, the nucleic acid-containing substrate is configured as a nucleic acid microarray.

The nucleic acid microarray may be suitable to perform comparative analyzes of genomic hybridization.

In one embodiment, the nucleic acid microarray comprises genomic DNA cloned into bacterial artificial chromosomes (BACs). In addition or alternatively, the detection of a genetic biomarker may include a method for preparing a substrate containing nucleic acid, as described above.

The method typically includes: (a) pretreating a substrate surface with a composition that includes an organosilane; (b) coupling a polymer to the surface pretreated with organosilane to form a polymer film; and (c) attaching a nucleic acid molecule to one or both surfaces pretreated with organosilane and the polymer film.

In preferred embodiments, the polymer film is formed from a polymer that comprises

it comprises reactive groups, and the nucleic acid has not been modified covalently to facilitate covalent bonding in the reactive groups. The nucleic acid molecule can be associated and / or linked to one or more polymer films and to the surface pretreated with organosilane through covalent and / or non-covalent interactions. Additionally or alternatively, the detection of a genetic biomarker may include a method for detecting the presence and / or quantity of a target nucleic acid molecule in a sample that includes: a) contacting the target molecule with a substrate containing nucleic acid, which it is prepared as described above, under conditions suitable to hybridize the target to the substrate nucleic acid; and b) detecting the presence of the target molecule bound to the substrate. In preferred embodiments, the nucleic acid-containing substrate is a nucleic acid microarray, and detection of the presence and / or amount of the nucleic acid target is performed using comparative genomic hybridization analysis.

[555] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include any of the various forms described in PCT Publication WO 2018/125892, which is incorporated herein by reference in its entirety . For example, the detection of a genetic biomarker may include methods and compositions of polynucleotide adapters related to the detection of mutations in ctDNA present in samples derived from an individual diagnosed as having or suspected of having cancer. In some embodiments, the methods allow rapid and sensitive detection and profile of ctDNA mutations in various target nucleic acid sequences in the exons and / or introns of one or more cancer-related genes, including, but not limited to a, ALK, BRAF, EGFR, ERBB2, KIT, KRAS, MET, NRAS, NTRK1, PIK3CA, ROS1, and RET The methods provide a framework for the ultrasensitive ctDNA profile obtained using accurate analytical models of detection limits.

These qualities improve the detection limits compared to the previous methods for samples with limited DNA content.

In addition or alternatively, the detection of a genetic biomarker may include a nucleic acid adapter comprising a first oligonucleotide strand and a second oligonucleotide strand, wherein (a) the first oligonucleotide strand (i) comprises a first proximal and a first distal region, where the first proximal region comprises a first sequence of unique molecular identifier and a first spacer sequence with the sequence 5'TGACT 3 '(SEQ ID NO :), where the first spacer sequence is localized 3 'to the first unique molecular identifier sequence and (ii) does not comprise a degenerate or semi-degenerate sequence; (b) the second oligonucleotide strip (i) comprises a second proximal region and a second distal region, wherein the second proximal region comprises a second sequence of unique molecular identifier and a second spacer sequence with the sequence 5'GTCA 3 '(SEQ ID NO :), where the spacer sequence is located 5' for the second unique molecular identifier; and (ii) does not comprise a degenerate or semi-degenerate sequence; (c) the first proximal region of the first oligonucleotide strip hybridizes with the second proximal region of the second oligonucleotide strip; and (d) the first distal region of the first oligonucleotide strip is not hybridized with the second distal region of the second oligonucleotide strip.

In some embodiments of the nucleic acid adapter, the "T" nucleotide located at the 3 'end of the first spacer sequence contains a phosphorothioate bond.

In some embodiments of the nucleic acid adapter, the 5 'end of the first oligonucleotide strand is marked with biotin.

In other nucleic acid adapter modes, the 3 'end of the second oligonucleotide strand is marked with biotin.

In some modalities,

the nucleic acid adapter is used to sequence a double-stranded target nucleic acid molecule selected from the group consisting of double-stranded DNA or double-stranded RNA.

Double-stranded DNA can be sheared genomic DNA or cellless DNA.

In some embodiments, the nucleic acid adapter of the present technology further comprises at least two binding sites to the PCR primer, at least two binding sites to the sequencing primer, or any combination thereof.

In addition or alternatively, in some embodiments, the nucleic acid adapter of the present technology further comprises a sample-specific barcode sequence, wherein the sample-specific barcode sequence comprises 2-20 nucleotides.

In addition or alternatively, the detection of a genetic biomarker may include a method to detect at least one mutation in a dual circulating viral DNA (ctDNA) molecule present in a sample obtained from a patient who comprises (a) binding a plurality of adapters for both ends of the double-stranded ctDNA molecule to form a double-stranded adapter-ctDNA complex, each Y-shaped adapter comprising a first oligonucleotide strand and a second oligonucleotide strand; (b) amplifying both strands of the adapter-ctDNA complex to produce first amplicons and second amplicons, where the first amplicons are derived from the first oligonucleotide strand and the second amplicons are derived from the second oligonucleotide strand; (c) sequencing the first and second amplicons; (d) detecting at least one mutation in the double-stranded ctDNA molecule, when a mutation detected in the first amplicons is consistent with a mutation detected in the second amplicons.

In some modalities of the method, the patient is diagnosed with ovarian cancer, breast cancer, colon cancer, lung cancer, prostate cancer, gastric cancer, pancreatic cancer, cervical cancer, liver cancer, bladder cancer, urinary tract cancer, thyroid cancer, kidney cancer, carcinoma, melanoma, head and neck cancer or brain cancer.

In some embodiments, the method further comprises enriching first amplicons and second amplifiers with a plurality of bait sequences, wherein the plurality of bait sequences comprises at least one genetic region that corresponds to each of a plurality of genes related to the cancer.

The plurality of cancer-related genes can comprise ALK, BRAF, EGFR, ERBB2, KIT, KRAS, MET, NRAS, NTRK1, PIK3CA, ROS1 and RET.

Additionally or alternatively, in some embodiments of the method, the plurality of bait sequences are RNA baits, DNA baits or a mixture of RNA baits and DNA baits.

In certain embodiments, the plurality of bait sequences comprises a 1: 1 mixture of RNA baits and DNA baits.

In other embodiments, the plurality of bait sequences comprises a mixture of RNA baits and DNA baits with a ratio of 2: 1, 1.5: 1, 0.75: 1 or 0.5: 1. In certain embodiments of the method, both 3 'ends of the double-stranded ctDNA molecule further comprise an "A" excess. In any of the above embodiments, each Y-shaped adapter further comprises at least two binding sites to the sequencing primer.

In addition or alternatively, in some embodiments, each Y-shaped adapter further comprises a patient-specific bar code sequence, wherein the patient-specific bar code sequence comprises 2-20 nucleotides.

Each Y-shaped adapter of the present technology can be labeled with biotin.

In some embodiments of the method, the sample comprises no more than 5 ng of DNA without cells.

In other embodiments, the sample comprises at least 6-30 ng of DNA without cells.

In certain modalities, the sample is whole blood,

serum, plasma, synovial fluid, lymphatic fluid, ascites fluid or interstitial fluid.

[556] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in U.S. Patent

9,389,234, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include methods and systems for creating a molecular profile and using the results of creating a molecular profile to identify treatments for individuals. In some modalities, treatments were not initially identified as a treatment for the disease. In addition or alternatively, the detection of a genetic biomarker may include a method for identifying a candidate for treatment for an individual in need, comprising: performing an immunohistochemical analysis (IHC) on a sample of the individual to determine an IHC expression profile on at least five proteins; perform a microarray analysis on the sample to determine a microarray expression profile in at least ten genes; perform a fluorescent in situ hybridization (FISH) analysis on the sample to determine a FISH mutation profile in at least one gene; perform DNA sequencing on the sample to determine a sequence mutation profile in at least one gene; and to compare the IHC expression profile, microarray expression profile, FISH mutation profile and sequencing mutation profile in a rules database. The rules database comprises a mapping of treatments whose biological activity is known against cancer cells that: i. overexpress or underexpress one or more proteins included in the IHC expression profile; ii. overexpress or overexpress one or more genes included in the microarray expression profile; iii. not having mutations or one or more mutations in one or more genes included in the FISH mutation profile; and / or iv. not having mutations or one or more mutations in one or more genes included in the sequencing mutation profile.

Candidate treatment is identified if: i. the comparison stage indicates that the treatment must have biological activity against cancer; and ii. the comparison stage does not contraindicate treatment for cancer treatment.

In some modalities, the IHC expression profile comprises the analysis of one or more SPARC, PGP, Her2 / neu, ER, PR, c-kit, AR, CD52, PDGFR, TOP2A, TS, ERCC1, RRM1, BCRP, TOP1 , PTEN, MGMT, and MRP1. In some modalities, the microarray expression profile comprises the analysis of one or more of ABCC1, ABCG2, ADA, AR, ASNS, BCL2, BIRC5, BRCA1, BRCA2, CD33, CD52, CDA, CES2, DCK, DHFR, DNMT1, DNMT3A, DNMT3B, ECGF1, EGFR, EPHA2, ERBB2, ERCC1, ERCC3, ESR1, FLT1, FOLR2, FYN, GART, GNRH1, GSTP1, HCK, HDAC1, HIF1A, HSP90AA1, ILPRA LYN, MGMT, MLH1, MS4A1, MSH2, NFKB1, NFKB2, OGFR, PDGFC, PDGFRA, PDGFRB, PGR, POLA1, PTEN, PTGS2, RAF1, RARA, RRM1, RRM2, RRM2B, RXRB, SRX, RXR SSTR2, SSTR3, SSTR4, SSTR5, TK1, TNF, TOP1, TOP2A, TOP2B, TXNRD1, TYMS, VDR, VEGFA, VHL, YES1, and ZAP70. In some modalities, the FISH mutation profile comprises the analysis of EGFR and / or HER2. In some modalities, the sequencing mutation profile comprises the analysis of one or more KRAS, BRAF, c-KIT and EGFR.

In addition or alternatively, the detection of a genetic biomarker may include a method to identify a candidate for treatment for an individual in need, comprising: performing an immunohistochemical analysis (IHC) on a sample of the individual to determine an expression profile of IHC in at least five of: SPARC, PGP, Her2 / neu, ER, PR, c-kit, AR, CD52, PDGFR, TOP2A, TS, ERCC1, RRM1, BCRP, TOPO1, PTEN, MGMT, and MRP1; perform a microarray analysis on the sample to determine a microarray expression profile in at least five of: ABCC1, ABCG2, ADA, AR, ASNS, BCL2, BIRC5, BRCA1, BRCA2, CD33, CD52, CDA, CES2, DCK , DHFR, DNMT1, DNMT3A, DNMT3B, ECGF1, EGFR, EPHA2, ERBB2, ERCC1, ERCC3, ESR1, FLT1, FOLR2, FYN, GART, GNRH1, GSTP1, HCK, HDAC1, HIF1A, HSPRAAA, KDR , LCK, LYN, MGMT, MLH1, MS4A1, MSH2, NFKB1, NFKB2, OGFR, PDGFC, PDG- FRA, PDGFRB, PGR, POLA1, PTEN, PTGS2, RAF1, RARA, RRM1, RRM2, RRM2B, RXRBR , SRC, SSTR1, SSTR2, SSTR3, SSTR4, SSTR5, TK1, TNF, TOP1, TOP2A, TOP2B, TXNRD1, TYMS, VDR, VEGFA, VHL, YES1, and ZAP70; perform a fluorescent in situ hybridization (FISH) analysis on the sample to determine a FISH mutation profile in EGFR and / or HER2; perform DNA sequencing on the sample to determine a sequence mutation profile in at least one of KRAS, BRAF, c-KIT and EGFR; and to compare the IHC expression profile, microarray expression profile, FISH mutation profile and sequencing mutation profile in a rules database.

The database of rules comprises a mapping of treatments whose biological activity is known against cancer cells that: i. overexpress or underexpress one or more proteins included in the IHC expression profile; ii. overexpress or underexpress one or more genes included in the microarray expression profile; iii. not having mutations or one or more mutations in one or more genes included in the FISH mutation profile; and / or iv. have no mutations or one or more mutations in one or more genes included in the sequencing mutation profile.

The candidate treatment is identified if: i. the comparison stage indicates that the treatment must have biological activity against cancer; and ii. the comparison stage does not contraindicate treatment for cancer treatment.

In some modalities, the profile of HCI expression is performed in at least 50%,

60%, 70%, 80% or 90% of the biomarkers listed.

In some modes, the microarray expression profile is carried out in at least 50%, 60%, 70%, 80% or 90% of the listed biomarkers.

Additionally or alternatively, the detection of a genetic biomarker may include a method to identify a cancer treatment candidate in an individual in need of it, including: performing an immunohistochemical analysis (IHC) in a sample of the individual to determine an IHC expression profile in at least the protein group consisting of: SPARC, PGP, Her2 / neu, ER, PR, c-kit, AR, CD52, PDGFR, TOP2A, TS, ERCC1 , RRM1, BCRP, TOPO1, PTEN, MGMT, and MRP1; perform microarray analysis on the sample to determine a microarray expression profile in at least the group of genes consisting of ABCC1, ABCG2, ADA, AR, ASNS, BCL2, BIRC5, BRCA1, BRCA2, CD33, CD52, CDA, CES2 , DCK, DHFR, DNMT1, DNMT3A, DNMT3B, ECGF1, EGFR, EPHA2, ERBB2, ERCC1, ERCC3, ESR1, FLT1, FOLR2, FYN, GART, GNRH1, GSTP1, HCK, HDAC1, HIF1A, HSAA90 , KIT, LCK, LYN, MGMT, MLH1, MS4A1, MSH2, NFKB1, NFKB2, OGFR, PDGFC, PDGFRA, PDGFRB, PGR, POLA1, PTEN, PTGS2, RAF1, RARA, RRM1, RRM2, RRM2B, RXR , SRC, SSTR1, SSTR2, SSTR3, SSTR4, SSTR5, TK1, TNF, TOP1, TOP2A, TOP2B, TXNRD1, TYMS, VDR, VEGFA, VHL, YES1, and ZAP70; perform a fluorescent in situ hybridization (FISH) analysis on the sample to determine a FISH mutation profile in at least the group of genes consisting of EGFR and HER2; perform DNA sequencing on the sample to determine a sequencing mutation profile in at least the group of genes consisting of KRAS, BRAF, c-KIT and EGFR; and to compare the IHC expression profile, microarray expression profile, FISH mutation profile and sequencing mutation profile in a rules database.

The rules database comprises a mapping of treatments whose biological activity is known against cancer cells that: i. overexpress or underexpress one or more proteins included in the IHC expression profile; ii. overexpress or underexpress one or more genes included in the microarray expression profile; iii. have more mutations in one or more genes included in the FISH mutation profile; and / or iv. have zero or more mutations in one or more genes included in the sequencing mutation profile. Candidate treatment is identified if: i. the comparison stage indicates that the treatment must have biological activity against cancer; and ii. the comparison stage does not contraindicate treatment for cancer treatment. In some modalities, the microarray expression profile is performed using a low density microarray, an expression microarray, a comparative genomic hybridization microarray (CGH), a single nucleotide polymorphism microarray (SNP), a proteomic arrangement or an array of antibodies.

[557] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in US Patent Application Publication 2018/0171337, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include a method, comprising: a) determining a molecular profile for at least one sample of the individual, evaluating a plurality of genes and / or genetic products; and b) identify, based on the molecular profile, at least one of: i) at least one treatment that is associated with the cancer treatment benefit; ii) at least one treatment that is associated with a lack of benefit for the treatment of cancer; and iii) at least one treatment associated with a clinical trial. The plurality of genes and / or genetic products can be chosen from genes and / or genetic products (for example,

transcripts and proteins) with efficacy known to be related to several chemotherapeutic agents. In addition or alternatively, the detection of a genetic biomarker can include mutational analysis performed on any desired gene panel. In one embodiment, assessing the plurality of genes and / or genetic products further comprises the mutational analysis of at least one, for example, at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36 , 37, 38, 39, 40, 41, 42, 43, 44, 45 or 46, from ABL1, AKT1, ALK, APC, ATM, BRAF, BRCA1, BRCA2, CDH1, CSF1R, CTNNB1, EGFR, ERBB2 (HER2) , ERBB4 (HER4), FBXW7, FGFR1, FGFR2, FLT3, GNA11, GNAQ, GNAS, HNF1A, HRAS, IDH1, JAK2, JAK3, KDR (VEGFR2), KIT (cKIT), KRAS, MET (cMET), MPL, NOTCH1 , NPM1, NRAS, PDGFRA, PIK3CA, PTEN, PTPN11, RB1, RET, SMAD4, SMARCB1, SMO, STK11, TP53 and VHL. Mutational analysis can understand any useful combination of these genes. Mutational analysis can be used to evaluate at least one, for example, at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 of a mutation, a polymorphism , an exclusion, an insertion, a substitution, a translocation, a fusion, a break, a duplication, an amplification, a repetition, a variation in the number of copies, a transcription variant and an amendment variant. Mutational analysis can be performed using any useful laboratory method or combination of methods. For example, mutational analysis can be performed using at least one of ISH, amplification, PCR, RT-PCR, hybridization, microarray, sequencing, pyrosquencing, Sanger sequencing, high throughput or next generation sequencing (NGS), analysis of fragments or RFLP. In some modes, mutational analysis comprises Next Generation Sequencing.

[558] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include any of the various forms described in US Patent Application Publication 2017/0175197, 2017/0039328 and 2015 / 0307947 and PCT Publication WO 2012/092336, which are incorporated herein by reference in their entirety.

[559] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include any of the various forms described in PCT Publication WO 2017/053915, which is incorporated herein by reference in its entirety . For example, the detection of a genetic biomarker can include systems, methods, devices and computer program products to provide a user interface for an application to analyze biological data. In addition or alternatively, the detection of a genetic biomarker may include a method of analyzing biological data, the method comprising: receiving, in a computing device comprising a processor and memory, patient data for a plurality of patients, the data of the patient corresponding to at least one of a biological sampling event, a biological processing event, at least one therapeutic regimen, at least one biomarker status and one patient status; determine at least one interrelation between any of the biological sampling events, the biological processing event, the at least one therapeutic regimen, the at least one biomarker status and the patient's status; perform a therapeutic regimen analysis to determine an interrelated status for the interrelated between at least one therapeutic regimen and at least one patient status and at least one biomarker status; and display at least one graphical interface on a user interface in communication with the computing device, the graphical interface including a plurality of visual elements, each visual element of the plurality of visual elements being associated with patient data, at least one element- visual development being associated with at least one interrelation, at least one visual element including an indicator corresponding to at least one of the interrelated statuses and the biomarker status.

In addition or alternatively, the detection of a genetic biomarker may include a method for analyzing biological data associated with a biological sample from a target patient, the method comprising: receiving, in a computing device comprising a processor and memory, data patient data associated with the target patient, patient data corresponding to a biological sampling event, a biological processing event, a therapeutic regimen, a marker status and a patient status; receiving reference data associated with a plurality of patients, reference data corresponding to a plurality of biological sampling events, biological processing events, therapeutic regimens, marker status and patient status; determine at least one interrelation between any of the biological sampling events, the biological processing events, the therapeutic regimens, the status of the markers and the status of the patients; perform a therapeutic regimen analysis to determine the interrelationship between at least one therapeutic regimen and at least one of at least one patient status and at least one marker status; display at least one graphical user interface, the graphical user interface configured to: i) display a plurality of graphical user interface objects associated with the reference data, ii) display a plurality of associated graphical user interface objects to patient data, iii) display, on at least one graphical interface on a user interface in communication with the computing device, a graphical interface object of the main user configured to, upon receiving an indication of an entry the user who defines a selection of the primary graphical user interface object, causing the graphical user interface to display a secondary graphical user interface object; and assist in providing patient care based on one or more interrelations displayed on the user interface.

In some modalities, the method may also comprise manipulating a primary visual element to display a secondary visual element, including additional information corresponding to the patient's data after selecting them.

The method may further comprise displaying the secondary visual element, so that the secondary visual element overlaps the primary visual element or the primary visual element is resized, so that the secondary visual element is displayed adjacent to the primary visual element .

In some modes, the method may also comprise assist in providing patient care based on one or more interrelations displayed on the user interface.

In some modalities, assisting in patient care comprises assisting at least one in providing a diagnosis, providing a prognosis, selecting a recommended therapeutic regime, generating a hypothesis and assessing the efficiency of the therapeutic regimen, with based on one or more interrelationships.

In some modalities, assisting in providing assistance to the patient comprises selectively manipulating the graphical interface and one or more of the plurality of visual elements displayed on it to visually compare a target patient against a set of reference patients.

The visual comparison of the target patient with the set of reference patients can be based on several desired attributes, including, without limitation, the patient's shared attributes, at least one therapeutic regimen and / or at least one biomarker status.

In addition or alternatively, the detection of a genetic biomarker may include a computer-readable storage medium that is non-transitory and has computer-readable portions of program code stored there that, in response to execution by a processor, cause at least one device: receive, on a computing device comprising processor and memory, patient data for a plurality of patients, patient data corresponding to at least one of a biological sampling event, a biological processing event, at least one therapeutic regime, at least a biomarker status, and a patient status; determine at least one interrelation between any of the biological sampling events, the biological processing event, the at least one therapeutic regimen, the at least one biomarker status and the patient's status; perform a therapeutic regimen analysis to determine an interrelated status for the interrelated between at least one therapeutic regimen and at least one of the patient's status and at least one biomarker status; and display at least one graphical interface on a user interface in communication with the computing device, the graphical interface including a plurality of visual elements, each visual element of the plurality of visual elements being associated with patient data, at least at least one visual element being associated with at least one interrelation, at least one visual element including an indicator corresponding to at least one of the interrelated statuses and the biomarker status.

In addition or alternatively, the detection of a genetic biomarker may include a device for analyzing biological data, the device including a user interface and a computing device in communication with the user interface, the computing device comprising a processor and memory including computer-readable program code stored on it, the computer-readable code configured, after the processor executes it, to make the device: receive patient data for a plurality of patients, data from the patient corresponding to at least one of a biological sampling event, a biological processing event, at least one therapeutic regime, at least a biomarker status and a patient status; determine at least one interrelation between any of the biological sampling events, the biological processing event, the at least one therapeutic regimen, the at least one biomarker status and the patient's status; perform a therapeutic regimen analysis to determine an interrelated status for the interrelated between at least one therapeutic regimen and at least one of the patient's status and at least one biomarker status; and display at least one graphical interface in the user interface, the graphical interface including a plurality of visual elements, each visual element of the plurality of visual elements being associated with the patient's data, at least one visual element being associated with at least one inter -relationship, at least one visual element, including an indicator corresponding to at least one of the interrelated statuses and the biomarker status.

[560] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include any of the various forms described in PCT Publication WO 2018/064229, which is incorporated herein by reference in its entirety . For example, the detection of a genetic biomarker may include parameters that bind biomarkers of interest. In some modalities, oligonucleotide probes are used to detect the presence or levels of biomarkers or another biological entity in a biological sample.

[561] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include any of the various forms described in PCT WO Publication

2017/205686, which is incorporated herein by reference in its entirety.

For example, the detection of a genetic biomarker may include oligonucleotide probes that recognize tissues with phenotypes of interest.

In several modalities, oligonucleotide probes are used in diagnostic, prognostic or teranosis processes to characterize a phenotype in this sample.

The diagnosis may be related to cancer.

In addition or alternatively, the detection of a genetic biomarker may include a method for enriching an oligonucleotide library comprising a plurality of oligonucleotides, comprising: (a) providing an arrayed support with a plurality of samples; (b) contacting the support with the plurality of oligonucleotides; and (c) recovering members of the oligonucleotide probe library that bound to members of the plurality of samples, thereby enriching the oligonucleotide probe library.

Additionally or alternatively, the detection of a genetic biomarker may include a method for enriching an oligonucleotide library comprising a plurality of oligonucleotides, the method comprising: (a) performing at least one positive selection cycle, in which the selection positive comprises: (i) simultaneously contacting a plurality of samples with the plurality of oligonucleotides; and (ii) recovering members of the plurality of oligonucleotides that have been associated with the plurality of samples; (iii) optionally carry out at least one negative selection cycle, in which the negative selection comprises: (i) simultaneously contacting a plurality of control samples with the plurality of oligonucleotides; (ii) members recovering from the plurality of oligonucleotides that were not associated with the plurality of control samples.

In the methods of enrichment methods, the plurality of samples is chosen to be representative of a phenotype of interest.

In addition or alternatively, the detection of a genetic biomarker may include a method for characterizing a phenotype in a sample comprising: (a) arranging at least one sample on a substrate; (b) contacting the substrate with a plurality of oligonucleotides; and (b) measure the presence or level of a complex formed between members of the oligonucleotide plurality and the samples arranged in the substrate, where the presence or level is used to characterize the phenotype. In addition or alternatively, the detection of a genetic biomarker may include a kit comprising at least one reagent to perform the methods, including enrichment and characterization methods. In addition or alternatively, the detection of a genetic biomarker may include the use of at least one reagent for the execution of the methods. The at least one reagent can be any useful reagent, including, without limitation, at least one among a support, a plurality of nucleotides, a filtration unit and PEG.

[562] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the U.S. Patent

10,011,826, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include a method of isolating / extracting several biomolecules, in particular nucleic acids and proteins, in parallel from the same fixed biological samples. In some modalities, the methods also include quantifying and analyzing the biomolecules isolated by the method, a kit to isolate / extract several biomolecules from a fixed sample in parallel and use that kit to diagnose, prognosis, decide the therapy and monitor the therapy of a disease. In some embodiments, a method of parallel purification of various types of biomolecules from the same initial biological material fixed by crosslinking comprises: a) a step of dissolving said crosslinking of the starting material, b) a step of separating the different biomolecules present in the starting material in at least one fraction (A) and at least one fraction (B), and c) isolate or detect, or isolate and detect biomolecules other than at least one of said fractions (A) and (B) from step b) and a kit to perform said method.

[563] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in U.S. Patent

9,797,000, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include a method for detecting the presence of a target RNA, the method comprising: a) providing at least one DNA capture probe, in which at least one DNA probe DNA capture is linked to a support; b) hybridizing the target RNA with said at least one DNA capture probe, producing an RNA capture probe complex: target DNA; c) isolate the RNA capture probe complex: target DNA; d) supply at least one DNA amplification probe and hybridize said at least one DNA amplification probe with the complex of RNA capture probe: target DNA, producing an amplification capture probe and RNA complex: Target DNA; e) providing a hybrid anti-RNA: DNA antibody and incubating said RNA: target DNA amplification capture probe complex with said antibody, producing an RNA: DNA: antibody complex; f) detecting said antibody, where said detection indicates the presence of said target RNA. In some embodiments, the antibody is conjugated to a detectable marker and the detection step comprises detecting the marker. In one aspect, the detectable marker is selected from the group consisting of alkaline phosphatase and horseradish peroxidase. In some embodiments, the detection step comprises providing a second antibody that binds to said hybrid anti-HIV antibody.

RNA: DNA, in which said second antibody is conjugated to a detectable marker and in which said detection further comprises the detection of the marker. In some embodiments, the support comprises a magnetic sphere. In one aspect, the magnetic sphere is conjugated to at least one streptavidin molecule and at least one DNA capture probe is conjugated to one biotin molecule. In addition or alternatively, the detection of a genetic biomarker may include a method for providing target RNA for detection, the method comprising: incubating a biological sample containing the target RNA with carboxyl beads; isolate the spheres; lyse the biological sample connected to the isolated spheres; and isolating the spheres from the lysed biological sample, where the resulting supernatant contains the target RNA for detection.

[564] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in U.S. Patent

9,689,047, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include a method for detecting a target nucleic acid in a sample comprising non-target nucleic acids, said method comprising: (a) purifying the target nucleic acid in the sample by a method comprising: (i) contacting the sample with at least one purification probe, wherein at least a portion of the nucleic acid probe hybridizes to at least one target nucleic acid to form a DNA: RNA hybrid; (ii) immobilize the DNA: RNA hybrid on a first solid support by a method that comprises contacting the DNA: RNA hybrid with at least one first antibody capable of binding to the DNA: RNA hybrid, in which the antibody is bound or adapted to be attached to the first solid support; and (iii) separating the first solid support from the sample to generate at least one purified target nucleic acid; B.

genotyping the purified target nucleic acid by a method comprising: (i) amplifying at least a portion of the purified target nucleic acid to generate an amplicon, such as by isothermal amplification, such as total genome amplification; (ii) immobilize the amplicon on a second solid support by a method comprising contacting the amplicon with at least one immobilization probe, in which: (α) the immobilization probe is attached or adapted to be attached to the second solid support; and (β) at least a portion of the immobilization probe hybridizes to at least one target nucleic acid; (iii) contacting the immobilized amplicon with at least one detection probe, wherein at least a portion of the detection probe hybridizes to at least one target nucleic acid to generate a detection complex; and (iv) detecting at least one first detectable signal generated by the detection complex, where the detectable signal indicates the target nucleic acid genotype.

In some embodiments, the plurality of purified target nucleic acids is contacted with a plurality of immobilization probes, where each of the plurality of immobilization probes is specific for a distinct purified target nucleic acid.

In addition or alternatively, the detection of a genetic biomarker may include a method that is provided comprising: a. a purification step comprising: generating a double-stranded nucleic acid hybrid of the at least one target nucleic acid by hybridizing the at least one target nucleic acid to a set of hybrid probes comprising at least a first specific nucleic acid probe remains for at least one target nucleic acid; immobilize the double-stranded nucleic acid hybrid to a first solid support by contacting the double-stranded nucleic acid hybrid with at least one first antibody capable of binding to the double-stranded nucleic acid hybrid and binding the at least one first antibody to the first solid support; and separating the double-stranded nucleic acid hybrid from the sample to generate at least one purified nucleic acid; B. an amplification step, wherein at least a portion of the at least one purified nucleic acid is amplified to generate amplified nucleic acids; and C. a genotyping step comprising: immobilizing the amplified nucleic acids to at least a second solid support by hybridizing the amplified nucleic acids with a set of immobilization probes comprising at least one polynucleotide probe specific for at least one target nucleic acid; and detecting the presence of at least one target nucleic acid with a set of detection probes comprising at least one polynucleotide probe specific for at least one target nucleic acid.

[565] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in U.S. Patent

9,422,593, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include a method for detecting at least one target nucleic acid comprising: (1) sequence-specific isolation of a target nucleic acid from a sample; (2) amplifying the isolated target nucleic acid; and (3) detecting the target nucleic acid using a plurality of detectably labeled nucleic acid detection probes, each of which (a) has a detectable marker different from the other detection probes and / or (b) has a different melting temperature than probes that contain the same detectable marker. In some embodiments, the method comprises: A. purifying at least one target nucleic acid by a method comprising: A1. generating a double-stranded nucleic acid hybrid of at least one target nucleic acid by hybridizing the at least one target nucleic acid to a set of hybrid probes comprising at least one first nucleic acid probe specific for at least one target nucleic acid; A2 separating the double-stranded nucleic acid hybrid from the sample to generate at least one purified nucleic acid; B. amplify at least a portion of the at least one purified nucleic acid; and C. detecting the target nucleic acid by a method comprising: C1. contact the amplified nucleic acid with at least one set of detection probes, where: C1 (a). each of the detection probes in the set of detection probes has a detectable marker; C1 (b). at least two of the detection probes in the set of detection probes carrying the same detectable marker; and C1 (c). each probe carrying the same detectable marker has a melting temperature (Tm) that differs from other probes with the same marker; C2 detects the amplified nucleic acid, determining whether the labeled probe hybridized to its nucleic acid sequence; and C3. detecting the temperature at which each detection probe dissociates from the nucleic acid sequence to which it has attached. In some embodiments, the double-stranded nucleic acid hybrid is separated from the sample by a method comprising contacting the double-stranded nucleic acid hybrid with a molecule that specifically binds to double-stranded nucleic acid hybrids, preferably a hybrid anti-DNA: RNA antibody.

[566] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in U.S. Patent

9,593,366, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include a method of randomly amplifying a target nucleic acid sequence, the method comprising bringing together a set of primers, DNA polymerase and a target sample, in which the Primers are G-deficient random primers and incubate the target sample under conditions that promote replication of the target sequence, where replication of the target sequence results in replicated strands. In addition or alternatively, the detection of a genetic biomarker may include a method of randomly amplifying a target nucleic acid sequence, the method comprising contacting a set of primers, DNA polymerase and a target sample, in which the initiators are G-deficient random primers and incubate the target sample under conditions that promote replication of the target sequence, in which the nucleic acids in the target sample are not separated from other material in the target sample.

In addition or alternatively, the detection of a genetic biomarker may include a method of randomly expanding messenger RNA, the method comprising the reverse transcription of messenger RNA to produce a first strand cDNA, putting in contact a set of random primers with G deficiency, DNA polymerase and the first strand cDNA and incubation under conditions that promote replication of the first strand cDNA, where replication of the first strand cDNA results in replicated strands, which during replication by at least one of the replicated tapes is displaced from the cDNA of the first tape by replicating the displacement of the replicating tape from another replicated tape.

In addition or alternatively, the detection of a genetic biomarker may include a method of randomly amplifying a target nucleic acid sequence, the method comprising: (a) mixing a set of G-deficient random primers with a sample target, to produce a target primer sample mix and incubate the target primer sample mix under conditions that promote hybridization between the G-deficient random primers and the target sequence in the target primer sample mix and (b) mixing DNA polymerase with the target primer sample mixture to produce a target polymerase sample mixture and incubating the target polymerase sample mixture under conditions that promote target sequence replication, where target sequence replication results in replicated tapes, where during replication, at least one of the replicated tapes is shifted from the target sequence by the tape offset replication of another rep tape licensed, where the target sequence is a nucleic acid sample of substantial complexity. In addition or alternatively, the detection of a genetic biomarker may include a method of random amplification of an entire genome, the method comprising contacting a set of G-deficient random primers, DNA polymerase and a target sample and incubate the target sample under conditions that promote replication of the target sequence.

[567] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in U.S. Patent

9,487,823, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include compositions and a method for amplifying nucleic acid sequences of interest. In some embodiments, the method is based on the replication of strand displacement of nucleic acid sequences by primers. In some modalities, the method is a form of multiple displacement amplification (MDA) useful for amplifying genomic nucleic acid samples and other highly complex nucleic acid samples. The method can be used to amplify highly complex nucleic acid samples using only one or a limited number of primers. It has been found that one or a small number of primers can effectively amplify entire genomes and other highly complex sequence nucleic acid samples. Primers are specially selected or designed to be able to efficiently initiate and amplify the wide range of sequences present in highly complex nucleic acid samples, despite the limited amount of primer sequences representing

sitting on the primers.

The method usually involves contacting one, a few or more primers with specific nucleic acid sequences, DNA polymerase and a nucleic acid sample, and incubating the nucleic acid sample under conditions that promote the replication of nucleic acid in the nucleic acid sample.

The replication of the nucleic acid molecules results in replicated strands, so that, during replication, the replicated strands are displaced from the nucleic acid molecules by replicating the strand by displacing another replicated strand.

Replication can result in the amplification of all or a substantial fraction of the nucleic acid molecules in the nucleic acid sample.

In some embodiments, the method, which uses a form of amplification of whole-genome strand displacement (WGSDA), one, a few or more primers are used to prepare a sample of genomic nucleic acid (or another sample of acid highly complex nucleic acid). In addition or alternatively, the detection of a genetic biomarker may include a method for amplifying human genomes, the method comprising: contacting a single DNA primer of at least 6 nucleotides in length that is not degenerate and non-random, human, DNA-shifting DNA polymerase and a sample of human genomic nucleic acid to form a mixture and incubate the mixture under conditions that promote the replication of nucleic acid molecules in the human genomic nucleic acid sample ; wherein the primer hybridizes to nucleic acid molecules in the genomic nucleic acid sample and where the primer has a specific nucleotide sequence, wherein the genomic nucleic acid sample comprises all or a substantial portion of a human genome; and replicating the nucleic acid molecules in the human genomic nucleic acid sample under isothermal conditions, where the replication of nucleic acid molecules in the genomic nucleic acid sample proceeds by ribbon shift replication, in which the replication of the molecules of nucleic acid in the genomic nucleic acid sample results in replication of all or a substantial fraction of the nucleic acid molecules in the genomic nucleic acid sample.

[568] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in U.S. Patent

9,115,410, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include a nucleic acid detection method, known as target-specific HYBRID CAPTURE ("TSHC"). Additionally or alternatively, the detection of a genetic biomarker may include a method for detecting and / or quantifying one or more target nucleic acids, comprising the steps of enrichment, amplification and detection of the target for the rapid and sensitive detection of the sequences of target nucleic acids. In some embodiments, one or more target nucleic acids are detected by: capturing the target nucleic acids in a solid support, mixing the target nucleic acids, nucleic acid probes complementary to the target nucleic acids, where one is RNA and the other is DNA, and a solid support; removing unbound target nucleic acids and nucleic acid probes; amplifying the captured target nucleic acids or nucleic acid probes, forming a plurality of amplicons, in which the presence of the amplicons is indicative of the presence of the target nucleic acids; and detecting target nucleic acids by mixing target nucleic acids with selectable and distinguishable oligonucleotides that hybridize to a portion of the target nucleic acids (i.e., capture sequence probes; CSPs) and nucleic acid probes complementary to a different portion of the acid target nucleic (ie, signal sequence probes; SSPs), where the probe or the target is an RNA and the other is DNA, where the DNA: RNA hybrids are detected by the specific binding agents of the DNA hybrid: RNA, which are labeled directly or indirectly, thus detecting target nucleic acids.

SSPs are not limited to serving only as a means to produce a signal for detection; but they can be used in the target enrichment stage by hybridization with the target nucleic acid, allowing the capture with a specific binding agent of the DNA: RNA hybrid.

In some embodiments, a plurality of target nucleic acids is detected by: hybridizing a plurality of target nucleic acids in nucleic acid probes that are complementary to the target nucleic acids, forming DNA: RNA hybrids; capture DNA: RNA hybrids with specific antibodies for DNA: RNA hybrids attached to solid supports; removing unbound target nucleic acids and nucleic acid probes; amplifying the captured target nucleic acids or nucleic acid probes, forming a plurality of amplifiers, using random primers and DNA polymerase, where the presence of the plurality of amplicons is indicative of the presence of the target nucleic acids; nucleic acid hybridization probes complementary to a portion of the target nucleic acid sequences, form DNA: RNA hybrids between targets and probes; hybridization oligonucleotides conjugated to a solid support to a different portion of the target nucleic acids, where the solid support is selectable; select oligonucleotide complexes; and detecting the plurality of target nucleic acids by binding specific DNA: RNA hybrid binding agents to the DNA: RNA hybrids.

In some embodiments, one or more target DNAs are detected by a multiplex method with the steps of: hybridizing a plurality of target DNAs in RNA probes that are complementary to the target DNAs, forming DNA: RNA hybrids; capture DNA: RNA hybrids with specific antibodies to DNA: RNA hybrids that are conjugated to beads; removing unbound nucleic acids and nucleic acid probes by flushing excess nucleic acids and probes; isothermally amplify target DNAs using random primers and DNA polymerase, forming a plurality of amplicons; hydrate RNA probes complementary to a portion of the target DNAs (i.e., SSPs), form DNA: RNA hybrids; hybridizing specific DNA oligonucleotides to a different portion of the target DNAs, where the DNA oligonucleotides are conjugated to selectable beads; and detecting the plurality of target DNAs, binding detectably labeled specific DNA: RNA hybrids to the DNA: RNA hybrids and selecting target DNA using beads conjugated to selectable oligonucleotides (ie, CSPs), in which the specific antibodies of DNA: RNA hybrids are marked in a detectable and distinguishable way. The presence of each target is detected by the DNA: RNA antibody labeled through SSPs that form DNA: RNA hybrids with the target, while the various targets are separated or selected based on the sphere conjugated to oligonucleotide (ie, CSP ). The presence of amplicons and DNA: RNA hybrids is indicative of the presence of the target DNAs.

[569] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in U.S. Patent

9,051,606, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include methods for producing and using multicomponent nucleic acid enzymes (MNAzymes). Additionally or alternatively, the detection of a genetic biomarker may include a composition comprising at least two or more oligonucleotide components in which at least one first oligonucleotide component and a second oligonucleotide component self-assemble in the presence of an MNAzyme assembly facilitator to form a catalytically active multi-component nucleic acid enzyme (MNAzyme), wherein each of said at least first and second oligonucleotide components comprises a substrate arm portion, a catalytic core portion and a sensor arm portion; where after self-assembly, the sensor arm portion of said first and second oligonucleotide components acts as MNAzyme sensor arms, the substrate arm portion of the first and second oligonucleotide components acts as MNAzyme substrate arms and the portion of the catalytic nucleus of the first and second oligonucleotide components act as a catalytic nucleus of MNAzyme; and in which the MNAzyme sensor arms interact with the said MNAzyme assembly facilitator, in order to keep the first and second oligonucleotide components close together to associate their respective portions of the catalytic nucleus to form the catalytic nucleus of the MNAzyme catalytic capable of modifying at least one substrate, and wherein said substrate arms of said MNAzyme engage with a substrate so that said catalytic core of said MNAzyme can modify said substrate.

In some embodiments, the composition may further comprise at least a third oligonucleotide component which acts to stabilize at least one of said substrate arm portions or sensor arm portions.

In some embodiments, the method may also comprise at least a third oligonucleotide component and a fourth oligonucleotide component that increases in the presence of at least one additional assembly facilitator to form at least one additional catalytically active MNAzyme, in that each of said at least the third and fourth oligonucleotide components comprise a substrate arm portion, a catalytic core portion and a sensor arm portion; wherein after self-assembly of said at least a third oligonucleotide component and a fourth oligonucleotide component, the sensor arm portion of said at least third and said at least four oligonucleotide components forms arms of said at least one catalytically active MNAzyme additional, the portion of the substrate arm of said at least third and said at least four oligonucleotide components forms substrate arms of said at least one additional catalytically active MNAzyme, and the portion of the catalytic core of said at least third and said at least four oligonucleotide components form a catalytic core of said at least one additional catalytically active MNAzyme, and wherein the sensor arms of said at least one additional MNAzyme interact with said at least one additional assembly facilitator, in order to keep those referred to at least third parties and referred to at least s four oligonucleotide components nearby to associate their respective portions of the catalytic nucleus to form the catalytic nucleus of said at least one additional MNAzyme, said catalytic nucleus capable of acting on at least one additional substrate, and in which the arms of the substrate of said at least one additional MNAzyme engaging in at least one additional substrate, so that the catalytic core of said at least one additional MNAzyme can act on said at least one additional substrate.

In addition or alternatively, the detection of a genetic biomarker may include a method for detecting the presence of at least one assembly facilitator comprising: (a) providing two or more oligonucleotide components, wherein at least one first oligonucleotide component and a second oligonucleotide component self-assembles in the presence of an assembly facilitator to form at least one catalytically active multi-component nucleic acid enzyme

(MNAzyme); (b) contacting the two or more oligonucleotide components with a sample containing the assembly facilitator under conditions that allow: (1) self-assembly of said at least one catalytically active MNAzyme and (2) the catalytic activity of said MNAzyme; and (c) determine the presence of the catalytic activity of said at least one MNAzyme, in which the presence of the catalytic activity is indicative of the presence of said at least one assembly facilitator.

In some modalities, the method may also comprise a step of amplifying the assembly facilitator.

The amplification step can comprise one or more of: polymerase chain reaction (PCR), ribbon displacement amplification (SDA), loop-mediated isothermal amplification (LAMP), rotating circle amplification (RCA), transcription-mediated amplification (TMA), self-sustained sequence replication (3SR), nucleic acid sequence-based amplification (NASBA) or reverse transcription polymerase chain reaction (RT-PCR). In addition or alternatively, the detection of a genetic biomarker may include a method for detecting the presence of at least one assembly facilitator comprising: (a) providing two or more oligonucleotide components, in which at least one first oligonucleotide component and a second oligonucleotide component self-assembles in the presence of at least one first assembly facilitator to form at least a first catalytically active multi-component nucleic acid enzyme (MNAzyme); (b) providing at least a first substrate, said first substrate capable of being modified by said first MNAzyme, wherein said modification of said substrate by said MNAzyme provides a detectable effect; (c) contact said two or more oligonucleotide components with a sample that contains at least the first assembly facilitator under conditions that allow: (1) self-assembly of said at least first MNAzyme and (2) catalytic activity of said at least first MNAzyme; and (d) detecting said detectable effect.

In some embodiments, the method may further comprise the step of amplifying the nucleic acid.

The amplification step may comprise one or more of: polymerase chain reaction (PCR), ribbon displacement amplification (SDA), loop-mediated isothermal amplification (LAMP), rotating circle amplification (RCA), transcription-mediated amplification (TMA), self-sustained sequence replication (3SR), nucleic acid sequence-based amplification (NASBA) or reverse transcription polymerase chain reaction (RT-PCR). In addition or alternatively, the detection of a genetic biomarker may include a method for detecting the presence of at least one target comprising: (a) providing two or more oligonucleotide components in which at least one first oligonucleotide component and at least one second oligonucleotide component are able to self-assemble in the presence of said target to form a catalytically active multi-component nucleic acid enzyme (MNAzyme); and wherein at least one of said first and second oligonucleotide components further comprises at least a portion of aptamer; (b) contacting said oligonucleotide components with a sample that contains at least one target under conditions that allow: (1) binding of said target to said aptamer portions and (2) catalytic activity of MNAzyme; and (c) determining the presence of MNAzyme's catalytic activity, in which the presence of catalytic activity is indicative of the presence of said target.

Additionally or alternatively, the detection of a genetic biomarker may include a method for detecting a target using an MNAzyme-mediated signal amplification cascade comprising: (a) providing a first oligonucleotide component and a second oligonucleotide component that self-assembles in the presence of said target to form a first catalytically active multi-component nucleic acid enzyme (MNAzyme); (b) providing an insoluble support with a first and a second substrate attached to it, said first and second substrates are capable of being modified by said first MNAzyme, wherein said first and second substrates comprise at least one third and a fourth oligonucleotide component, respectively, capable of forming a second catalytically active MNAzyme, in which said third and fourth oligonucleotide components are released by modifying said first and second substrates by said first MNAzyme; (c) providing said insoluble support with a third and a fourth substrate attached to it, said third and fourth substrates are capable of being modified by said second MNAzyme, in which said third and fourth substrates comprise at least one fifth and one sixth oligonucleotide component, respectively, capable of forming a third catalytically active MNAzyme, wherein said fifth and sixth oligonucleotide components are released by modifying said third and fourth substrates by said second MNAzyme; (d) providing an assembly facilitator capable of facilitating the assembly of said second and third MNAzyme, and; (e) providing a fifth substrate that is capable of being modified by said second MNAzyme to provide a detectable effect; (f) contacting said first and second oligonucleotide components with a sample containing said target, in the presence of said assembly facilitator and in the presence of said insoluble support having said first, second, third and fourth substrates attached to it under conditions that allow: (1) self-assembly of said first, second and third MNAzymes and (2) catalytic activity of said first, second and third MNAzymes; and (g) in which said third MNAzyme modifies said first and second substrates, further providing said second MNAzyme, in which said second MNAzyme further modifies at least one of said third, fourth and fifth substrates, further providing said third MNAzyme, further providing said detectable effect, and; (h) wherein the detection of said detectable effect is indicative of the presence of said target.

[570] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in U.S. Patent

9,012,149, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include a method for synthesizing a cDNA that contains the sequence of a miRNA or other small RNA that can be amplified using standard nucleic acid amplification methods, such as the reaction in polymerase chain. The method can provide greater specificity of cDNA synthesis from small RNAs, while allowing researchers to perform the two main enzymatic reactions necessary for this synthesis under substantially the same reaction conditions, conditions that include the presence of divalent cations in concentrations of 10 millimolar and 80 millimolar. When these reaction conditions are used as part of an assay for a small RNA, especially for a miRNA, it results in greater specificity and sensitivity. In some embodiments, the method for preparing a cDNA copy of a small RNA molecule comprises: (a) providing a small RNA from a biological sample, where said RNA is 18 to 28 nucleotides from length; (b) incubate the small RNA with an enzyme capable of catalyzing the addition of nucleotides at the 3 'end of the small RNA in the presence of a single ribonucleotide triphosphate selected from the group consisting of ATP, GTP, UTP and CTP and in a final concentration of divalent magnesium cation between 20 millimolar and 80 millimolar in a reaction to add nucleotides to the small RNA to generate a small tail RNA; (c) annealing a DNA primer on the small-tailed RNA, whereby the DNA template extends from the 3 'end of the small-tailed RNA, thus providing a single stranded DNA region that can be used to direct the polymerization of deoxyribonucleotide triphosphates; and (d) incubate the annealed small-tailed RNA and DNA primer in the presence of reverse transcriptase triphosphates and deoxyribonucleotide and at a final concentration of divalent magnesium cation between 20 millimolar and 80 millimeter in conditions that allow reverse transcription in cDNA and amplification of annealed small-tailed RNA to produce an amplification product.

[571] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the U.S. Patent

8,962,250, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include methods of amplifying and quantifying nucleic acid, such as a method of amplifying a plurality of nucleic acid molecules selected from a set of nucleic acid molecules comprising: (a ) amplify a plurality of selected nucleic acid molecules in a first rounded multiplex amplification reaction including a plurality of external primer pairs, each pair being specific to a selected nucleic acid sequence, where the amplification reaction is allowed to proceed to a point earlier than that in which there was significant competition between amplicons for the reaction components; and (b) further amplifying the selected nucleic acid molecules in a plurality of second cycle amplification reactions, each including a portion of the multiplex reaction completed as a model and at least one pair of internal primers, each pair being specific to one of the selected nucleic acid sequences, so that each second amplification reaction further amplifies a subset of the plurality of selected nucleic acid molecules, respectively.

Additionally or alternatively, the detection of a genetic biomarker may include a method for amplifying a plurality of nucleic acid molecules selected from a set of nucleic acid molecules comprising: (a) amplifying a plurality of acid molecules selected nucleic acid in a first round multiplex amplification reaction including a plurality of pairs of external primers, each pair being specific to a selected nucleic acid sequence, in which the amplification reaction is allowed to proceed to a point prior to that in that there was a significant competition between amplicons for the reaction components; and (b) further amplifying the selected nucleic acid molecules in a plurality of second cycle amplification reactions, each including a portion of the multiplex reaction completed as a template and at least a pair of primers, each pair comprising a primer internal pain and one of the external primers and being specific for one of the selected nucleic acid sequences, so that each reaction of the second cycle further amplifies a subset of the plurality of selected nucleic acid molecules, respectively.

In addition or alternatively, the detection of a genetic biomarker may include a method for estimating the number of nucleic acid molecules selected from a set of nucleic acid molecules comprising: (a) amplifying a plurality of acid molecules selected nucleic acid in a first round multiplex amplification reaction including a plurality of pairs of external primers, each pair being specific to a selected nucleic acid sequence, where the amplification reaction is allowed to proceed to an earlier point that in which there was a significant competition among amplicons for the components of the reaction; (b) further amplifying selected nucleic acid molecules in a plurality of second cycle amplification reactions, each including a detectable reporter, a portion of the multiplex reaction completed as a template and at least one pair of internal primers each pair being specific for one of the selected nucleic acid sequences, by which each second amplification reaction further amplifies a subset of the plurality of selected nucleic acid molecules, respectively; and (c) monitoring each second amplification reaction through the detectable reporter, so that the number of selected nucleic acid molecules from each selected sequence is estimated.

In addition or alternatively, the detection of a genetic biomarker may include a method for estimating the number of selected nucleic acid molecules from a set of nucleic acid molecules comprising: (a) amplifying a plurality of selected nucleic acid molecules in a first round multiplex amplification reaction including a plurality of pairs of external primers, each pair being specific to a selected nucleic acid sequence, in which the amplification reaction is allowed to proceed to a point earlier than the one in which it occurred a significant competition between amplicons for the reaction components; and (b) further amplifying selected nucleic acid molecules in a plurality of second cycle amplification reactions, each including, a detectable reporter, a portion of the multiplex reaction completed as a template and at least a pair of primers, each pair comprising an inner primer and one of the outer primers and being specific to one of the selected nucleic acid sequences, so that each second cycle reaction further amplifies a subset of the plurality of selected nucleic acid molecules, respectively; and (c) monitor each second amplification reaction by means of the detectable reporter, so that the number of selected nucleic acid molecules from each selected sequence is estimated.

In some embodiments, the fully nested form of the Multiplex Tandem Polymerase Chain Reaction (MT-PCR) method is used according to the first and third aspects, by which each selected nucleic acid molecule is amplified using a pair of external initiators in the first round of amplification and two internal initiators in the second round of amplification.

In some embodiments, the hemi-nested MT-PCR method is used according to the second and fourth aspects, in which each selected nucleic acid molecule is amplified using a pair of external primers in the first round of amplification and the sequence of Selected nucleic acid is further amplified in the second round of amplification using a pair of primers comprising one of the external primers used in the first round of amplification paired with an internal primer.

In some embodiments, the second round amplification reaction includes a plurality of primer pairs and a plurality of fluorescent probes, so that a plurality of nucleic acid molecules selected from each selected sequence is amplified and quantified using the fluorescent probes. each being specific for a nucleic acid sequence selection.

In some embodiments, at least one of the external primers includes UTP nucleotides, where the primer is digestible by an UNG enzyme and the external primers are removed at the end of the first round of amplification by digestion with an UNG enzyme, thus substantially preventing contamination of the amplification reaction of the second cycle by the initiators of the first cycle. In some modalities, the methods are used in a method of detecting polymorphisms, mutations, insertions and deletions. Additionally or alternatively, the detection of a genetic biomarker may include a method to identify and / or quantify at least one selected nucleic acid sequence including the steps of: (i) mixing one or more selected nucleic acid sequences with one or more detectable reporters; (ii) generate a fusion curve measuring the signal generated by said one or more detectable reporters; (iii) identifying and / or quantifying said one or more nucleic acid sequences selected from said fusion curve.

[572] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the U.S. Patent

8,877,436, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include a method for determining the presence of a target nucleic acid molecule in a sample containing biological material. In some modes, the method for determining the presence of a target nucleic acid molecule in a sample comprises: a) suspending the sample in a collection medium; b) release target nucleic acid molecules from the sample into the collection medium; c) converting double-stranded target nucleic acid molecules to single-stranded target nucleic acid molecules; d) contacting one or more probes with the single-stranded target nucleic acid molecules under conditions that allow the probes and single-stranded target nucleic acid molecules to hybridize, forming double-stranded nucleic acid hybrids; e) capture the double-stranded nucleic acid hybrids; f) separating the double-stranded nucleic acid hybrids from the unbound single-stranded target nucleic acid molecules; and g) detecting double-stranded nucleic acid hybrids, thereby indicating the presence of the target nucleic acid. In some modalities, the detection method can be automated, fully automated or partially automated - in other words, requiring some human contribution. In some embodiments, the detection of target nucleic acid molecules in several samples at the same time or within a very short period of time, for example, in one machine or in a series of machines. In addition or alternatively, the detection of a genetic biomarker may include a collection medium in which a sample containing a target nucleic acid molecule is collected. The target nucleic acid molecule can be kept in the collection medium with minimal degradation of the target nucleic acid molecule over a period of weeks or months. In some modalities, the target sample material based on DNA can be kept in the collection medium with minimal degradation of the target nucleic acid molecule over a period of weeks or months. In some modalities, the detergent-based collection medium allows rapid analysis and processing of a sample. In some embodiments, the collection medium comprises about 0.5% to about 2.0% NP-40, about 0.10% to about 0.40% sodium deoxycholate, about 25 mM at about 75 mM Tris-HCl, about 10 mM to about 50 mM EDTA, about 50 mM to about 200 mM NaC1 and about 0.01% to about 0.10% sodium azide .

[573] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the U.S. Patent

8,372,637, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include a method for stabilizing a biological sample that has the following steps:

[574] a) prepare a biological sample and b) count the biological sample with a composition with a substance according to the following structural formula:

[575] where R1 is a hydrogen residue or a methyl residue, R2 and R3 are identical or different hydrocarbon residues with a carbon chain length of 1-20 and R4 is an oxygen, sulfur or selenium residue. The hydrocarbon residues R2 and / or R3 can be selected independently of each other from the group comprising alkyl, long chain alkyl, alkenyl, alkoxy, long chain alkoxy, cycloalkyl, aryl, haloalkyl, alkylsilyl, alkylsilyloxy, alkylene, alkenyl- i, arylene, carboxylates and carbonyl. In some embodiments, the length of the n chain in R2 and / or R3 can have the values 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 , 17, 18, 19 and 20. In some embodiments, R2 and R3 have carbon chain lengths of 1-10. In some embodiments, R2 and R3 have carbon chain lengths of 1-5. In this case, the length of the chain n can, in particular, have the values 1, 2, 3, 4 and 5. In some modalities, methyl and ethyl residues are used in R2 and / or R3. The length of the current n has values of 1 and 2. In some embodiments, the substance used in the composition can be used as the only agent for preservation. In some embodiments, the substance can also be used in conjunction with other preservative substances or even just as an additive to other preservative substances in the composition. In some embodiments, the volume ratio or weight ratio between the substance and one or more other preserving substances in the composition can be in the range of 0.01: 100 to 100: 0. Preferably, it is in the range of 0.1: 100 to 100: 0 and, especially, preferably,

it is in the range of 1: 100 to 100: 0 and, particularly preferably, it is in the range of 5: 100 to 100: 0. In some embodiments, the class of substances used is, for example, dialkylacetamides (if R1 is a methyl residue) or dialkylformamides (if R1 is a hydrogen residue). In some modalities, the biological sample is a material selected from the group comprising sample material, plasma, body fluids, blood, serum, cells, leukocyte fractions, phlogistic crust, sputum, saliva, urine, semen, feces , forensic samples, smears, aspirates, biopsies, tissue samples, tissue parts and organs, food samples, environmental samples, plants and plant parts, bacteria, viruses, viroids, prions, yeasts and fungi and fragmen - components or constituents of the materials mentioned above and / or isolated, synthetic or modified proteins, nucleic acids, lipids, carbohydrates, metabolic products and / or metabolites. In some modalities, the substance is a substance selected from the group comprising N, N-dimethylacetamide, N, N-diethylacetamide, N, N-dimethylformamide, N, N-diethylformamide, N, N-dimethylthioformmamide and N, N-diethylthioformate. Its structural formulas are as follows: N, N-dimethylformamide N, N-diethylformamide N, N-dimethylacetamide N, N-diethylacetamide Dimethylthioformamide Dietiltioformamide.

[576] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the U.S. Patent

8,043,834, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include compositions and methods useful for labeling and detecting analytes. In some embodiments, the compositions are associations of three components: reporter-binding agents, target amplification circles and DNA polymerase. The compositions are assembled before use in a rotating circle amplification reaction and can be stored and transported before use without substantial loss of activity. In some embodiments, the composition comprises a reporter binding agent, a target circle of amplification and DNA polymerase, wherein the reporter binding agent comprises a specific binding molecule and a broken circle replication primer. - active, where the specific binding molecule is specific to a target molecule, where the specific binding molecule is not linked to the target molecule, where the composition does not comprise tandem sequence DNA, where the agent of connection to the reporter, the target amplification circle and the DNA polymerase are directly associated with each other and form a complex, in which the composition is not in an assay or reaction. In some embodiments, the composition can be used as universal reagents for rotating circle amplification. To this end, the compositions can have specific binding molecules that can interact with specific portions or molecules that are present or are used to label any target molecule of interest. For example, the specific binding molecule in the composition of the reagent can be streptavidin or another specific biotin molecule (such as an anti-biotin antibody). Any biotin-labeled target molecule can then be associated with the composition of the reaction.

people and marked and / or detected through amplification of the mediated circle by the composition. This reagent composition can be used with any biotinylated target molecule. Likewise, the use of an antibody specific to a class of antibodies (eg, and anti-mouse antibody) as a specific binding molecule in reagent compositions. Such reagent compositions can be used to label and detect a class of antibodies in an assay. For example, the reagent composition can be used to label and detect all mouse antibodies bound to the antigen in immunoassays, regardless of the specificity of individual mouse antibodies. This is analogous to the use of antibodies specific to a class of antibodies in sandwich immunoassays. The reagent compositions provide greater signal amplification and tighter signal localization than in traditional immunoassays.

[577] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the U.S. Patent

7,682,790, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include compositions for the isolation and / or stabilization of nucleic acids in materials of biological origin. The compositions contain as an essential ingredient a cationic compound of general formula

[578] Y + R1R2R3R4X−

[579] where

[580] Y can denote nitrogen or phosphorus;

[581] R1, R2, R3 and R4 independently of each other may note a branched or unbranched C1-C20 alkyl group and / or a C6-C20 aryl group, as well as a C6-C26 aralkyl group; and

[582] X− can represent an anion of an inorganic or organic acid, mono- or polybasic and at least one proton donor as an additive.

In some embodiments, the cationic compounds consist of an ammonium salt in which R1 indicates a higher alkyl group, preferably with 12, 14 or 16 carbon atoms, and R2, R3 and R4 in each case denote a methyl group .

In some modalities, R1 indicates an aralkyl group, preferably a benzyl group, R2 indicates a higher alkyl group - preferably with 12, 14 or 16 carbon atoms - and R3 and R4 denote a methyl group.

In some embodiments, the anion is bromide, chloride, phosphate, sulfate, formate, acetate, propionate, oxalate or succinate.

In some embodiments, aliphatic hydroxy-di- and tricarboxylic acids, for example, tartronic acid, D - (+), L - (-) or DL-malic acid, (2R, 3R) - (+) - tartaric acid , (2S, 3S) - (-) - tartaric acid, meso-tartaric acid and citric acid are used.

In some embodiments, aliphatic ketodicarboxylic acids can also be used as additives, such as, for example, mesoxalic acid and oxaloacetic acid, of which oxaloacetic acid is more particularly preferred.

In some modalities, amino acids can be used, of which α-amino acids - such as, for example, aminoacetic acid (glycine), α-aminopropionic acid (alanine), α-amino-iso-valeric acid (valine), α acid -amino-iso-caproic (leucine) and α-amino-β-methylvaleric acid (isoleucine) are preferred.

As additional additives, mineral acids and their salts can also be used.

Preferably, mineral acid salts - such as phosphoric acid or sulfuric acid - are used with alkali metals or ammonium salts thereof.

Phosphoric acid and ammonium sulphate are more preferentially used.

In addition or alternatively, the detection of a genetic biomarker may include a method of stabilizing nucleic acids in a biological sample, the method comprising: mixing a storage stabilization composition

with a solution containing nucleic acids, in which the composition comprises a cationic compound of the general formula

[583] Y + R1R2R3R4X−

[584] where Y represents nitrogen or phosphorus;

[585] R1, R2, R3 and R4 independently represent a branched or unbranched C1-C20 alkyl group and / or a C6-C20 aryl group, as well as a C6-C26 aralkyl group;

[586] X represents an anion of an inorganic or organic acid, mono- or polybasic; and

[587] at least one proton donor

[588] in which the proton donor is present in the composition at a concentration above 50 mM until saturation and in which the proton donor is selected from the group consisting of saturated aliphatic monocarboxylic acids, unsaturated alkenyl carboxylic acids saturated and / or unsaturated C2-C6-dicarboxylic acids, aliphatic hydroxyl-di- and tricarboxylic acids, aliphatic ketocarboxylic acids, amino acids or inorganic acids or salts thereof, alone or in combination; stabilizing nucleic acids, in which nucleic acids are stabilized by forming an ionic complex with the cationic compound; optionally, separate the insoluble ionic complex from the solution; and optionally releasing nucleic acids from the insoluble ionic complex.

[589] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the U.S. Patent

7,683,035, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include (1) a method to stabilize and / or isolate nucleic acids from a biological sample, comprising the following step: contacting the biological sample with at least one cationic compound of formula (I ):

[590] in which conjugated bases of strong and / or weak inorganic and / or organic acids are used as anion (A) and in which the substance consisting of (I) and the anion is neutral in charge as a whole , and where

[591] X represents nitrogen (N) or phosphorus (P),

[592] k represents the integer 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, or 24,

[593] Bk represents aliphatic alkanediyl bridges, which can be substituted for none, one or more carbon atoms, and in which one or more non-adjacent carbon atoms can be substituted for oxygen and which have the structure

[594] - (CH2) n— (OCH2) m—

[595] where n and m independently represent the integer 0, 1, 2, 3, 4, 5 or 6, with n + m> 0; alternatively,

[596] Bk represents a substituted phenyl, naphthyl or biphenyl bridge which, in addition, can be replaced by one or more carbon atoms and has the structure or

[597] where n, m, l, p, q independently represent the integer 0, 1, 2, 3, 4, 5 or 6;

[598] R1, R2, R3k, which may be identical or different and which may be unsubstituted or substituted on one or more carbon atoms, represent hydrogen, straight or branched C1-C6 alkyl, straight or branched C1-C6 alkenyl, C1-C6 linear or branched alkynyl, phenyl, benzyl and phenoxyethyl with the structure

[599] where n, m independently represents the integer 0, 1, 2, 3, 4, 5 or 6, and

[600] Z represents one of the structures —O—, —CO—, —CO2—, —OCO—, —CO — N—, —N — CO—, —O — CO — N—, —N — CO — O -, —S—, or —S — S—;

[601] or R1, R2, R3k represent phenyl, benzyl, phenoxyethyl having the structure

[602] where n, m independently represents the integer 0, 1, 2, 3, 4, 5 or 6;

[603] RA, RBk, RC, which may be identical or different and which may be unsubstituted or substituted on one or more carbon atoms, represent hydrogen, C1-C21 straight or branched alkyl, C1-C21 straight or branched alkenyl, C1-C21 linear or branched alkynyl and a structure

[604] CH3— (CH2) n — Z— (CH2) m—

[605] where n, m independently represents the integer 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18 , 19, 20, 21, 22, 23 or 24 and

[606] Z stands for —O—, —CO—, —CO2—, —OCO—, —CO— N—, —N — CO—, —O — CO — N—, —N — CO — O—, - S—, or —S— S—;

[607] alternatively, RA and RC together form a RAC residue with a cyclic structure

[608] wherein the RAC residue, which may be unsubstituted or substituted on one or more carbon atoms, represents straight or branched C1-C8 alkyl, straight or branched C1-C8 alkyl or straight or branched C1-C8 alkynyl, and

[609] if k> 1, the bridge groups Bk and the groups RBk and R3k are the same or different;

[610] (2) A kit to stabilize and / or isolate nucleic acids, comprising at least one cationic compound as defined above by formula (I); (3) A complex, comprising a nucleic acid and at least one cationic compound, formed as a result of the method in (1); (4) A composition of matter, comprising at least one cationic compound as defined above by formula (I); (5) A pharmaceutical composition, comprising the composition of the material in (4); (6) A diagnostic composition, comprising the composition of the matter in (4); and (7) A composition for research, comprising the composition of the material in (4).

[611] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the U.S. Patent

7,323,310, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include an amplification reaction mixture to selectively amplify RNA from a target RNA model comprising: an RNA-polymerase

cell-dependent RNA rase and at least two primers complementary to the said target RNA model. In some embodiments, RNA-dependent RNA polymerase (RdRp) is a tomato or other cellular RdRp. In some embodiments, RdRp is a cellular RdRp and, in a particularly preferred embodiment, RdRp is selected from the group consisting of tomato RdRp, tobacco RdRp, cucumber RdRp and wheat RdRp. In some embodiments, the amplification reaction mixture comprises a cellular RdRp and at least two primers complementary to a target RNA model. In a preferred embodiment, the reaction mixtures further comprise an RNA helicase, an energy source and, optionally, a divalent cation, such as, for example, Mg2 +, Mn2 + or Co2 +. Amplification reaction mixtures may also include RNase inhibitors, RNA stabilizing agents, single-stranded binding proteins, rNTPs and rNTP analogs, for the amplification of the target RNA in the product's RNA. An amplification buffer that supports both RdRp and RNA helicase activity is also provided. In addition or alternatively, the detection of a genetic biomarker may include a method for selectively amplifying RNA from an RNA model, comprising: contacting the model RNA with an amplification reaction mixture according to claim 1; and incubating said amplification reaction mixture to produce the amplified RNA product, wherein said incubation step comprises at least one denaturation condition.

[612] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the U.S. Patent

6,977,153, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include a method that involves synthesizing first strand cDNA molecules from RNA molecules, circulating the first strand cDNA molecules, and replicating the first circularized strand cDNA molecules using rotation circle replication.

In addition or alternatively, the detection of a genetic biomarker may include a method of amplifying RNA sequences, the method comprising: incubating a cDNA primer and a sample of RNA comprising RNA molecules under conditions that promote the synthesis of first strand cDNA molecules from the RNA molecules; incubating a circularization probe and the first strand cDNA molecules under conditions that promote the circularization of the first strand cDNA molecules; and incubating the first circularized strand cDNA molecules under conditions that promote rotational circle replication of the first circularized strand cDNA molecules, thus amplifying the RNA sequences.

Additionally or alternatively, the detection of a genetic biomarker may include a method of amplifying RNA sequences, the method comprising: incubating a cDNA primer and an RNA sample comprising RNA molecules under conditions that promote the synthesis of first strand cDNA molecules from RNA molecules, wherein the conditions that promote the synthesis of first strand cDNA molecules comprise incubating the cDNA primer and the RNA sample in the presence of a reverse transcriptase; incubating the first strand cDNA molecules in the presence of an RNAse H activity; incubating the first strand cDNA molecules under alkaline conditions; neutralize the cDNA molecules on the first strand; purify the cDNA molecules on the first strand; incubate a circularization probe and the first strand cDNA molecules under conditions that promote the circularization of the first strand cDNA molecules, where the conditions that promote the circularization of the first strand cDNA molecules comprise incubating the circularization probe and the first strand cDNA molecules in the presence of ligase; incubate the cDNA molecules of the first circularized strand under conditions that promote replication of the rotating circle of the cDNA molecules of the first circularized strand, amplify the RNA sequences, in which the conditions that promote rotational circle replication of the first molecules of Circularized cDNAs comprise the incubation of the first circularized strand cDNA molecules in the presence of a DNA polymerase. In addition or alternatively, the detection of a genetic biomarker may include a method of amplifying RNA sequences, the method comprising: incubating a cDNA primer and a sample of RNA comprising RNA molecules under conditions that promote the synthesis of first strand cDNA molecules from RNA molecules; incubating a circularization probe and the first strand cDNA molecules under conditions that promote the binding of the first strand cDNA molecules to each other to form first strand cDNA competitors; and incubating the first strand cDNA competitors under conditions that promote the ribbon shift replication of the first strand cDNA concatamers, thereby amplifying the RNA sequences.

[613] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the U.S. Patent

6,815,212, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include methods provided to detect the binding of a first member to a second member of a pair of ligands, comprising the steps of (a) combining a set of first tagged members with a sample that may contain one or more second members, under conditions and for a time sufficient to allow the connection of a first member to a second member, in which said label is

relative to a specific first member and detectable by non-fluorescent spectrometry or potentiometry, (b) separating the first and second connected members from unconnected members, (c) cleaving the label of the first marked member and (d) detecting the label by non-fluorescent spectrometry or potentiometer and then detect the connection of the first member to the second member.

A wide variety of first and second member pairs can be used, including, for example, nucleic acid molecules (eg, DNA, RNA, nucleic acid analogs, such as PNA, or any combination thereof), proteins or polypeptides (for example, an antibody or antibody fragment (for example, monoclonal antibody, polyclonal antibody or a binding partner such as a CDR), oligosaccharides, hormones, organic molecules and other substrates (for example, xenobiotics such as glucuronidase - drug molecule) or any other pair of ligands.

In some embodiments, the first and second members may be of the same type of molecule or of different types.

For example, representative first and second member ligand pairs include: nucleic acid molecule / nucleic acid molecule; antibody / nucleic acid molecule; anti-body / hormone; antibody / xenobiotic; and antibody / protein.

In addition or alternatively, the detection of a genetic biomarker may include methods for analyzing the pattern of gene expression from a selected biological sample, comprising the steps of (a) exposing nucleic acids from a biological sample, (b) combining the nucleic acids exposed with one or more selected labeled nucleic acid probes, under conditions and for a time sufficient for the probes to hybridize with said nucleic acids, where the label is correlated with a specific nucleic acid probe and detectable by spectrometry non-fluorescent or potentiometry, (c)

separate hybridized probes from non-hybridized probes, (d) cleave the label of the labeled fragment and (e) detect the label by non-fluorescent spectrometry or potentiometry and then determine the pattern of genetic expression of the biological sample .

Within a modality, the biological sample can be stimulated with a selected molecule before the nucleic acid exposure step.

Representative examples of "stimulants" include nucleic acid molecules, recombinant gene delivery vehicles, organic molecules, hormones, proteins, inflammatory factors, cytokines, drugs, drug candidates, paracrine and autocrine factors and the like.

In other modalities, tags can be detected by fluorometry, mass spectrometry, infrared spectrometry, ultraviolet spectrometry or potentiostatic amperometry (for example, using coulometric or amperometric detectors). Representative examples of suitable spectrometric techniques include flight time mass spectrometry, quadrupole mass spectrometry, magnetic sector mass spectrometry and electrical sector mass spectrometry.

Specific modalities of such techniques include ion trap mass spectrometry, electrospray ionization mass spectrometry, ion spray mass spectrometry, liquid ionization mass spectrometry, atmospheric pressure ionization mass spectrometry, electron ionization mass spectrometry, fast atom bombardment mass spectrometry, MALDI mass spectrometry, photoionization flight time mass spectrometry, laser droplet mass spectrometry, MALDI-TOF mass, APCI mass spectrometry, nanoparse mass spectrometry, nebulized spray ionization mass spectrometry, chemical ionization mass spectrometry, resonance ionization mass spectrometry, secondary ionization mass spectrometry and thermospray mass spectrometry.

[614] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the U.S. Patent

6,361,940, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include compositions and methods to increase the specificity of nucleic acid hybridization and initiation of nucleic acids in PCR. In some modalities, the composition comprises a nucleic acid and a salt, the salt comprising an anion and a cation, the anion selected from halogenated acetate, propionate and halogenated propionate, the selected cation of primary, secondary and tertiary ammonium, comprising 1 -36 carbon atoms and quaternary ammonium comprising 4-48 carbon atoms. In some embodiments, the composition is not fluid and comprises an oligonucleotide of 6-100 nucleotides and a salt, the salt comprising an anion and a cation, the anion selected from acetate, halogenated acetate, propionate and halogenated propionate , the selected cation of primary, secondary and tertiary ammonium comprising 1-36 carbon atoms and quaternary ammonium comprising 4-48 carbon atoms. In some embodiments, the composition is free of organic solvent and comprises an oligonucleotide of 6-100 nucleotides and a salt, the salt comprising an anion and a cation, the anion selected from acetate, halogenated acetate, propionate and halogenated propionate, the selected cation primary, secondary and tertiary ammonium, comprising 1-36 carbon atoms and quaternary ammonium comprising 4-48 carbon atoms. In some embodiments, the composition comprises a nucleic acid and a salt, the nucleic acid immobilized on a solid support, the salt comprising an anion and a cation,

the anion selected from acetate, halogenated acetate, propionate and halogenated propionate, the cation selected from primary, secondary and tertiary ammonium, comprising 1-36 carbons and quaternary ammonium comprising 4-48 carbons. In addition or alternatively, the detection of a genetic biomarker may include a salt selected from the group: (a) an acetate salt of a cation of the formula HN (CH3) 2Ra where Ra is a hydrocarbyl C4-C7; (b) a halogenated acetate salt of a cation of the formula HN (CH3) 2 Rb wherein Rb is a C7-C12 hydrocarbyl; (c) acetate and halogenated acetate salts of a cation of the formula H2N (C5-C7cycloalkyl) Rc where Rc is a hydrocarbyl C1-C12; and (d) acetate and halogenated acetate salts of N-substituted piperdin, in which the piperidine nitrogen is replaced by hydrocarbyl C1-C12. Additionally or alternatively, the detection of a genetic biomarker can include an oligonucleotide in solution, in which the oligonucleotide is formed from constituents that include a plurality of fragments, each fragment shown schematically by the structure (1)

[615] where,

[616] represents a sequence of at least three nucleotides, as found in wild-type DNA, where "B" represents a base selected independently at each location; - represents a series of covalent chemical bonds called "specificity spacer", which separates and connects two bases B 3 and B 5; the specificity spacer having steric and chemical properties so that (a) does not prevent hybridization between a structure fragment (1) and an oligonucleotide fragment with a complementary base sequence, as shown schematically as structure (2) and

[617] and (b) cannot enter a hydrogen bond with a base positioned opposite itself in a sequence of complementary hybridized bases of the structure (2). Additionally or alternatively, the detection of a genetic biomarker may include an array that includes a plurality of oligonucleotides immobilized in an array format on a solid support, each oligonucleotide of the plurality formed from components that include a plurality of fragments, each fragment shown schematically by the structure (1)

[618] where,

[619] represents a sequence of at least three nucleotides, as found in wild-type DNA, where "B" represents a base selected independently at each location; - represents a series of covalent chemical bonds called "specificity spacer", which separates and connects two bases B 3 and B 5; the specificity spacer having steric and chemical properties so that (a) does not prevent hybridization between a structure fragment (1) and an oligonucleotide fragment with a complementary base sequence, as shown schematically as structure (2) and

[620] and (b) cannot enter hydrogen bond with a base positioned opposite itself in a sequence of complementary hybridized bases of the structure (2). In addition or alternatively, the detection of a genetic biomarker may include a method to distinguish between hybridization of a complementary nucleic acid target and a nucleic acid probe in which the probe and the target are perfectly complementary and in which the probe and the target have one or more base incompatibilities, comprising: (a) mixing the nucleic acid target with the nucleic acid probe in a solution comprising a hydrotrope; (b) hybridizing a discriminating temperature; and (c) detecting the probe hybridized to the target, thereby determining whether the probe and the nucleic acid target are perfectly complementary or incompatible. In a preferred embodiment, the nucleic acid probe is labeled with a radioactive molecule, fluorescent molecule, mass spectrometry tag or enzyme. In preferred embodiments, the nucleic acid probe and / or the target nucleic acid is 6 to 40 bases. Preferably, the hydrotrope is an ammonium salt. Specific preferred ammonium salt hydrotropes include, without limitation, bis (2-methoxyethyl) amine acetate, 1-ethylpiperidine acetate, 1-ethylpiperidine trichloroacetate, 1-ethylpiperidine trifluoroacetate, 1-acetate -methylimidizole, 1-methylpiperidine acetate, 1-methylpiperidine trichloroacetate, 1-methylpyrrolidine acetate, 1-methylpyrrolidine trichloroacetate, 1-methylpyrrolidine trifluoroacetate, 2-methoxyethylamine acetate, nitrate acetate, nitrate acetate, acetate -hexylamine, N, N- trifluoroacetate

dimethylcyclohexylamine, N, N-dimethylcyclohexylamine, N acetate, N-dimethylheptylamine, NN-dimethylheptylamine acetate, N, N-dimethylhexylamine acetate, N, N-dimethyl- acetate hexylamine, N acetate, N-dimethylisopropylamine, N-ethylbutylamine acetate, N-ethylbutylamine trifluoroacetate, N, N-dimethylaminobutane trichloroacetate, N, N-dimethylisopropylamine trichloroacetate, triethylamine acetate, triethanolamine acetate, triethanolamine acetate triethylamine, tripropylamine acetate and tetraethylammonium acetate.

Other suitable hydrotropes include LiTCA, RbTCA, GuSCN, NaSCN, NaClO 4, KI, TMATCA TEATCA, TMATBA, TMTCA, TMTBA, TBATCA and TBATBA.

Preferably, the hydrotrope is present at a molarity of about 0.005 M to about 6 M.

Preferably, the probe's nucleic acid is DNA or RNA, and the target nucleic acid is DNA or RNA.

Preferably, the target nucleic acid is attached to a solid substrate.

Preferably, the method further comprises the polymerase chain reaction.

Additionally or alternatively, the detection of a genetic biomarker may include a method to distinguish between hybridization of a complementary nucleic acid target and a nucleic acid probe in which the probe and the target are perfectly complementary and in which the probe and the targets have one or more base incompatibilities, comprising: (a) mixing a nucleic acid target with a nucleic acid probe containing at least one abasic or deoxyNebularine substitution; (b) hybridize at a temperature of discrimination; and (c) detecting a probe attached to the target, thereby determining whether the probe and the nucleic acid target are perfectly complementary or incompatible.

Additionally or alternatively, the detection of a genetic biomarker may include a method to increase discrimination in a nucleic acid synthesis procedure, comprising: (a) mixing a single-stranded nucleic acid target with a primer oligonucleotide in a solution comprising a hybrid and a polymerase; (b) annealing the primer to the target at a temperature of discrimination; and (c) synthesizing a chain complementary to the target to form a duplex. In addition or alternatively, the detection of a genetic biomarker may include a method for distinguishing a single base change in a nucleic acid molecule from a wild-type sequence, comprising: (a) mixing a nucleic acid target of single strand with an oligonucleotide primer in a solution comprising an amine-based salt and a polymerase, wherein the oligonucleotide primer has a 3 'base more complementary to the wild type sequence or single base change; (b) bringing the initiator back to the target at a temperature of discrimination; (c) extending the initiator, in which a complementary ribbon to the target is synthesized when the base 3 'of the initiator is complementary to the target; and (d) detecting the extension of the initiator.

[621] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the U.S. Patent

6,248,521, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker can include methods of amplifying nucleic acid molecules from a model comprising (a) mixing single stranded nucleic acid models on a solid substrate with a solution comprising an oligonucleotide primer that hybridizes with the models and a DNA polymerase, in which the mixing occurs in different areas on the substrate and where the solution remains in different areas; (b) synthesize a complementary tape to the model to form a duplex; (c) denaturing the duplex; and (d) synthesize strands complementary to the model, amplifying the nucleic acid molecules; where mixing, synthesis and denaturation are carried out at the dew point. The solid substrate can be a silicone tablet or glass slide. The models can be fixed

to the solid substrate or deposited on the substrate surface. The model can be applied uniformly to the entire arrangement before mixing or applied individually to each distinct area in the substrate. When applied individually, the application is preferably carried out using spring probes. In a more preferred embodiment, an apparatus is used to control the dew point. In addition or alternatively, the detection of a genetic biomarker may include a method for performing a single nucleotide extension assay, comprising (a) mixing oligonucleotides on a solid substrate with a solution comprising single-stranded nucleic acid molecules they hybridize with oligonucleotides, a single nucleotide and a DNA polymerase, in which the mixing occurs in different areas of the substrate and where the solution remains in different areas; and (b) detecting an oligonucleotide extension product; wherein the oligonucleotide will be extended only when the single nucleotide is complementary to the nucleotide adjacent to the hybridized oligonucleotide, where mixing is carried out at the dew point.

[622] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in US Patent Application Publication 2018/0155705, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include a method for isolating extracellular nucleic acids from a biological sample, comprising (a) preparing a sample from the sample comprising: i) extracellular nucleic acids; ii) particles that provide an anion exchange surface; iii) at least one non-ionic detergent which is a polyoxyalkylene fatty alcohol ether; iv) optionally at least one salt, wherein the ligation mixture has a pH so that the extracellular nucleic acids bind to the particles, (b) separating the particles with the attached extracellular nucleic acids from the remaining ligation mixture; (c) optionally washing the attached extracellular nucleic acids; and (d) optionally eluting linked extracellular nucleic acids. In some embodiments, the bonding mixture is prepared by forming a suspension by contacting the particles with a lysis and / or bonding composition comprising at least one polyoxyalkylene fatty ether and optionally comprising a salt and / or a plug; contacting the suspension with the sample comprising extracellular nucleic acids; and optionally add a proteolytic enzyme before, at the same time or after contacting the sample with the suspension. In addition or alternatively, the detection of a genetic biomarker may include a kit to perform the method, which comprises (a) a lysis and / or bond composition comprising: i) at least one nonionic detergent which is an ether polyoxyalkylene fatty alcohol; ii) optionally at least one salt; iii) at least one buffer; wherein said composition has an acidic pH; (b) particles that provide an anion exchange surface; (c) optionally a proteolytic enzyme; (d) optionally one or more washing solutions and (e) optionally one or more elution solutions.

[623] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in US Patent Application Publication 2018/0148716, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include methods for generating a double circular stranded DNA (dsDNA) or a sequencing library, in which the method comprises circulating a dsDNA or ligating a first and a second dsDNA into the presence of a DNA ligase and a single-stranded DNA-binding protein or a double-stranded DNA-binding protein.

In some embodiments, the method for generating a sequencing library comprises additional steps, prior to binding, to: (i) provide DNA fragments; (ii) repair the end of the DNA fragments by a polynucleotide kinase enzyme and an enzyme with polymerase and exonuclease activities; and (iii) optionally adding a terminal adenine to the end of the DNA fragments repaired at the end by a deoxynucleotidyl transferase enzyme.

In some embodiments, said method further comprises the subsequent steps of purification and size selection of the bound fragments for sequencing.

In some embodiments, fragments attached to the adapter are amplified before sequencing.

In addition or alternatively, the detection of a genetic biomarker may include a kit comprising: (i) a DNA ligase; and (ii) a single-stranded DNA binding protein (ssDNA) or a double-stranded DNA binding protein (dsDNA). In some embodiments, the kit comprises: (i) a polynucleotide kinase and an enzyme with polymerase and exonuclease activities; (ii) optionally a deoxynucleotidyl transferase; (iii) a DNA ligase; (iv) a single- or double-stranded DNA-binding protein; and (v) optionally a reaction buffer.

In preferred embodiments, either kit comprises a mixture of a ligase, a single-stranded DNA-binding protein (ssDNA) or a double-stranded DNA-binding protein (dsDNA) and, optionally, a reaction buffer.

In a preferred embodiment, the enzyme with polymerase and exonuclease activities is a DNA polymerase.

In the methods or kits mentioned above, the polynucleotide kinase enzyme is T4 polynucleotide kinase (PNK), the enzyme with polymerase and exonuclease activities is T4 DNA polymerase and / or the deoxynucleotidyl transferase enzyme is a Taq polymerase or a Klenow fragment exo- . In some embodiments, binding methods are referred to, where the first and second dsDNAs comprise two ssDNA ends, where each of the ssDNA ends of the first dsDNA binds to each of the complementary ss ends of the second dsDNA to provide connected circular dsDNA. In some embodiments, the first or second DNA is able to confer the ability to automatically replicate within the competent cells. In the methods or kits mentioned above, the DNA binding protein is a viral, bacterial, archaeal or eukaryotic single-stranded DNA-binding protein or double-stranded DNA-binding protein. In some embodiments, the DNA ligase in any of the above methods or kits is a T3 DNA ligase or a T4 DNA ligase. In other embodiments, the ligase is a T7 DNA ligase or an Ampligase®. In some modalities of the above methods, each of the first and second dsDNAs has one or two ends of single-stranded DNA (ssDNA). These ssDNA end (s) are / are less than 20 nucleotides (nt) in length.

[624] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in US Patent Application Publication 2018/0002738, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include methods for amplifying target nucleic acids in a nucleic acid sample and kits useful in those methods. In some embodiments, the method for amplifying target nucleic acids in a nucleic acid sample, comprises: (a) extending each of a plurality of barcode primers (BC primers) to obtain extension products using the acids target nucleicles as templates, where (i) each barcode primer comprises, from 5 'to 3', a 1st universal primer sequence (US1), a molecular marker sequence (MT) and a specific 1st target sequence

(TS1), (ii) a plurality of barcode primers comprise at least 20 different barcode primers and (iii) among the plurality of barcode primers (BC primers), the first universal primer sequences (US1) are the same, but the 1st target specific sequences (TS1) are different; (b) separating the plurality of barcode initiators that were not extended in step (a) from the extension products; and (c) amplifying the extension products of step (b) in the presence of a plurality of limited amplification primers (LA primers) to obtain a plurality of first amplification products, where (i) each amplification initiator Limited comprises, from 5 'to 3', a 2nd universal primer sequence (US2) and a 2nd specific target sequence (TS2) and (ii) among the plurality of limited amplification primers, the 2nd universal primer sequences (US2) are the same, but the 2nd target specific sequences (TS2) are different.

In addition or alternatively, the detection of a genetic biomarker may include a kit comprising: (1) a plurality of barcode primers (BC primers), where (i) each barcode primer comprises, from 5 'to 3', a 1st universal primer sequence (US1), a molecular labeling sequence (MT) and a 1st target specific sequence (TS1), (ii) a plurality of barcode primers comprise at least 20 different barcode primers and (iii) among the plurality of barcode primers, the 1st universal primer sequence (US1) is the same, the molecular tag (MT) sequences are different and the 1st target specific sequence (TS1) is different; and (2) a plurality of limited amplification primers (LA primers), wherein (i) each limited amplification primer comprises, from 5 'to 3', a second universal primer sequence (US2) and a second target specific sequence (TS2) and (ii) among the plurality of limited amplification primers, the second universal primer sequences (US2) are the same, but the second target specific sequence (TS2) is different.

[625] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in US Patent Application Publication 2016/0374330, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include a suitable method for stabilizing an extracellular nucleic acid population comprised in a biological sample containing cells, comprising contacting the sample containing cells with at least one poly (oxyethylene) polymer. as a stabilizing agent or with mono-ethylene glycol as a stabilizing agent. In addition or alternatively, the detection of a genetic biomarker may include a method for isolating extracellular nucleic acids from a biological sample containing cells, wherein said method comprises: a) stabilizing the biological sample containing cells according to the method defined above; and b) isolating extracellular nucleic acids from the stabilized sample. In addition or alternatively, the detection of a genetic biomarker may include a composition suitable for stabilizing a biological sample containing cells, comprising: i) a poly (oxyethylene) polymer as a stabilizing agent or ii) mono-ethylene glycol as a stabilizing agent and one or more, preferably two or more additional additives selected from the group consisting of: one or more primary, secondary or tertiary amides; a caspase inhibitor; an anticoagulant and / or a chelating agent. Preferably, the composition comprises a poly (oxyethylene) polymer, which is preferably a high molecular weight poly (oxyethylene) polymer with a molecular weight of at least 1500,

as a stabilizing agent and furthermore comprises one or more, preferably two or more additives selected from the group consisting of at least one more poly (oxyethylene) polymer with a molecular weight that is at least 100, preferably at least 200 at least 300 or at least 400 below the molecular weight of the first poly (oxyethylene) polymer, which is preferably a high molecular weight poly (oxyethylene) polymer, wherein said additional poly (oxyethylene) polymer is preferably a low molecular weight poly (oxyethylene) polymer having a molecular weight of 1000 or less; one or more primary, secondary or tertiary amides; a caspase inhibitor; an anticoagulant and / or a chelating agent.

In addition or alternatively, the detection of a genetic biomarker may include a collection device for collecting a biological sample containing cells, where the collection device comprises i) a poly (oxyethylene) polymer as a stabilizing agent or ii) monoethylene glycol as a stabilizing agent and one or more additional additives selected from the group consisting of: one or more primary, secondary or tertiary amides; a caspase inhibitor; an anticoagulant and / or a chelating agent.

Preferably, the collection device according to the fifth aspect comprises a poly (oxyethylene) polymer, which is preferably a high molecular weight poly (oxyethylene) polymer with a molecular weight of at least 1500, as this agent biliser and furthermore comprises one or more, preferably two or more additional additives selected from the group consisting of at least one more poly (oxyethylene) polymer with a molecular weight that is at least 100, preferably at least 200, at least 300 or at least 400 below the molecular weight of the first poly (oxyethylene) polymer which is preferably a high molecular weight poly (oxyethylene) polymer, wherein said additional poly (oxyethylene) polymer is preferably a poly (oxyethylene) polymer low molecular weight with a molecular weight of 1000 or less; one or more primary, secondary or tertiary amides; a caspase inhibitor; an anticoagulant and / or a chelating agent.

[626] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in US Patent Application Publication 2016/0048564, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include a method to build a database of the variant observation community, including: receiving human variant data sets derived from samples generated by a plurality of distinct users, in which users consented to share observations of combined variants with other users; store the human variant data sets received in a knowledge base of genomic information; search the knowledge base to identify a plurality of variant observations that meet the inclusion criteria of a set; add each variant observation identified to the set; and calculate one or more anonymous statistics for alleles in the set, where at least one of receiving, storing, searching, adding or calculating is performed by one or more computers. Additionally or alternatively, the detection of a genetic biomarker may include a method for determining a candidate for a clinical trial, comprising: receiving criteria for enrollment in clinical trials from a user, including criteria for genetic targeting; search a knowledge base of patient testing information received from a plurality of independent entities in search of patients who meet the criteria for enrollment in clinical trials; and provide the user with search results for consenting patients who match

meet the criteria for enrollment in clinical trials; in which at least one of receiving, researching or providing is carried out by one or more computers.

[627] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in US Patent Application Publication 2016/0017320, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include methods to reduce errors in sequencing and thus improve accuracy in detecting mutations and creating transcriptome profiles, including methods that use semi-random bar codes to mark sequencing fragments before amplification to reduce bias and sequencing errors. In addition or alternatively, the detection of a genetic biomarker can include oligonucleotides that comprise semi-random bar code sequences, including sequencing adapters, reverse transcription primers and PCR primers. The term "semi-random barcode strings", "semi-random strings" or "semi-random barcodes" refers to a population of semi-random nucleotide sequences, each consisting of (Xmer) n, where Xmer is 3-mer (that is, a 3-nucleotide oligonucleotide, also known as "trimer"), 4-mer (that is, a 4-nucleotide oligonucleotide, also known as "tetramer"), 5- mer (that is, a 5-nucleotide oligonucleotide, also known as a "pentamer") or 6-mer (that is, a 6-nucleotide oligonucleotide, also known as "hexamer") and n is an integer from 2 to 10. Each The nucleotide sequence in the population is called "semi-random barcode sequence", "semi-random barcode" or "semi-random sequence". In certain embodiments, the semi-random sequence consists of (Xmer) n, where Xmer is 3-mer and n is 2, 3, 4, 5, 6, 7, 8, 9 or 10, preferably 4, 5, 6, 7, 8 or 9. In certain embodiments, Xmer is 4-mer, and n is 2, 3, 4, 5, 6, 7, 8, or 9, preferably 2, 3, 4, 5, 6, or 7. In certain embodiments, Xmer is 5-mer, n is 2, 3, 4, 5, 6, 7 or 8, preferably 2, 3, 4, 5 or 6. In certain ways, Xmer is 6-mer, and n is 2, 3, 4, 5, 6 or 7, preferably 2, 3, 4 or

5. Semi-random barcode sequences can be synthesized from a mixture of Xmers with defined sequences. For example, in certain embodiments, semi-random bar codes consist of (Xmer) n, where Xmer is 3-mer and n is 7. In other words, semi-random bar codes are a population of 21 nucleotides bp consisting of 7 trimers. Such semi-random bar codes can be synthesized with 7 successive steps during each step, a random trimer from a defined trimer mixture can be incorporated. An Xmer blend defined to synthesize semi-random bar codes can have 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, or 25, or at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, or 25 different Xmers. The number of different semi-random barcode sequences synthesized from a defined Xmer mix can be at least 100, 200, 300, 400, 500, 1,000,

2,000, 3,000, 4,000, 5,000, 10,000, 50,000, 100,000, 50,000,

100,000,500,000 or 1,000,000. Preferably, each Xmer (for example, trimer, tetramer, pentamer and hexamer) has at least 2 different bases from another Xmer in a defined Xmer mix, so that any single base variant within each Xmer block in the sequencing readings can be identified as errors, not a different barcode. In certain embodiments, each Xmer (for example, tetramer, pentamer and hexamer) has at least 3 different bases from another Xmer in a defined Xmer mix, so that any one or two base variant within each Xmer block in the readings sequencing can be identified as errors, not a different barcode.

In certain embodiments, each Xmer (for example, pentamer and hexamer) has at least 4 bases other than another Xmer in a defined Xmer mix, so that any 1, 2, or 3 base variant within each Xmer block in the sequencing readings can be identified as errors, not a different barcode.

In addition or alternatively, the detection of a genetic biomarker may include a plurality of single-stranded oligonucleotides (ss), wherein each oligonucleotide comprises from the 5 'to 3' direction a 1st sequence and a 2nd sequence; (a) the 1st sequence is a semi-random sequence consisting of (Xmer) n, where Xmer is 3-mer, 4-mer, 5-mer or 6-mer, and n is an integer from 2 to 8, and (b ) the 2nd sequence is (i) at least 10 nucleotides in length, (ii) totally or substantially complementary to a target sequence and (iii) the same among the plurality of oligonucleotides.

Additionally or alternatively, the detection of a genetic biomarker can include a plurality of double-stranded sequencing adapters (ds), wherein each sequencing adapter comprises: (a) an oligonucleotide ("1st oligonucleotide") from above and (b) a 2nd ss oligonucleotide that comprises from the 3 'to 5' direction (i) a sequence ("Sequence A") that is completely complementary to the 1st sequence of the 1st oligonucleotide of the sequencing adapter and (ii) the target sequence ("Sequence B”), and in which the 1st oligonucleotide anneals with the second ss oligonucleotide.

Additionally or alternatively, the detection of a genetic biomarker can include a plurality of sets of double-stranded sequencing adapters (ds), where (A) each set comprises a plurality of single-stranded sequencing adapters (ss ), where each sequencing adapter in each set comprises: (a) an oligonucleotide ("1st oligonucleotide") from the plurality of ss oligonucleotides from above and (b) a 2nd ss oligonucleotide comprising: (i) one sequence ("Sequence A") that is fully complementary to the 1st sequence of the 1st sequence adapter oligonucleotide and (ii) the target sequence ("Sequence B") located 5 'to Sequence A and (iii) a sequence ( "Sequence C") which is located 3 'to Sequence B and is completely complementary to the 3rd sequence of the 1st oligonucleotide, and in which the 1st oligonucleotide anneals with the 2nd oligonucleotide; and (B) in which the plurality of ds sequencing adapters in different sets is identical to each other, except in the 3rd sequence of the 1st oligonucleotide and in Sequence C of the 2nd oligonucleotide. Additionally or alternatively, the detection of a genetic biomarker may include a method for preparing a sequencing library that comprises (1) linking the plurality of ds sequencing adapters that comprise a semi-random bar code sequence (that is, the 1st sequence of the 1st oligonucleotide) to dsDNA molecules or fragments of a sample.

[628] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in US Patent Application Publication 2015/0275267, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include a method provided for the preparation of a composition lacking target RNA from a composition containing initial RNA, comprising: a) contacting the composition containing initial RNA with one or more groups probe molecules, wherein a group of probe molecules has the following characteristics: i) the group comprises two or more different probe molecules with a length of 100 nt or less; ii) the probe molecules comprised in said group are complementary to a target region present in a target RNA; iii) when hybridized to said target region, the two or more different probe molecules are located adjacent to each other in the formed double-stranded hybrid; and generating a double-stranded hybrid between the target RNA and the probe molecules; b) capture the double-stranded hybrid using a binding agent that binds the double-stranded hybrid, thus forming a hybrid / binding agent complex; c) separate the hybrid / binding agent complexes from the composition, thus providing a composition devoid of target RNA.

In addition or alternatively, the detection of a genetic biomarker may include specifically designed groups of probe molecules that hybridize and thus mark the unwanted target RNA, such as different species of rRNA, for depletion.

Each group of probe molecules targets a specific region in a target RNA, also known as a target region, and comprises two or more different short probe molecules that hybridize to that target region.

When hybridized to their target region, the short probe molecules of a group are located adjacent to each other in the formed double-stranded hybrid and, therefore, located in the vicinity.

The formed double-stranded hybrid extends and thus covers the target region.

The double-stranded hybrid formed that comprises the short probe molecules of a group is then linked by an anti-hybrid linker, by which a hybrid / linker complex is formed.

Said complexes can be easily separated from the remaining composition, thus removing the unwanted target RNA and thus providing a composition lacking the target RNA.

In addition or alternatively, the detection of a genetic biomarker may include a method for sequencing RNA molecules of interest comprised in a sample, comprising: a) obtaining a composition containing RNA, preferably isolating the total RNA from the sample; b) depleting the unwanted target RNA of the RNA-containing composition, which is preferably total RNA, using the method according to the first aspect, thus providing a deprived target RNA composition; c) optionally removing unbound probe molecules; d) sequencing RNA molecules comprised in the composition without target RNA.

[629] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in US Patent Application Publication 2015/0225775, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include a method for performing the polymerase chain reaction (PCR), comprising: a) amplifying one or more different target nucleic acids in the presence of one or more pairs of different primers, specific for one or more different target nucleic acids in a single reaction mixture via PCR, where each primer from one or more pairs of different primers contains one or more clickable bases and in substantially all primers from one or more pairs of different primers, each or more cleavable bases are at least 4 nucleotides from the 3 'terminal of the primer comprising the one or more cleavable bases. In certain embodiments, in substantially all primers of one or more pairs of different primers, each of the one or more cleavable bases is at least 5 nucleotides away from the 3 'terminus of the primer comprising the one or more cleavable bases. In certain other embodiments, in substantially all primers of one or more pairs of different primers, each of the one or more cleavable bases is at least 6 nucleotides away from the 3 'terminal of the primer comprising the one or more cleavable bases. In certain other embodiments, in substantially all primers of one or more pairs of different primers, each of the one or more cleavable bases is at least 7 nucleotides away from the 3 'terminal of the primer comprising the one or more bases cleavable. In certain other embodiments, in substantially all primers from one or more pairs of different primers, each of the one or more cleavable bases is at least 8 nucleotides away from the 3 'terminal of the primer comprising the one or more bases clickable.

Step a) may comprise amplifying a single target nucleic acid in the presence of a specific primer pair for the single target nucleic acid in the single reaction mixture.

Alternatively, step a) can comprise amplifying a plurality of different pairs of primers specific to the plurality of different target nucleic acids in the single reaction mixture.

In certain embodiments, in all primers of one or more pairs of different primers, each of the one or more cleavable bases is at least 4 nucleotides away from the 3 'terminal of the primer comprising the one or more cleavable bases.

In certain other embodiments, in all primers of one or more pairs of different primers, each of the one or more cleavable bases is at least 5 nucleotides away from the 3 'terminal of the primer comprising the one or more cleavable bases.

In certain other embodiments, in all primers of one or more pairs of different primers, each of the one or more clickable bases is at least 6 nucleotides away from the 3 'terminal of the primer comprising the one or more cleavable bases .

In certain other embodiments, in all primers of one or more pairs of different primers, each of the one or more cleavable bases is at least 7 nucleotides away from the 3 'terminal of the primer comprising the one or more cleavable bases.

In certain other modalities, in all primers of one or more pairs of different primers, each of the one or more cleavable bases is at least 8 nucleotides away from the 3 'terminal of the primer comprising the one or more cleavable bases.

Preferably, the cleavable base is uracil.

Alternatively, the cleavable base is inosine, an oxidized pyrimidine, an oxidized purine, 5-hydroxyuracil, 5-hydroxylmethyluracil or 5-formyluracil.

The one or more different primer pairs can comprise at least 100 different primer pairs.

In some modalities,

the method may further comprise one or more: b) cleaving the one or more cleavable bases in the amplification product (s) from step a) to produce single-stranded DNA protrusions in the amplification product (s) - cation, c) digest the single-stranded DNA protrusions obtained in step b) to generate trimmed amplification product (s), d) binding adapters to the trimmed amplification product (s) to produce pro - trimmed amplification duct (s) connected to the adapter and e) sequence the trimmed amplification product (s) connected to the adapter in step d). In addition or alternatively, the detection of a genetic biomarker may include a set of primer pairs, comprising: one or more different primer pairs, specific for one or more different target nucleic acids, where each primer one or more pairs of different primers contains one or more cleavable bases, and in substantially all primers of one or more pairs of different primers, each of the one or more cleavable bases is at least 4 nucleotides away from the 3 'terminal of the primer comprising the one or more clickable bases.

In certain embodiments, in substantially all primers of one or more pairs of different primers, each of the one or more cleavable bases is at least 5 nucleotides away from the 3 'terminal of the primer comprising the one or more cli s bases. - feasible.

In certain other embodiments, in substantially all primers of one or more pairs of different primers, each of the one or more cleavable bases is at least 6 nucleotides away from the 3 'terminal of the primer comprising the one or more cleavable bases.

In certain other embodiments, in substantially all primers of one or more pairs of different primers, each of the one or more cleavable bases is at least 7 nucleotides away from the 3 'terminal of the primer comprising the one or more cleavable bases.

In certain other embodiments, in substantially all primers of one or more pairs of different primers, each of the one or more cleavable bases is at least 8 nucleotides away from the 3 'terminal of the primer comprising the one or more cleavable bases. In addition or alternatively, the detection of a genetic biomarker may include a PCR reaction mixture, comprising: the set of primer pairs, a DNA polymerase, dNTPs and a PCR reaction buffer.

[630] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in US Patent Application Publication 2015/0197787, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include a method for enriching target sequences from a sequencing library to provide an enriched sequencing library, wherein the sequencing library is suitable for massive parallel sequencing and comprises a plurality of molecules double-stranded nucleic acid, in which the method comprises: a) providing nucleoprotein filaments comprising (i) a single-stranded invasion probe, in which the invasion probe has a region of substantial complementarity to a strand a double-stranded target sequence, (ii) a recombinase; b) forming a complex between the invasion probe and a complementary portion of the target sequence in which the formation of the complex is mediated by recombinase; c) separate the complexes from the remaining sequencing library, thus enriching the target sequences. In addition or alternatively, the detection of a genetic biomarker may include a method for sequencing a target region of interest, comprising: a) providing a suitable sequencing library for massive parallel sequencing and comprising a plurality of acid molecules double-stranded nucleic acid, wherein a portion of the double-stranded nucleic acid molecules comprised in the sequencing library, the target sequences comprise a sequence that is in the target region of interest; b) enrich target sequences corresponding to the target region of interest according to the method above, thus providing a library of enriched target sequences; c) sequencing the enriched target sequences in parallel. Additionally or alternatively, the detection of a genetic biomarker may include the use of the sequencing method for exome sequencing, exon sequencing, targeted genomic resequencing, targeted panel genomic resequencing, transcriptome sequencing and / or molecular diagnosis. In addition or alternatively, the detection of a genetic biomarker may include a kit to perform a method according to the first aspect, which comprises: a) adapters to create a suitable sequencing library for massive parallel sequencing; b) optionally one or more ligation reagents to couple the adapters to a nucleic acid fragment; c) a recombinase, preferably a RecA-type recombinase; d) a non-hydrolyzable cofactor for recombinase, preferably 5 '- (gamma-thio) triphosphate adenosine; e) a plurality of different invasion probes, in which the invasion probes differ in their region of complementarity with a region of target interest; f) a plurality of different stabilization probes, at least partially complementary to the plurality of invasion probes; and g) a solid support suitable for capturing synaptic complexes formed between the invasion probes and the target sequences.

[631] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in US Patent Application Publication 2015/0093756, which is incorporated herein by reference in its entirety.

For example, the detection of a genetic biomarker may include methods of amplifying a target nucleic acid in a helicase-dependent reaction.

In addition or alternatively, the detection of a genetic biomarker may include methods of amplifying and detecting a target nucleic acid in a helicase-dependent reaction, as well as modified detection tags to aid detection.

In some embodiments, the method for amplifying a target nucleic acid in a helicase-dependent reaction, the method comprises: (a) providing target nucleic acid to be amplified; wherein the target nucleic acid is double-stranded and is denatured by heating to 65 ° C for 10 minutes in the presence of 50 mM NaOH before step (b); (b) adding oligonucleotide primers for hybridization with the target nucleic acid of step (a); (c) synthesizing an extension product of the oligonucleotide primers that are complementary to the molds, using a DNA polymerase to form a duplex; (d) contacting the duplex from step (c) with a helicase preparation to unwind the duplex, so that the helix case preparation comprises a helicase and a simple ribbon-binding protein (SSB), unless the helicase preparation comprises a thermostable helicase in which the single-stranded binding protein is optional; and (e) repeat steps (b) (d) to exponentially and selectively amplify the target nucleic acid in a helicase-dependent reaction.

In addition or alternatively, the detection of a genetic biomarker may include a method that amplifies a target nucleic acid in a helicase-dependent reaction, in which the target nucleic acid is subjected to a "pre" step involving RNA probes and hybrid RNA-DNA capture antibodies.

This method comprises: (a) providing target nucleic acid to be amplified; wherein the target nucleic acid is single-stranded DNA and where a complementary RNA probe is added to the single-stranded DNA to bind to the DNA to form a target nucleic acid RNA-DNA hybrid; and wherein a hybrid capture antibody that recognizes RNA-DNA hybrids bound to a magnetic sphere is added to the RNA-DNA hybrid to be used in step (b) (b) adding oligonucleotide primers to hybridize to the RA- Target nucleic acid DNA from step (a); (c) synthesize an extension product of the oligonucleotide primers that are complementary to the models, using a DNA polymerase to form a duplex; (d) contacting the duplex from step (c) with a helicase preparation to unroll the duplex, so that the helicase preparation comprises a helicase and a single-stranded binding protein (SSB), unless the helicase preparation comprises a thermostable helicase in which the single-stranded binding protein is optional; and (e) repeating steps (b) - (d) to exponentially and selectively amplify the target nucleic acid in a helicase-dependent reaction. In addition or alternatively, the detection of a genetic biomarker may include a modified TaqMan probe (and method using that probe). The probe has a short tail at the 3 'or 5' end, complementary to the 5 'or 3' end, and where the TaqMan probe is complementary to the target nucleic acid, except for this short tail and where the short tail sequence forms a rod handle structure.

[632] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in US Patent Application Publication 2015/0011416, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include a method for determining the presence of one or more target polynucleotides, the method comprising; perform a PCR amplification scheme comprising cycles of stripping, annealing of primers and extension of primers in a reaction mixture comprising a nucleic acid sample and a set of oligonucleotide primers specific for each target polynucleotide ; wherein each set of oligonucleotide primers comprises a first subset of at least one truncated double domain direct primer and a second subset of at least one reverse primer; wherein each truncated double domain primer in a set comprises a 5 'tail region that differs from the 5' tail region in other truncated double domain primers in the set and a 3 'core region complementary to a sequence in a double-stranded nucleic acid strand comprising the target; wherein for each truncated double domain primer, the 3 'core substantially anneals its complementary target site sequence to a first annealing temperature and the sequence comprised of the 5' core and 3 'tail region substantially anneal to its complement at a second annealing temperature, the second annealing temperature being higher than the first annealing temperature, so that, at the second annealing temperature, the 3 'core of the truncated double domain initiator cannot anneal substantially a model molecule that also lacks the 5 'tail truncated double domain primer sequence complement; wherein for each truncated double domain primer of a set of primers: the 5 'tail sequence has no homology to the target sequence, and the 5' tail sequence has 6 or less contiguous homologous bases with respect to any another 5 'tail sequence of the truncated double domain primer set; wherein the PCR amplification regime comprises first and second stages, the first stage comprising annealing at the first annealing temperature for a first set of cycles, and; the second phase comprising annealing at the second annealing temperature for a second set of cycles; and detecting an amplified product for each target polynucleotide, where detection indicates the presence of the target polynucleotide.

In some embodiments, a reverse primer comprises a double domain primer.

In some embodiments, a reverse primer comprises a truncated double domain primer.

In some embodiments, a reverse primer comprises an amplification primer.

In some embodiments, the primer set comprises at least one direct dual domain primer; wherein each double domain primer in a set comprises a 5 'tail region that differs from the 5' tail region of other double domain primers in the set, a 3 'core region complementary to a sequence in a double-stranded nucleic acid strand comprising said target and a terminal nucleotide complementary to one of the variant nucleotides occurring at said target site; wherein for each double domain primer, the 3 'core substantially anneals its complementary sequence from the target site at a first annealing temperature and the sequence comprised of the 5' core and 3 'tail region substantially anneals to its complement at a second annealing temperature, the second annealing temperature being higher than the first annealing temperature, so that, at said second annealing temperature, said 3 ′ core of said double domain initiator cannot anneal substantially a model molecule that also lacks the 5 ′ tail complement of the double domain primer sequence; and wherein for each truncated double domain primer of a set of primers: the 5 'tail sequence has no homology to the target sequence; and the 5 'tail sequence has 6 or less homologous bases contiguous to any other 5' tail sequence of the truncated double or double domain primer set.

In addition or alternatively, the detection of a genetic biomarker may include a composition for determining the presence of one or more target polynucleotides, comprising; at least a set of specific oligonucleotide primers for each target polynucleotide; wherein each set of oligonucleotide primers comprises a first subset of at least one truncated double domain forward primer and a second subset of at least one reverse primer; wherein each truncated double domain primer in a set comprises a 5 'tail region that differs from the 5' tail region in other truncated double domain primers in the set and a 3 'nucleus region complementary to a sequence copying onto a strand of a double stranded nucleic acid comprising the target; wherein for each truncated double domain primer, the 3 'core substantially anneals its complementary sequence from the destination site at a first annealing temperature, and the sequence comprised of the 5' tail and 3 'core anneal subs - substantially to its complement to a second annealing temperature, the second annealing temperature being higher than the first annealing temperature, so that, in the second annealing temperature, the 3 ′ core of the domain initiator truncated double cannot substantially anneal a model molecule that also lacks the 5 'tail complement of the primer sequence; wherein for each member of the truncated double domain primer set: the 5 'tail sequence has no homology to the target sequence; and the 5 'tail sequence has 6 or less contiguous homologous bases with respect to the other 5' tail sequences of the truncated double domain primer set.

In some embodiments, the composition may further comprise a sample of nucleic acid.

Additionally or alternatively, the detection of a genetic biomarker can be a method to determine the presence of one or more target polynucleotides, the

all comprising; perform a PCR amplification regimen comprising strand separation cycles, annealing of primers and extension of primers in a reaction mixture comprising a nucleic acid sample and a set of specific oligonucleotide primers for each target polynucleotide; wherein each set of oligonucleotide primers comprises a first subset of at least one truncated double domain direct primer and a second subset of at least one reverse primer; wherein each truncated double domain primer in a set comprises a 5 'tail region that differs from the 5' tail region in other truncated double domain primers in the set and a 3 'core region complementary to a sequence in a double-stranded nucleic acid strand comprising the target; wherein for each truncated double domain primer, the 3 'core substantially anneals its target site sequence complementary to a first annealing temperature and the sequence comprised of the 5' core and 3 'tail region substantially anneal to its complement at a second annealing temperature, the second annealing temperature being higher than the first annealing temperature, so that, at the second annealing temperature, the 3 'core of the truncated double domain initiator is not can substantially anneal a model molecule that also lacks the 5 'tail truncated double domain primer sequence complement; where for each truncated double domain primer in a set of primers: the 5 'tail sequence has no homology to the target sequence, and the 5' tail sequence has 6 or less contiguous homologous bases with respect to any other 5 'tail sequence of the truncated double domain primer set; wherein the PCR amplification regime comprises first and second stages, the first stage comprising annealing at the first annealing temperature for a first set of cycles, and; the second stage comprising annealing at the second annealing temperature for a second set of cycles; and detecting an amplified product for each target polynucleotide, where detection indicates the presence of the target polynucleotide.

In addition or alternatively, the detection of a genetic biomarker may include a composition for determining the presence of one or more target polynucleotides, comprising; at least one set of oligonucleotide primers specific for each target polynucleotide; wherein each set of oligonucleotide primers comprises a first subset of at least one truncated double domain direct primer and a second subset of at least one reverse primer; wherein each truncated double domain primer in a set comprises a 5 'tail region that differs from the 5' tail region in other truncated double domain primers in the set and a 3 'nucleus region complementary to a sequence in a double-stranded nucleic acid strand comprising the target; wherein for each truncated double domain primer, the 3 'core substantially anneals its complementary sequence from the destination site to a first annealing temperature, and the sequence comprised of the 5' and core tail region 3 'anneal substantially to its complement at a second annealing temperature, the second annealing temperature being higher than the first annealing temperature, so that, at the second annealing temperature, the 3 ′ core of the initiator double-truncated domain cannot substantially anneal a model molecule that also lacks the 5 'tail complement of the primer sequence; where for each member of the truncated double domain primer set: the following

5 'tail sequence has no homology to the target sequence; and the 5 'tail sequence has 6 or less contiguous homologous bases with respect to the other 5' tail sequences of the truncated double domain primer set.

[633] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in US Patent Application Publication 2010/0113758, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include a process for the purification of biomolecules from a sample that comprises the following steps: a) disposing in a reaction vessel with a connection arrangement in a centrifuge, in which a solution or suspension a sample containing the biomolecules is prepared in the reaction vessel or introduced into the reaction vessel before or after this step; and b) include at least one multistage centrifugation step, comprising at least a first centrifugation step at a first acceleration value and at least a second centrifugation step at a second acceleration value which is greater that the first acceleration value; wherein c) step b) can be a binding step, a washing step and / or an elution step. Preferably, the multi-stage step b) is a binding step in which the biomolecules are linked to the binding matrix by centrifugation. Particularly preferably, biomolecules are expected to be substances chosen from the group containing nucleic acids, amino acids, oligopeptides, polypeptides, monosaccharides, oligosaccharides, polysaccharides, fats, fatty acids and / or lipids. In some embodiments, the binding matrix comprises a silicate substrate and, in addition, the sample containing biomolecules is mixed with at least one chaotropic salt prior to centrifugation. The modality is particularly suitable for nucleic acids. Preferably, the following steps are foreseen in this modality: a) dispose in a column-type reaction vessel with a connection arrangement comprising a silicate substrate in a centrifuge, in which a solution or suspension of a sample containing nucleic acid and at least one chaotropic salt is prepared in the reaction vessel or introduced into the reaction vessel before or after this step; b) include a first centrifugation step in the first acceleration value; c) include a second centrifugation step at a second acceleration value higher than the first acceleration value; d) optionally include other centrifugation steps between step c) and step d) or after step d); e) optionally include one or more washing steps; and f) eluting nucleic acids bound to the silicate substrate with an elution solution. In this embodiment, the multi-stage centrifugation step is a binding step in which the nucleic acids are linked to the silicate matrix.

[634] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in US Patent Application Publication 2009/0298187, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include a method for determining the presence of a target nucleic acid in a sample. The method comprises: a) contacting one or more polynucleotide probes with the sample under sufficient hybridization condition so that one or more polynucleotide probes hybridize with the target nucleic acid in the sample to form nucleic acid hybrids double-stranded, wherein the one or more polynucleotide probes do not hybridize to a variant of the target nucleic acid; and b) detecting double-stranded nucleic acid hybrids, wherein the detection comprises contacting the double-stranded nucleic acid hybrids with a first anti-hybrid antibody that is immunospecific to double-stranded nucleic acid hybrids, whereby the detection of double-stranded nucleic acid hybrids determine the target nucleic acid in the sample. In some embodiments, the hybridization of the nucleic acids and the detection of the double-stranded nucleic acid hybrids are carried out at the same time. In some embodiments, after double-stranded nucleic acid hybrids are contacted with a first anti-hybrid antibody that is immunospecific to double-stranded nucleic acid hybrids, a second anti-hybrid antibody is added to detect the nucleic acid hybrids double-stranded by which the detection of double-stranded nucleic acid hybrids by these second anti-hybrid antibodies determines the presence of target nucleic acid in the sample.

[635] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the U.S. Patent

6,686,157, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include a method and compositions for the sensitive detection of the quantity and location of specific nucleic acid sequences. In some ways, the method uses a branched oligomer, referred to as a lollipop oligomer, which has a tail portion, a right arm portion and a left arm portion. These three components are joined in a common junction, forming a three-way structure. The two arms each end with sequences complementary to the adjacent sequences in a target sequence. This allows the right and left arms to be linked when the oligomer is hybridized to the target sequence, topologically linking the oligomer to the target sequence. The tail portion can then be detected at the location of the target sequence. When using the tail of the oligomer to prepare for replication of the rotating circle of a DNA circle, a DNA repeated over a long period is associated with the target sequence. The replication of the rotating circle does not disturb the association of the arms and the target sequence, thus maintaining a close association of the tandem repeated DNA and the target sequence. In addition or alternatively, the detection of a genetic biomarker may include a method of amplifying nucleic acid sequences, the method comprising: (a) mixing one or more different lollipop oligomers with one or more target samples, each comprising a or more target sequences and incubate under conditions that promote hybridization between the oligomers and the target sequences, in which the lollipop oligomers comprise a branched oligomer comprising a tail portion, wherein the tail portion comprises a primer rotating circle replication, wherein the rotating circle replication primer comprises a complementary portion that is complementary to an initiator complement portion of an amplifying target circle, (b) before, simultaneously with, or after step (a) , mix one or more amplification target circles with the oligomers and incubate under conditions that promote hybridization between the amplification target circles and the p primary replication prayers of the rotating circle of the oligomers and (c) mixing the DNA polymerase with the oligomers and the target circles of the amplification and incubating under conditions that promote the replication of the target circles of the amplification, in which the replication of the target circles amplification results in the formation of the tandem sequence DNA.

[636] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the U.S. Patent

6,090,935, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include a method for isolating nucleic acid from a sample, said method comprising boiling said sample, cooling the boiled sample, allowing the nucleic acid in the liquid phase of the chilled sample to connect directly to a solid support comprising magnetic particles and separate the solid support with the nucleic acid attached to it from the rest of said liquid phase. Additionally or alternatively, the detection of a genetic biomarker may include a method for isolating the nucleic acid from a sample that is fixed or aged, said method comprising boiling the fixed or aged sample, cooling the boiled sample by allowing the nucleic acid in the cooled sample connect directly to a solid support with a high surface area comprising magnetic particles and separate the solid support with the nucleic acid attached to it from the rest of the cooled sample.

[637] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include any of the various forms described in PCT Publication WO 2017/032808, which is incorporated herein by reference in its entirety . For example, the detection of a genetic biomarker may include an RNA sequencing method, whereby said method comprises: (i) providing RNA; (ii) generate (a) first strand of single-stranded DNA (cDNA), which is / are complementary to RNA, subject RNA to reverse transcription using reverse transcriptase, a first set of oligonucleotide primers and the RNA from step (i) and (iii) generate a second strand of DNA using a DNA polymerase, a second set of oligonucleotide primers and the single strand cDNA from (ii), where a) the first set of oligonucleotide primers - dicos comprises a fraction covalently coupled in its 5 nucleotide (s) ends (5), which blocks the connection at the 5 'terminal of the first generated DNA strand; or b) the second set of oligonucleotide primers comprises a fraction covalently coupled to its 5 'terminating nucleotide (s), which blocks binding at the 5' end of the second generated DNA strand. In some modalities,

the method further comprises the subsequent steps of: (iv) optionally repairing the double-stranded DNA strands using a polynucleotide kinase and an enzyme with polymerase and exonuclease activities to obtain DNA strands repaired at the end; (v) optionally adding a terminal adenine to the 3 'ends of the DNA strands using a deoxynucleotidyl transferase enzyme; and (vi) attaching adapters, which optionally comprise terminal strips, to DNA strands, which optionally comprise 3 'terminal adenines. Said methods may also comprise sequence analysis of the generated DNA.

In some embodiments, said method comprises: (i) providing RNA; (ii) generate (a) first strand of single-stranded DNA (cDNA), which is / are complementary to RNA, subject RNA to reverse transcription using a reverse transcriptase, a first set of primers oligonucleotides and the RNA from step (i), (iii) generate a second strand of DNA using a DNA polymerase, a second set of oligonucleotide primers and the single strand cDNA from (ii), (iv) attaching adapters to the Double-stranded DNA; from step (iii) and (v) sequencing the generated DNA, in which a) the first set of oligonucleotide primers comprises a fraction covalently coupled to its 5 'end nucleotide (s), which blocks the binding at the 5 'end of the first generated DNA strand; or b) the second set of oligonucleotide primers comprises a fraction covalently coupled to its 5 'terminating nucleotide (s), which blocks binding at the 5' end of the second generated DNA strand.

When generating the second DNA strand, a double stranded DNA is generated.

In some modalities of the method mentioned above, before step (iv), the method comprises the step of: (iii) (a) repairing double-stranded DNA strands using a polynucleotide kinase and an enzyme with activity of polymerase and exo-nuclease to obtain DNA strands repaired at the end.

In some

but in embodiments, step (iii) (a) is followed by step (iii) (b) comprising adding a terminal adenine to the 3 'ends of the DNA strands using a deoxynucleotidyl transferase enzyme, where the adapters comprise 3 terminal thymines ', which in step (iv) bind to DNA strands comprising 3' terminal adenines. In some embodiments, the oligonucleotide primers, which are covalently coupled to a blocking portion and / or unmodified oligonucleotide primers, are random oligonucleotide primers.

In some embodiments, said methods comprise the initial step of optionally extracting and enriching the RNA of interest.

In some embodiments, the extracted RNA is fragmented to an average size of 19-510 bp.

In some embodiments of the above methods, the molecules can be attached to a solid support for paired end sequencing.

In some embodiments, a "binding blocking portion" or a "blocking portion" refers to a specific part of a larger molecule, which is more than one atom, here the part of a modified oligonucleotide, which is covalently coupled to the 5 'nucleotide of a modified primer oligonucleotide.

Said portion preferably blocks any binding at the site, where the portion is located, preferably at the 5 'terminal nucleotide of the 5' terminal of an oligonucleotide.

In some embodiments of the above methods, the oligonucleotide primer comprising a blocking fraction is characterized by (i) the oligonucleotide comprising at the 5 'terminal nucleotide a 5' phosphate which is not free, where optionally an OH 5 'group or a 5 'phosphate group at the 5' terminal nucleotide is covalently coupled to the fraction, which blocks the bond; (ii) the base of the 5 'terminal nucleotide is not any one of thymine, adenine, cytosine, guanine and uracil; (iii) one or both 2 'hydrogen (s) of the 5' terminal nucleotide deoxyribose is / are replaced

replaced by another atom or a blocking portion; and / or (iv) the oligonucleotide comprises a 5 'terminal nucleotide with a pentose in a steric conformation, which is not the steric ribose or deoxyribose conformation in RNA or DNA. In some embodiments of the above methods, oligonucleotide primers comprising a covalently coupled fraction, which blocks binding, comprise a 5'OH group or a free 5 'phosphate group at the 5' terminal nucleotide before being covalently coupled to a fraction, which confers the blocking property of the connection.

[638] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include any of the various forms described in PCT Publication WO 2016/193490, which is incorporated herein by reference in its entirety . For example, the detection of a genetic biomarker may include a selective DNA size isolation method based on a poly (alkylene oxide) polymer to isolate DNA molecules with a size above a certain cutoff value of a sample. containing DNA, including (a) preparing a ligation mixture comprising the DNA-containing sample, at least one poly (alkylene oxide) polymer and at least one divalent cation, wherein said ligation mixture has a pH which is in the range of 8 to 10 and that binds precipitated DNA molecules to a solid phase having a surface containing unmodified silicon, thus providing a solid phase that binds to DNA molecules with a size above the cutoff value, where under the binding conditions used, DNA molecules with a size less than the cutoff value do not bind to the solid phase; (b) separating the bound DNA molecules from the remaining sample; optionally washing the bound DNA molecules; and optionally eluting the bound DNA molecules from the solid phase. In some embodiments, the molecules of

DNA with a size above the desired cut value binds efficiently to the solid phase with high yield, while DNA molecules with a size below that cut value are predominantly unbound and, therefore, are not recovered in the step (a) The cut-off value can be adjusted by modifying the concentration of the poly (alkylene oxide) polymer in the bonding mixture, as is known in the prior art and also demonstrated by the examples. The presence of the divalent cation and the alkaline pH value, as specified, guarantees an efficient binding of DNA molecules with a size above the cutoff value, even if a solid phase is used with an unmodified silicon-containing surface. . The method is particularly suitable for isolating DNA molecules attached to the adapter as target DNA molecules from an adapter binding sample and for removing adapter monomers and adapter-adapter binding products.

[639] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the U.S. Patent

7,811,759, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include a method for creating a signature ncRNA profile for a disease state or condition, said method comprising: a. determining a first ncRNA profile from a first source, said first source being characterized as free from said disease condition or condition; B. determining a second ncRNA profile from a second source, said second source characterized as positive for said disease condition or condition, said first and second ncRNA profile being obtained according to a method for determining a profile of a plurality of target ncRNA molecules in a sample RNA, said method comprising the steps of: i. providing said RNA sample from an individual, said sample containing said plurality of target ncRNAs; ii. contacting said sample with a first specific oligonucleotide for each of said target ncRNAs to be detected under appropriate conditions to form a complex between said first oligonucleotides and said target ncRNAs, each of said first oligonucleotides comprising a first signal generator to generate a first detectable signal and each of said first oligonucleotides having a first Tm to bind each of said target ncRNAs which are substantially the same; iii. contacting said sample with a second oligonucleotide capable of binding each of said target ncRNAs to be detected under appropriate conditions to form a complex between said second oligonucleotides and said target ncRNAs, said second oligonucleotide comprises a second signal generator for generating a second detectable signal and each of said second oligonucleotides having a second Tm for binding each of said target ncR-NAs which are substantially the same; iv. determining the presence of said plurality of target ncRNA in said sample by measuring the first and second detectable signals; and v. generate a sample profile based on the detected ncRNAs c. compare said first and second ncRNA profiles and identify the ncRNA molecules that are altered in said second ncRNA profile to create a signature ncRNA profile for said disease condition or condition.

[640] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the Publication of the

U.S. Patent Application 2005/0244847, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include methods for amplifying a circular nucleic acid model, comprising contacting the model with a reaction mixture comprising a thermostable polymerase, individual nucleotides and forward and reverse primers complementary to a common region within model, where the common region is preferably about 80 to 150 base pairs in length. In one fashion, the 5 'ends of the primers hybridize to the opposite strands of the mold with about 10 to 50 base pairs, even more preferably from zero to twenty-five base pairs. In some modalities, the 5 'end of the forward primer will generally be proximal to the 5' end of the reverse primer and distal to the 3 'end of the reverse primer when the primers are hybridized to the model. In a particularly preferred embodiment, the common region is a conserved region, for example, an origin of replication, within an extrachromosomal nucleic acid. The reaction mixture may also include a reaction buffer comprising a weak organic base and a weak organic acid. In addition or alternatively, the detection of a genetic biomarker may include reagents to perform in vitro amplification of extrachromosomal DNA, including solutions that support amplification and subsequent binding reactions and solutions that support a combined amplification and binding reaction, or that is, they provide the appropriate environment for simultaneous polymerase and ligase enzyme activity.

[641] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include any of the various forms described in PCT Publication WO 2018/137826, which is incorporated herein by reference in its entirety .

For example, the detection of a genetic biomarker can include methods for enriching model nucleic acids and methods for generating a sequencing library.

In some embodiments, the method for enriching model nucleic acids or for generating a sequencing library comprises: a) hybridizing model nucleic acids with oligonucleotides attached to a solid surface and initially comprising at least two funnel elements - national; b) extending the surface-bound oligonucleotides hybridized with the template nucleic acids to form a double strand; c) optionally modifying the double-stranded nucleic acids generated in step b); d) truncate 3 'of the surface-bound oligonucleotides that were not used in the standard nucleic acid hybridization of step a); and e) optionally modify the oligonucleotides attached to the single strip surface generated in step d). In some embodiments, the additional also includes the additional step of: f) hybridizing other model nucleic acids with surface-bound oligonucleotides or using functional sequence elements in the surface-bound oligonucleotides for a downstream application.

In some modalities, the downstream application is the amplification of nucleic acid on the solid surface, uncoupling of the solid surface, in vitro transcription by the use of an RNA polymerase promoter in the oligonucleotide, marking of immobilized nucleic acid by use a primer binding site or a molecular barcode region for identification within the oligonucleotide, sequencing or a combination thereof.

In some embodiments, at least one of the functional elements of the sequence is a hybridization site or preferably a sequence useful for downstream application.

In another modality, the elements of functional sequence are consecutive or overlapping.

In certain embodiments, the sequence functional elements are separated by predefined cleavage sites or generated by hybridization of protective oligonucleotides with the oligonucleotides bound to the surface.

In some embodiments, steps a) to c) are repeated at least once with model nucleic acids from different samples.

In other modalities, steps a) to e) are repeated at least once with model nucleic acids from the same sample or from different samples, optionally in which the repetition (s) is (are) performed in parallel .

In some embodiments, the density of surface-bound oligonucleotides is between 500 - 500000 oligonucleotides / μηι2, more preferably 750 - 200000 oligonucleotides / μηι2, more preferably 1000 - 100000 oligonucleotides / μηη2. In other embodiments, surface-bound oligonucleotides comprise 2 to 20 elements of functional sequence, more preferably 2 to 10, more preferably 2 to 5. In certain embodiments, the length of the surface-bound oligonucleotides is in the range of 4 -200 nt, preferably 10-200 nt, more preferably 6-180 nt, more preferably 8-160 nt, more preferably 10-140 nt, most preferably 20-100 nt.

In some embodiments, the length of surface-bound oligonucleotides is 10 nt, preferably 20 nt.

In some embodiments, all elements of the functional sequence of the same position within a surface-bound oligonucleotide have a single sequence or comprise 2-100000, preferably 2-50000, more preferably 2-25000, more preferably 2 -10000, more preferably 2-5000, more preferably 2-2500, more preferably 2-1000 different sequences.

In certain embodiments, the 3 'truncation is achieved enzymatically or chemically.

In some embodiments, double-stranded nucleic acids attached to the surface are modified by introducing barcode sequences, adding sequencing adapters, adding fluorophore at the terminal, incorporating mo-

difficulties or other modifications. In other embodiments, single-stranded oligonucleotides attached to the surface are modified by the addition of biotin, labeling portions, blocking portions or other modifications.

[642] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include any of the various forms described in PCT Publication WO 2018/013598, which is incorporated herein by reference in its entirety . For example, the detection of a genetic biomarker may include methods, adapters and kits for performing duplex single-ended DNA sequencing. In some embodiments, the method for performing DNA sequencing comprises: (a) performing a binding reaction in the presence of a plurality of double-stranded target nucleic acids and a first set of substantially complementary double-stranded adapters to generate products of connection, where each adapter of the first set comprises a first strand and a second strand, the first strand comprises, from 5 'to 3', a 5 'region 10 or more nucleotides in length, a molecular tag sequence and an optional 3 'region, the second strand comprises, from 3' to 5 ', a 3' region comprising a sequence completely complementary to a 10 nucleotide or longer portion of the 5 'region of the first strand, a sequence Completely complementary to the molecular label sequence of the first strand and an optional 5 'region, at least one incompatibility between the first and second strands is located in the 3' region of the first strand if the 3 'region is present between and / or in the 5 'region which is 3' for the 10 nucleotide or longest portion of the 5 'region of the first strand and different adapters of the first set comprise different molecular label sequences in their first strands and corresponding fully complementary sequences corresponding to the sequences of different molecular tags on their second strands, but are identical to each other, (b) perform an amplification reaction using the ligation products from step (a) as models to generate amplification products, in which the amplification products comprise one or more locations that do not form complementary base pairs on the first and second strands of the substantially complementary double-strand adapters (c) perform sequencing reactions using amplification products from step (b) or their additional amplification products as models for obtaining sequence readings that comprise the one or more locations where a complementary base pair is not available. the first and second filaments of the double ribbon adapters.

In addition or alternatively, the detection of a genetic biomarker may include a set of substantially complementary double-stranded adapters, comprising at least 16 different adapters, each adapter in the set comprising a first strand and a second strand, the first strip comprises, from 5 'to 3', a 5 'region, a sequence of molecular tags and an optional 3' region, the second strip comprises, from 3 'to 5', a 3 'region comprising a fully complementary sequence to a 10 nucleotide or longer portion of the first strand 5 'region, a fully complementary sequence to the first strand molecular tag sequence and an optional 5' region, at least one incompatibility between the first and the second strand is located in the 3 'region of the first strand if the 3' region is present and / or in the 5 'region which is 3' for the 10 nucleotide or longer portion of the 5 'region of the first strand, and different adapters they comprise different sequences of molecular tags on their first tapes corresponding to totally complementary sequences of the different sequences of molecular tags on their second tapes, but they are identical to each other.

In addition or alternatively, the detection of a genetic biomarker may include a kit, comprising: (1) a set of substantially complementary double-stranded adapters and (2) a ligase.

[643] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in US Patent Application Publication 2017/165289, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker can include primers, sets of primers, kits and methods for multiple displacement amplification (MDA). In some embodiments, the method for amplifying nucleic acids by multiple displacement amplification comprises: performing one or more separate multiple displacement amplification reactions, in which each reaction is performed in the presence of: (1) a set of primers , where each primer in the primer set comprises a self-complementary sequence at its 5 'end and a random sequence or a semi-random sequence at its 3' end, and where the self-complementary sequences in each set of primers are the same, but different from the sequences self-complementary in another set of primers, (2) a DNA polymerase with a strand displacement activity and (3) target nucleic acids. In certain embodiments, the self-complementary sequences in one or more sets of primers are each 6 to 20 nucleotides in length. In certain embodiments, the random or semi-random sequences in one or more sets of primers are 4 to 20 nucleotides in length. Preferably, the primers are resistant to 3'-5 'exonuclease review activity. In certain embodiments, the DNA polymerase that has a ribbon-shifting activity is Phi29 polymerase. In certain modalities, at least 2 separate multiple displacement amplification reactions are performed. In certain embodiments, the target nucleic acids used in one or more separate multiple displacement amplification reactions are genomic DNA from one or more different single cells, such as human cells. In certain embodiments, multiple displacement amplification is performed at a temperature of about 20 ° C to about 40 ° C, as cycling between two temperatures within the above mentioned range or under an isothermal condition. In certain modalities in which a plurality of separate multiple displacement amplification reactions is performed, the method further comprises: grouping the amplified nucleic acids from the plurality of multiple displacement amplification reactions together, generating a library sequencing using the clustered amplified nucleic acids and sequencing the set of amplified nucleic acids. Additionally or alternatively, the detection of a genetic biomarker can include a set of primers, where each primer in the set of primers comprises a self-complementary sequence at its 5 'end and a random sequence or a semi-random sequence at its 3 end ', and in which the self-complementary sequences of the primers are identical to each other. Additionally or alternatively, the detection of a genetic biomarker may include a plurality of sets of primers, where each primer comprises a self-complementing sequence at its 5 'end and a random sequence or a semi-random sequence at its 3 end ', in which the self-complementary primer sequences in each primer set are the same, but different from the self-complementary primer sequences in another primer set. In certain embodiments, the plurality of primer sets comprises at least 3 different primer sets.

[644] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include any of the various forms described in PCT Publication WO

2017/085321, which is incorporated herein by reference in its entirety.

For example, the detection of a genetic biomarker may include a method for producing a sterile composition suitable for stabilizing an extracellular nucleic acid population from a biological sample, the method comprising: a) providing a composition comprising: i. at least one caspase inhibitor, and ii. at least one compound selected from a thioalcohol (preferably N-acetylcysteine or glutathione), a water-soluble vitamin and vitamin E or a derivative thereof; and b) irradiating the composition for sterilization.

In addition or alternatively, the detection of a genetic biomarker may include a method to stabilize an extracellular nucleic acid population comprised in a biological sample containing a cell, comprising: a) obtaining i) a sterile composition suitable to stabilize a population of extracellular nucleic acid from a biological sample or ii) a composition above in sterile form; and b) contact the biological sample containing cells with the sterile composition for stabilization.

Additionally or alternatively, the detection of a genetic biomarker may include a method for isolating extracellular nucleic acids from a biological sample containing stabilized cells comprising: a) stabilizing the biological sample containing cells according to the method; and b) isolating extracellular nucleic acids.

In addition or alternatively, the detection of a genetic biomarker may include a method for processing and / or analyzing extracellular nucleic acids comprising: a) isolating extracellular nucleic acids from a stabilized biological sample containing cells according to the method; and b) processing and / or analyzing the isolated extracellular nucleic acids.

In addition or alternatively, the detection of a genetic biomarker may include a method for producing a sterilizable composition, where the composition in sterile form is suitable for stabilizing an extracellular nucleic acid population from a biological sample, the method comprising: a) preparing a composition comprising: i. at least one caspase inhibitor, and ii. at least one compound selected from a thioalcohol (preferably N-acetyl-cysteine or glutathione), a water-soluble vitamin and vitamin E or a derivative thereof, and optionally b) sterilize the composition. In addition or alternatively, the detection of a genetic biomarker may include a sterilizable composition, in which the composition in sterile form is suitable for stabilizing an extracellular nucleic acid population in a biological sample, where the composition is a composition as provided in step a) of the method. Understands i. at least one caspase inhibitor, and ii. at least one compound selected from a thioalcohol (preferably N-acetyl-cysteine or glutathione), a water-soluble vitamin and vitamin E or a derivative thereof. The sterilizable composition according to the sixth aspect, which can be a composition as provided in step a) of the method, in the modalities can be sterilized to provide a sterile composition. Additionally or alternatively, the detection of a genetic biomarker may include a sample collection device, such as a container, preferably a sample collection tube, comprising the sterilizable composition above. Additionally or alternatively, the detection of a genetic biomarker may include using at least one compound selected from the group consisting of a thioalcohol (preferably N-acetyl-cysteine or glutathione), a water-soluble vitamin and vitamin E or a derivative thereof, to protect a composition suitable for stabilizing an extracellular nucleic acid population from a biological sample or components thereof during sterilization by irradiation.

[645] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include any of the various forms described in PCT Publication WO 2016/170147, which is incorporated herein by reference in its entirety .

For example, the detection of a genetic biomarker may include methods for generating dsDNA, where the method comprises binding a first and a second dsDNA, both optionally having one or two single stranded ends, in the presence of a DNA ligase and an agent, which modulates the melting temperature of the dsDNA.

In addition or alternatively, the detection of a genetic biomarker may include a method to bind a first and a second dsDNA, where the first and second dsDNAs comprise two ssDNA regions, where each end of the ssDNA regions of the first dsDNA binds to each end of the complementary ss region of the second dsDNA to provide circular dsDNA bound in the presence of an agent, which modifies the fsDNA fusion temperature.

In some embodiments, the first or second DNA is able to confer the ability to automatically replicate within the competent cells.

In addition or alternatively, the detection of a genetic biomarker may include a method for generating a sequencing library, in which the method comprises the steps of: (i) providing DNA fragments; (ii) repair the end of the DNA fragments by a polynucleotide kinase enzyme and an enzyme with polymerase and exonuclease activities to obtain 5 'blunt-ended phosphorylated DNA fragments; (iii) optionally adding a terminal adenine to the end of the DNA fragments repaired at the end by a deoxynucleotidyl transferase enzyme; and (iv) ligating the DNA fragments, optionally having the terminal adenine, with sequencing adapters where the adapters preferably have a terminal thymidine if the fragments have a terminal adenine.

In some modalities, this method also comprises step (v),

in which the linked fragments from step (iv) are purified and selected by size for sequencing. In some embodiments, said method further comprises step (vi), in which fragments attached to the adapter are amplified and the amplification product is optionally purified before sequencing. In another mode, fragments from step (v) or (vi) are subjected to sequencing. In addition or alternatively, the detection of a genetic biomarker may include a kit comprising: (i) a DNA ligase; and (ii) an agent that modulates the melting temperature of the dsDNA. In another embodiment, the dsDNA melting temperature agent is selected from either tetramethylammonium chloride (TMAC), piperazinium chloride, tetramethylpiperazinium chloride, tetramethylpiperazinium chloride, tetraethylammonium chloride (TEAC), N-oxide trimethylamine (TMANO), 2-methyl-4-carboxy-5-hydroxy-3,4,5,6-tetrahydro-pyrimidine THP (A), 2-methyl-4-carboxy-3,4,5,6 -tetrahydropyrimidine THP (B), non-ionic detergents, such as NP-40 and Triton® X-100, and mixtures thereof. In a preferred embodiment, the agent that modulates the fusion temperature of the dsDNA is selected from any one of tetramethylammonium chloride (TMAC), piperazinium chloride, methmethylpiperazinium chloride, tetramethylpiperazinium chloride, tetraethylammonium chloride ( TEAC), trimethylamine N-oxide (TMANO), 2-methyl-4-carboxy-5-hydroxy-3, 4,5,6-tetrahydropyrimidine THP (A), 2-methyl-4-carboxy-3, 4,5,6- tetrahydropyrimidine THP (B) and mixtures thereof. In some modes, the agent that modulates the fusion temperature of the dsDNA is in a binding buffer. In some embodiments, the ligase and the agent that modulates the fusion temperature of the dsDNA are in separate containers.

[646] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include any of the various forms described in PCT WO Publication

2016/135300, which is incorporated herein by reference in its entirety.

For example, the detection of a genetic biomarker may include methods and kits for specific inhibition of enzymes involved in preparing DNA for protocols for building NGS libraries.

In addition or alternatively, the detection of a genetic biomarker may include a method for generating a sequencing library, in which the method comprises the steps of: (i) providing DNA fragments; (ii) repair the final end of the DNA fragments by a polynucleotide kinase enzyme and an enzyme with polymerase and exonuclease activities to obtain 5 'blunt-ended phosphorylated DNA fragments; (iii) optionally adding a terminal adenine to the end of the DNA fragments repaired at the end by a deoxynucleotidyl transferase enzyme; and (iv) ligating the DNA fragments, optionally having the terminal adenine base, with sequencing adapters by a DNA ligase; therefore, after the completion of step (ii) and / or optional step (iii), the enzyme or enzymes used in that step (s) is / are inactivated by the addition of (a) inhibitor (s) specific (s). None of the inhibitors of the above method inhibits the enzymatic activity of a subsequent step (s). In some modalities, the steps of the above method, which comprise enzymatic inactivation by a specific inhibitor, do not comprise the heat inactivation of said enzyme (s). In some embodiments, the steps of the above method, which comprise inactivating the enzyme by a specific inhibitor, do not comprise a subsequent purification step for said enzymes.

In some alternative embodiments, the steps of the method above, which include adding specific inhibitor (s), comprise a heat inactivation upstream of the referred enzyme (s), but do not comprise a purification step of the enzyme (s). In some modalities, the method above may also comprise step (v), in which the fragments linked from step (iv)

are purified and selected by size for sequencing.

[647] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include any of the various forms described in PCT Publication WO 2001/023618, which is incorporated herein by reference in its entirety . For example, the detection of a genetic biomarker can include a composition comprising an oligonucleotide and an annealing promoting compound (APC). The composition can be used, for example, to reduce the specificity of a hybridization reaction involving the oligonucleotide. The oligonucleotide can optionally be immobilized on a solid surface, where suitable solid surfaces include a nylon tip, a nylon sphere and a nylon membrane. In a preferred embodiment, the APC is an amino alcohol, wherein the amino alcohol comprises at least one amine group and at least one hydroxyl group. The composition may further comprise an acid and / or a buffer, so that the amino alcohol can be present in the composition, in whole or in part, as a salt of the amino alcohol. The composition is preferably aqueous and has a pH between 4 and

10. Examples of amino alcohol APCs are 4-hydroxypiperidine, 1-methyl-3-piperidinemethanol, 4,4'-trimethylenebis (1-piperidineethanol), 3-piperidinemethanol, 1-ethyl-4-hydroxy-piperidine, 2- piperidineethanol, 3-hydroxy-1-methylpiperidine, 1-ethyl-3-hydroxy-piperidine, 4-hydroxy-1-methylpiperidine, 1-methyl-2-piperidinomethanol, 2-piperidine-methanol, 2,2,6,6- tetramethyl-4-pi-peridinol, 1,4-bis (2-hydroxyethyl) piperazine and 1- (2-hydroxyethyl) piperazine. In addition or alternatively, the detection of a genetic biomarker can include a method to decrease the specificity of a hybridization reaction between two oligonucleotides. The method comprises adding an annealing promoting compound (APC) to a hybridization reaction between two oligonucleotides. In addition or alternatively, the detection of a genetic biomarker may include a method to decrease the specificity of a hybridization reaction between two oligonucleotides. The method comprises mixing a first oligonucleotide, a second oligonucleotide and an annealing promoting compound (APC) under conditions suitable for the formation of an oligonucleotide duplex. In addition or alternatively, the detection of a genetic biomarker may include a method for identifying a target oligonucleotide. The method comprises: (a) mixing a first oligonucleotide with a sequence complementary to the target oligonucleotide, a second oligonucleotide with a sequence complementary to the complement of the target oligonucleotide, an annealing promoting compound (APC), a polymerase, a compatible buffer with polymerase activity and a target oligonucleotide; (b) heating the mixture of (a) to a temperature above the melting temperature of the first oligonucleotide and the second oligonucleotide and their respective complementary sequences; (c) reducing the temperature of the mixture from (b) to below the melting temperature, to thereby allow hybridization between the first oligonucleotide, the second oligonucleotide and the target oligonucleotide; (d) raising the temperature of the mixture of (c) to a temperature compatible with the polymerase activity; and (e) detecting a polymerization product.

[648] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in U.S. Patent

9,792,403, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include a system for generating a characterization model (including, for example, type of variant and / or zygosity) for a variant (for example, a mutation) in a tissue or sample , for example, a tumor or tumor sample, from an individual, for example, a human individual, for example, a cancer patient. The system comprises: at least one processor operationally connected to a memory, the at least one processor during execution is configured to: a) acquire: i) a sequence coverage input (SCI), which comprises, for each a plurality of selected subgenomic intervals (for example, exons) a value for sequence coverage in the selected subgenomic intervals (including, for example, a normalized sequence coverage value); ii) an SNP allele frequency input (SAFI), which comprises, for each of a plurality of selected germline SNPs, a value for the frequency of the allele in the tissue or sample, for example, tumor sample; iii) a variant allele frequency input (VAFI), which comprises the allele frequency for said variant, for example, mutation, in the tissue or sample, for example, tumor sample; b) acquire values, determined according to SCI and SAFI, for: a total number of copies (C) of the genomic segment, for each of a plurality of genomic segments; a smaller allele copy number of the genomic segment (M), for each of a plurality of genomic segments; and sample purity (p); and c) calculate one or both of: i) a value for the type of variant, for example, type of mutation, for example, g, which indicates that the variant is somatic, germline, somatic subclonal or undistinguishable, in that at least one processor during execution is configured to calculate the value for the type of variant, for example, type of mutation, depending on VAFI, p, C and M; ii) an indication of the zygosity (for example, homozygous, heterozygous and absent) of the variant, for example, mutation, in the tissue or sample, for example, tumor sample, depending on C and M.

In one embodiment, the system is configured so that the analysis can be performed without the need to analyze the individual's non-tumor tissue.

In one embodiment, the system is configured to determine for at least one tumor sample, the selected subgenomic intervals and the selected SNPs of the germline that the type of variant, for example, type of mutation, cannot be determined for analyzed values. In one embodiment, at least one processor when running acquires the SCI calculated as a function (for example, the reason log) of the number of readings for a subgenomic interval and the number or readings for a control (for example, a process corresponded to the control). In one embodiment, at least one processor during execution is configured to calculate SCI as a function (for example, the reason log) of the number of readings for a subgenomic interval and the number or readings for a control (for example, control corresponding to the process). In one mode, at least one processor when running is configured to validate a minimum number of subgenomic intervals that have been selected or analyzed. In one embodiment, at least one processor when running is configured to validate a minimum number of a plurality of germline SNPs have been selected or analyzed. In a modality, the minimum number of SNPs of the germ line comprises at least 10, 25, 50, 100, 150, 200, 250, 300, 350, 400, 450, 500, 1,000, 2,000, 3,000, 4,000 , 5,000, 6,000, 7,000, 8,000, 9,000,

10,000 or 15,000 germline SNPs. In one embodiment, SAFI is based, at least in part, on a lower allele frequency in the tumor sample. In one embodiment, at least one processor during execution is configured to calculate or acquire SAFI based, at least in part, on a lower allele frequency in the tumor sample. In one embodiment, SAFI is based, at least in part, on an alternative allele frequency (for example, a different allele frequency than a standard allele in a human genome reference database). In one embodiment, at least one processor when running is configured to calculate or acquire SAFI based, at least in part, on an alternative allele frequency (for example, a different allele frequency than a standard allele in a database reference of the human genome). In one embodiment, at least one processor during execution is configured to access C, M and p values calculated from the adaptation of a copy number model for the entire genome to the SCI and SAFI.

In one embodiment, at least one processor when running is configured to calculate C, M and p.

In one embodiment, at least one processor when running generates a better fit between the copy number model across the genome and the SCI and SAFI to calculate C, M and p.

In one embodiment, the values of C, M and p fit into a plurality of model entries for the number of copies across the SCI and SAFI genomes.

In one embodiment, at least one processor when running is configured to generate a user interface.

In one embodiment, the user interface is configured to accept as input any one or more of: a sequence coverage entry (SCI), which comprises, for each of a plurality of selected subgenomic intervals, for example , exons, a value for sequence coverage in the selected subgenomic intervals (including, for example, a normalized sequence coverage value); an SNP allele frequency input (SAFI), which comprises, for each of a plurality of selected germline SNPs, a value for the allele frequency in the tumor sample; a variant allele frequency input (VAFI), which comprises the allele frequency for said variant, for example, mutation, in the tumor sample; a total number of copies (C) of the genomic segment, for each of a plurality of genomic segments; a smaller allele copy number of the genomic segment (M), for each of a plurality of genomic segments; and sample purity (p). In a modality, responsive to the user interface input, for example, for one or more (for example, 2, 3, 4, 5 or all) of SCI, SAFI, VAFI, C, M or p, the system generates a characterization model, for example, a characterization model for a variant.

In addition or alternatively, the detection of a genetic biomarker may include a method for characterizing a variant, for example, a mutation, in a tissue or sample, for example, a tumor or tumor sample, from an individual, for example. example, a human, for example, a cancer patient, comprising: a) acquiring: i) a sequence coverage entry (SCI), which comprises, for each of a plurality of selected subgenomic intervals, for example, exons, a value for normalized sequence coverage in the selected subgenomic intervals; ii) an SNP allele frequency input (SAFI), which comprises, for each of a plurality of selected germline SNPs, a value for the allele frequency in the tumor or sample, for example, sample tumor; iii) a variant allele frequency input (VAFI), which comprises the allele frequency for said variant, for example, mutation, in the tumor or sample, for example, tumor sample; b) to acquire values, according to SCI and SAFI, for: C, for each of a plurality of genomic segments, where C is a total number of copies of the genomic segment; M, for each of a plurality of genomic segments, where M is a minor allele copy number of the genomic segment; and p, where p is the purity of the sample; and c) acquire one or both: i) a value for the type of variant, for example, type of mutation, for example, g, which is indicative of the variant, for example, a mutation, being somatic, a subclonal somatic variant , germ line or not distinguishable and is a function of VAFI, p, C and M; ii) an indication of the zygosity of the variant, for example, mutation, in the tumor or sample, for example, tumor sample, depending on C and M. In one embodiment, the analysis can be performed without the need to analyze tumor tissue of the individual. In one embodiment, the analysis is performed without analyzing the individual's non-tumor tissue, for example, the non-tumor tissue of the same individual is not sequenced. In one embodiment, the SCI comprises values that are a function, for example, the log of the ratio, the number of readings for a subgenomic range, for example, the sample, and the number or readings for a control, for example, a process matched to control. In one embodiment, the SCI comprises values, for example, log r values, for at least 10, 25, 50, 100, 150, 200, 250, 300, 350, 400, 450, 500, 1,000, 2,000, 3,000,

4,000, 5,000, 6,000, 7,000, 8,000, 9,000 or 10,000, subgenomic intervals, for example, exons. In one embodiment, the SCI comprises values, for example, log r values, for at least 100 subgenomic intervals, for example, exons. In one modality, the SCI comprises values, for example, log r values, for 1,000 to 10,000,

2,000 to 9,000, 3,000 to 8,000, 3,000 to 7,000, 3,000 to 6,000 or 4,000 to

5,000, subgenomic intervals, for example, exons. In a modality, the SCI comprises values, for example, values of log r, for subgenomic intervals, for example, exons, of at least 10, 25, 50, 100, 150, 200, 250, 300, 350, 400 , 450, 500, 1,000, 2,000, 3,000 or

4,000 genes. In one modality, at least one, a plurality or substantially all values included in the SCI are corrected for correlation with the GC content. In one embodiment, a subgenomic interval, for example, an exon, in the sample has at least 10, 20, 30, 40, 50, 100, 150, 200, 250, 300, 350, 400, 450, 500, 600 , 700, 800, 900 or 1,000 readings. In one embodiment, a plurality, for example, at least 10, 25, 50, 100, 150, 200, 250, 300, 350, 400, 450, 500, 1,000, 2,000, 3,000, 4,000, 5,000, 6,000, 7,000, 8,000, 9,000 or 10,000, subgenomic intervals, for example, exons, in the sample have a predetermined number of readings. In one embodiment, the predetermined number of readings is at least 10, 20, 30, 40, 50, 100, 150, 200, 250, 300, 350, 400, 450, 500, 600, 700, 800, 900 or 1,000. In one embodiment, the plurality of germline SNPs comprises at least 10, 25, 50, 100, 150, 200, 250, 300, 350, 400, 450, 500, 1,000, 2,000, 3,000, 4,000, 5,000, 6,000, 7,000, 8,000, 9,000,

10,000 or 15,000 germline SNPs. In one embodiment, the plurality of germline SNPs comprises at least 100 germline SNPs. In one embodiment, the plurality of germline SNPs comprises 500 to 5,000, 1,000 to 4,000, or 2,000 to 3,000 germline SNPs. In one mode, the allelic frequency is a lower allelic frequency. In one embodiment, the allele frequency is an alternative allele, for example, an allele other than a standard allele in a human genome reference database. In one embodiment, the method comprises characterizing a plurality of variants, for example, mutants, in the tumor sample. In one embodiment, the method comprises at least 2, 3, 4, 5, 6, 7, 8 9, 10, 25, 50, 100, 150, 200, 250, 300, 350, 400, 450 or 500 variants for example, mutants. In one embodiment, the method comprises characterizing variants, for example, mutants, in at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 25, 50, 100, 150, 200, 250, 300, 350, 400, 450 or 500 different genes. In one embodiment, the method comprises acquiring a VAFI for at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 25, 50, 100, 150, 200, 250, 300, 350, 400, 450 , or 500 variants, for example, mutants. In one embodiment, the method comprises performing one, two or all steps a), b) and c) for at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 25, 50, 100 , 150, 200, 250, 300, 350, 400, 450 or 500 variants, for example, mutants. In one embodiment, the values of C, M and p are, have or can be obtained by adjusting a model of number of copies in the entire genome to one or both SCI and

SAFI.

In one embodiment, the values of C, M, and p fit into a plurality of model number of copy entries across the SCI and SAFI genome.

In one embodiment, a genomic segment comprises a plurality of subgenomic intervals, for example, exons, for example, subgenomic intervals to which an SCI value has been assigned.

In one embodiment, a genomic segment comprises at least 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 125, 150, 175, 200, 225, 250, 275, 300, 400 or 500 subgenomic intervals, for example, exons.

In one embodiment, a genomic segment comprises 10 to 1,000, 20 to 900, 30 to 700, 40 to 600, 50 to 500, 60 to 400, 70 to 300, 80 to 200, 80 to 150 or 80 to 120, 90 to 110, or about 100, subgenomic intervals, for example, exons.

In one embodiment, a genomic segment comprises between 100 and 10,000, 100 and 5,000, 100 and 4,000, 100 and 3,000, 100 and 2,000 or 100 and 1,000, subgenomic ranges, for example, exons.

In one embodiment, a genomic segment comprises 10 to 1,000, 20 to 900, 30 to 700, 40 to 600, 50 to 500, 60 to 400, 70 to 300, 80 to 200, 80 to 150 or 80 to 120, 90 110, or about 100 genomic SNPs, which have been assigned a SAFI value.

In one embodiment, a genomic segment comprises between 100 and 10,000, 100 and 5,000, 100 and 4,000, 100 and 3,000, 100 and 2,000 or 100 and 1,000 genomic SNPs that have been assigned a SAFI value.

In one embodiment, each of a plurality of genomic segments is characterized by having one or both: a measure of standardized sequence coverage, for example, log r, which differ by no more than a pre-selected quantity, for example , the values for log2 r for subgenomic intervals, for example, exons, within the limits of the genomic segment, differ no more than a reference value or are substantially constant; and frequencies of SNP alleles for germline SNPs that differ by no more than a pre-selected amount, for example, values for germline SNP allele frequencies for subgenomic intervals, for example, exons, within limits of the genomic segment, do not differ more than a reference value, or are substantially constant. In one embodiment, the number of subgenomic intervals, for example, exons, that are contained in or combined to form a genomic segment is at least 2, 5, 10, 15, 20, 50 or 100 times the number of genomic segments. In one embodiment, the number of subgenomic intervals, for example, exons, is at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 or 15 times the number of segments genomics. In one modality, a limit for a genomic segment is provided. In one embodiment, the method comprises assembling sequences for subgenomic intervals, for example, exons, in genetic segments. In one embodiment, the method comprises assembling sequences for subgenomic intervals, with a method, for example, a method comprising a circular binary segmentation (CBS), a method based on HMM, a method based on Wavelet or a Cluster method along Chromosomes. In one embodiment, adjusting the copy number model across the genome to the SCI comprises using the equation of:

[649] where ψ is tumor ploidy. In one embodiment, ψ = (ΣiliCi) / Σili, let li be the length of a genomic segment. In one embodiment, adjusting the copy number model across the genome to SAFI involves using the equation of:

[650] where AF is the frequency of the allele. In one embodiment, the adjustment comprises using Gibbs sampling. In one modality, the adjustment comprises using, for example, the Monte Carlo algorithm of the Markov chain (MCMC), for example, ASCAT (Analysis of the Number of

Copies of Tumor-specific Alleles), OncoSNP or PICNIC (Prediction of Full Copy Numbers in Cancer). In a modality, the adjustment comprises using Metropolis-Hastings MCMC. In one embodiment, the adjustment comprises using a non-Bayesian approach, for example, a frequentist approach, for example, using the least squares adjustment. In one modality, g is determined by determining the value adjustment for VAFI, p, C and M to a model for somatic status / germline. In a modality, the method comprises acquiring an indication of heterozygosity for the referred variant, for example, mutation. In one mode, the purity of the sample (p) is the overall purity, for example, it is the same for all genomic segments. In one mode, the value of g is acquired by:

[651] where AF is the frequency of the allele. In a modality, a value of g that is close to 0, for example, does not differ significantly from 0, indicates that the variant is a somatic variant. In one embodiment, a value of g that is 0, or close to 0, for example, within a predetermined distance of 0, for example, a value of g less than 0.4, indicates that the variant is a variant somatic. In one embodiment, a value of g that is close to 1, for example, does not differ significantly from 1, indicates that the variant is a variant of the germ line. In one embodiment, a value of g that is 1, or close to 1, for example, within a predetermined distance of 1, for example, a value of g greater than 0.6, indicates that the variant is a variant of the germ line. In one embodiment, a value of g is less than 1, but greater than 0, for example, if it is less than 1 by a predetermined amount and more than 0 by a predetermined amount, for example, if g is between 0, 4 and 0.6, this indicates an indistinguishable result. In one modality, a value of g that is significantly less than 0, is indicative of a subclonal somatic variant. In one mode, the value of g is acquired by:

[652] where AF is allele frequency and M ′ = C − M (for example, when M is a non-secondary allele frequency), for example, the variant is a germline polymorphism if g = 1 and the variant is a somatic mutation if g = 0.

[653] In one embodiment, the somatic status / germline is determined, for example, when the purity of the sample is below about 40%, for example, between about 10% and 30%, for example, between about 10% and 20%, or between about 20% and 30%. In one embodiment, when: a value of M equal to 0 other than C is indicative of the absence of the variant, for example, mutation, for example, nonexistent in the tumor;

[654] a non-zero value of M equal to C is indicative of variant homozygosity, for example, mutation, for example, with loss of heterozygosis (LOH); a value of M equal to 0 equal to C indicates a homozygous deletion of the variant, for example, mutation, for example, nonexistent in the tumor; and a non-zero value of M other than C is indicative of heterozygosity of the variant, for example, mutation. In one embodiment, the method comprises acquiring a ziggy indication for that variant, for example, mutation. In a modality, the mutation status is determined to be homozygous (for example, LOH) if M = C ≠ 0. In one embodiment, the mutation status is determined to be homozygous deletion if M = C = 0. In one mode - dality, the mutation status is determined as heterozygous is 0 <M <C. In one embodiment, the mutation is absent in the tumor if M = 0 and C ≠ 0. In one embodiment, zygosity is determined, for example, when the purity of the sample is greater than about 80%, for example,

between about 90% and 100%, for example, between about 90% and 95%, or between about 95% and 100%. In one embodiment, the control is a sample of euploid tissue (for example, diploid) from an individual other than the individual the tumor sample is from or a sample of euploid tissues (for example, diploids) mixed from one or more (for example, at least 2, 3, 4 or 5) individuals other than the individual from which the tumor sample is taken. In one embodiment, the method comprises sequencing each of the selected subgenomic intervals and each of the germline SNPs selected, for example, by next-generation sequencing (NGS). In one embodiment, the sequence coverage before normalization is at least about 10 ×, 20 ×, 30 ×, 50 ×, 100 ×, 250 ×, 500 ×, 750 × or 1000 × the depth of the sequence.

[655] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include any of the various forms described in PCT Publication WO 2018/0218113, which is incorporated herein by reference in its entirety .

[656] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in US Patent Application Publication 2017/0356053, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include the use of sample nucleic acid hybridization with a set of baits to assess a region of interest, for example, to assess the clonal profile of a region of interest in the sample. In addition or alternatively, the detection of a genetic biomarker may include a method for evaluating or providing a clonal profile of an individual range, for example, a subgenomic range or an expressed subgenomic range (or a cell containing the same) in an individual, comprising: (a) acquiring a nucleic acid library comprising a plurality of members, each member of the plurality comprising a nucleic acid from the individual, for example, a plurality of members of tumors from a sample solid tumor or haematological malignancy (or pre-malignancy); (b) contacting the library with a set of baits to provide a plurality of selected members, each of which comprises the individual range or a portion of it (sometimes referred to here as a library capture); optionally, (c) amplifying each member of the plurality of selected members, for example, to provide an amplified sequence of the individual range; (d) acquire the sequence of one or more occurrences of the individual's interval; providing or evaluating the clonal profile of an individual range.

In one modality, the method comprises assessing the clonal profile of a subgenomic range and an expressed subgenomic range.

In one embodiment, the method comprises comparing the sequence of a first allele or signature (for example, a first segment V) in the individual range with a comparison value, for example, a pre-selected value, for example, a value that is a function of the sequence of a second allele or signature (for example, a second V segment). In one embodiment, the method further comprises: (e) acquiring: (i) a value for distribution, expression (the occurrence or level of transcribed copies of a subgenomic signature), abundance or identity of a sequence, signature or allele in the individual range, for example, the relative abundance of a sequence, a signature or allele, or the relative abundance of each of a plurality of sequences, signatures or alleles in the individual range; or (ii) a value for variability, for example, sequence variability resulting from somatic hypermutation, sequence variability resulting from a VD, DJ or VJ junction, for example, by the formation of an indel at the junction or a CDR, for example example, heavy chain CDR3, sequence variability, within a signature or individual range, for example, where a value for variability is a function of the number of different variants present for the individual range in one individual or sample.

In one embodiment, the method comprises providing the clonal profile of a sequence, allele or signature, for example, a V segment, or rearrangement of VDJ or VJ, in a first individual interval; and i) a phenotype, for example, disease state, of the individual; or ii) the genotype in a second individual range.

In one embodiment, step (d): (i) comprises acquiring the sequence of each of a plurality of occurrences in the individual range, for example, acquiring the sequence of the first occurrence of an individual range comprising a segment V and a second occurrence of the interval comprising segment V, in which the first and second occurrences differ by diversity in a VD, DJ or VJ junction; or (ii) it comprises acquiring the sequence of a first range of individuals and a second range of different individuals, for example, wherein the first range of individuals comprises a sequence of a first gene and the second range of individuals comprises sequence of a second gene.

In one embodiment, step (d) comprises acquiring sequence from each of a plurality of occurrences in the individual range, for example, a plurality of occurrences from an individual interval comprising a VDJ sequence, for example, a plurality of occurrences of an individual interval comprising a VDJ sequence comprising a specific V segment, a specific D segment and a specific J segment.

In a fashion, the method comprises acquiring a value for e (i). In a modality, the value of (e) (i) comprises a value for the abundance of a sequence, signature or allele (for example, a first segment V) in an individual interval in relation to a value of comparison, for example, a pre-selected value, for example, a value that is a function of the abundance of a second sequence, signature or allele (for example, a second segment V). In a modality, the value of (e) (i) comprises a value for the abundance of an event, for example, a sequence, allele or signature, for example, a mutation or rearrangement, in an individual interval, in relation to a comparison value, for example, a pre-selected value, for example, a value that is a function of the abundance of a sequence without the event, for example, an unmuted or non-rearranged sequence in the individual range .

In one embodiment, the value of (e) (i) comprises a relative abundance value for each of the X sequences, signatures or unique alleles (that is, different from each other), in an individual range.

Additionally or alternatively, the detection of a genetic biomarker may include a method of assessing an individual for the occurrence of an entire arm or large rearrangement, for example, a rearrangement, for example, a translocation, duplication, insertion or exclusion, comprising, for example, at least 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80% or 90%, or an entire chromosomal arm, comprising: (a) acquiring a nucleic acid library comprising a plurality of members, each member of the plurality comprising the individual's nucleic acid; (b) contacting the library with a set of baits, for example, under solution hybridization conditions, to provide a plurality of selected members, each of which comprises an individual range or a part of it (sometimes referred to here as library capture); (c) amplifying each member of the plurality, for example, by a method that does not depend on a specific interaction of the sequence with the target / individual nucleic acid in the member, for example, amplifying each member of the plurality with a primer that does not bind to target / individual nucleic acid in the limb; and (d) acquiring the sequence of a plurality of intervals of individuals, wherein said plurality of intervals of individuals is arranged on a chromosome, in order to allow the determination of an entire arm or a major rearrangement.

In addition or alternatively, the detection of a genetic biomarker may include a method of assessing an individual, comprising: (a) acquiring a nucleic acid library comprising a plurality of members, each member of the plurality comprising the individual's nucleic acid, for example example, a plurality of tumor members from a blood cancer sample; (b) contacting the library with a set of baits, for example, under solution hybridization conditions, to provide a plurality of selected members, each of which comprises the individual range or a portion thereof (sometimes referred to herein) as a capture from the library); (c) amplifying each member of the plurality of selected members, for example, by a method that does not depend on a specific sequence interaction with the target nucleic acid / individual in the member, for example, amplifying each member of the plurality of members selected with a primer that does not bind to the target / individual nucleic acid in the member; (d) acquire the sequence of a subgenomic interval and an expressed subgenomic interval; thus evaluating the individual, in which: (i) the method comprises contacting the library with a set of baits that provide a subgenomic range and an expressed subgenomic range; (ii) the method comprises contacting the library with a first set of baits that provides a subgenomic interval and a second set of baits that provides an expressed subgenomic interval; (iii) where the library comprises genomic DNA and is contacted with a set of baits that provides a subgenomic range and the method further comprises a second library comprising cDNA which is contacted with the set of baits to provide an expressed subgenomic range ; (iv) where the library comprises genomic DNA and is contacted with a set of baits that provides a subgenomic range and the method further comprises a second library which comprises cDNA which is contacted with a second set of baits to provide a subgenomic range express; or (v) the method comprises performing one of steps (a), (b) and (c) in a first reaction mixture to provide a first individual interval, for example, a subgenomic interval and a second reaction mixture to provide a second individual interval, for example, an expressed subgenomic interval, for example, which corresponds to the subgenomic interval.

In addition or alternatively, the detection of a genetic biomarker may include a method of analyzing a sample, for example, a tumor sample of a hematological (or pre-malignant) malignancy, for example, a hematological (or pre- malignancy). The method comprises: (a) acquiring one or a plurality of libraries comprising a plurality of members of a sample, for example, a plurality of tumor members of a tumor sample; (b) optionally enriching one or a plurality of libraries for pre-selected strings, for example, by contacting one or a plurality of libraries with a set of baits (or a plurality of sets of baits) to provide selected members (sometimes referred to here as library capture); (c) acquiring a reading for an individual range, for example, a subgenomic range or an expressed subgenomic range, from a member, for example, a tumor member from a library or library capture, for example, by a method which comprises sequencing, for example, with a next generation sequencing method; (d) aligning said reading with an alignment method; and

(e) assigning a nucleotide value (for example, calling a mutation, for example, with a Bayesian method) from said reading to the pre-selected position of the nucleotide, thus analyzing said tumor sample, optionally in that: a reading of each of the X unique individual intervals (for example, subgenomic intervals, expressed subgenomic intervals or both) are aligned with a unique alignment method, where the exclusive individual interval (for example, subgenomic interval or expressed subgenomic interval) means different from other X-1 individual intervals (for example, subgenomic intervals, expressed subgenomic intervals, or both), and in which unique alignment method means different from other X-1 alignment methods and X is at least 2. Additionally or alternatively, the detection of a genetic biomarker may include a method of analyzing a sample, for example, a tumor sample of a m hematological alignment (or pre-malignancy), for example, a hematological malignancy (or pre-malignancy). The method comprises: (a) acquiring one or a plurality of libraries comprising a plurality of members of a sample, for example, a plurality of tumor members of the sample, for example, the tumor sample; (b) optionally enriching one or a plurality of libraries for pre-selected strings, for example, contacting the library with a set of baits (or a plurality of sets of baits) to provide selected members, for example, a library capture; (c) acquiring a reading for an individual range (for example, a subgenomic range or an expressed subgenomic range) from a member, for example, a tumor member of said library or library capture, for example, by a method that comprises sequencing, for example, with a next generation sequencing method; (d) aligning said reading with an alignment method; and (e) assigning a nucleotide value (for example, calling a mutation, for example, with a Bayesian method or a calling method) from said reading to the pre-selected nucleotide position, thus analyzing said sample of tumor. optionally, where a nucleotide value is assigned to a nucleotide position in each of the X unique individual intervals (subgenomic intervals, expressed subgenomic intervals, or both) is assigned by a unique calling method, where the unique individual range (eg, subgenomic range or expressed subgenomic range) means different from the individual's other X-1 ranges (eg, subgenomic ranges, expressed subgenomic ranges, or both) and where the unique calling method means different of the other calling methods X-1 and X is at least 2. The calling methods can differ and thus be unique, for example, based on different Bayesian previous values.

[657] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in US Patent Application Publication 2015/0324519, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include methods, systems and apparatus for making the most accurate variant calls based on a sample's sequencing readings, for example, obtained from a targeted sequencing. For example, when sequence readings are received and aligned to a reference sequence, sequence readings with a variant in one location can be counted. A first variant frequency of a specific variant measured at one location in a sample can be compared to one or more frequencies of the second variant of the specific variant measured at other positions and / or other samples.

rear.

The second variant frequency can correspond to an expected value for sequencing errors for a sequencing run.

In some modalities, a probability value indicating the level of confidence that a variant is a true positive in a location can be calculated based on the variance counts and the total readings in a plurality of locations in the target region in one or more samples.

The probability value can then be compared with a threshold level to determine whether the detected variant is a true positive.

In other modalities, a difference in the variant counts and total readings at the same location in a test sample and a reference sample (for example, it is assumed that there are only sequencing errors at the location) can be used to determine whether a variant is a true positive in a test sample.

According to one embodiment, a method can detect true positives for rare variants in a target region of a test sample.

For each sample, the frequencies of variant variants of the same variant class in locations where there is a reference allele in a reference sequence can be calculated using variant counts and total reading counts.

A distribution of the variant frequencies for variants of the same class can be used to determine the probability value of a variant at a location in the test sample with a determined variant frequency.

Based on the probability value, the variant at the location of the test sample is classified as true positive (mutation) or false positive.

In other embodiments, a method can detect true positives for rate variants in a target region of a test sample using a comparison with one or more reference samples.

A variant count and a wild type count for a specific variant at a specific location in the test sample can be determined from the readings of the aligned sequence and compared to a variant count and a wild type count for the specific variant in the specific location in one or more reference samples to determine a probability value.

Based on the probability value, the specific variant at the specific location in the test sample is classified as true positive or false positive.

In one embodiment, a computer-implemented method for detecting low-frequency variants in a target region in a first sample is provided.

In some embodiments, the method comprises (in a computer system) receiving a plurality of sequence readings obtained from the sequencing of DNA fragments from one or more samples, to one or more samples including the first sample, where sequencing includes targeting the target region in the DNA fragments; align the plurality of sequence readings to the target region of a reference sequence; identify a first candidate variant that has a first allele at a first location in the target region based on sequence readings from the first sample that differ from a reference allele at the first location in the reference sequence; determining a first variant frequency for the first allele at the first site based on the sequence readings of the first sample that align with the first site of the reference sequence; identify the first candidate variant as corresponding to a first class of variant selected from a plurality of classes of variants, each class of variant of the plurality of classes of variants corresponding to a different type of variant; identify a set of second locations in the target region of the reference sequence that has the reference allele, where at least 50% of the other locations in one or more samples exhibit a false positive for the first allele and where the set of second locations includes the first location; in each of the sets of local seconds and for each of the one or more samples: determine a second variant frequency of the first allele based on sample sequence readings that align with the second location of the reference sequence, the frequencies of the second variant forming a statistical distribution; comparing the frequency of the first variant with a statistical value of the statistical distribution to determine a probability value of the first variant frequency in relation to the statistical value of the statistical distribution; and comparing the probability value with a threshold value as part of determining whether the first candidate variant is a true positive in the first sample of the first allele, the threshold value that differentiates between false positives and true positives for the first allele.

In certain modalities, the reference sequence corresponds to a consensus sequence, as determined from normal cells.

In some embodiments, the one or more samples are derived from DNA fragments without cells.

In some embodiments, the one or more samples are derived from the RNA of a biological sample.

In some modes, the plurality of samples is sequenced in a single sequencing run.

In other modalities, the statistical value of the statistical distribution includes an average value.

In other modalities, the probability value is a z score, modified z score, cumulative probability, Phred quality score or modified Phred quality score.

In other modalities, the statistical distribution is the statistical distribution of logarithmic transformations of the second variant frequencies.

In other modalities, the threshold is determined using the support vector machine classifier based on training data obtained from one or more sequencing runs.

In other modalities, the threshold is a function of the variant frequency.

In another modality, an im-

computerized to detect a variant that has a first allele at a first location in a target region in a first sample.

In some embodiments, the method comprises (on a computer system): receiving a plurality of sequence readings obtained from sequencing DNA fragments from at least two samples, the at least two samples including the first sample , where sequencing includes targeting the target region in the DNA fragments; align the plurality of sequence readings to the target region of a reference sequence; identify whether the first allele exists at the first location in each sample of at least two samples based on aligned sequence readings from each sample at the first location, differ from a reference allele at the first location in the reference sequence; determining a count of variants of the first allele at the first site and a wild-type count of the reference allele at the first site for each sample of at least two samples; select from at least two samples, at least one sample as a reference sample; compare a first count of variants of the first allele at the first site and a first wild-type count of the reference allele at the first site for the first sample with a second count of variants of the first allele at the first site and a second type count wild-type of the reference allele at the first location of the reference sample to determine a probability value of the variant that has the first allele at the first location of the first sample; and comparing the probability value with a threshold value as part of determining whether the first allele at the first site of the first sample is a true positive for the first allele, the threshold value differentiating between false positives and true positives for the first allele at the first location.

In certain embodiments, the reference sample comprises two samples with lower variant frequencies for the first allele at the first location between at least two samples other than the first sample.

In some modalities, the probability value is determined using the chi-square cumulative distribution function.

In some modalities, the probability value is determined using Pearson's proportion test.

In some modalities, the probability value is one or more of z score, modified z score, p value, chi-square value, cumulative probability value and quality score.

In some embodiments, the quality score is determined using a lookup table.

In some modalities, the threshold is determined using the support vector machine classifier based on training data obtained from one or more sequencing runs.

In some modalities, the threshold is a function of the variant frequency.

In another embodiment, a computer product is provided comprising a non-transitory, computer-readable medium that stores a plurality of instructions that, when executed, control a computer system to detect true variants in a target region of a first sample.

In some embodiments, the instructions comprise receiving a plurality of sequence readings obtained from the sequencing of DNA fragments from one or more samples, to one or more samples including the first sample, where sequencing includes directing the target region in the DNA fragments; align the plurality of sequence readings to the target region of a reference sequence; identify a set of sequence sites in the target region of the reference sequence that has a reference allele of variants in a class of variants, where at least 50% of the sequence sites in one or more samples exhibit a false positive for the variants in the variant class in the sequence readings, and where the set of sequence locations includes a first location; at each location in the set of sequence locations and for each sample of one or more samples: determine a reading count at each location for each sample; identify candidate variants with variant alleles for variants in the variant class based on the sequence readings for each sample that differ from the reference allele at the same location in the reference sequence, a total number of candidate variants at each location in each sample , with the variance count at each location for each sample; determine a variant frequency of variants in the variant class based on the reading count and the variant count, the variant frequency for each location in each sample forming a statistical distribution, where the variant frequency at a first location in the set of sequence locations for the first sample is a first variant frequency; compare the frequency of the first variant with a value of the statistical distribution to determine a probability value of the first variant frequency in relation to the value of the statistical distribution; and comparing the probability value with a threshold value as part of determining whether candidate variants in the first sample are true positives, the threshold value that differentiates between false positives and true positives for variants in the variant class. In certain modalities, the statistical distribution is the statistical distribution of a logarithmic transformation of the variant frequency at each location for each sample.

[658] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in U.S. Patent

9,340,830, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include a method of analyzing a tumor sample. The method comprises: (a) acquiring a library comprising a plurality of target members, for example, tumor members, from a sample, for example, a tumor sample; (b) optionally, contact the library with a set of baits (or plurality of sets of baits) to provide selected members (sometimes referred to here as "library capture"); (c) acquiring a reading for a subgenomic interval of a tumor member from said library or library capture, for example, by sequencing, for example, with a next generation sequencing method; (d) align said reading; and (e) assigning a nucleotide value (for example, calling a mutation, for example, with a Bayeisan method) from said reading to a pre-selected nucleotide position, for example, to a pre-selected nucleotide position in each of a plurality of subgenomic intervals, for example, each of a plurality of genes, thus analyzing the said sample, in which: (i) each of the X nucleotide positions is analyzed under a unique set of conditions for one or a combination of steps (b), (c), (d) or (e) (where unique means different from other sets of conditions X-1 and where X is at least 2, 5, 10, 20, 30, 40, 50, 100, 200, 300 or 500). For example, a first set of conditions is used for a first nucleotide position, for example, in a first subgenomic range or gene, and a second set of conditions, for example, a second set of conditions, is used for a second position of nucleotide, for example, in a second subgenomic range or gene; (ii) for each of the X nucleotide positions, responsive to a characteristic of a pre-selected change, for example, mutation that can occur in the nucleotide position, the nucleotide position is analyzed under a single set of conditions (in which unique means differ of the other X-1 sets of conditions and where X is at least 2, 5, 10, 20, 30, 40, 50, 100, 200, 300 or 500). For example, in response to a characteristic, for example, a characteristic of a pre-selected change, for example, mutation, which can occur in a nucleotide position in a first subgenomic interval, the nucleotide position is analyzed under a first con - together with conditions and responsive to a characteristic, for example, a characteristic, of a pre-selected alteration, for example, mutation, which can occur in a nucleotide position in a second subgenomic interval, the nucleotide position is analyzed under the second set of conditions; (iii) in which said method is performed on a sample, for example, a preserved tumor sample, under conditions that allow 95, 98 or 99% sensitivity or specificity for nucleotide positions in at least 2, 5, 10, 20, 50 or 100 subgenomic intervals, for example, genes; or (iv) in which the method comprises one or more or all of: a) sequencing a first subgenomic interval to provide about 500 × or more of the sequencing depth, for example, sequencing a mutation present in no more than 5% of the sample cells; b) sequencing a second subgenomic interval to provide about 200 × or higher, for example, about 200 × - about 500 ×, sequencing depth, for example, to sequence a mutation present in no more than 10% of the sample cells; c) sequencing a third subgenomic interval to provide about 10-100 × depth of sequencing, for example, sequencing one or more subgenomic intervals (eg exons) that are chosen from: a) a single nucleotide polymorphism (SNP) pharmacogenomics (PGx) that can explain the patient's ability to metabolize different drugs; or b) genomic SNPs that can be used to uniquely identify (for example, fingerprint) a patient; d) sequencing a fourth subgenomic interval to provide about 5-50 × depth of sequencing, for example, to detect a structural breakpoint, such as a genomic translocation or an indel.

For example, the detection of an intronic breakpoint requires a depth of coverage of 5-50 × pair of sequence to ensure high detection reliability.

These sets of baits can be used to detect, for example, translocation / cancer genes prone to indel; or e) sequence a fifth subgenomic range to provide about 0.1 to 300 × depth of sequencing, for example, to detect changes in the number of copies.

In one embodiment, the sequencing depth varies from about 0.1-10 × sequencing depth to detect changes in the number of copies.

In other modalities, the depth of sequencing varies from about 100 to 300 × to detect a SNP / genetic loci that is used to assess gains / losses in the number of copies of genomic DNA or loss of heterozygosity (LOH). In addition or alternatively, the detection of a genetic biomarker may include a method of analyzing a sample, for example, a tumor sample.

The method comprises: (a) acquiring a library comprising a plurality of members of a sample, for example, a plurality of tumor members of a tumor sample; (b) optionally enriching the library for pre-selected strings, for example, contacting the library with a set of baits (or a plurality of sets of baits) to provide selected members (sometimes referred to here as library capture) ; (c) acquiring a reading for a subgenomic interval of a member, for example, a tumor member from said library or library capture, for example, by a method comprising sequencing, for example, with a next sequence method generation; (d) aligning said reading with an alignment method; and (e) assigning a nucleotide value (for example, calling a mutation, for example, with a Bayesian method) from that reading to the pre-selected position of the nucleotide, thus analyzing that sample

tumor, in which a reading of each of the unique X subgenomic intervals is aligned with a unique alignment method, where unique subgenomic interval means different from the other X-1 subgenomic intervals and in which unique alignment method means different from the other alignment methods X-1 and X is at least 2. In one embodiment, step (b) is present.

In one embodiment, step (b) is absent.

In one embodiment, X is at least 3, 4, 5, 10, 15, 20, 30, 50, 100, 500 or 1,000. In a modality, a method (for example, element (d) of the method mentioned above) comprises selecting or using an alignment method to analyze, for example, align, a reading, in which said alignment method is a function de, is selected in response to, or is optimized for, one or more or all of: (i) type of tumor, for example, the type of tumor in said sample; (ii) the gene, or type of gene, in which said subgenomic range being sequenced is located, for example, a gene or type of gene characterized by a pre-selection or variant or type of variant, for example, a mutation or a change in a pre-selected frequency; (iii) the site (for example, nucleotide position) being analyzed; (iv) the type of variant, for example, a substitution, within the subgenomic range being evaluated; (v) the type of sample, for example, an FFPE sample; and (vi) sequence at or close to the subgenomic range being evaluated, for example, the expected propensity for misalignment for that subgenomic range, for example, the presence of repeated sequences in the next subgenomic range.

In addition or alternatively, the detection of a genetic biomarker may include a method of analyzing a sample, for example, a tumor sample.

The method comprises: (a) acquiring a library comprising a plurality of members of a sample, for example, a plurality of members of the tumor sample; (b) optionally enriching the library for pre-selected strings, for example, contacting the library with a set of baits (or a plurality of sets of baits) to provide selected members, for example, a library capture; (c) acquiring a reading for a subgenomic interval of a member, for example, a tumor member of said library or library capture, for example, by a method comprising sequencing, for example, with a next sequence method generation; (d) align said reading with an alignment method; and (e) assigning a nucleotide value (for example, calling a mutation, for example, with a Bayesian method or a calling method) from said reading to the pre-selected nucleotide position, thus analyzing said tumor sample, where a nucleotide value is assigned to a nucleotide position in each of the unique X subgenomic intervals is assigned by a unique calling method, where unique subgenomic interval means different from the other subgenomic X-1 intervals and in which exclusive calling method means different from other calling methods X-1 and X is at least 2. The calling methods may differ and thus be unique, for example, based on different previous Bayesian values .

In one embodiment, step (b) is present.

In one embodiment, step (b) is absent.

In addition or alternatively, the detection of a genetic biomarker may include a method of analyzing a sample, for example, a tumor sample.

The method comprises: (a) acquiring a library comprising a plurality of members (for example, target members) of a sample, for example, a plurality of tumor members of a tumor sample; (b) contacting the library with a set of baits to provide selected members (for example, a library capture); (c) acquiring a reading for a subgenomic interval of a member, for example, a tumor member of said library or library capture, for example, by a method comprising sequencing, for example, with a sequencing method next generation; (d) aligning said reading with an alignment method; and (e) assigning a nucleotide value (for example, calling a mutation, for example, with a Bayesian method or method) from said reading to the pre-selected nucleotide position, thereby analyzing said tumor sample, wherein the method comprises contacting the library with a plurality, for example, at least two, three, four or five of baits or sets of baits, wherein each bait or set of baits of said plurality has a pre-selected efficiency (as opposed to the other sets of baits in plurality) for selection.

For example, each unique bait or set of baits provides a unique depth of sequencing.

In one mode, the efficiency of selecting a first bait fixed in plurality differs from the efficiency of a second bait fixed in plurality at least 2 times.

In one embodiment, the first and second set of lures provide a depth of sequencing that differs by at least 2 times.

In one embodiment, the method comprises contacting one or more sets of baits below with the library: a) a set of baits that selects enough members that comprise a subgenomic range to provide about 500 × or more of sequencing depth, for example, to sequence a mutation present in no more than 5% of the sample cells; b) a set of baits that selects enough members comprising a subgenomic range to provide about 200 × or more, for example, about 200 × - about 500 × sequencing depth, for example, to sequence a mutation present in no more than 10% of the sample cells; c) a set of baits that selects enough members that comprise a subgenomic interval to provide about 10 to 100 × depth of sequencing, for example, to sequence one or more subgenomic intervals (for example, exons) that are chosen from: a) pharmacogenomic single nucleotide polymorphism (SNP) (PGx) that can explain the patient's ability to metabolize different drugs, or b) genomic SNPs that can be used to exclusively identify (for example, fingerprint) a patient; d) a set of baits that selects enough members comprising a subgenomic interval to provide about 5-50 × depth of sequencing, for example, to detect a structural breakpoint, such as a genomic translocation or an indel.

For example, detecting an intronic breakpoint requires a depth of coverage of 5-50 × pair of sequence to ensure high detection reliability.

These sets of baits can be used to detect, for example, translocation / cancer genes prone to indel; or e) a set of baits that selects sufficient members comprising a subgenomic range to provide about 0.1 to 300 × sequencing depth, for example, to detect changes in the number of copies.

In one embodiment, the sequencing depth varies from about 0.1-10 × sequencing depth to detect changes in the number of copies.

In other modes, the sequencing depth varies from about 100 to 300 × to detect a SNP / genomic loci that is used to assess gains / losses in the number of copies of genomic DNA or loss of heterozygosity (LOH). These sets of baits can be used to detect, for example, cancer genes prone to amplification / exclusion.

In addition or alternatively, the detection of a genetic biomarker may include a sample, for example, a tumor sample.

The method comprises: (a) acquiring a complete library

comprising a plurality of members of a sample, for example, a plurality of tumor members of a tumor sample; (b) optionally enriching the library for pre-selected strings, for example, contacting the library with a set of baits (or a plurality of sets of baits) to provide selected members (for example, a library capture ); (c) acquiring a reading for a subgenomic range of a member, for example, a tumor member of said library or library capture, for example, by a method comprising sequencing, for example, with a sequencing method next generation; (d) aligning said reading with an alignment method; and (e) assigning a nucleotide value (for example, calling a mutation, for example, with a Bayesian method or a method) from said reading to the pre-selected nucleotide position, thereby analyzing said tumor sample, where the method comprises sequencing, for example, by a next generation sequencing method, a subgenomic interval of at least five, six, seven, eight, nine, ten, fifteen, twenty, twenty-five, thirty or more genes or gene products in the sample, where the genes or gene products are chosen from: ABL1, AKT1, AKT2, AKT3, ALK, APC, AR, BRAF, CCND1, CDK4, CDKN2A, CEBPA, CTNNB1, EGFR, ERBB2, ESR1, FGFR1, FGFR2, FGFR3, FLT3, HRAS, JAK2, KIT, KRAS, MAP2K1, MAP2K2, MET, MLL, MYC, NF1, NOTCH1, NPM1, NRAS, NTRK3, PDGFRA, PIK3CA, PIK3CG, PIK3R1, PTCH1, PTCH RB1, RET, SMO, STK11, SUFU, or TP53. In one embodiment, step (b) is present. In one embodiment, step (b) is absent.

[659] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include any of the various forms described in PCT Publication WO 2017/151524, which is incorporated herein by reference in its entirety .

For example, the detection of a genetic biomarker may include methods for assessing the mutation load in a sample, providing a sequence of a set of subgenomic intervals in the sample; and determining a value for the mutation load, where the value is a function of the number of changes in the set of subgenomic intervals.

In certain embodiments, the set of subgenomic intervals is from a predetermined set of genes, for example, a predetermined set of genes that does not include the entire genome or exome.

In certain embodiments, the set of subgenomic intervals is a set of subgenomic coding intervals.

In other modalities, the set of subgenomic intervals contains a subgenomic coding interval and a subgenomic non-coding interval.

In certain embodiments, the value for the mutation load is a function of the number of a change (for example, a somatic change) in the set of subgenomic intervals.

In certain modalities, the number of a change excludes a functional change, a change in the germ line or both.

In some modalities, the sample is a tumor sample or a sample derived from a tumor.

In addition or alternatively, the detection of a genetic biomarker may include methods comprising, for example, one or more of the following: acquiring a library comprising a plurality of tumor members of the sample; contacting the library with a set of baits to provide tumor members selected by hybridization, thereby providing a capture of the library; acquire a reading for a subgenomic interval comprising an alteration of the tumor limb from the capture of the library; align the reading with an alignment method; assigning a nucleotide value of the reading to a pre-selected nucleotide position; and select a set of subgenomic intervals from a set of positions

assigned nucleotide sections, where the set of subgenomic intervals are from a predetermined set of genes.

In addition or alternatively, the detection of a genetic biomarker may include a method for assessing the mutation load in a sample, for example, a tumor sample (for example, a sample acquired from a tumor), the method includes: ) provide a sequence, for example, a nucleotide sequence, from a set of subgenomic intervals (eg, subgenomic coding intervals) of the sample, where the set of subgenomic intervals are from a predetermined set of genes; and b) determine a value for the mutation load, where the value is a function of the number of a change (for example, one or more changes), for example, a somatic change (for example, one or more somatic changes) in the set of subgenomic intervals.

In certain modalities, the number of an alteration excludes a functional change in a subgenomic interval.

In other modalities, the number of a change excludes a change in the germ line in a subgenomic interval.

In certain modalities, the number of a change excludes a functional change in a subgenomic interval and a change in the germline in a subgenomic interval.

In certain modalities, the set of subgenomic intervals comprises subgenomic coding intervals.

In other modalities, the set of subgenomic intervals comprises subgenomic non-coding intervals.

In certain modalities, the set of subgenomic intervals comprises subgenomic coding intervals.

In other modalities, the set of subgenomic intervals comprises one or more subgenomic coding intervals and one or more subgenomic non-coding intervals.

In certain embodiments, about 5% or more, about 10% or more, about 20% or more, about 30% or more, about 40% or more, about 50% or more, about 60% or more, about 70% or more, about 80% or more, about 90% or more, or about 95% or more, of the subgenomic intervals in the set of subgenomic intervals are subgenomic coding intervals.

In other embodiments, about 90% or less, about 80% or less, about 70% or less, about 60% or less, about 50% or less, about 40% or less, about 30% or less, about 20% or less, about 10% or less, or about 5% or less, of the subgenomic intervals in the set of subgenomic intervals are subgenomic non-coding intervals.

In other modalities, the set of subgenomic intervals does not comprise the entire genome or the whole exome.

In other modalities, the set of subgenomic coding intervals does not comprise the whole exome.

In certain modes, the mutation load is expressed as a percentile, for example, among the mutation loads in samples from a reference population.

In certain modalities, the reference population includes patients with the same type of cancer as the individual.

In other ways, the reference population includes patients who are receiving or have received the same type of therapy as the individual.

In addition or alternatively, the detection of a genetic biomarker may include a method for assessing the mutation load in a sample, for example, a tumor sample or a sample derived from a tumor.

The method includes: (i) acquiring a library comprising a plurality of tumor members from the sample; (ii) contacting the library with a set of baits to provide selected tumor members, wherein said set of baits hybridizes to the tumor member, thereby providing a capture of the library; (iii) acquiring a reading for a subgenomic interval comprising an alteration (for example, a somatic alteration) of a tumor member from said library capture, for example, by a sequencing method

next generation funding; (iv) align said reading with an alignment method; (v) assigning a nucleotide value from said reading to a pre-selected nucleotide position; (vi) selecting a set of subgenomic intervals (for example, encoding subgenomic intervals) from a set of designated nucleotide positions, where the set of subgenomic intervals are from a predetermined set of genes; and (vii) determining a value for the mutational load, where the value is a function of the number of a change (for example, one or more changes), for example, a somatic change (for example, one or more changes somatic), in the set of subgenomic intervals.

In certain embodiments, the number of a change (for example, a somatic change) excludes a functional change in a subgenomic range.

In other modalities, the number of a change excludes a change in the germ line in a subgenomic interval.

In certain embodiments, the number of a change excludes a functional change (for example, a somatic change) in a subgenomic interval and a change in germ line in a subgenomic interval.

In certain modalities, the predetermined set of genes comprises a plurality of genes, which in mutant form, are associated with an effect on cell division, growth or survival, or are associated with cancer.

In certain embodiments, the method further comprises acquiring a library comprising a plurality of tumor members from the sample.

In certain embodiments, the method further comprises contacting a library with a set of baits to provide selected tumor members, wherein said set of baits hybridizes to a tumor member of the library, thereby providing a library capture.

In certain embodiments, the method further comprises acquiring a reading for a subgenomic interval comprising altering (for example, somatic alteration) a tumor member of a library or library capture, thus acquiring a reading for the subgenomic interval, for example, by a next generation sequencing method.

In certain modalities, the method also comprises aligning a reading for the subgenomic interval with an alignment method.

In certain embodiments, the method further comprises assigning a nucleotide value to a pre-selected nucleotide position from a reading for the subgenomic range, for example, by a method called mutation.

In certain modalities, the method also comprises one, two, three, four or all: (a) acquiring a library comprising a plurality of tumor members of the sample; (b) contacting the library with a set of baits to provide selected tumor members, wherein said set of baits hybridizes to the tumor member, thereby providing a capture of the library; (c) acquire a reading for a subgenomic interval comprising the alteration (for example, somatic alteration) of a tumor limb from the aforementioned library capture, thus acquiring a reading for the subgenomic interval, for example , by a next generation sequencing method; (d) aligning said reading with an alignment method; or (e) assigning a nucleotide value from said reading to a pre-selected nucleotide position, for example, by a so-called mutation method.

In certain modalities, the alteration of the germ line is excluded by a method or system that includes the use of an SGZ algorithm.

In certain embodiments, the method further comprises characterizing a variant, for example, a change in the tumor sample by: a) acquiring: i) a sequence coverage entry (SCI), which comprises, for each of a plurality of selected subgenomic intervals, a value for normalized sequence coverage in the selected subgenomic intervals, where SCI is a function of the number of readings for a subgenomic interval and the number of readings for a control corresponding to the process; ii) an SNP allele frequency input (SAFI), which comprises, for each of a number of selected germline SNPs, a value for the allele frequency in the tumor sample, on which the SAFI is based, at least in part, at a lower or alternative allelic frequency in the tumor sample; and iii) a variant allele frequency input (VAFI), which comprises the allele frequency for said variant in the tumor sample; b) acquire values, according to SCI and SAFI, for: i) a total number of copies (C) of the genomic segment for each of a plurality of genomic segments; ii) a lower allele copy number of the genomic segment (M) for each of a plurality of genomic segments; and iii) sample purity (p), in which the values of C, M and p are obtained by adjusting a copy number model across the genome to SCI and SAFI; and c) acquire: a value for the type of mutation, g, for which it is indicative of the variant, being somatic, a subclonal somatic variant, germ line or not distinguishable and is a function of VAFI, p, C and M.

The SGZ algorithm is described in International Application Publication WO 2014/183078 and U.S. Order Publication 2014/0336996, the content of which is incorporated by reference in its entirety.

In addition or alternatively, the detection of a genetic biomarker may include a system for assessing the mutation load in a sample (for example, a tumor sample or a sample derived from a tumor). The system includes at least one processor operationally connected to a memory, the at least one processor when running is configured to: a) acquire a sequence, for example, a nucleotide sequence, from a set of subgenomic intervals (for example, encode subgenomic intervals) of the sample, where the set of subgenomic intervals encoding are from a predetermined set of genes; and b) determining a value for the mutational load, where the value is a function of the number of a change (for example, a somatic change) in the set of subgenomic intervals.

In addition or alternatively, the detection of a genetic biomarker may include a method of analyzing a sample, for example, a tumor sample of a hematological malignancy (or pre-malignancy). The method comprises: (a) acquiring one or a plurality of libraries comprising a plurality of members of a sample, for example, a plurality of tumor members of a tumor sample; (b) optionally enriching one or a plurality of libraries for pre-selected strings, for example, by contacting one or a plurality of libraries with a set of baits (or a plurality of sets of baits) to provide selected members (sometimes referred to here as library capture); (c) acquiring a reading for an individual interval, for example, a subgenomic interval or an expressed subgenomic interval, for a member, for example, a tumor member of a library or a library capture, for example, by a method that comprises sequencing, for example, with a next generation sequencing method; (d) aligning said reading with an alignment method; and (e) assigning a nucleotide value (for example, calling a mutation, for example, with a Bayesian method) from that reading to the pre-selected nucleotide position, thus analyzing the said sample of tumor, optionally in which: a reading of each of the X unique individual intervals (for example, subgenomic intervals, expressed subgenomic intervals or both) are aligned with a unique alignment method, where the exclusive individual interval ( subgenomic interval or expressed subgenomic interval) means different from other X-1 individual intervals (eg, subgenomic intervals, expressed subgenomic intervals, or both), and in which unique alignment method means different from other methods of alignment X-1 and X is at least 2. In one embodiment, a method (for example, element (d) of the method mentioned above) comprises selecting or using an alignment method to analyze, eg example, align, a reading, in which said alignment method is a function of, is selected in response to, or is optimized for, one or more or all of: (i) type of tumor, for example, the type of tumor in said sample; (ii) the gene, or type of gene, in which said individual range (for example, subgenomic interval or expressed subgenomic range) being sequenced is located, for example, a characterized gene or type of gene by a pre-selection or variant or type of variant, for example, a mutation or a mutation of a pre-selected frequency; (iii) the site (for example, nucleotide position) being analyzed; (iv) the type of variant, for example, a substitution, within the range of the individual (for example, subgenomic range or expressed subgenomic range) being evaluated; (v) the type of sample, for example, an FFPE sample, a blood sample or a bone marrow aspirate sample; and (vi) sequence at or close to the referred subgenomic interval being evaluated, for example, the expected propensity for misalignment for said individual interval (for example, subgenomic interval or expressed subgenomic interval), for example, the presence of sequences repeated in the individual interval at or near that individual interval (for example, subgenomic interval or expressed subgenomic interval). In some embodiments, the method may comprise using a suitable alignment method which includes: selecting a rearrangement reference sequence for alignment with a reading, wherein said rearrangement reference sequence is pre-selected to align with a pre-arrangement -selected (in modalities the reference sequence is not identical to the genomic rearrangement); compare, for example, align, a reading with that preset rearrangement reference sequence. In the modalities, other methods are used to align problematic readings. These methods are particularly effective when the alignment of readings for a relatively large number of different subgenomic intervals is optimized. As an example, a method of analyzing a tumor sample may comprise: performing a comparison, for example, an alignment comparison, of a reading under a first set of parameters (for example, a first mapping algorithm or with a first reference sequence) and determine if that reading meets a predetermined first alignment criterion (for example, the reading can be aligned with said first reference sequence, for example, with less than a pre-defined number selected from incompatibilities); if said reading fails to meet the first predetermined alignment criterion, perform a second alignment comparison under a second set of parameters (for example, a second mapping algorithm or with a second reference sequence); and, optionally, determine whether said reading meets said second predetermined criterion (for example, the reading can be aligned with said second reference sequence with less than a pre-selected number of incompatibilities), where said second set of parameters comprises using a set of parameters, for example, the said second reference sequence, which, compared to the said first set of parameters, is more likely to result in an alignment with a reading for a pre-selected variant, for example, a rearrangement, for example, an insertion, exclusion or translocation.

[660] In some modalities, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the several methods described in the Publication of the

U.S. Patent Application 2013/0266938, which is incorporated herein by reference in its entirety.

For example, the detection of a genetic biomarker may include methods for detecting the presence or absence of a target nucleic acid sequence in a biological sample.

In some modalities, the method comprises: a. contacting a detectably labeled probe comprising an anchor nucleic acid domain and a reporter nucleic acid domain with the sample; and B. detecting the presence or absence of binding of the probe to the target nucleic acid, where the anchor and reporter domains are linked by a non-nucleoside ligand, and neither the anchor domain nor the reporter form a stem loop in the absence the acid target nucleus; and wherein (i) the probe is not extensible by a polymerase; (ii) the linker is linked to the anchor domain within 2 nucleotides of the 3 'end of the anchor domain and the linker is linked to the reporter domain within 2 nucleotides of the 5' end of the reporter domain, where the anchor domain is not connected to a detectable marker; and / or (iii) the anchor domain and the reporter domain comprise a contiguous sequence of at least 6 nucleotides complementary to the same strand of the target nucleic acid.

In some embodiments, the probe is not extensible by a polymerase In some embodiments, the ligand is linked to the anchor domain within 2 nucleotides of the 3 'end of the anchor domain and the ligand is linked to the reporter domain within 2 nucleotides from the 5 'end of the reporter domain, where the anchor domain is not linked to a detectable marker.

In some embodiments, the anchor domain and the reporter domain comprise a contiguous sequence of at least 10 nucleotides complementary to a strand of the target nucleic acid.

In some embodiments, the detection step comprises measuring the melting temperature of a complex formed between the reporter domain and the target nucleic acid.

In some modalities, the length of the reporter domain is between 4 to 20 nucleotides.

In some modalities, the length of the reporter domain is between 6 to 12 nucleotides.

In addition or alternatively, the detection of a genetic biomarker may include reaction mixtures to detect the presence or absence of a target sequence.

In some embodiments, the reaction mixture comprises: a. a target nucleic acid comprising an anchor binding region and a reporter binding region, and b. a detectably labeled probe comprising an anchor nucleic acid domain and a reporter nucleic acid domain, where the anchor and reporter domains are linked by a non-nucleoside linker, and neither the anchor domain nor the reporter forms a rod handle in the absence of the acid target nucleus; and wherein (i) the probe is not extensible by a polymerase; (ii) the linker is linked to the anchor domain within 2 nucleotides of the 3 'end of the anchor domain and the linker is linked to the reporter domain within 2 nucleotides of the 5' end of the reporter domain, where the anchor domain it is not linked to a detectable marker; and / or (iii) the anchor domain and the reporter domain comprise a contiguous sequence of at least 6 nucleotides complementary to the same strand of the target nucleic acid.

In some embodiments, the reaction mixture further comprises nucleoside triphosphates, a DNA polymerase and / or an oligonucleotide primer.

In some embodiments, the probe is not extensible by a polymerase.

Additionally or alternatively, the detection of a genetic biomarker may include a detectably labeled probe comprising an anchor nucleic acid domain and a reporter nucleic acid domain, wherein: the anchor and reporter domains are linked by a non-nucleoside linker ; neither the anchor domain nor the reporter form a stem loop in the absence of the target nucleic acid; and where: (i) the probe is not extensible by a polymerase; and / or (ii) the linker is linked to the anchor domain within 2 nucleotides of the 3 'end of the anchor domain and the linker is linked to the reporter domain within 2 nucleotides of the 5' end of the reporter domain, where the anchor domain does not is connected to a detectable marker. In some modalities, the probe is not extensible by a polymerase. In some embodiments, the linker is linked to the anchor domain within 2 nucleotides of the 3 'end of the anchor domain and the linker is linked to the reporter domain within 2 nucleotides of the 5' end of the reporter domain, where the domain anchor is not attached to a detectable marker. In some modalities, the length of the reporter domain is between 4 to 20 nucleotides. In some modalities, the length of the reporter domain is between 6 to 12 nucleotides. In some embodiments, the anchor domain is between 6-40 nucleotides. In some modalities, the marker is a fluorescent marker. In some embodiments, the probe comprises at least one unnatural nucleotide, wherein the unnatural nucleotide increases the melting temperature of the reporter domain compared to a corresponding natural nucleotide in place of the unnatural nucleotide. In some embodiments, the binder is polyethylene glycol. In some modalities, the binder is hexa-ethylene glycol.

[661] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in US Patent Application Publication 2012/0225428, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker can include a set of probes comprising DNA and LNA nucleotides. In some embodiments, at the 5 'end of the probes, nucleobases are determined, while at the 3' end, there are one or more (e.g., two or three) random nucleotides (also called "oscillation" positions). One modality consists of a short nucleic acid chain that can be used universally for the detection of several target sequences. The short nucleic acid sequence is also allele-specific and allows the detection of a specific mutation, such as a single nucleotide polymorphism (SNP). Some embodiments include a composition comprising a first probe and a second probe.

According to these modalities, the first probe has a 5 'end opposite a 3' end and at least eight nucleotides, the at least eight nucleotides comprising at least one nucleotide of DNA and at least five nucleotides of blocked nucleic acid and a pri - the first position of discrimination; and a second probe with an end 5 'opposite the 3' end and the same number of nucleotides as the first probe.

The nucleotides of the second probe comprise the same number of DNA nucleotides and blocked nucleic acid nucleotides as the first probe and a second discrimination position located in a position corresponding to the first discrimination position on the first probe.

In addition, according to these modalities, the nucleotides of the first and second probes comprise one of an adenine nucleobase, a cytosine nucleobase, a guanine nucleobase, a thymine nucleobase, an uracil nucleobase and an uracil nucleobase methyl cytosine and the first and second probes comprise different nucleobases in the first and second discrimination positions.

However, according to these modalities, the first and second probes comprise the same nucleobases in all other nucleotide positions of the probes.

Other modalities comprise a composition that includes a first and a second set of probes.

Each probe in the first and second sets has a 5 'end opposite the 3' end and eight nucleotides.

The nucleotides of each probe in the first set have at least one DNA nucleotide, at least five blocked nucleic acid nucleotides and a first

first discriminatory position, at least one blocked nucleic acid nucleotide being a random blocked nucleic acid nucleotide, while each probe in the second set of probes has a corresponding number of DNA nucleotides, blocked nucleic acid nucleotides and nucleotides of randomly blocked nucleic acid as a probe in the first set, and each probe in the second set has a second discrimination position located in the same nucleotide location as a first probe discrimination position in the first set.

In addition, according to some of these modalities, all probes in the first and second sets have the same nucleotide sequence, with the exception of (i) the nucleobase in the nucleotides of nucleic acid randomly blocked; and (ii) the nucleobase in the first and second discrimination positions.

Furthermore, the nucleobase of the second discrimination position differs from the nucleobase of the first discrimination position in the same nucleotide location, and the at least one random nucleotide of blocked nucleic acid from each probe in the second set comprises the same nucleobase located in the same nucleotide location as at least one blocked nucleic acid random nucleotide from a probe in the first set.

According to these modalities, the nucleobase of the blocked nucleic acid random nucleotide is selected from one of adenine, cytosine, guanine and thymine, and any possible nucleobase sequence resulting from variations of nucleobases in one or more random positions of blocked nucleobases (s) is represented by at least one probe in the first and second set of probes.

In addition or alternatively, the detection of a genetic biomarker may include a method for determining a genotype at a site of interest in a sample comprising genetic material.

The method includes the steps of contacting the genetic material with a first probe and a second probe and detecting the connection of the first or second probe to the genetic material, thereby determining the genotype in the locus.

According to these modalities, the first and second probes have a 5 'end opposite the 3' end and eight nucleotides comprising at least one nucleotide of DNA and at least five nucleotides of blocked nucleic acid.

The nucleotides of the first probe comprise a first discrimination position and the nucleotides of the second probe comprise a second discrimination position in the same nucleotide location on the second probe as the first discrimination position on the first probe.

In addition, the first discrimination position comprises a nucleobase different from the second discrimination position, in which the nucleobases in the other nucleotides of the first and second probes are the same.

In addition or alternatively, the detection of a genetic biomarker may include a composition that includes a first set of probes and a second set of probes, each of the probes with eight nucleotides being composed of one to three nucleotides of DNA and five to seven nucleotides of LNA (blocked nucleic acid). According to these modalities, all probes in the first and second set of probes have identical nucleotide sequences, with the exception of (i) base (s) in one, two or three random positions of the LNA; and (ii) the base in a position of discrimination, in which one, two or three random positions of the LNA and the position of discrimination are located in identical positions in all probes of the first and second sets.

In addition, according to these modalities in each LNA random position, the base is selected independently from adenine, cytosine, guanine and thymine and any possible sequence resulting from the base variation (s) in one, two or three random positions of the LNA is represented by at least one probe in each set of probes.

In addition, according to these modalities, the base in the discrimination position is identical within each set of probes, but differs between the first and the second set of probes. In addition or alternatively, the detection of a genetic biomarker can include a library of at least two sets of probes. According to these modalities, the library comprises a plurality of sets of probes, each of the probes with eight nucleotides with the general structure 5′-D- LLLLLXX-3 ′ or 5′-DLLLLXXX-3 ′ (where D is a DNA nucleotide, each L is an LNA nucleotide, and each X is a random LNA nucleotide). In addition, within a set of probes, all probes have identical nucleotide sequences, with the exception of the two and / or three random nucleotides LNA (with each position of a random nucleotide base LNA being selected independently from adenine, cytosine, guanine and thymine). In addition, according to these modalities, any possible sequence resulting from the base variation (s) in the two positions is represented by a probe in each set of probes and a set of probes differing from the other set of probes in the sequence of at least the DNA D nucleotide or an LNA L nucleotide. In addition or alternatively, the detection of a genetic biomarker may include a method for determining the genotype at a locus of interest in a sample obtained from an individual . The method includes the steps of contacting the sample that comprises the genetic material with any of the compositions of the above composition modalities and detecting the connection of a probe from the first or second set of probes to the genetic material, thereby determining the genotype at the locus.

[662] In some modalities, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the several methods described in the Publication of the

U.S. Patent Application 2010/0248991, which is incorporated herein by reference in its entirety.

For example, the detection of a genetic biomarker may include a solid support comprising at least two sequence specific amplification primers, wherein at least one primer is attached to said support with a cleavable inducible linker.

Preferably, said cleavable linker is a photo-cleavable linker.

In a first main embodiment, said solid support is a sphere.

This sphere is composed of a material selected from the group consisting of silicon, titanium dioxide, aluminum oxide, lanthanide oxide, glass, silicates, polystyrene, cellulose, sepharose and polyamide.

A sphere is made of a pure material or composed of two or more materials, while the two or more materials are mixed or assembled in an orderly manner, as in the wrapper particles of the core.

The surface of a sphere is functionalized in such a way that the oligonucleotides can be attached.

Additionally or alternatively, the detection of a genetic biomarker may include a sphere library, as disclosed above.

Preferably, each member of the plurality of primers that are connected to the sphere via a cleavable linker carries a different detectable tag or a single mix of multiple tags.

In some embodiments, the solid support is a microtiter or picotiter plate (PTP) comprising a plurality of wells, characterized in that a plurality of said wells comprises a surface with at least two sequence-specific amplification initiators in which at least at least one primer is attached to said support with a cleavable linker.

In addition or alternatively, the detection of a genetic biomarker may include a method for preparing a solid support and, preferably, a sphere comprising at least two sequence specific primers, further characterized by at least one of said primers is cleavable, said method comprising the steps of providing a solid support carrying at least one or more functional groups and reacting said one or more functional groups with the reactive group or groups of two sequence specific primers, wherein one reactive cleavable fraction is present within one of the spacers that connect said solid support with its functional group or one of its functional groups or said cleavable portion is present within one of the spacers that connect one of said sequence specific primers to your active group.

In addition or alternatively, the detection of a genetic biomarker may include a method comprising the steps of providing a solid support comprising two functional groups, each carrying a different protective group, unprotecting a first functional group and reacting said group with the reactive group of a first initiator, and deprotect the second functional group and react said group of said sphere with the reactive group of a second initiator.

Said two functional groups are connected to the sphere via two separate ligands, but in a specific embodiment, said two functional groups are connected to the sphere via a two-arm ligand.

In some embodiments, the method comprises the steps of providing a solid support carrying exactly one functional group, unprotecting said functional group and reacting said group with a mixture of a sequence specific first and second primer, said first and second initiators comprising identical reactive groups, characterized in that at least one of said initiators is connected to its reactive group via a cleavable fraction.

In some embodiments, the method comprises the steps of providing a sphere carrying exactly one functional group and unprotecting said functional group and reacting said group with an oligonucleotide representing a first and a second amplification initiator which are connected by a cleavable fraction. In some embodiments, the method comprises the steps of providing a sphere carrying protected OH groups, protected with two different orthogonal protecting groups, cutting one of the said orthogonal protecting groups and synthesizing the first initiator in the sphere and cutting the second of the said protecting group orthogonal and synthesize the second primer in the sphere.

[663] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in US Patent Application Publication 2009/0105081, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include methods and systems for the capture and enrichment of target nucleic acids and analysis of enriched target nucleic acids. Additionally or alternatively, the detection of a genetic biomarker may include enrichment of targeted sequences in a solution-based format. In some embodiments, the solution-based capture methods comprise probe-derived amplicons, wherein said probes for amplification are affixed to a solid support. The solid support comprises nucleic acid probes immobilized on support to capture specific nucleic acid sequences (for example, target nucleic acids) from, for example, a genomic sample. Probe amplification provides amplicons of the probe in solution that are hybridized to target sequences. After hybridization of the probe's amplicons to the target sequences, the target nucleic acid sequences present in the sample are enriched by capture (for example, via ligand chemistry, such as biotin, digoxigenin, etc.) and probe washing and elution of the target nucleic acids hybridized from the captured probes (FIG. 1). The target nucleic acid sequence (s) can be further amplified using, for example, non-specific binding-mediated PCR (LM-PCR), resulting in an amplified set of PCR products from reduced complexity compared to the original target sample.

In some embodiments, hybridization between the probes and the target nucleic acids is preferably performed under stringent conditions sufficient to support the hybridization between the solution-based probe amplicons, wherein said probes comprise the ligand chemistry and complementary regions of the target nucleic acid sample to provide probe / target hybridization complexes.

The complexes are subsequently captured via ligand chemistry and washed under conditions sufficient to remove non-specifically bound nucleic acids and the hybridized target nucleic acid sequences are eluted from the captured probe / target complexes.

In addition or alternatively, the detection of a genetic biomarker may include methods to isolate and reduce the genetic complexity of a plurality of nucleic acid molecules, the method comprising the steps of exposing denatured and fragmented nucleic acid molecules from that population multiple multiple oligonucleotide probes that are attached to a solid support under hybridization conditions to capture nucleic acid molecules that specifically hybridize to said probes or expose denatured and fragmented nucleic acid molecules from said population to several different oligonucleotide probes under hybridization conditions followed by connecting the complexes of hybridized molecules to a solid support to capture nucleic acid molecules that specifically hybridize with said probes, in which in both cases the denatured and fragmented nucleic acid molecules are of average size from about 100 to about 1000 nucleotide residues, preferably about 250 to about 800 nucleotide residues and more preferably about 400 to about 600 nucleotide residues,

separating unbound and not specifically hybridized nucleic acids from the captured molecules, eluting the captured molecules and, optionally, repeating the aforementioned processes for at least one additional cycle with the eluted captured molecules.

In addition or alternatively, the detection of a genetic biomarker may include an enrichment method for target nucleic acid sequences in a genomic sample, such as exons or variants, preferably SNP sites.

This can be achieved by synthesizing genomic probes specific to a region of the genome to capture complementary target nucleic acid sequences contained in a complex genomic sample.

In some embodiments, the method further comprises determining the nucleic acid sequence of the captured and eluted target molecules, in particular by performing sequencing by synthesis reactions.

In addition or alternatively, the detection of a genetic biomarker may include a method for detecting the variation of the coding region in relation to a reference genome, in particular in relation to a reference genome comprising denatured genomic nucleic acid molecules, fragmented, the method as previously described further comprising determining the nucleic acid sequence of the captured and eluted target molecules, in particular by performing sequencing by synthesis reactions and comparing the determined sequence with a sequence in a database, in particular with a sequence in a database of polymorphisms in the reference genome to identify variants of the reference genome.

In the modalities, the nucleic acid capture probes (pre-selected) are immobilized on a solid support (for example, slide, chip, sphere, etc.) using any number of recognized methods (for example, stains, photolysis). in situ synthesis, etc.). In preferred embodiments, the probes are synthesized in situ by unmasked array synthesis on a substrate and subsequently amplified by, for example, PCR resulting in amplicons derived from the probe in solution.

In some embodiments, the probe sequences as synthesized comprise primer binding sites for amplification at one or both 3 'and 5' terminals (for example, at or near the ends) of the probes.

In some embodiments, the sequence of the primer binding sites on the probes is the same at the 3 'and 5' primary ends or on the probes, while in other embodiments the sequence of the primer binding sites is different at the 3 'primary end so sequence at the primary 5 'end. In some embodiments, amplification primers for probe amplification further comprise a restriction endonuclease site, for example, a MlyI site for easy removal of primers from the final captured target, in which one of the primers (for example, primer direct or reverse) further comprises the chemistry of the ligand as a binding portion or sequence (eg biotin, digoxigenin, HIS tag, etc.) and is deposited on the support with the immobilized probes together with the reagents necessary for the exponential amplification of the PCR (for example, PCR procedures for the exponential amplification of targets, as is known to a skilled technician). PCR is performed in this way, creating amplicons of probe capture sequences, so that one of the tapes comprises the chemistry of the ligand, as a fraction or binding sequence.

The solution containing amplicons is transferred to a container (eg, tube, 96-well plate well, etc.) and, in some embodiments, purified from reaction components.

An additional amplification cycle is preferably carried out on probe-derived amplicons using asymmetric PCR, in which the ligand chemistry-labeled primer is in abundance compared to the unlabeled primer to preferentially synthesize fraction / sequence-labeled amplicons. simple ribbon link.

The amplicons are purified away from the reaction components and transferred to a vessel, denatured nucleic acid sample is added and hybridization is allowed.

After hybridization, the target nucleic acid / labeled amplicon complexes are captured.

For example, when biotin is the binding moiety, a substrate coated with streptavidin (SA), such as beads coated with SA (eg, paramagnetic beads / particles), is used to capture the biotin-labeled target / amplicon complex. .

The SA-bound complex is washed and the hybridized target nucleic acids are eluted from the complex and used in downstream applications, such as sequencing applications.

Additionally or alternatively, the detection of a genetic biomarker may include methods for isolating and reducing the complexity of a plurality of nucleic acid sequences comprising providing a solid support wherein said solid support comprises hybridization probes for hybridization to targeting nucleic acid sequences and providing a fragmented sample of nucleic acid comprising target nucleic acid sequences, amplifying the hybridization probes in which the amplification products comprise a binding fraction and in which the amplification products are in solution , hybridize the nucleic acid sample to the amplification products in solution under conditions such that hybridization between the amplification products and the target nucleic acid sequences is allowed, separate the hybridized target nucleic acid sequence complexes / amplification products from the hybridized nucleic acids not specifically by that fraction of ligation and elute the hybridized target nucleic acid sequences of the complex thereby isolating and reducing the complexity of a plurality of nucleic acid sequences.

In some embodiments, the solid support is a microarray blade.

In some embodiments, the target nucleic acid sample is fragmented genomic DNA with or without adapter molecules at one or both ends of the fragments.

In some embodiments, the hybridization probes comprise a restriction endonuclease site, for example, a MlyI site.

In some embodiments, probe amplification comprises exponential polymerase chain reaction and may also comprise asymmetric non-exponential amplification.

In some embodiments, the binding fraction is biotin and the capture substrate, such as a sphere, for example, a paramagnetic particle, is coated with streptavidin to separate the target nucleic acid complex / amplification product from the target nucleic acids. specifically hybridized.

In some embodiments, the captured target nucleic acid / amplification product complexes are washed before eluting the bound target nucleic acids.

In some embodiments, the eluted target nucleic acids are sequenced.

In addition or alternatively, the detection of a genetic biomarker may include methods for isolating and reducing the complexity of a plurality of nucleic acid sequences comprising providing a solid support wherein said solid support comprises hybridizable hybridization probes to target sequences nucleic acid and provide a fragmented sample of nucleic acid comprising target nucleic acid sequences, amplify the hybridization probes in which the amplification products comprise a binding fraction and in which the amplification products are in solution, hybridize the nucleic acid sample with the amplification products in solution under conditions such that hybridization between the amplification products and the target nucleic acid sequences is permitted, separating the hybridized target nucleic acid sequence complexes / amplification products from the nucleic acids hybridized not specifically by that fraction of ligation, eluting the hybridized target nucleic acid sequences from the complex thereby isolating and reducing the complexity of a plurality of nucleic acid sequences and sequencing the eluted target nucleic acid sequences. In some embodiments, the solid support is a microarray blade. In some embodiments, the target nucleic acid sample is fragmented genomic DNA with or without adapter molecules at one or both ends of the fragments. In some embodiments, the hybridization probes comprise a restriction endonuclease site, for example, a MlyI site. In some modalities, probe amplification comprises exponential polymerase chain reaction and may also comprise asymmetric non-exponential amplification. In some embodiments, the binding fraction is biotin and the capture substrate, such as a sphere, for example, a paramagnetic particle, is coated with streptavidin to separate the target nucleic acid complex / amplification product from the target nucleic acids. specifically hybridized. In some embodiments, the captured target nucleic acid / amplification product complexes are washed before eluting the bound target nucleic acids.

[664] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include any of the various forms described in PCT Publication WO 2018/077847, which is incorporated herein by reference in its entirety . For example, the detection of a genetic biomarker may include a method for making a library of target nucleic acid molecules from a sample comprising a plurality of target molecules, the method comprising substantially for each target molecule: attaching a single adapter to a target molecule forming a circular molecule, where the adapter comprises two bar codes, two primer binding sites located between the two bar codes, where the primers annealing to the binding sites are facing each other and at least one modified nucleotide effecting a strand synthesis termination by a nucleic acid polymerase located between the two primer binding sites; annealing a direct starter complementary to the adapter on a tape of the target molecule; extending the direct primer to the modified nucleotide, thereby producing a first strand; annealing a reverse primer complementary to the adapter for the first ribbon; extending the first primer, thus producing the second strand and a double strand molecule comprising the first strand and the second strand in which the two bar codes are flanking the target sequence.

In some embodiments, at least one of the forward and reverse primers comprises a 5'-flap sequence not complementary to the adapter and comprising an additional primer binding site.

Then, the method further comprises a step of annealing an additional primer to the complementary sequence to the flap sequence in the direct primer and extending the additional primer thus producing a double strand molecule comprising two additional primer sites and the two codes of bars that flank the target sequence.

In some embodiments, the target molecule and the adapter are single-stranded.

In other embodiments, the target molecule and the adapter are double-stranded and the circular molecule is at least partially denatured initiator to anneal the initiator.

In some ways, the barcode is a sequence of nucleotides 4-20 bases in length.

The modified nucleotide that carries out a termination of strand synthesis by a nucleic acid polymerase can be selected from abasic nucleotides, nucleotides with protein side groups, AraC synthetic nucleotide (cytarabine) or deoxyacryl, isoguanine, 5- methylisocytosine, ethylene glycol spacers, nucleotides with bulky analogs, such as fluorophores, or unnatural base pair "d5SICS-dNaM" nucleic acid analogs

(UBP). The bond can be selected from the protruding bond, T-A bond, blunt tip bond and topoisomerase catalyzed bond.

In some embodiments, the adapter has a photoconductive binder at one end.

In these modalities, the ligand is attached at one end and exposed to UV light to allow bonding at the other end.

In some embodiments, the additional primers are sequencing primers.

In addition or alternatively, the detection of a genetic biomarker can include a library of target nucleic acid molecules in which each molecule is a circular molecule comprising a target sequence and an adapter that links the ends of the target sequence, the adapter comprising : two bar codes; two primer binding sites located between the two barcodes, where the annealing primers at the binding sites face each other; at least one modified nucleotide that performs a chain synthesis termination by a nucleic acid polymerase located between the two primer binding sites.

In some embodiments, the barcode is a sequence of nucleotides 4-20 bases in length.

The modified nucleotide that performs a tape synthesis termination by a nucleic acid polymerase can be selected from abasic nucleotides, nucleotides with side groups of proteins, AraC synthetic nucleotide (cytarabine) or deoxyuracil, isoguanine, 5-methylisocytosine, ethylene glycol spacers , nucleotides with bulky analogues, such as fluorophores, or non-natural base pair (UBP) nucleic acid analogs (UBP). Additionally or alternatively, the detection of a genetic biomarker may include a method of sequencing target nucleic acids in a sample comprising a plurality of target molecules, the method comprising: creating a library of target nucleic acid molecules from the sample connecting a single-stranded adapter to substantially each double-stranded target molecule forming a circular double-stranded molecule, where the adapter comprises two barcodes, two connection sites to the initiator located between the two barcodes, where the annealing primers at the binding sites face each other and at least one modified nucleotide effecting a chain synthesis termination by a nucleic acid polymerase located between the two binding sites initiator; denature at least a portion of the circular double-stranded target molecule; annealing a direct primer complementary to the adapter on a ribbon of the target molecule; extending the direct primer to the modified nucleotide, thus producing a first strand; annealing a reverse primer complementary to the adapter for the first ribbon; extending the first primer, thereby producing the second strand and a double strand molecule comprising the first strand and the second strand in which the two bar codes are flanking the target sequence; amplify the double-stranded molecule; and sequencing the amplified products of the double-stranded molecule. In some modes, at least one of the forward and reverse primers comprises a 5'-flap sequence not complementary to the adapter and comprising an additional primer binding site. In some modalities, the method further comprises, after extending the first primer, annealing an additional primer to the sequence complementary to the flap sequence in the direct primer and extending the additional primer thus producing a double strand molecule comprising two sites of additional initiators and the two barcodes that flank the target sequence. In some embodiments, amplification or sequencing can be performed with additional primers.

[665] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include any of the various forms described in PCT Publication WO 2017/123316, which is incorporated herein by reference in its entirety .

For example, the detection of a genetic biomarker may include a targeted sequencing workflow in which an input sample comprising a sufficient amount of genetic material is provided so that minimal or nonexistent amplification processes are required prior to sequencing.

In some embodiments, the incoming sample is derived from an intact tumor or lymph nodes.

In some embodiments, the incoming sample is obtained by homogenizing an intact tumor sample (total or partial) and / or one or more lymph nodes obtained from a patient or mammal.

In some embodiments, the incoming sample is derived from a sufficient amount of blood, including whole blood or any fraction thereof.

In some embodiments, the incoming sample is derived from cancerous tissue.

In some embodiments, the incoming sample is derived from precancerous tissue.

In some modalities, the targeted sequencing workflow comprises one or more amplification steps (for example, a pre-capture amplification step, a post-capture amplification step) before sequencing, where each amplification step before sequencing comprises 0 to 3 amplification cycles, and in which an aggregate of amplification cycles before sequencing does not exceed 4. In other embodiments, the targeted sequencing workflow comprises one or more amplification steps (for example, a pre-capture amplification step, a post-capture amplification step) before sequencing, where each amplification step before sequencing comprises 0 to 2 amplification cycles, and in which an aggregate of amplification cycles before sequencing does not exceed 3. In still other embodiments, the targeted sequencing workflow comprises an amplification step before sequencing (p for example, a pre-capture amplification step or a post-capture amplification step

capture), where the only amplification step before sequencing comprises 0 to 3 amplification cycles.

In other modalities, the targeted sequencing workflow comprises an amplification step before sequencing, where the only amplification step before sequencing comprises 1 to 3 cycles.

In yet other modalities, the targeted sequencing workflow comprises an amplification step before sequencing, where the only amplification step before sequencing comprises 1 cycle.

In even other modalities, the targeted sequencing workflow comprises an amplification step before sequencing, in which the only amplification step before sequencing comprises 2 cycles.

In some modalities, one or both of the pre-capture amplification stage or the post-capture amplification stage, but before sequencing, use LM-PCR.

In addition or alternatively, the detection of a genetic biomarker may include a method for sequencing genomic material within a sample that comprises: homogenizing a tumor sample and / or lymph node sample to provide a homogenized sample; isolate at least 0.5 microgram of genomic material from the homogenized sample; prepare at least 0.5 micrograms of isolated genomic material for sequencing; and sequence the prepared genomic material.

In some modalities, the method does not include any amplification step before sequencing.

In some modalities, the method comprises at least one pre-capture or post-capture amplification step, in which an aggregate number of amplification cycles performed during at least one pre-capture or post-capture amplification step is a maximum of 4 cycles.

In some modalities, the aggregate number of amplification cycles is 3. In some modalities

the aggregate number of amplification cycles is 2. In some modalities, the preparation of at least 0.5 micrograms of isolated genomic material for sequencing comprises hybridizing at least 0.5 micrograms of isolated genomics for capture probes and capture hybridized genomic material.

In some modalities, the amount of genomic material captured varies from about 90ng to about 900ng.

In some modalities, 1 or 2 amplification cycles are performed on the captured genomic material.

In some modalities, the homogenized sample comprises a representative sample of cells.

In some embodiments, at least 1 microgram of genomic material is isolated from homogenized samples.

In some modalities, at least 5 micrograms of genomic material are isolated from homogenized samples.

In some embodiments, at least 10 micrograms of genomic material are isolated from homogenized samples.

In addition or alternatively, the detection of a genetic biomarker may include a method of DNA sequencing within a sample comprising isolating at least 0.5 micrograms of DNA from a blood sample; prepare at least 0.5 microgram of isolated DNA for sequencing and sequence the prepared DNA.

In some embodiments, the method comprises the amplification step before sequencing.

In some embodiments, the preparation of at least 0.5 micrograms of isolated DNA for sequencing comprises hybridizing at least 0.5 micrograms of isolated genomics to capture probes and capture the hybridized genomic material.

In some modalities, the amount of genomic material captured varies from about 90ng to about 900ng.

In some modalities, 1 or 2 amplification cycles are performed on the captured genomic material.

In some modalities, at least 1 microgram of DNA is isolated from the blood sample.

In addition or alternatively, the detection of a genetic biomarker may include a targeted representational sequencing method comprising: (i) homogenizing at least a portion of a tumor, one or more whole or partial lymph nodes, or any combination of them to provide a homogenized sample; (ii) extracting genomic material from the homogenized sample; (iii) capture the genomic material extracted in spheres; and (iv) sequencing the captured genomic material; where the targeted representational sequencing comprises performing a maximum of 4 amplification cycles before sequencing the captured genomic material.

In some modalities, a maximum of 3 amplification cycles can be carried out before the capture of the extracted genomic material or after the capture of the extracted genomic material, or any combination thereof.

In some modalities, no pre-capture amplification cycle is conducted.

In some modalities, the amount of captured genetic material varies from about 90ng to about 900ng.

In some modalities, 1 to 3 amplification cycles are performed after the capture of the extracted genomic material, but before sequencing.

In some modalities, at least 0.5 microgram of genomic material is extracted from the homogenized sample.

In some embodiments, at least 100 times more genomic material is derived from the homogenized sample compared to an amount of input material used in a sequencing method that requires more than 4 amplification cycles.

Additionally or alternatively, the detection of a genetic biomarker may include a method of DNA sequencing within a sample comprising: providing at least 0.5 micrograms of input genomic material, at least 0.5 micrograms of derived genomic material of a tumor sample, a lymph node sample, or a blood sample, isolate the DNA from the incoming genomic sample, preparing the isolated DNA for sequencing and sequencing the prepared DNA, in which the method does not comprise any amplification step .

In some modalities, at least 0.5 micrograms of incoming genomic material is derived from multiple histological and / or biopsy samples.

In some embodiments, the at least 0.5 microgram of incoming genomic material is derived from a homogenized tumor sample.

In some embodiments, the at least 0.5 microgram of input genomic material is derived from a sample from a homogenized lymph node sample.

In some embodiments, the at least 0.5 microgram of input genomic material is a representative sample of the tumor sample, lymph node sample or blood sample from which it is derived.

In some embodiments, sequencing is performed using a next generation sequencing method.

In some embodiments, sequencing is performed using a synthetic sequencing methodology.

In addition or alternatively, the detection of a genetic biomarker may include a method for reducing mutations introduced by PCR during sequencing comprising isolating DNA from a sample comprising a sufficient amount of genomic material; prepare the isolated DNA for sequencing; and sequencing the prepared DNA, in which the method comprises a maximum of 3 amplification cycles before sequencing.

In some embodiments, the method comprises 1 or 2 cycles of amplification before sequencing.

In some modes, a sufficient amount of input genomic material is such that no pre-capture amplification cycle is used.

In some modalities, the sample is derived from a patient with suspected cancer.

In some modalities, the sample is derived from a patient diagnosed with cancer.

In some modalities, the sample is derived from a patient at risk of developing cancer.

In some embodiments, the sample is derived from healthy tissue samples.

In some embodiments, 0.5 micrograms of DNA are isolated from the sample.

In some modalities, at least 1 microgram of genomic material is isolated from homogenized samples.

In some embodiments, at least 5 micrograms of genomic material is extracted from the homogenized sample.

In some modalities, at least 10 micrograms of genomic material are isolated from the sample.

In addition or alternatively, the detection of a genetic biomarker may include a sequencing method in which the mutations introduced by PCR are reduced, comprising capturing at least 0.05 micrograms of genomic material and performing between 0 and 2 cycles amplification before sequencing.

In some modalities, 0 amplification cycles are performed.

In other ways, 1 amplification cycle is performed.

In still other modalities, 2 amplification cycles are performed.

Additionally or alternatively, the detection of a genetic biomarker may include a sequence capture method in which the bias introduced by PCR in the proportional representation of the genome content is reduced, the sequencing method comprises providing an input sample comprising at least 0.5 microgram of genomic material and where the sequence capture method comprises performing between 0 and 2 amplification cycles before sequencing.

In some modalities, 0 amplification cycles are performed.

In other modalities, 1 amplification cycle is performed.

In still other modes, 2 amplification cycles are performed.

In some ways, the input sample comprises at least 1 microgram of genomic material.

In some embodiments, the input sample comprises at least 5 micrograms of genomic material.

In some modalities, the input sample comprises at least 10 micrograms of genomic material.

Additionally or alternatively, the detection of a genetic biomarker may include a sequence capture method in which mutations introduced by PCR are eliminated, the sequence capture method comprising preparing an input sample comprising at least 0.5 microgram of genomic material. In some embodiments, the input sample comprises at least 1 microgram of genomic material. In some modalities, the input sample comprises at least 5 micrograms of genomic material. In some modalities, the input sample comprises at least 10 micrograms of genomic material. In another aspect, it is a sequence capture method in which a step of removing duplicate PCR readings before sequencing is eliminated, the sequence capture method comprising providing an input sample comprising at least 0.5 microgram of genomic material. In some embodiments, the input sample comprises at least 1 microgram of genomic material. In some modalities, the input sample comprises at least 5 micrograms of genomic material. In some embodiments, the input sample comprises at least 10 micrograms of genomic material. In addition or alternatively, the detection of a genetic biomarker may include a sequencing method in which mutations introduced by PCR are virtually eliminated, the sequencing method comprising capturing at least 0.05 micrograms of genomic material. In some modalities, about 0.05 micrograms of genomic material is provided after the capture of the genomic material. In some modalities, 1 or 2 post-capture amplification cycles are performed before sequencing.

[666] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include any of the various forms described in PCT Publication WO 2017/132276, which is incorporated herein by reference in its entirety .

For example, the detection of a genetic biomarker may include methods in which regions of a tumor sample that do not predict response to a first therapeutic agent are excised from the sample with an automatic dissection tool, mutations correlated with predictive biomarkers. they are detected in the excised region using NGS and additional tumor samples are stained for one or more predictive biomarkers identified by NGS.

In addition or alternatively, the detection of a genetic biomarker may include a method which comprises: obtaining a first sample of a tumor, in which the first sample is stained histochemically for a first predictive biomarker for a first therapeutic agent; extract one or more regions from the first sample with an automated dissection tool, in which the excised region has a staining pattern for the first predictive biomarker, indicating that the region is unlikely to respond to the first therapeutic agent; detecting with a next-generation sequencer one or more predictive mutations of a response to one or more additional therapeutic agents in a nucleic acid sample derived from the excited region (s) of the first sample; staining of one or more additional tumor samples for one or more additional predictive biomarkers correlated with one or more mutations identified in the samples, with one or more additional predictive biomarkers predictive of a response to one or more of the agent (s) additional therapeutic (s). In addition or alternatively, the detection of a genetic biomarker may include a system comprising: (a) a sample of nucleic acid derived from one or more regions excised from a first tumor sample, wherein the first tumor sample it is stained for a first predictive biomarker and yet the one or more regions excised from the section have a staining pattern of the first predictive biomarker, indicating that it is unlikely that at least a portion of the tumor will respond to a first therapeutic agent; (b) a next generation sequencer adapted to identify the presence or absence of mutations correlated with one or more additional predictive biomarkers; (c) a laboratory information system (LIS) comprising a database, the database containing: (cl) analyzing mutation of a nucleic acid sample by next generation sequencing, in which the mutation analysis indicates at least the presence or absence of mutations in the nucleic acid sample, mutations correlated to one or more additional predictive biomarkers for one or more additional therapeutic agents; and (c2) instructions for directing an automated slide stain to stain a second tumor sample with one or more additional predictive biomarkers identified by the mutation analysis.

[667] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the U.S. Patent

10,023,917, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include high-resolution fusion assays (HRM) as a pre-screening diagnostic method to diagnose mutations in the access point regions of the most common genes (KRAS, BRAF, PIK3CA, AKT1) related to the RAS / RAF / MAPK and PI3K / PTEN / AKT path. In addition or alternatively, the detection of a genetic biomarker may include amplification primer pairs, which are useful for the analysis of HRM of genes that are important for predicting the responsiveness to cancer therapeutic agents. Additionally or alternatively, the detection of a genetic biomarker may include the following pairs of amplification primers for amplification and analysis of KRAS, exons 2 and 3, BRAF, exon 15, PIK3CA, exons 7, 9 and 20 and AKT1, exon 2.

In addition or alternatively, the detection of a genetic biomarker may include a reaction composition or mixture comprising at least one pair of amplification primers, as disclosed above.

The composition can be used for PCR amplification of nucleic acids during a nucleic acid amplification reaction, as well as real-time PCR amplification and monitoring.

According to some embodiments, a mixture comprises at least: a pair of amplification primers as disclosed above; a thermostable DNA polymerase; a mixture of deoxynucleoside triphosphates which is generally dA, dG, dC and dT, or dA, dG, dC and dU; and a buffer.

In some additional embodiments, when suitable for amplification and real-time detection, of one or more specific nucleic acid target sequence (s), such composition additionally comprises a nucleic acid detection entity, such as a probe. fluorescent hybridization or a double-stranded DNA-binding fluorescent dye.

In some embodiments, this double stranded DNA dye is a dye that can be used to perform HRM curve analysis.

In some embodiments, the pair of amplification primers is designed to amplify a specific sequence of interest according to standard methods known in the art of molecular biology.

In some embodiments, when contacted with a sample to be analyzed, this PCR reaction mixture additionally comprises at least partially purified DNA or another nucleic acid that putatively comprises a specific sequence of interest.

In addition, in some of these modes, the concentrations of all included reagents are generally known to those skilled in the art and can be optimized for specific adaptations according to standard protocols.

In some of these modalities, the concentration of the double-stranded DNA-binding fluorescent dye is between approximately 0.1 and 10.0 µg

/ ml. In some embodiments, a kit is provided. Some illustrative kit modalities include at least a pair of amplification primers. Some kit types may also comprise one, several or all of the following additional ingredients; a thermostable DNA polymerase; a mixture of triphosphate deoxynucleosides which is generally dA, dG, dC and dT, or dA, dG, dC and dU, and a fluorescent double-stranded DNA binding buffer and dye, which may be suitable for use for HRM. In addition or alternatively, the detection of a genetic biomarker may include a method to determine the increased probability of a response to a targeted treatment of a cancer disease, comprising the steps of: a) isolating genomic DNA from a patient sample; b) amplifying at least one fragment of said DNA by means of PCR with a specific pair of amplification primers; c) determine whether the said amplified fragment has a wild-type sequence or comprises a mutation using High Resolution Fusion Analysis (HRM); and d) correlate the presence or absence of a mutation with a greater probability of success of the referred targeted treatment. In some embodiments, the mutation is identified through hybridization analysis or through sequencing. For example, the patient sample can be tissue Embedded in Paraffin Formalin-Fixed (FFPE). In some of these cases, HRM analysis can be performed without any addition of DNA.

[668] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in U.S. Patent

9,873,908, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include methods for enriching low abundance alleles (for example, mutant DNA) in a sample that allows for the subsequent detection of such alleles.

In addition or alternatively, the detection of a genetic biomarker may include a method for enriching a variant of a target nucleic acid in a mixture of nucleic acids in a sample, the target nucleic acid existing in the form of two variant sequences, where the said variants differ in a single nucleotide position, the method comprising, providing the sample which includes the target nucleic acid in which the variant to be enriched is present in the sample in low abundance among a large excess of the other variant; provide an oligonucleotide that is complementary to a strand of the target nucleic acid at a concentration that is in molar excess over the target nucleic acid, where the oligonucleotide is attached to an affinity marker and blends perfectly into the single nucleotide position with the variant to be enriched and has an incompatibility in the position of single nucleotide with the other variant; providing suitable conditions for hybridizing the oligonucleotide to the target nucleic acid to generate duplex polynucleotides consisting of the oligonucleotide and a strand of any variant of the target nucleic acid; contacting the duplex polynucleotides with an incompatible interchangeable compound that binds preferentially only to the duplex polynucleotides that contain an incompatibility in which said compound is still capable of catalyzing the cleavage of a duplex polynucleotide ribbon at the light incompatibility site; submitting the duplex polynucleotides to light, resulting in cleaved and non-cleaved duplex polynucleotides; apply cleaved and non-cleaved duplex polynucleotides to an affinity matrix that recognizes and binds the affinity marker on the oligonucleotide; providing conditions in which only the cleaved duplex polynucleotide is denatured and removing the denatured single strand from the affinity matrix; and providing a buffer under conditions to denature the uncleaved polynucleotide duplex; and collecting the buffer containing a strand of the enriched variant of the target nucleic acid.

In one embodiment, the intercalating compound of incompatibility is Rh (bpy) 2 (chrysi) 3+ or Rh (bpy) 2 (phzi) 3+ or its respective analogues.

In addition or alternatively, the detection of a genetic biomarker may include an additional step of amplification and detection of the enriched variant of the target nucleic acid.

Additionally or alternatively, the detection of a genetic biomarker may include a method for detecting a mutant allele of a target nucleic acid in a mixture of nucleic acids in a sample in which the mutant allele differs from a wild-type allele in a single nucleotide position and is present in the sample in low abundance among a large excess of the wild type allele, the method comprising enriching the mutant allele in the sample in which the enrichment is performed by providing an oligonucleotide that is complementary to a strand of the target nucleic acid at a concentration that is in molar excess in relation to the target nucleic acid, where the oligonucleotide is linked to an affinity marker and combines perfectly at the single nucleotide position with the mutant allele and has an incomparable patibility in single nucleotide position with the wild type allele; providing suitable conditions for hybridizing the oligonucleotide to the target nucleic acid to generate duplex polynucleotides consisting of the oligonucleotide and a strand of the mutant allele or wild type allele; contacting the duplex polynucleotides with an incompatible intercalating compound that preferably binds only to the duplex polynucleotides that contain an incompatibility, wherein said compound is further capable of catalyzing the cleavage of a duplex polynucleotide ribbon at the light incompatibility site; submitting the duplex polynucleotides to light, resulting in cleaved and non-cleaved duplex polynucleotides; apply cleaved and non-cleaved duplex polynucleotides to an affinity matrix that recognizes and binds the affinity marker on the oligonucleotide; providing conditions under which only the cleaved duplex polynucleotide is denatured and removing the denatured single strand from the wild type allele of the affinity matrix; providing a buffer capable of denaturing the uncleaved polynucleotide duplex; and collecting the buffer containing a strand of the target nucleic acid enriched mutant allele; amplify the enriched mutant allele; and detecting the product of the enriched amplified mutant allele or the signal generated from the enriched amplified mutant allele. In one embodiment, the intercalating incompatibility compound is Rh (bpy) 2 (chrysi) 3+ or Rh (bpy) 2 (phzi) 3+ or its analogues.

[669] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in U.S. Patent

9,399,794, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include a method for detecting the presence or absence of a target nucleic acid in a test sample that comprises: insertion into data of a statistical learning classifier system from a control together with training samples in which the amount of the target nucleic acid and a control nucleic acid are known, using the statistical learning classifier system, calculating a plurality of weights for a general linear classifier; build a general linear classifier with the plurality of weights calculated by the statistical learning classifier system; contacting the test sample with a reaction mixture containing reagents necessary to amplify the target and the control nucleic acids by polymerase chain reaction (PCR) under conditions that allow PCR; measure at least one amplification-dependent parameter for the target and the control nucleic acids to obtain a set of test data; apply the general linear classifier to the test data set to classify the test sample as containing or not the target nucleic acid, thereby detecting the presence or absence of the target nucleic acid in the test sample.

In the variations of this modality, the statistical learning classifier system is selected from SVM, LDA and QDA.

In other variations of this modality, the amplification-dependent parameter is detected by fluorescence during each amplification cycle.

In other variations of this modality, the data are from the value of the cycle to the threshold (Ct). In other variations of this modality, the general linear classifier is a linear classifier in parts.

In other variations of this modality, a function in parts determined by restrictions imposed on the amplification-dependent parameter for the control nucleic acid is inserted in the linear classifier in parts.

In other variations of this modality, the target nucleic acid is a nucleic acid variant of a human sequence.

In addition or alternatively, the detection of a genetic biomarker may include a method for detecting the presence or absence of a target nucleic acid in a test sample that comprises: insertion into data of a statistical learning classifier system from a con - together with training samples in which the amount of the target nucleic acid and a control nucleic acid are known, using the statistical learning classifier system, calculating a plurality of weights for a general linear classifier; build a general linear classifier with the plurality of weights calculated by the statistical learning classifier system; subject the sample to the polymerase chain reaction (PCR); measure at least one amplification-dependent parameter for the target and the control nucleic acids to obtain a set of test data; apply the general linear classifier to the test data set; classify the test sample as containing or not the target nucleic acid, thereby detecting the presence or absence of the target nucleic acid in the test sample.

In the variations of this modality, the statistical learning classifier system is selected from SVM, LDA and QDA.

In other variations of this mode, the amplification-dependent parameter is detected by fluorescence during each amplification cycle.

In other variations of this modality, the data are from the cycle value to the threshold (Ct). In other variations of this modality, the general linear classifier is a linear classifier in parts.

In other variations of this modality, a function in parts determined by restrictions imposed on the amplification-dependent parameter for the control nucleic acid is inserted in the linear classifier in parts.

In other variations of this modality, the target nucleic acid is a nucleic acid variant of a human sequence.

In addition or alternatively, the detection of a genetic biomarker may include a method for determining whether a target nucleic acid is present in a test sample which comprises: submitting a sample training set in which the amount of the target nucleic acid and a nucleic acid control is known as polymerase chain reaction (PCR) and measurement of at least one amplification dependent parameter for the target and the control nucleic acids to obtain a training data set; inputting data into a statistical learning classifier system; use the statistical learning classifier system, calculate a plurality of weights for a general linear classifier; build a general linear classifier with the plurality of weights determined by the statistical learning classifier system; submit the test sample to the PCR and measure at least one amplification-dependent parameter for the target and the control nucleic acids to obtain a set of test data; apply the general linear classifier to the test data set; classify the test sample as containing or not the target nucleic acid.

In the variations of this modality, the statistical learning classifier system is selected from SVM, LDA and QDA.

In other variations of this modality, the amplification-dependent parameter is detected by fluorescence during each amplification cycle.

In other variations of this mode, the data are from the value of the cycle to the threshold (Ct). In other variations of this modality, the general linear classifier is a linear classifier in parts.

In other variations of this modality, a function in parts determined by restrictions imposed on the amplification-dependent parameter for the control nucleic acid is inserted in the linear classifier in parts.

In other variations of this embodiment, the target nucleic acid is a nucleic acid variant of a human sequence.

In addition or alternatively, the detection of a genetic biomarker may include a method for determining whether a target nucleic acid is present in a test sample which comprises: submitting a sample training set in which the amount of the target nucleic acid and a Control nucleic acid is known for polymerase chain reaction (PCR) and measurement of at least one amplification dependent parameter for the target and the control nucleic acids to obtain a training data set; inputting data into a statistical learning classifier system; use the statistical learning classifier system, calculate a plurality of weights for a general linear classifier; build a general linear classifier with the plurality of weights determined by the statistical learning classifier system; subjecting the test sample to PCR and measuring at least one amplification dependent parameter for the target and the control nucleic acids to obtain the test data set; apply the general linear classifier to the test data set obtained; classify the test sample as containing or not the target nucleic acid.

In the variations of this modality, the statistical learning classifier system is selected from SVM,

LDA and QDA.

In other variations of this modality, the amplification-dependent parameter is detected by fluorescence during each amplification cycle.

In other variations of this modality, the data are from the cycle value to the threshold (Ct). In other variations of this modality, the general linear classifier is a linear classifier in parts.

In other variations of this modality, a function in parts determined by restrictions imposed on the amplification-dependent parameter for the control nucleic acid is inserted in the linear classifier in parts.

In other variations of this embodiment, the target nucleic acid is a nucleic acid variant of a human sequence.

In addition or alternatively, the detection of a genetic biomarker may include a computer-readable medium, including code to control one or more processors to classify whether a test sample contains a target nucleic acid, the code including instructions for: applying a statistical classification system from learning to a training data set in which the amount of the target nucleic acid and a control nucleic acid is known, in order to build a general formula I linear classifier; applying the general linear classifier to a test data set comprising the test sample data to produce a statistically derived decision whether to classify the test sample as containing or not the target nucleic acid.

In the variations of this modality, the statistical learning classifier system is selected from SVM, LDA and QDA.

In other variations of this mode, the data in the data sets are from the cycle value to the threshold (Ct). In other variations of this modality, the general linear classifier is a linear classifier in parts.

In other variations of this modality, the target nucleic acid is a nucleic acid variant of a human sequence.

In addition or alternatively, the detection of a genetic biomarker may include a system for detecting a target nucleic acid in a test sample comprising: a data acquisition module configured to produce a data set from a set of training samples and one or more test samples, the data set indicating the presence and quantity of the target nucleic acid and a control nucleic acid; a data processing unit configured to process the data acquired by the acquisition module by applying a statistical learning classifier system to the training data set to build a general Formula I linear classifier and then apply the classifier general linear from Formula I to the test data set comprising the test sample data, to produce a statistically derived decision by classifying the test sample as containing or not the target nucleic acid; a display module configured to display the data produced by the data processing unit. In the variations of this modality, the statistical learning classifier system is selected from SVM, LDA and QDA. In other variations of this modality, the general linear classifier is a linear classifier in parts.

[670] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in U.S. Patent

9,382,581, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include an allele-specific amplification method of a variant of a target sequence, the target existing in the form of several variant sequences, the method comprising: (a) hybridizing a first oligonucleotide and a second oligonucleotide for at least one variant of the target sequence; wherein the first oligonucleotide is at least partially complementary to one or more variants of the target sequence and the second oligonucleotide is at least partially complementary to one or more variants of the target sequence and has at least one selective nucleotide complementary to only one variant of the target sequence; wherein said second oligonucleotide comprises a nucleotide with a base covalently modified in the exocyclic amino group and a phosphate modified with a structure:

[671] where A and B represent a nucleotide chain, D is OH or CH3 and Acc is an electron acceptor or an electron acceptor substituted by a residue R where R is an organic substituent, where Acc is selected from the group consisting of CN, SO2— R ', where R' comprises at least one alkyl substituted by amino, an optionally substituted aryl or an optionally substituted heterocycle and a six membered N + heterocycle with at least one N atom - alkylated in the ortho or para position, said heterocycle selected from the group consisting of pyridinium, pyrimidinium and quinoline; (b) providing suitable conditions for extending the oligonucleotide by a nucleic acid polymerase; (c) extending said first oligonucleotide and said second oligonucleotide by said nucleic acid polymerase, wherein said nucleic acid polymerase is capable of efficiently extending said second oligonucleotide when said oligonucleotide is hybridized to a variant of the target sequence that is complementary to said at least one selective nucleotide and substantially less efficient when said second oligonucleotide is hybridized to a variant of the target sequence that is not complementary to said at least one nucleotide selective. In addition or alternatively, the detection of a genetic biomarker may include a method for detecting a variant of a target sequence, the target existing in the form of several variant sequences, the method comprising: (a) hybridizing a first oligonucleotide and a second oligonucleotide in at least one variant of the target sequence; wherein the first oligonucleotide is at least partially complementary to one or more variants of the target sequence and the second oligonucleotide is at least partially complementary to one or more variants of the target sequence and has at least one selective nucleotide complementary to only one variant of the target sequence; wherein said second oligonucleotide comprises a nucleotide with a base covalently modified in the exocyclic amino group and a phosphate modified with a structure:

[672] here A and B represent a nucleotide chain, D is OH or CH3 and Acc is an electron acceptor or an electron acceptor substituted by a residue R where R is an organic substituent, where Acc is selected from the group consisting of CN, SO2— R ', where R' comprises at least one amino substituted alkyl, one optionally substituted aryl or one optionally substituted heterocycle and a six membered N + heterocycle with at least one N - alkylated atom in the ortho position or for, said heterocycle selected from the group consisting of pyridinium, pyrimidinium and quinoline; (b) providing suitable conditions for extending the oligonucleotide by a nucleic acid polymerase; (c) extending said first oligonucleotide and said second oligonucleotide by said nucleic acid polymerase, wherein said nucleic acid polymerase is capable of efficiently extending said second oligonucleotide when said oligonucleotide is hybridized with a variant the target sequence that is complementary to said at least one selective nucleotide and substantially less efficient when said second oligonucleotide is hybridized to a variant of the target sequence that is not complementary to said at least one selective nucleotide; (d) detecting products of said oligonucleotide extension, wherein said extension means the presence of the variant of said target sequence to which said second oligonucleotide has a complementary selective nucleotide. In addition or alternatively, the detection of a genetic biomarker may include an oligonucleotide to perform allele-specific amplification of a target sequence, the target existing in the form of several variant sequences, the oligo-nucleotide comprising (a) a sequence at least partially complementary to a portion of one or more variants of said target sequence; (b) at least one selective nucleotide complementary to only one variant of the target sequence; (c) a nucleotide with a base covalently modified in the exocyclic amino group; (d) a modified phosphate with a structure:

[673] where A and B represent a nucleotide chain, D is OH or CH3 and Acc is an electron acceptor or an electron acceptor substituted by a residue R where R is an organic substituent, where Acc is selected from the group consisting of CN, SO2— R ', where R' comprises at least one alkyl substituted by amino, an optionally substituted aryl or an optionally substituted heterocycle and a six membered N + heterocycle with at least one N atom alkylated in the ortho or para position, said heterocycle selected from the group consisting of pyridinium, pyrimidinium and quinoline. Additionally or alternatively, the detection of a genetic biomarker may include a reaction mixture for allele-specific amplification of a target sequence, the target existing in the form of several variant sequences, the mixture comprising: (a) a first oligonucleotide, at least least partially complementary to one or more variants of the target sequence; and (b) a second oligonucleotide, at least partially complementary to one or more variants of the target sequence, and has at least one selective nucleotide complementary to only one variant of the target sequence; wherein said second oligonucleotide comprises a nucleotide with a base covalently modified in the exocyclic amino group and a phosphate modified with a structure:

[674] where A and B represent a nucleotide chain, D is OH or CH3 and Acc is an electron acceptor or an electron acceptor substituted by a residue R where R is an organic substituent, where Acc is selected from the group consisting of CN, SO2— R ', where R' comprises at least one alkyl substituted by amino, an optionally substituted aryl or an optionally substituted heterocycle and a six membered N + heterocycle with at least one N atom alkylated in the ortho or para position, said heterocycle selected from the group consisting of pyridinium, pyrimidinium and quinoline; (c) a nucleic acid polymerase; (d) nucleoside triphosphates; and (e) a buffer suitable for the extension of nucleic acids by the nucleic acid polymerase.

[675] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in U.S. Patent

9,279,146, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include methods and compositions for enriching low abundance alleles (for example, mutant DNA) in a sample that allows for the subsequent detection of such alleles. In addition or alternatively, the detection of a genetic biomarker may include a method for enriching a variant of a target nucleic acid sequence in a mixture of nucleic acids in a sample, the target nucleic acid existing in the form of two variant sequences, where the said variants differ in a single nucleotide position, the method comprises, providing the sample which includes the target nucleic acid sequence in which the variant to be enriched is present in the sample in low abundance between a large excess of the other variant ; provide an oligonucleotide that is complementary to a strand of the target nucleic acid sequence, where the oligonucleotide has an incompatibility in the position of the single nucleotide with the variant to be enriched and is perfectly compatible in the position of the single nucleotide with the other variant; providing suitable conditions for hybridizing the oligonucleotide to the target nucleic acid to generate duplex polynucleotides consisting of the oligonucleotide and a strand of any variant of the target nucleic acid sequence; contacting the duplex polynucleotides with an incompatible intercalating compound that is attached to an affinity marker to generate a reaction mixture, in which the said incompatible intercalating compound is able to bind to the duplex polynucleotides that contain an incompatibility and is not able to bind to duplex polynucleotides that do not contain incompatibility; subjecting the reaction mixture to an affinity matrix that recognizes and binds to the affinity marker on the incompatible intercalating compound; washing the reaction mixture and separating the affinity matrix from all material that is not bound to the affinity matrix; and providing a buffer to elute the nucleic acid from the affinity matrix and collecting the eluted buffer containing the enriched variant of the target nucleic acid sequence.

Additionally or alternatively, the detection of a genetic biomarker may include a method for detecting a mutant allele of a target nucleic acid sequence in a mixture of nucleic acids in a sample in which the mutant allele differs from a wild-type allele in a single nucleotide position and is present in the sample in low abundance among a large excess of the wild-type allele, the method comprising enriching the mutant allele in the sample in which enrichment is performed by providing an oligonucleotide that is complementary to a strand of the target nucleic acid sequence, in which the oligonucleotide has an incompatibility in the position of single nucleotide with the mutant allele and is perfectly compatible in the position of single nucleotide with the wild-type allele; providing suitable conditions for hybridizing the oligonucleotide to the target nucleic acid to generate duplex polynucleotides consisting of the oligonucleotide and a strand of the mutant or wild-type allele; contacting the duplex polynucleotides with an incompatible intercalating compound that is attached to an affinity marker to generate a reaction mixture, in which the incompatible intercalating compound is able to bind to the duplex polynucleotides that contain an incompatibility and is not capable to bind to duplex polynucleotides that do not contain incompatibility; subjecting the reaction mixture to an affinity matrix that recognizes and binds to the affinity marker on the incompatible intercalating compound; wash the reaction mixture and separate the affinity matrix from all material that is not bound to the affinity matrix; and providing a buffer to elute the nucleic acid from the affinity matrix and collecting the eluted buffer containing the enriched mutant allele; amplify the enriched mutant allele; and detecting the product of the enriched amplified mutant allele or the signal generated from the enriched amplified mutant allele.

In addition or alternatively, the detection of a genetic biomarker may include a method for enriching a variant of a target nucleic acid sequence in a mixture of nucleic acids in a sample, the target nucleic acid existing in the form of two variant sequences, where the said variants differ in a single nucleotide position, the method comprises,

providing the sample that includes the target nucleic acid sequence in which the variant to be enriched is present in the sample in low abundance between a large excess of the other variant; heating the sample so that the nucleic acid mixture is denatured; providing suitable conditions for annealing the target nucleic acid, in which duplex polynucleotides can be formed between a strand of one variant sequence and a strand of the other variant sequence to generate an incompatibility at the single nucleotide position, where the variant differ; contacting the duplex polynucleotides with an incompatible intercalating compound that is attached to an affinity marker to generate a reaction mixture, wherein said incompatible intercalating compound is capable of binding to the duplex polynucleotides that contain an incompatibility and it is unable to bind to duplex polynucleotides that do not contain an incompatibility; subjecting the reaction mixture to an affinity matrix that recognizes and binds to the affinity marker on the incompatible intercalating compound; washing the reaction mixture and separating the affinity matrix from all material that is not bound to the affinity matrix; and providing a buffer to elute the nucleic acid from the affinity matrix and collecting the eluted buffer containing the enriched variant of the target nucleic acid sequence.

In addition or alternatively, the detection of a genetic biomarker may include a method for detecting a mutant allele of a target nucleic acid sequence in a mixture of nucleic acids in a sample in which the mutant allele differs from a wild-type allele in a single nucleotide position and is present in the sample in low abundance among a large excess of the wild type allele, the method comprising enriching the mutant allele in the sample in which the enrichment is carried out: heat the sample so that the nucleic acid mixture is denatured; providing suitable conditions for annealing the target nucleic acid, in which duplex polynucleotides can be formed between a strand of the mutant allele and a strand of the wild type allele to generate an incompatibility at the position of the single nucleotide, where the alleles differ; contacting the duplex polynucleotides with an incompatible intercalating compound that is linked with an affinity marker to generate a reaction mixture, wherein said incompatible intercalating compound is able to bind to the duplex polynucleotides that contain an incompatibility and it is unable to bind to duplex polynucleotides that do not contain an incompatibility; subjecting the reaction mixture to an affinity matrix that recognizes and binds to the affinity marker on the incompatible intercalating compound; washing the reaction mixture and separating the affinity matrix from all material that is not bound to the affinity matrix; and providing a buffer to elute the nucleic acid from the affinity matrix and collecting the eluted buffer containing the enriched variant of the target nucleic acid sequence; amplify the enriched mutant allele; and detecting the product from the enriched amplified mutant allele or the signal generated from the enriched amplified mutant allele.

[676] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in U.S. Patent

9,238,832, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include an allele-specific amplification method of a variant of a target sequence, the target existing in the form of several variant sequences, the method comprising (a) hydrating a first and a second oligo-nucleotide for at least one variant of the target sequence; wherein the first oligonucleotide is at least partially complementary to one or more variants of the target sequence and the second oligonucleotide is at least partially complementary to one or more variants of the target sequence and has at least one internal selective nucleotide complementary to just one variant the target sequence; (b) extending the second oligonucleotide with a nucleic acid polymerase, wherein said polymerase is capable of extending said second oligonucleotide preferably when said selective nucleotide forms a base pair with the target and substantially smaller when the said selective nucleotide does not form a base pair with the target.

In addition or alternatively, the detection of a genetic biomarker may include a method for detecting a variant of a target sequence, the target existing in the form of several variant sequences, the method comprising (a) hybridizing a first and second oligonucleotide cleotides for at least one variant of the target sequence; wherein said first oligonucleotide is at least partially complementary to one or more variants of the target sequence and said second oligonucleotide is at least partially complementary to one or more variants of the target sequence and has at least one complementary internal selective nucleotide only one variant of the target sequence; (b) extending the second oligonucleotide with a nucleic acid polymerase; wherein said polymerase is able to extend said second oligonucleotide preferably when said selective nucleotide forms a base pair with the target and substantially smaller when said selective nucleotide does not form a base pair with the target; and (c) detecting the products of said oligonucleotide extension, wherein the extension means the presence of the target sequence variant to which the oligonucleotide has a complementary selective nucleotide.

In addition or alternatively, the detection of a genetic biomarker may include an oligonucleotide to perform allele-specific amplification of a target sequence, said target existing in the form of several variant sequences, the oligonucleotide-

deo comprising (a) a sequence at least partially complementary to a portion of one or more variants of said target sequence; (b) at least one internal selective nucleotide complementary to only one variant of the target sequence. Additionally or alternatively, the detection of a genetic biomarker may include a reaction mixture for allele-specific amplification of a target sequence, said target existing in the form of several variant sequences, the mixture comprising (a) a first oligonucleotide , at least partially complementary to one or more variants of the target sequence; and (b) a second oligonucleotide, at least partially complementary to one or more variants of the target sequence, but having at least one internal selective nucleotide complementary to only one variant of the target sequence.

[677] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in US Patent Application Publication 2016/0092630, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker can include accurate and rapid mapping of the sequencing readings obtained from a targeted sequencing. For example, once a target region is selected, alternative regions of the genome that are sufficiently similar to the target region can be identified. If a sequencing reading is more similar to the target region than to an alternative region, the reading can be determined to be aligned with the target region. The readings aligned to the target region can then be analyzed to determine if there is a mutation in the target region. Therefore, a sequencing reading can then be compared with the target region and the corresponding alternative regions, and not with the entire genome, thus providing computational efficiency. In addition or alternatively, the detection of a genetic biomarker may include a method that detects variants in a target region of an organism's sample genome. A plurality of sequence readings is received. Sequence readings are obtained from the sequencing of genomic segments in a sample obtained from the organism, where sequencing includes directing genomic segments of the target region. One or more alternative regions that have a respective first number of variations in the target region of a reference genome are identified. Each respective first number is greater than one and less than a first threshold number. A computer system performs an alignment of the plurality of sequence readings with the target region of the reference genome to identify a set of sequence readings that align with the target region of the reference genome with less than a second number variation threshold. Sequence readings aligned to one of the alternate regions with a second number of variations less than a third threshold number can be removed from the pool. The remaining readings of the set sequence are analyzed to determine variants in the target region of the sample genome.

[678] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the U.S. Patent

7,977,108, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include approaches for rapid and reliable detection and differentiation between mutant and non-mutant forms of nucleic acids that comprise repetitive nucleotide sequences. In certain modalities, for example, methods are used to assess microsatellite instability in patients as part of diagnostic or prognostic applications. In many modalities, several polymorphisms of a given sequence

repetitive nucleotide are detected using a single nucleic acid from the probe.

In addition or alternatively, the detection of a genetic biomarker may include a method for detecting a mutant form of a target nucleic acid.

The method includes providing at least one target nucleic acid and / or an amplicon of the target nucleic acid.

The target nucleic acid includes at least one repetitive nucleotide sequence.

The method also includes attaching (e.g., hybridizing, etc.) at least one nucleic acid probe to the target nucleic acid and / or the amplicon of the target nucleic acid.

The probe's nucleic acid includes at least one first nucleotide sequence that is at least substantially complementary to at least a portion of a non-mutant form of the repetitive nucleotide sequence.

In addition, the method also includes detecting a bimodal dissociation of the probe's nucleic acid from the target nucleic acid and / or amplicon from the target nucleic acid.

In some embodiments, the bimodal dissociation detected comprises a bimodal distribution of fusion peaks.

In addition, the detected bimodal dissociation generally correlates with at least one mutant form of the repetitive nucleotide sequence.

In some embodiments, a non-bimodal dissociation detected (for example, a single mode, etc.) correlates the repetitive nucleotide sequence with a non-mutant form.

Typically, the probe nucleic acid, the target nucleic acid and / or the amplicon of the target nucleic acid includes or is associated with at least a tag fraction and / or at least a extinguisher fraction.

In these embodiments, the detection step generally includes detecting a detectable signal produced by the tag fraction.

In addition, bimodal dissociation of the nucleic acid from the target nucleic acid and / or amplicon from the target nucleic acid is typically detected under at least one varied condition, such as a varied temperature or the like.

In addition or alternatively, the detection of a genetic biomarker can include a reaction mixture.

The reaction mixture will include at least one target nucleic acid and / or an amplicon of the target nucleic acid.

The target nucleic acid includes at least one repetitive nucleotide sequence.

The reaction mixture also includes at least one nucleic acid probe that includes at least one first nucleotide sequence that is at least substantially complementary to at least a portion of a non-mutant form of the repetitive nucleotide sequence.

In addition, the probe's nucleic acid dissociates bimodally from a bound target nucleic acid that includes at least one mutant form of the repetitive nucleotide sequence under at least one varying condition.

In certain embodiments, the reaction mixture also includes several other components.

For example, the reaction mixture optionally includes at least one salt (for example, NaCl, KCl and / or the like). In some embodiments, the reaction mixture also includes at least one buffer.

The buffer normally maintains a pH of the reaction mixture between about 5.5 and about 10.0. The reaction mixture also optionally includes at least one cofactor, such as Mg2 + (for example, MgSO4, MgCl2, etc.), Mn2 + (for example, MnSO4, MnCl2, etc.) and / or the like.

In addition or alternatively, the detection of a genetic biomarker can include a probe nucleic acid.

The probe's nucleic acid includes at least one first nucleotide sequence that is at least substantially complementary to at least a portion of a non-mutant form of a repetitive nucleotide sequence.

In addition, the probe nucleic acid dissociates bimodally from a bound target nucleic acid that includes at least one mutant form of the repetitive nucleotide sequence under at least one varying condition.

In addition or alternatively, the detection of a genetic biomarker may include a system for detecting mutant forms of target nucleic acids.

The system includes at least one nucleic acid probe that includes at least one first nucleotide sequence that is at least substantially complementary to a non-mutant form of a repetitive nucleotide sequence.

The probe's nucleic acid dissociates bimodally from a bound target nucleic acid that comprises at least one mutant form of the repetitive nucleotide sequence under at least one varying condition.

Typically, at least one container comprises the probe's nucleic acid, for example, in solution.

The system also includes at least one detector that detects dissociation of the probe nucleic acid from a target nucleic acid and / or an amplicon from the target nucleic acid when the probe nucleic acid is bound to the target nucleic acid and / or the amplicon of the target nucleic acid and subjected to one or more varied conditions.

In some embodiments, the system also includes at least one thermal modulator that modulates the temperatures to which the probe's nucleic acid is exposed when the probe's nucleic acid is bound to the target nucleic acid and / or the amplicon of the target nucleic acid to effect to varying conditions.

In certain embodiments, the system also includes at least one controller operationally connected to at least the detector, which correlates detected bimodal dissociations of the probe's nucleic acid from linked target nucleic acids and / or linked amplicons to diagnosed target nucleic acids of at least one genetic disorder and / or at least one disease state for individuals from whom the target nucleic acids were obtained.

In some embodiments, the target nucleic acid typically comprises a DNA or an RNA and is generally obtained from at least one individual.

Mutant forms of the target nucleic acid usually correlate with the diagnosis of at least one genetic disorder (eg, Fragile X Syndrome, etc.) and / or at least one disease state (eg at least a form of cancer, etc.) for an individual comprising the mutant form of the target nucleic acid.

In addition, the mutant form of the repetitive nucleotide sequence typically comprises at least one deletion with respect to the non-mutant form of the repetitive nucleotide sequence.

In some modalities, for example, the repetitive nucleotide sequence corresponds to a microsatellite marker, a mononucleotide repeat and / or the like.

In some embodiments, the repetitive nucleotide sequence comprises at least one mononucleotide repeat (for example, An, Tn, Gn, Cn, Un, etc., where n is an integer greater than 1). For example, the mononucleotide repeat optionally comprises a BAT-25 repeat, a BAT-26 repeat, among many others.

In certain embodiments, the detected mutant forms of the mononucleotide repeat comprise 22 or less adenine nucleotides.

To further illustrate, the repetitive nucleotide sequence of the target nucleic acid includes at least one AT repeat, at least one GC repeat, at least one CGG repeat, at least one CGC repeat, at least one TAT repeat, at least one ATT repetition, and / or at least one complementary repetition of it in certain modalities.

In some modalities, for example, the first nucleotide sequence is larger than the non-mutant form of the repetitive nucleotide sequence.

In these modalities, the portion of the first nucleotide sequence that extends beyond the length of the non-mutant form of the repetitive nucleotide sequence is typically not substantially complementary to the nucleotide sequences of the target nucleic acid that are adjacent to the repetitive nucleotide sequence.

Although not restricted to a specific theory, in these modalities, it is believed that at least one segment of the probe's nucleic acid forms a triple helix when the probe's nucleic acid is linked to the mutant form of the target nucleic acid or to an amplicon of the mutant form of the target nucleic acid under at least one selected condition.

In certain embodiments, the probe nucleic acid comprises at least one modified nucleotide

dified. The probe nucleic acids, target nucleic acids and / or amplicons of the target nucleic acids (for example, through nucleic acid initiators used to produce the amplicons, etc.) optionally comprise or are associated with at least one fraction of marking and / or at least a fraction of extinguisher. To illustrate, the label fraction optionally comprises one or more of, for example, a fluorescent dye, a weakly fluorescent marker, a non-fluorescent marker, a colorimetric marker, a chemiluminescent marker, a bioluminescent marker, an antibody, an antigen, biotin, a hapten, an enzyme or the like. To further illustrate, the fluorescent dye is optionally selected from the group consisting of, for example, Cy3, Cy3.5, Cy5, Cy5.5, JOE, VIC, TET, HEX, FAM, R6G, R110, TAMRA, ROX, SYBR-Green, EtBr, and the like.

[679] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the U.S. Patent

7,745,125, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include methods related to the polymerization and amplification of nucleic acids. In certain modalities, for example, the methods related to pyrophospholysis activated polymerization (PAP) involve the serial coupling of pyrophospholysis and polymerization. These methods can be used, for example, for SNP analysis and detection of rare somatic mutation, among many other applications. In some embodiments, the methods increase the general specificity of oligonucleotide-mediated synthesis reactions. For example, similarly to other "hot start" methods (eg chemically modified reversible enzymes, aptamer or antibody mediated "hot start"),

zerotic cycle tension "(pre-PCR) is reduced or eliminated. In contrast to these other methods, primer activation is carried out at each new oligonucleotide-mediated synthesis step. This improves the overall specificity of the reaction, minimizing generation of unwanted side products. Therefore, the detection of low copy and even single copy sequences is improved. In addition, the performance in multiplex amplification reactions (where several or many different targets are being amplified) it is also improved by reducing or eliminating the generation of unwanted and unwanted nonspecific synthesis products, for example, primary dimers in the case of PCR. Additionally or alternatively, the detection of a genetic biomarker may include a reaction mixture that includes at least one oligonucleotide (for example, a primer nucleic acid, a probe nucleic acid, etc.) comprising a 2 'terminator nucleotide (for example, the 3'-terminus). In certain embodiments, the oligonucleotide comprises the formula:

[680] where Z is O or CH2; B is at least one homocyclic ring, at least one heterocyclic ring, at least one aryl group or combinations thereof; BG is a blocking group; R1 is H, OH, a hydrophilic group or a hydrophobic group; X is a nucleotide or nucleotide analog; n is an integer greater than 0; and represents a single or double bond. Optionally, the oligonucleotide comprises at least one marker. In certain embodiments, at least one nucleotide position in the oligonucleotide corresponds to a polymorphic nucleotide position in a target nucleic acid. In some of these embodiments, for example, the 2 'terminator nucleotide corresponds to the position of the polymorphic nucleotide in the target nucleic acid.

The reaction mixture typically includes additional reagents according to the particular application in which the reaction mixture is used.

In some embodiments, for example, additional reagents are selected from, for example, a first biocatalyst comprising a nucleotide removal activity (for example, a pyrophospholysis activity and / or a nuclease activity), a second biocatalyst comprising a nucleotide-incorporating activity, a target nucleic acid comprising at least one sequence that is at least partially complementary to the oligonucleotide, an amplicon, a primer nucleic acid, a probe nucleic acid (for example, a probe nucleic acid) hybridization probe, a 5'-nuclease probe, a hook probe, etc.), an additional nucleotide (for example, an extensible nucleotide, a terminator nucleotide, a ribonucleoside triphosphate, a deoxyribonucleoside triphosphate, etc.), an additional oligonucleotide (eg, a starting nucleic acid, a probe nucleic acid, etc.), a soluble light emission modifier, a cosolvent, an i alternating agent, a clinical sample, a sample, a buffer, a salt, a metal ion, pyrophosphate, glycerol, dimethyl sulfoxide, poly rA and the like.

In some embodiments, the target nucleic acid, amplicon, initiator nucleic acid, probe nucleic acid, additional nucleotide and / or additional oligonucleotide comprise at least one marker.

In certain embodiments, the buffer comprises N- [Tris (hydroxymethyl) methyl] glycine at a concentration of at least 90 mM (for example, about 95 mM, about 100 mM, about 105 mM, etc.). ). In some embodiments, the first biocatalyst comprises a nucleotide-incorporating activity (that is, in addition to the nucleotide removal activity). The nucleotide-incorporating activity of the first and / or the second biocatalyst typically comprises a polymerase activity and / or a ligase activity.

Optionally, the first and / or the second biocatalyst comprises a nuclease activity.

To further illustrate, the first and / or second biocatalyst optionally comprises an enzyme selected from, for example, a polymerase, a terminal transferase, a reverse transcriptase, a polynucleotide phosphorylase, a ligase, an endonuclease AP and an telome- rase.

In certain embodiments, the first and / or second biocatalyst comprises a CS5 DNA polymerase that includes one or more mutations at the amino acid positions selected from the group consisting of: G46, L329, Q601, D640, I669, S671 and E678 . In some of these modalities, for example, the mutations comprise a G46E mutation, an L329A mutation, a Q601R mutation, a D640G mutation, an I669F mutation, an S671F mutation and / or an E678G mutation.

In some embodiments, for example, the 2 'terminating nucleotide comprises a 2′-monophosphate-3′-hydroxyl nucleoside.

In addition or alternatively, the detection of a genetic biomarker may include a method for removing a nucleotide from an oligonucleotide.

The method includes incubating at least one target nucleic acid with: at least a first biocatalyst comprising a nucleotide removal activity (for example, a pyrophospholysis activity and / or a nuclease activity) and at least one oligonucleotide ( for example, a primer nucleic acid, a probe nucleic acid, etc.) comprising a 2 'terminator nucleotide (for example, in a 3'-terminus), the oligonucleotide of which is at least partially complementary to at least a first substring of the target nucleic acid, under conditions under which the first biocatalyst removes at least the 2 'terminator nucleotide from the oligonucleotide to produce a removed 2' terminator nucleotide and a shortened oligonucleotide, thereby removing the nucleotide from the oligonucleotide.

In some embodiments, the method includes incubating the target nucleic acid with the first biocatalyst, the oligonucleotide and the pyrophosphate, whose pyrophosphate is added to the nucleotide removed from the 2 'terminator. In some exemplary embodiments, the target nucleic acid comprises at least one polymorphic nucleotide position, and the method comprises detecting the removal of the 2 ′ terminator nucleotide from the oligonucleotide, the removal of which correlates with the oligonucleotide comprising at least one nucleotide position that corresponds to the position polymorphic nucleotide.

In these embodiments, the 2 'terminator nucleotide typically corresponds to the polymorphic nucleotide position.

In certain embodiments, the oligonucleotide comprises at least one marker, and the method comprises detecting a detectable signal emitted from the marker.

In some of these modalities, the marker comprises a donor fraction and / or an acceptor fraction and the detectable signal comprises light emission, and the method comprises incubating the target nucleic acid with the first biocatalyst, the oligonucleotide and at least one emission modifier. soluble light and detection of light emission from the donor fraction and / or the acceptor fraction.

Optionally, the 2 'terminator nucleotide comprises the donor fraction and / or the acceptor fraction.

In some embodiments, the first biocatalyst comprises nucleotide incorporating activity (that is, in addition to the nucleotide removal activity), and the method comprises incubating the target nucleic acid with the first biocatalyst, the shortened oligonucleotide and at least one additional nucleotide under conditions under which the first biocatalyst incorporates the additional nucleotide at one end of the shortened oligonucleotide to produce an extended oligonucleotide.

Optionally, the method comprises incubating the target nucleic acid with at least one second biocatalyst comprising a nucleotide incorporating activity, the oligonucleotide.

shortened kleotide and at least one additional nucleotide under conditions where the second biocatalyst incorporates the additional nucleotide at the end of the shortened oligonucleotide to produce an extended oligonucleotide.

To illustrate, nucleotide uptake activity typically includes a polymerase activity and / or a ligase activity.

The first and / or second biocatalyst typically comprises an enzyme selected from, for example, a polymerase, a terminal transferase, a reverse transcriptase, a polynucleotide phosphorylase, a ligase, an endonuclease AP, a telomerasee and the like.

In certain embodiments, the first and / or the second biocatalyst comprises a CS5 DNA polymerase comprising one or more mutations at the amino acid positions selected from, for example, G46, L329, Q601, D640, 1669, S671 and E678. In some of these embodiments, the mutations comprise a G46E mutation, an L329A mutation, a Q601R mutation, a D640G mutation, an I669F mutation, an S671F mutation and / or an E678G mutation.

In some embodiments, one or more nucleotides of the oligonucleotide extend beyond a target nucleic acid terminus when the oligonucleotide and the target nucleic acid hybridize to form a hybridized nucleic acid.

In some embodiments, at least one additional oligonucleotide comprises the additional nucleotide.

The additional nucleotide comprises an extensible nucleotide and / or a terminating nucleotide.

In certain embodiments, the additional nucleotide comprises at least one marker, and the method comprises detecting a detectable signal emitted from the marker.

For example, the marker optionally comprises a donor portion and / or an acceptor portion and the detectable signal comprises light emission, and the method comprises incubating the target nucleic acid with at least one light emission modifier. soluble and detect the emission of light from the marker.

In addition or alternatively, the detection of a genetic biomarker may include incubating the target nucleic acid with at least one nucleic acid probe comprising at least one marker, whose probe nucleic acid is at least partially complementary to at least a second subsequence of the target nucleic acid, and detecting a detectable signal emitted from the probe nucleic acid marker or a fragment thereof.

In some embodiments, the detectable signal comprises light emission, and the method further comprises incubating the target nucleic acid with at least one soluble light emission modifier and detecting light emission from the tag.

For example, the probe nucleic acid optionally comprises a 5'-nuclease probe and the first and / or second biocatalyst extends the shortened oligonucleotide in the 5 'to 3' direction and comprises a 5 'exonuclease activity to 3 '. Optionally, the probe nucleic acid comprises a hybridization probe and / or a hook probe.

In certain embodiments, the target nucleic acid comprises at least one polymorphic nucleotide position, and the method comprises detecting the extent of the shortened oligonucleotide, the extent of which correlates with the extended oligonucleotide comprising at least one nucleotide position that corresponds to put it to the polymorphic nucleotide position.

In some of these modalities, the 2 'terminating nucleotide corresponds to the polymorphic nucleotide position.

In addition or alternatively, the detection of a genetic biomarker may include a system that includes (a) at least one container or support comprising an oligonucleotide that comprises a 2 'terminator nucleotide. The system also includes at least one of: (b) at least one thermal modulator configured to communicate thermally with the container or support to modulate the temperature in the container or support; (c) at least one component

fluid transfer component that transfers fluid to and / or from the container or support; and (d) at least one detector configured to detect detectable signals produced in the container or holder. In some embodiments, the system includes at least one controller operationally connected to: thermal modulator for modulating the temperature in the container or holder, the fluid transfer component for transferring the fluid to and / or the container or the support and / or the detector to detect the detectable signals produced in the container or the support.

[681] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in US Patent Application Publication 2018/0135103, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include methods based on the digital polymerase chain reaction (dPCR) in combination with a reference sample that is used in a dual function. First, it is added to a dPCR run as an external standard. Second, the same reference sample is used as an internal standard, preferably adding it to the primary sample. It goes through the entire sample preparation process in the same way as the nucleic acid of interest (target nucleic acid). Internal and external references are quantified using dPCR. The ratio between internal and external reference quantification provides the sample preparation yield before dPCR. Knowing this yield, the initial target concentration in the primary sample can be calculated. The reference used with dPCR leads to a complete understanding of the standards used in dPCR and helps to avoid calculation errors due to pipetting and dilution errors. Even with inaccurate standards, the absolute accuracy of the dPCR is further enhanced and the standards can be recalibrated as a bonus. In addition or alternatively, the detection of a genetic biomarker may include a method to determine the amount or concentration of a nucleic acid of interest in an unprocessed sample, the method comprising the steps of: a) providing an unprocessed sample suspected of containing the nucleic acid of interest and a reference sample known to contain a reference nucleic acid, which is different from the nucleic acid of interest; b) combining the unprocessed sample with a defined quantity of the reference sample, thus obtaining a combined sample; c) process the combined sample, thus obtaining a processed sample suitable for the digital polymerase chain reaction (dPCR); d) perform dPCR with the processed sample, thus determining the amount or concentration of the nucleic acid of interest and the amount or concentration of the reference nucleic acid in the processed sample; e) running the dPCR with a defined amount of the reference sample, thereby determining the amount or concentration of the reference nucleic acid in the defined amount of the reference sample;

[682] f) comparing the amount or concentration of the reference nucleic acid determined in step d) with that determined in step e), thus determining the yield of the nucleic acid in step c); and

[683] g) determining the amount or concentration of the nucleic acid of interest in the unprocessed sample based on the amount or concentration of the nucleic acid of interest in the processed sample determined in step d) and the yield determined in step f). In addition or alternatively, the detection of a genetic biomarker may include a method for determining the amount or concentration of a nucleic acid of interest in an unprocessed sample, the method comprising the steps of: a) providing an unprocessed sample suspected of contain the nucleic acid of interest;

[684] b) providing a reference sample known to contain a reference nucleic acid, which is different from the nucleic acid of interest; c) processing the reference sample, thus obtaining a processed reference sample suitable for dPCR; d) perform the dPCR with the processed reference sample, thus determining the amount or concentration of the reference nucleic acid in the processed reference sample; e) execute dPCR with a defined amount of unprocessed reference sample, thereby determining the amount or concentration of the reference nucleic acid in the defined amount of the unprocessed reference sample; f) comparing the amount or concentration of the reference nucleic acid determined in step d) with that determined in step e), thus determining the yield of the nucleic acid in step c); g) processing the unprocessed sample, thus obtaining a processed sample suitable for dPCR, in which the processing steps c) and g) are identical; h) perform the dPCR with the processed sample, thus determining the amount or concentration of the nucleic acid of interest; and i) determining the amount or concentration of the nucleic acid of interest in the unprocessed sample based on the amount or concentration of the nucleic acid of interest in the processed sample determined in step i) and the yield determined in step f). In some modalities, (i) the amount or concentration of the reference nucleic acid in the reference sample is compared with a reference value, thereby controlling the reference sample; (ii) the amount or concentration of the reference nucleic acid in the reference sample is unknown or not predetermined; and / or (iii) the quantity or concentration of the reference sample in step e) is identical to that in step b). In addition, the reference nucleic acid has one or more of the following characteristics: (i) it is a nucleic acid selected from the group consisting of DNA, cDNA, RNA and a mixture thereof; (ii) has the same primer binding site as the nucleic acid of interest; (iii) it has a different primer binding site than the nucleic acid of interest; (iv) has a nucleic acid length that differs from that of the nucleic acid of interest by a maximum of 50%, a maximum of 25%, a maximum of 10% or a maximum of 5%; (v) has a sequence that is at least 50% identical, at least 60%, at least 70% or at least 80% identical to the nucleic acid of interest; (vi) it has a G and C content that differs from the nucleic acid of interest by a maximum of 50%, a maximum of 25%, a maximum of 10% or a maximum of 5%; and (vii) comprises a part that is not part of the nucleic acid of interest and that is used to detect the reference nucleic acid.

In addition, the nucleic acid of interest has one or more of the following characteristics: (i) it is a nucleic acid selected from the group consisting of DNA, cDNA, RNA and a mixture thereof; (ii) comprises a part that is not part of the reference nucleic acid and that is used to detect the nucleic acid of interest; and (iii) it is indicative of a microorganism, a cell, a virus, a bacterium, a fungus, a species of mammal, a genetic status or a disease.

In addition, the unprocessed sample has one or more of the following characteristics: (i) it was obtained from a cell culture, a source suspected of being contaminated or an individual, particularly in which the individual is selected from the group consisting of in a human being, an animal and a plant, especially a human; and (ii) is selected from the group consisting of body fluid, blood, blood plasma, blood serum, urine, bile, cerebrospinal fluid, a smear, a clinical sample, an organ sample and a tissue sample .

The processing step can include one or more of the following processes: dilution, lysis, centrifugation, extraction, precipitation, filtration and purification.

In some embodiments, the dPCR is characterized by one or more of the following items: (i) it is performed in a liquid, in a gel, in an emulsion, in a drop, in a microarray of miniaturized chambers, in a a microfluidic device, on a microwell plate, on a chip, on a capillary, on a nucleic acid binding surface or on a sphere, especially on a microarray or chip; (ii) is carried out identically in at least 100 reaction areas, particularly in at least 1,000 reaction areas, especially in at least 5,000 reaction areas; and (iii) is carried out identically in at least 10,000 reaction areas, particularly in at least 50,000 reaction areas, especially in at least

100,000 reaction areas. More specifically, steps d) and e) are performed in the same dPCR run and / or on the same dPCR device. In an additional embodiment, the dPCR comprises using one or more fluorescent probes, alone or in combination with an inhibitor, to detect the nucleic acid of interest and / or the reference nucleic acid. In a specific embodiment, the fluorescent probe comprises fluorescein, rhodamine or cyanine. In this embodiment, the determination step comprises detecting a fluorescent signal. In some embodiments, an external control is used. In a specific modality, the method is used to diagnose the presence or absence of a disease, a pathogen, a rare genetic sequence, a rare mutation, a variation in the number of copies or relative expression of the gene. Optionally, the method is used to monitor disease progression, therapeutic response and combinations of the same.

[685] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in US Patent Application Publication 2014/0128270, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include a method for interrogating a target nucleic acid sequence with a sense and an antisense by a microarray analysis comprising a sequence determination computation, comprising omitting the computation of a signal from one of the sense and antisense tapes to one or more nucleotide positions in the target nucleic acid sequence. In variations of this modality, omitting the signal from one of the sense and antisense tapes in a nucleotide position comprises the steps of using a plurality of microarrays, measuring hybridization signals in the nucleotide position using one or more sets of probes for each of the tapes of sense and antisense; for each set of probes, determine the basic discrimination capacity by comparing the hybridization signals within each set of probes; for each nucleotide position, computational discrimination capacity for the sense and antiseptic tape separately, use the calculated discrimination capacity of each of the probe sets; for each nucleotide position, compare the discrimination capacity calculated between the sense and antisense tapes; omit the signal from the tape with less base discrimination capability. In the variations of this modality, the base discrimination is measured using Formula 1. In other variations of this modality, the discrimination capacity for the sense and antiseptic tape is calculated as a percentile of the discrimination capacity for sets of probes on the ribbon in the base position. In still other variations of this modality, the discrimination capacity between the sense and antisense tape is compared using Formula 3: for Q75i <PT For Q75i> PT Formula 3

[686] In addition or alternatively, the detection of a genetic biomarker may include a method for detecting the presence or absence of a target nucleic acid with sense and antisense tapes in a test sample using a microarray analysis, including a determination of sequence or mutation detection computation, comprising omitting the computation of a signal from one of the sense and antisense tapes to one or more nucleotide positions in the target nucleic acid sequence.

In variations of this modality, the omission of the signal from one of the sense and antisense tapes in a nucleotide position comprises the steps of using a plurality of microarrays, measuring hybridization signals in the nucleotide position using one or more sets of probes for each of the tapes of sense and antisense; for each set of probes, determine the basic breakdown by comparing the hybridization signals within each set of probes; for each nucleotide position, computational discrimination capacity for the sense and antisense tape separately, use the discrimination capacity of each of the probe sets; for each nucleotide position, compare the discrimination capacity between the sense and antisense tapes; omit the signal from a tape with less basic discrimination capability.

In the variations of this modality, the base discrimination is measured using Formula 1. In other variations of this modality, the discrimination capacity of the sense and antisense tape is calculated as a percentile of the discrimination capacity for sets of probes in the tape in the base position measured using a plurality of microarrays.

In addition or alternatively, the detection of a genetic biomarker can include a computer-readable medium, including code to control one or more processors to detect the presence or absence of a target nucleic acid with sense and antisense tapes in a sample. test using microarray analysis, which includes sequence determination or mutation detection computation, comprising omitting computing a signal from one of the sense and antisense tapes to one or more nucleotide positions in the nucleic acid sequence target.

In variations of this modality, the computer-readable medium comprises a code that controls the steps of: using a plurality of microarrays, measuring hybridization signals in the nucleotide position using one or more sets of probes for each of the sense and antisense tapes ; for each set of probes, determine the basic discrimination capacity by comparing the hybridization signals within each set of probes; for each nucleotide position, computational discrimination capacity for the sense and antisense tape separately, use the discrimination capacity of each of the probe sets; for each nucleotide position, compare the discrimination capacity between the sense and antisense tapes; omit the signal from a tape with less basic discrimination capability.

In addition or alternatively, the detection of a genetic biomarker may include a system for detecting a target nucleic acid in a test sample comprising: a data acquisition module configured to acquire hybridization data from a microarray; a data processing unit configured to process data to determine a target nucleotide sequence, omitting the signal from one of the sense and antisense tapes in one or more nucleotide positions in the target sequence through the steps of: using a plurality microarray, measure hybridization signals at the nucleotide position using one or more sets of probes for each of the sense and antisense tapes; for each set of probes, determine the basic discrimination capacity by comparing the hybridization signals within each set of probes; for each nucleotide position, calculate the discrimination capacity for the sense and antisense tape separately, use the discrimination capacity of each of the probe sets; for each nucleotide position, compare the discrimination capacity between the sense and antisense tapes; omitting the signal from a tape with less basic discrimination capability; and a display module configured to display the data produced by the data processing unit.

[687] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in US Patent Application Publication 2002/0160404, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include a method for amplifying nucleic acid fragments from a sample comprising two or three thermocyclic amplification reactions in which completely random primers are used in the first amplification reaction and primers are used. - specifics are used in the second amplification reaction, characterized by the fact that, to amplify the DNA, a mixture of at least two DNA polymerases is used, at least one of which has 3'-5 'exonuclease activity. An amplification reaction can comprise about 20 to 60 thermal cycles. The first amplification reaction preferably comprises at least 40 thermal cycles and, more preferably, at least 50 thermal cycles. The second amplification reaction preferably comprises at least 30 thermal cycles and, more preferably, at least 40 thermal cycles. Each thermal cycle comprises a denaturation phase, an annealing phase and at least one elongation phase. Denaturation in single tapes occurs preferably at temperatures between 90 ° C and 96 ° C. The annealing phase to hybridize the primers to the target nucleic acid occurs preferably at temperatures between 30 ° C and 50 ° C., The annealing phase occurs at temperatures between 35 ° C and 45 ° C. During the first amplification reaction, the annealing phase preferably takes place at about 37 ° C.

The stretching phase is carried out at temperatures between 50 ° C and 75 ° C.

In a preferred embodiment, the elongation phase of the first amplification reaction occurs at temperatures between 50 ° C and 60 ° C.

A temperature of about 55 ° C is especially preferred.

It is advantageous that the stretching is performed during the first amplification reaction in most cycles, using two or more stretching steps, with a stretching performed at a lower temperature and continuing the stretching at a higher temperature.

Using this approach, especially long populations of amplicons are created during the first amplification reaction.

In this modality, the first amplification reaction occurs preferably at about 55 ° C, and the second amplification reaction occurs at about 65 ° C to 72 ° C.

A temperature of around 68 ° C is ideal.

The primers used in the first amplification reaction are completely randomized, that is, a population of single-stranded oligonucleotides is used in which each single nucleotide in each single position can comprise one of the four nucleotide components A, T, G or Ç.

Such primers are preferably 10-20 nucleotides in length.

Most preferably, the primers are about 15 nucleotides.

The specific primers used in the second amplification reaction are characterized by having a sequence that is identical to a sequence of the target nucleic acid or its complementary sequence over a range of at least 10 nucleotides.

The specific primers used to perform a "nested PCR" in a potential third amplification reaction are selected according to the same criteria as the primers used in the second amplification reaction.

The sequences of the primers used that are identical to the target nucleic acid or its complement must be a component of the amplified sequence in the second reaction.

amplification.

The DNA polymerase mixture preferably contains a thermostable DNA polymerase without 3'-5 'exonuclease activity, such as Taq DNA polymerase, for example, and another thermostable DNA polymerase with 3'- 5 exonuclease activity. ', such as the Pwo DNA polymerase obtained from Pyrokokkus woesii (order from Boehringer Mannheim No. 1644947). Other DNA polymerases without 3'-5 'exonuclease activity can also be used as a component of the polymerase mixture.

In addition or alternatively, the detection of a genetic biomarker may include a method for amplifying DNA.

To guarantee the sensitivity of the detection of certain sequences, it is advantageous to carry out the cellular analysis of the material to be analyzed using enzymatic digestion with protease to obtain the sample DNA.

Proteinase K can be used, for example.

Additionally or alternatively, the detection of a genetic biomarker may include a method in which RNA is first isolated from the physical material to be analyzed.

The sample of physical material can comprise one cell, less than 10 cells or less than 100 cells.

To obtain RNA, it is preferable to use chemical lysis using buffers that contain guanidine isothiocinate.

A corresponding cDNA is then created using a reverse transcriptase reaction.

This cDNA is then used as the starting material for pre-amplifying the primer extension.

The cDNA is preferably obtained by reverse transcription of the poly-A RNA.

The use of polymerase mixtures in the pre-amplification PCR of primer extension leads to a surprisingly high sensitivity of DNA detection that cannot be achieved using the methods known in the prior art.

In addition or alternatively, the detection of a genetic biomarker may include a method for amplifying nucleic acid fragments comprising two or three thermocyclic amplification reactions.

Completely randomized primers are used in the first amplification reaction and specific primers are used in the second amplification reaction. In addition, the sample contains an amount of nucleic acid corresponding to an equivalent of no more than 100 cells. In some modalities, the probability of the formation of amplifiers is greater than 90%. In some modalities, the probability is greater than 90% that the amplified ones will be formed from an equivalent of no more than 5-10 cells. In a special mode, the probability of amplifiers being formed from the equivalent of a cell is greater than 50%. The method is suitable for use in amplifying nucleic acid fragments between 100 and 1000 base pairs in length. The method is especially suitable for use in the amplification of nucleic acid fragments between 150 and 550 base pairs in length.

[688] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the U.S. Patent

8,658,572, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include a microarray with a high density of oligopeptide characteristics, thus allowing the detection of protein interactions through an organism's proteome. One embodiment is a microarray comprising at least 50,000 oligopeptide characteristics per cm2. Another modality is a microarray with oligopeptide characteristics representing at least 50%, at least 60%, at least 70%, at least 80%, at least 90%, at least 95%, at least 99% or 100 % of the proteome of a selected target of a virus or organism. Additionally or alternatively, the detection of a genetic biomarker may include a microarray comprising at least 50,000 oligopeptide characteristics per cm2, where the characteristics represent between about 90% and 100% of a target proteome, the target selected from a virus and an organism and in which at least a portion of the characteristics comprise oligopeptides having a tyrosine residue protected with 2- (2-nitro-4-benzoyl-phenyl) -propoxycarbonyl (benzoyl-NPPOC).

[689] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the U.S. Patent

8,822,158, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include a method for the treatment of multiple nucleic acid molecules of interest, comprising the steps of: (a) providing a plurality of spheres, characterized by each sphere comprising at least a pair of sequence-specific amplification primers, further characterized by at least one of said primers being linked to the sphere by means of a photo-cleavable linker, (b) capturing the nucleic acid molecules of interest in a sample, ( c) isolating said plurality of spheres clonally, (d) photo-cleaving said at least one primer, (e) clonically amplifying said nucleic acid, thus creating multiple amplification products and (f) analyzing said products amplification ducts. In a first main modality, step c) comprises generating an emulsion in which each sphere is encapsulated in a single micelle. Preferably, step f) comprises distributing said plurality of spheres in the wells of a microtiter or picotiter plate and detecting said amplification products. In a first particular modality, step f) further comprises a sequencing reaction for said amplification products. Preferably, said sequencing reaction is a synthesis sequencing reaction, for example, a sequencing reaction. If the multiple nucleic acid molecules are variants of the same type of nucleic acid, this method can be used for quantitative mutational analysis. In the event that the plurality of molecules corresponds to a plurality of different cell RNAs or their corresponding cDNAs, this method can be used to monitor gene expression. In a second particular mode, the generation of the referred amplification products, for example, by means of PCR is monitored. Preferably, said amplification products are detected by means of a fluorescent double-stranded DNA binding entity specifically, a sequence specific hybridization probe: In addition, said amplification products can be analyzed by submitting the referred amplification products to a thermal gradient. In a second main mode, step c) comprises distributing said plurality of spheres in the wells of a microtiter or picotiter plate. Preferably, steps e) and f) are performed simultaneously by real-time PCR. After PCR, an analysis of the melting curve can be performed. In some embodiments, at least one primer that is attached to the sphere via a cleavable linker carries a detectable tag. Preferably, said detectable tag is selected from a group consisting of a mass tag, colored marker, electronic tag and a hapten that is detectable by an antibody. Highly preferred is a fluorescent marker that is preferably extinct as long as said marked primer has not been elongated. Alternatively; the cleavable initiator carries a discoverable tag. In this case, said amplification products are detected using labeled primers or labeled dNTPs. For example, the detectable tag can be a hapten, such as biotin or digoxigenin. In a particular embodiment, each member of the plurality of primers that is connected to the sphere via a cleavable linker carries a different detectable tag.

[690] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in US Patent Application Publication 2015/0024948, which is incorporated herein by reference in its entirety.

For example, the detection of a genetic biomarker may include a system and methods for enriching and analyzing nucleic acid sequences.

In addition or alternatively, the detection of a genetic biomarker may include enriching targeted sequences in a format, representing a fusion partner gene on a capture platform and allowing subsequent sequencing of chimeric nucleic acids, such as acid strands. - nucleic acids that carry information about different regions of DNA in a genome.

Additionally or alternatively, the detection of a genetic biomarker may include a method for detecting balanced chromosomal aberrations in a genome.

The method comprises the steps of: (a) exposing denatured and fragmented nucleic acid molecules of that genome to multiple different oligonucleotide probes located at multiple different sites on a solid support under hybridization conditions to capture nucleic acid molecules that they specifically hybridize with said probes, wherein said denatured and fragmented nucleic acid molecules have an average size of about 100 to about 1000 nucleotide residues, preferably about 250 to about 800 nucleotide residues and more preferably about 400 to about 600 nucleotide residues, in particular about 500 nucleotide residues, wherein said oligonucleotide probes have an average size of about 20 to about 100 nucleotides, preferably about 40 to about 85 nucleotides, more preferred about 45 to about 75 nucleotides, in particular about 55 to about 65 nucl residues eotide or about 60 nucleotide residues, (b) separating unbound and hybridized nucleic acids not specifically from the captured molecules; (c) eluting the captured molecules from the solid support, (d) optionally repeating steps (a) to (c) for at least one additional cycle with the eluted captured molecules, (e) determining the nucleic acid sequence of the captured molecules, in particular by performing sequencing by synthesis reactions, (f) comparing the determined sequence with the sequences in a reference genome database, (g) identifying sequences in the determined sequence that correspond only partially or not to the reference sequences (h) detect at least one balanced chromosomal aberration.

In addition or alternatively, the detection of a genetic biomarker may include pre-selected immobilized nucleic acid probes to capture target nucleic acid sequences, for example, a genomic sample by hybridizing the sample to probes in a solid support.

According to some modalities, the captured target nucleic acids can be washed and eluted from the probes.

In some cases, eluted genomic sequences may be more amenable to detailed genetic analysis than a sample that has not been subjected to the methods.

Additionally or alternatively, the detection of a genetic biomarker may include the solution-based capture method comprising probe-derived amplicons, wherein said probes for amplification are affixed to a solid support.

The solid support comprises nucleic acid probes immobilized on support to capture specific nucleic acid sequences from a genomic sample.

Probe amplification provides amplicons of the probe in solution that are hybridized to target sequences.

After hybridization of the probe's amplicons to the target sequences, the target nucleic acid sequences present in the sample are enriched by capturing and washing the probes and eluting the hybridized target nucleic acids from the captured probes.

The target nucleic acid sequence (s) can be further amplified using, for example, nonspecific binding-mediated PCR (LM-PCR), resulting in an amplified set of PCR products from reduced complexity compared to the original target sample, which is further analyzed by sequencing as described above.

Additionally or alternatively, the detection of a genetic biomarker may include a method for detecting balanced chromosomal aberrations in an organism's genome.

The method comprises the steps of exposing denatured and fragmented nucleic acid molecules from the genome to a plurality of oligonucleotide probes linked to different positions of a solid support.

Nucleic acid molecules have an average size of about 100 to about 1000 nucleotide residues and oligonucleotide probes have an average size of about 20 to about 100 nucleotide residues.

The method also includes the step of separating nucleic acid molecules attached to one or more oligonucleotide probes from nucleic acid molecules not attached to one or more oligonucleotide probes and then eluting the nucleic acid molecules attached to one or more oligonucleotide probes from the solid support.

After that, the nucleic acid molecules that were eluted in the elution step are sequenced, thereby obtaining a determined sequence for the nucleic acid molecules.

In addition, the method includes the step of comparing the determined sequence with a database that comprises a sequence of the reference genome and identifying sequences in the determined sequence that only partially or do not match the sequences of the reference genome, thus detecting at least a balanced chromosomal aberration.

In some embodiments, the oligonucleotide probes include a linker for binding to the solid support.

In various embodiments, the linker may comprise a chemical linker.

In some embodiments, the method may also include the steps of binding at least one adapter molecule to at least one end of the nucleic acid molecules prior to the step of exposing and amplifying the nucleic acid molecules that bind to a or more oligonucleotide probes with at least one primer comprising a sequence that specifically hybridizes to the adapter molecule, in which the amplification step is carried out after the elution step.

In addition, according to some modalities, the solid support is a microarray of nucleic acid or a population of spheres.

In some modalities, the method for detecting balanced chromosomal aberrations in a genome.

The method includes the steps of providing a solid support comprising a plurality of different oligonucleotide probes linked to different positions of the solid support, wherein the oligonucleotide probes have an average size of about 20 to about 100 nucleotides and they provide a plurality of fragmented and denatured nucleic acid molecules with an average size of about 100 to about 1000 nucleotide residues.

The method also includes the step of amplifying oligonucleotide probes, thus generating amplification products including a fraction of binding and being kept in solution.

Subsequently, the method includes the steps of hybridizing the target nucleic acid molecules to the amplification products in solution under specific hybridization conditions, thereby generating a plurality of hybridization complexes and separating the hybridization complexes from nucleic acid molecules not hybridized to the amplification products.

Then, according to the method, the hybridized target nucleic acid molecules are separated from the amplification product comprising the hybridization complex and sequenced, whereby a determined sequence is obtained for the nucleic acid molecules.

According to the method, the determined sequence is compared to a database comprising a genome of

The transfer and sequences in the determined sequence that only partially or not correspond to the sequences of the reference genome are determined to detect at least one balanced chromosomal aberration. In some embodiments, the binding fraction is a biotin fraction. According to some modalities, oligo-nucleotide probes with highly repetitive sequences are not used. In addition, in some modalities, the identified balanced chromosomal aberrations may include translocations or inversions.

[691] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include using any of the various methods described in the U.S. Patent

6,514,736, which is incorporated herein by reference in its entirety. For example, the detection of a genetic biomarker may include a process for amplifying one or more specific nucleic acid sequences present in a nucleic acid or a mixture thereof using primers and a thermostable enzyme. The primer extension product, when hybridized to the other, becomes a model for producing the desired specific nucleic acid sequence and vice versa, and the process is repeated as many times as necessary to produce the desired amount of the sequence. The method improves the specificity of the amplification reaction, resulting in a very distinct signal of amplified nucleic acid. In addition, the method eliminates the need to transfer reagents from one container to another after each amplification cycle. This transfer is not necessary because the thermostable enzyme withstands the high temperatures required to de-inactivate the nucleic acid strips and therefore does not need to be replaced. The temperature cycle can furthermore be automated to further reduce manpower and the steps required to carry out the amplification reaction. Additionally or alternatively, the detection

A genetic biomarker can include a process to amplify at least one specific nucleic acid sequence contained in a nucleic acid or a mixture of nucleic acids, where if the nucleic acid is double-stranded, it consists of two separate complementary strands of equal or unequal length, the process of which includes: (a) contacting each nucleic acid strand with four different nucleoside triphosphates and an oligonucleotide primer for each different specific sequence that is amplified, in which each primer is selected to be substantially complementary to different strands of each specific sequence, so that the extension product synthesized from an initiator, when it is separated from its complement, can serve as a model for synthesis of the extension product the other primer, said contact being at a temperature that promotes the hybridization of each primer to its complementary nucleic acid chain; (b) contacting each strand of nucleic acid, at the same time as or after step (a), with a thermostable enzyme that allows the combination of the; nucleoside triphosphates to form primer extension products complementary to each strand of each nucleic acid; (c) keep the mixture from step (b) at an effective temperature for an effective time to activate the enzyme and synthesize, for each different sequence being amplified, an extension product from each primer that is complementary to each acid strip model nucleic, but not so high (a temperature) that separates each extension product from its complementary tape model; (d) heat the mixture from step (c) for an effective time and at an effective temperature to separate the starter extension products from the models in which they were synthesized to produce simple, but not so high, ribbon molecules (one temperature) how much to irreversibly denature the enzyme; (e) cooling the mixture from step (d) to an effective temperature for an effective time to promote the hybridization of each primer with each of the single strand molecules produced in step (d); and (f) maintaining the mixture of step (e) at an effective temperature for an effective time to promote the enzyme activity and synthesize, for each different sequence being amplified, an extension product for each primer that is complementary to each model of nucleic acid strip produced in step (d), but not so high (one temperature) that separates each extension product from its complementary strip model, in which steps (e) and (f) are performed simultaneously or sequentially.

Steps (d), (e) and (f) can be repeated until the desired level of sequence amplification is achieved.

The preferred thermostable enzyme is a polymerase extracted from Thermus aquaticus (Taq polymerase). More preferably, if the enzyme is Taq polymerase, in step (a) the nucleic acid strips are contacted with a buffer that comprises about 1.5-2 mM of a magnesium salt, 150-200 μM of each of nucleotides and 1 μM of each primer, steps (a), (e) and (f) are performed at about 45-58 ° C, and step (d) is performed at about 90- 100 ° Ç.

In a preferred embodiment, the nucleic acid (s) are double-stranded and step (a) is performed (i) by heating each nucleic acid in the presence of four different nucleoside triphosphates and an oligonucleotide initiator for each different specific sequence that is amplified, for an effective time and effective temperature to denature each nucleic acid, where each primer is selected to be substantially complementary to different strands of each specific sequence, so that the synthesized extension product from an initiator, when separated from the complement, it can serve as a model for the synthesis of the extension product of the other initiator; and (ii) cooling the denatured nucleic acids to a temperature that promotes the hybridization of each primer in its complementary nucleic acid strand.

Additionally or alternatively, the detection of a biomarker

Genetic pain may include a process for detecting the presence or absence of at least one specific nucleic acid sequence in a sample that contains a nucleic acid or mixture of nucleic acids or the distinction between two different sequences in that sample, in that the sample is suspected of containing said sequence or sequences and in which if the nucleic acid (s) are double-stranded, each consists of two separate complementary strands of equal or unequal length, the length of which The process comprises steps (a) to (f) mentioned above, resulting in amplification in quantity of the specific nucleic acid sequence, if present; (g) adding to the product of step (f) a labeled oligonucleotide probe, for each sequence being detected, capable of hybridizing to said sequence or a mutation thereof; and (h) determine whether said hybridization has occurred.

Additionally or alternatively, the detection of a genetic biomarker may include a process for detecting the presence or absence of at least one variation of a nucleotide in sequence in one or more nucleic acids contained in a sample, where if the nucleic acid is double ribbon, consists of two separate complementary chains of equal or unequal length, the process of which comprises steps (a) - (f) mentioned above, in which steps (d), (e) and (f) are repeated a sufficient number of times to result in detectable amplification of the nucleic acid containing the sequence, if present; (g) fixing the product of step (f) to a membrane; (h) treating the membrane under hybridization conditions with a labeled sequence-specific oligonucleotide probe, capable of hybridizing to the amplified nucleic acid sequence only if a probe sequence is complementary to a region of the amplified sequence; and (i) detecting whether the probe hybridized to an amplified sequence in the nucleic acid sample.

If the sample comprises cells, they are preferably heated before step (a) to expose the nucleic acids in the reagents.

This step prevents the extraction of nucleic acids before adding the reagent.

In a variation of this process, the nucleoside primer (s) and / or triphosphates are labeled so that the resulting amplified sequence is labeled.

The labeled primer (s) and / or nucleoside triphosphates may be present in the reaction mixture initially or added during a later cycle.

The sequence-specific oligonucleotide (unlabeled) is affixed to a membrane and treated under hybridization conditions with the marked amplification product so that hybridization occurs only if the membrane-bound sequence is present in the product amplification.

Additionally or alternatively, the detection of a genetic biomarker may include a process for cloning into a cloning vector one or more specific nucleic acid sequences contained in a nucleic acid or a mixture of nucleic acids, whose nucleic acids, when streaked double, consist of two separate complementary strands and which nucleic acids are amplified in quantity before cloning, whose process comprises the steps (a) - (f) mentioned above, with steps (d), (e) and (f) being repeated enough times to result in the detectable amplification of the nucleic acid (s) containing the sequence (s); (g) adding to the product of step (f) a restriction enzyme for each of said restriction sites to obtain products cleaved in a restriction digest; and (h) ligating the cleaved product (s) from step (g) containing the specific sequences) to be cloned into one or more cloning vectors containing a promoter and a selectable marker.

Additionally or alternatively, the detection of a genetic biomarker may include a process for cloning into a cloning vector one or more specific nucleic acid sequences contained in a nucleic acid or nucleic acid mixture, in which the acids nucleic acids, when double-stranded, consist of two separate complementary strands of equal or unequal length, whose nucleic acids are amplified in quantity before cloning, whose process comprises the steps (a) - (f) mentioned above, with the steps (d), (e) and (f) being repeated enough times to result in effective amplification of the nucleic acid (s) containing the sequence (s) for binding the blunt end on one or more cloning vectors; and (g) linking the specific amplified sequence (s) to be cloned (s) obtained from step (f) into one or more of said cloning vectors in the price. - presence of a ligase, with the amplified sequence (s) and the vector (s) present in sufficient quantities to make the connection.

Additionally or alternatively, the detection of a genetic biomarker may include a composition of matter useful in amplifying at least one specific nucleic acid sequence contained in a nucleic acid or a mixture of nucleic acids, comprising four different nucleoside triphosphates and an oligonucleotide primer for each different specific sequence being amplified, where each primer is selected to be substantially complementary to different strands of each specific sequence, so that the extension product synthesized from a primer, when - stopped from its complement, it can serve as a model for the synthesis of the extension product of the other initiator.

In addition or alternatively, the detection of a genetic biomarker may include a sample of one or more nucleic acids comprising multiple strands of a specific nucleic acid sequence contained in the nucleic acid (s). The sample can comprise about 10-100 tapes, about 100-1000 tapes or more than about 1000 tapes.

In addition or alternatively, the detection of a genetic biomarker may include a nucleic acid sequence amplified from a nucleic acid or mixture of nucleic acids comprising several copies of the sequence produced by the above amplification processes.

[692] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include any of the various forms described in PCT Publication WO 2017/181134, which is incorporated herein by reference in its entirety . For example, the detection of a genetic biomarker may include techniques to identify genes, mutations and / or driver pathways for various types of cancer. For example, the identified driver genes can be used for diagnosis, identifying mutations that occur in the identified driver genes, or for targeted treatment of the identified driver genes. In some embodiments, a conducting gene can be identified by determining a gene's specific background mutation rate. In some modalities, a statistical model for the specific background mutation rate of a gene can be determined by optimizing parameters estimated from the modeling of single genes and cross-genes. In one example, the specific background mutation of the gene can be statistically determined by recursively optimizing a specific gene mean and a specific gene dispersion using negative binomial regression and Bayesian inference. Genes, mutations and / or pathways that have significantly more mutations than the background mutations expected in the samples can be identified as genes, mutations and / or candidate pathways for drivers. In addition or alternatively, the detection of a genetic biomarker may include a method that comprises: for each sample of a plurality of samples from different individuals with the same type of cancer, receiving a set of one or more mutations in the DNA measured in the sample, the DNA including a plurality of genes; for each sample of the plurality of samples, determine a mutation rate of the sample based on the total number of mutations measured in the sample; for each mutation context of a plurality of mutation contexts, determine a context mutation rate based on a first number of mutations identified in the mutation sets for the mutation context, where a mutation context corresponds to a type of mutation replacement or exclusion; for each gene of the plurality of genes, determine, for each sample of the plurality of samples, a second number of silent mutations measured in the sample gene; determining an expected silent mutation rate using a sum of the context mutation rates of silent mutations in the gene, where a silent mutation does not cause a change in an amino acid sequence of a protein translated into the gene; determine a probability distribution of the specific gene mutation rate across the plurality of samples for the gene based on the expected silent mutation rate for the gene and the sample mutation rates for the plurality of samples, in which to determine the distribution of probability of the gene specific background mutation rate includes: optimizing one or more parameters of the probability distribution of the gene specific background mutation rate for the gene to increase an adjustment of the probability distribution to the second number of mutations silent; determining an expected non-silent mutation rate using a sum of the context mutation rates for a subset of non-silent mutations in the gene; determining an expected number of samples with at least one non-silent mutation using the expected non-silent mutation rate and the probability distribution of the gene-specific background mutation rate for the gene; and compare the expected number with the measured number of samples with at least one non-silent mutation to obtain a probability value for the measured number; and identifying a group of genes with probability values above a threshold as candidate conductor genes.

[693] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include any of the various forms described in PCT Publication WO 2017/201315, which is incorporated herein by reference in its entirety .

For example, the detection of a genetic biomarker may include an automated method of nucleic acid amplification that may comprise the following steps: (a) providing at least two drops, each drop comprising primers that anneal a target nucleic acid; (b) amplifying target nucleic acid in each said parallel drop; (c) quantifying the amplified target nucleic acid in at least one drop; and (d) after obtaining a desired amount of the target nucleic acid, recover at least one drop for further analysis or processing of said at least one drop.

In addition or alternatively, the detection of a genetic biomarker may include an automated nucleic acid amplification method that may comprise the following steps: (a) supply at least two drops, each drop comprising a nucleic acid target; (b) amplifying the target nucleic acid in each said drop in parallel; (c) quantifying the amplified target nucleic acid in at least one drop; and (d) after obtaining a desired amount of the target nucleic acid, recover at least one drop for further analysis or processing of said drop by at least one drop.

In one embodiment, the said drops can be supplied in an electrodeposition based device.

Additionally or alternatively, the detection of a genetic biomarker may include an automated method of nucleic acid amplification that comprises the following steps: (a) providing an electrodeposition-based device; (b) providing at least two drops in said electrodeposition device, each drop comprising primers that anneal with a target nucleic acid; (c) amplifying the target nucleic acid in each said drop in parallel; (d) quantifying the amplified target nucleic acid in at least one drop; and (e) after obtaining a desired amount of the target nucleic acid, recover at least one drop using for further analysis or processing of said at least one drop.

In addition or alternatively, the detection of a genetic biomarker may include an automated method of nucleic acid amplification that comprises the following steps: (a) providing an electrodeposition based device; (b) providing at least two droplets in said electrodeposition device, wherein each droplet comprises a target nucleic acid; (c) amplifying the target nucleic acid in each said drop in parallel; (d) quantifying the amplified target nucleic acid in at least one drop; and (e) after obtaining a desired amount of the target nucleic acid, recover at least one drop using for further analysis or processing of said at least one drop.

In some embodiments, said drops may each comprise a different target nucleic acid.

In addition or alternatively, the detection of a genetic biomarker may include a method in which said drops may comprise the same target nucleic acid.

Additionally or alternatively, the detection of a genetic biomarker can include a method in which said drop can comprise a mixture of drops containing the same target nucleic acid and different target nucleic acids.

In addition or alternatively, the detection of a genetic biomarker may include a method in which the electrodeposition-based device may comprise a biplane configuration of parallel electrode arrays for effecting electrodeposition-mediated droplet manipulations.

Some modalities refer to a method in which the electrodeposition-based device can comprise a flat electrode configuration that affects electrodeposition-mediated droplet manipulations.

Some fashion-

labilities refer to a method in which the electrodeposition-based device may comprise square electrodes, optionally in which said electrodes are about 5 mm by 5 mm.

Some modalities refer to a method in which the device based on electrodeposition can comprise electrodes, in which said electrodes are square, triangular, rectangular, circular, trapezoidal and / or irregularly shaped.

Some modalities refer to a method in which the electrodeposition-based device can comprise electrodes in which said electrodes can comprise electrode sizes ranging from about 100 μηι per 100 μηι to about 10 cm by 10 cm.

Some modalities refer to a method in which the device based on electrodeposition can comprise interdigitated electrodes.

Some modalities refer to a method in which the electrodeposition-based device may comprise electrodes, in which said electrodes may comprise indium and tin oxide ("ITO"), transparent conductive oxides ("TCOs"), conductive polymers , carbon nanotubes ("CNT"), graphene, nanowire meshes and / or ultrafine metal films, for example, ITO.

In addition or alternatively, the detection of a genetic biomarker can include a method in which the detection zone can detect electrochemical and / or fluorescent signals.

In addition or alternatively, the detection of a genetic biomarker may include a method in which said detection zone can detect the capacitance of a drop.

An additional embodiment refers to a method in which said detection zone can be a fixed location.

Another embodiment relates to a method in which said detection zone can comprise any location within the device based on electrodeposition.

Yet another embodiment generally relates to a method in which the amplification method may comprise hot start PCR.

Additionally or alternatively, the detection of a genetic biomarker may include a method in which said amplification may comprise isothermal amplification.

In addition or alternatively, the detection of a genetic biomarker may include a method in which said amplification may comprise thermocycling.

Said thermocycling can comprise temperatures ranging from about 50 ° C to about 98 ° C, for example, about 50 ° C, about 60 ° C, about 65 ° C, about 72 ° C, about 95 ° C or about 98 ° C.

The said thermocycling can comprise times ranging from about 1 to 5 min., For example, about 1 s, about 5 s, about 10 s, about 20 s, about 30 s, about 45 s, about 1 s, and / or about 5 min.

In addition, said thermocycling can comprise three thermocycle steps and said three thermocycle steps can be completed in one minute or less.

In some embodiments, each drop may further comprise a detection agent.

In addition or alternatively, the detection of a genetic biomarker may include a method in which each drop can contain the same detection agent.

In addition or alternatively, the detection of a genetic biomarker may include a method in which each drop may contain a different detection agent.

Another modality generally relates to a method in which the drops may comprise a marked subset of drops, in which each drop within the subset contains an agent for detecting the target nucleic acid and an unmarked subset of drops, in that each drop within the subset does not contain said agent to detect the target nucleic acid.

An additional embodiment generally includes a method in which each drop within the subset containing a detection agent can comprise a different detection agent.

In addition or alternatively, the detection of a genetic biomarker may include a method in which each drop within the subset containing a detection agent can comprise the same detection agent.

In some modalities,

the nucleic acid polymerase can be a naturally occurring modified Type A polymerase.

Another modality generally refers to a method in which the modified Type A polymerase can be selected from any species of the genus Meiothermus, Thermotoga or Thermomicrobium.

Another modality generally refers to a method in which the polymerase can be isolated from any one of Thermus aquaticus (Taq), Thermus thermophilus, Thermus caldophilus or Thermus mus flliformis.

An additional embodiment generally includes a method in which the modified Type A polymerase can be isolated from Bacillus stearothermophilus, Sphaerobacter thermophilus, Dictoglomus thermophilum or Escherichia coli.

In addition or alternatively, the detection of a genetic biomarker may include a method in which the modified Type A polymerase can be a mutant 7a-E507K polymerase.

Another modality generally belongs to a method in which a thermostable polymerase can be used to effect amplification of the target nucleic acid.

An additional modality generally refers to a method in which the thermostable polymerase can be selected from the following: Thermotoga maritima, Thermus aquaticus, Thermus thermophilus, Thermus flavus, Thermus flliformis, Thermus Sps 1 7 species. Species Thermus Z05, Thermus caldophilus, Bacillus cal- dotenax, Thermotoga neopolitana and Thermosipho africanus.

Additionally or alternatively, the detection of a genetic biomarker may include a method in which a modified polymerase can be used to effect amplification of the target nucleic acid, for example, in which said modified polymerase can be selected from the following: G46E E678G CS5 DNA polymerase, G46E L329A E678G CS5 DNA polymerase, G46E L329A D640G S671F CS5 DNA polymerase, G46E L329A D640G S671F E678G CS5 DNA polymerase, G46E E678G05 DNA polymerase, G6E DNA polymerase,

AZ05-Gold polymerase, AZ05R polymerase, E615G Taq DNA polymerase, E678G TMA-25 polymerase, and E678G TMA-30 polymerase.

In addition or alternatively, the detection of a genetic biomarker may include a method in which the detection agent can be detected at the end of an amplification cycle.

In some embodiments, the method can detect a single nucleotide polymorphism.

An additional modality generally refers to a method in which the method can be used to generate amplicons.

In some modalities, the method can be used for a melting curve analysis.

In some embodiments, the method can be used for target nucleic acid enrichment.

In some embodiments, the method can be used to enrich the primer extension target ("PETE"). In some modalities, the method can be used to amplify the library.

In some embodiments, the method can be used to quantify the number of target nucleic acid molecules attached to the adapter during library preparation.

Additionally or alternatively, the detection of a genetic biomarker may include a method in which said quantification of the number of target nucleic acid molecules attached to the adapter can occur (a) after the adapter is attached to determine the amount of input material converted into molecules linked to the adapter (conversion rate) and / or the amount of model used to amplify the library; (b) after amplifying the library, determine whether a sufficient amount of each library has been generated and / or ensure an equal representation of the indexed libraries assembled for target capture or cluster amplification; and / or (c) before amplifying the cluster, to confirm that individual libraries or sample sets are diluted to the ideal concentration for loading NGS flow cells.

In addition, said quantification of the number of target nucleic acid molecules attached to the adapter can occur after the post-ligation cleaning steps (before amplifying the library). In some embodiments, after recovering at least one drop, said analysis or further processing of said at least one drop may comprise a nucleic acid sequencing reaction, a next generation sequencing reaction, complete genome shotgun sequencing, complete exome or targeted sequencing, sequencing of amplicons, sequencing of coupling pairs, RIP-seq / CLIP-seq, ChlP-seq, RNA-seq, transcriptome analysis and / or methyl-seq.

In addition or alternatively, the detection of a genetic biomarker may include a method in which the droplets can be surrounded by a filling fluid, for example, wherein said filling fluid can be an oil.

In some embodiments, said oil may comprise a clear oil.

In some embodiments, said oil may comprise liquid polymerized siloxane, silicone oil and / or paraffin oil.

Another embodiment generally refers to the method in which the droplets can be surrounded by a gas, for example, in which said gas can be air.

Yet another modality generally refers to a method in which the method can be used to avoid over-amplification bias.

Another modality generally refers to a method in which the method can be used to produce a representative sample of a population of mutations.

In addition or alternatively, the detection of a genetic biomarker may include a method in which the method can be used to determine the number of amplification cycles required to generate the desired concentration of a target nucleic acid.

Additionally or alternatively, the detection of a genetic biomarker can include a method in which the method can be controlled via a computer in communication with the electro-wetting device.

Some embodiments generally refer to a method in which said method comprises a main mixture.

In some embodiments, said master mixture may comprise a polymerase, dNTP (s), MgCl2 and / or oligonucleotide (s) primer (s). In some embodiments, said master mixture may comprise dNTP (s) in a concentration comprising from about 1 mM to about 100 mM, for example, about 1 mM, about 10 mM or about 100 mM; MgC12 in a concentration comprising from about 1 mM to about 100 mM, for example, about 1 mM, about 10 mM or about 100 mM; and / or an oligonucleotide primer (s) in a concentration comprising from about 1 nM to about 1 mM, for example, about 1 nM, about 1 μM or about 1 mM.

Additionally or alternatively, the detection of a genetic biomarker may include a device for amplifying a target nucleic acid, wherein said device may (a) comprise a biplane configuration of parallel electrode arrays to perform electron-mediated drop manipulations -wetting; (b) understand or contact at least one heating element; and (c) understand or contact at least one detection zone.

In one embodiment, said heating element may comprise an inductive heating element.

Another aspect generally relates to a device for amplifying a target nucleic acid, wherein said device can (a) comprise a planar configuration of electrodes to perform droplet manipulations mediated by electro-wetting; (b) understand or contact at least one heating element; and (c) understand or contact at least one detection zone.

In one embodiment, said heating element may comprise an inductive heating element.

In some modes, said electrodes may comprise square shapes, optionally about 5 mm by 5 mm.

In some modalities

Accordingly, said electrodes may comprise square, triangular, rectangular, circular, trapezoidal and / or irregular shapes.

In some embodiments, said electrodes may comprise electrode sizes ranging from about 100 μιτι per 100 μιτι to about 10 cm by 10 cm.

In some modalities, said electrodes can be interdigitated.

In some embodiments, said electrodes may comprise indium and tin oxide ("ITO"), transparent conductive oxides ("TCOs"), conductive polymers, carbon nanotubes ("CNT"), graphene, nanowire meshes and / or ultra-thin metal films, for example, ITO.

In some embodiments, said device may comprise droplets ranging in volume from about 1 picoliter to about 5 ml, for example, about 12.5 μΐ.

In some embodiments, said device may comprise a gap between an upper plate and a lower plate of about 0.5 mm.

In some embodiments, said device may comprise a plurality of input / output ports.

In some embodiments, said device may comprise between 1 to about 400 inlet / outlet ports for loading and removing the same sample or from different samples, and / or said device further comprises between 1 to about 100 inlet / ports. outlet to introduce and remove filling fluid (s).

In some embodiments, said device may comprise entry / exit doors in which the spacing between adjacent doors varies from about 5 mm to about 500 mm.

In another embodiment, said heating element may comprise a contact heater.

An additional aspect concerns a modality in which said amplification comprises adhesion of thermocytes.

Said thermocycling can comprise three thermocycle stages and said three thermocycle stages can be completed in one minute or less.

In another embodiment, said amplification may comprise isothermal amplification.

In another embodiment, said amplification may comprise hot start PCR.

In yet another mode, the detection zone can detect electrochemical and / or fluorescent signals.

An additional modality refers to a detection zone that can detect a drop's capacitance.

In another embodiment, said detection zone can be a fixed location.

In another embodiment, said detection zone can comprise any location within the electro-wetting device.

In another embodiment, the target nucleic acid can be delivered to the device within at least three drops.

In another embodiment, said droplets can each comprise the same target nucleic acid.

In yet another embodiment, said drops may comprise a mixture of drops containing the same target nucleic acid and different target nucleic acids.

In another embodiment, each drop can further comprise a detection agent.

In an additional embodiment, each drop can contain the same detection agent.

In another embodiment, each drop can contain a different detection agent.

In addition, in another embodiment, the droplets may comprise a labeled subset of droplets that each contain an agent for detecting the target nucleic acid and an unlabeled subset of droplets that each do not contain said agent for detecting nucleic acid target.

In an additional embodiment, each drop within the subset containing a detection agent can comprise a different detection agent.

In another embodiment, each drop within the subset containing a detection agent can comprise the same detection agent.

In an additional embodiment, each subset of drops may comprise 1 or more, 2 or more, 10 or more, 100 or more, 1,000 or more, or 10,000 or more drops.

In some modalities, the device can detect a single nucleotide polymorphism.

In yet another modality, the device can affect the generation of amplicons.

In an additional embodiment, the device can perform a melting curve analysis.

In yet another embodiment, the device can affect target nucleic acid enrichment.

In yet another embodiment, the device can effect PETE.

In an additional modality, the device can affect the amplification of the library.

In another embodiment, the device can quantify the number of target nucleic acid molecules attached to the adapter during library preparation.

For example, said quantification can occur (a) after the adapter is attached to determine the amount of input material converted to molecules attached to the adapter (rate conversion) and / or the amount of model used for amplifying the library; (b) after amplifying the library, determine whether a sufficient amount of each library was generated and / or ensure an equal representation of the indexed libraries gathered for target capture or cluster amplification; and / or (c) before cluster amplification, to confirm that individual libraries or sample sets are diluted to the ideal concentration for loading NGS flow cells.

In addition, said quantification can occur after the post-ligation cleaning steps (before the amplification of the library). In yet another embodiment, after obtaining a desired amount of the target nucleic acid, at least one drop can be recovered from said device before further analysis or processing of said drop.

For example, said analysis or further processing of said at least one drop may comprise a nucleic acid sequencing reaction, a next generation sequencing reaction, complete genome shotgun sequencing, complete exome or targeted sequencing, sequencing of amplicons, sequencing of coupling pairs, RIP-seq / CLIP-

seq, ChlP-seq, RNA-seq, transcriptome analysis and / or methyl-seq.

In addition or alternatively, the detection of a genetic biomarker may include a system for automated amplification of a target nucleic acid which may comprise: (a) an electro-wetting device; (b) at least one heating element that comprises or is in contact with the electro-wetting device; (c) at least one detection zone that comprises or is in contact with the electro-wetting device.

In one embodiment, said heating element may comprise an inductive heating element.

In another embodiment, said heating element may comprise a contact heater.

An additional aspect concerns a modality in which said amplification comprises adhesion of thermocytes.

The said thermocycling can comprise three thermocycle steps and said three thermocycle steps can be completed in one minute or less.

In another embodiment, said amplification may comprise isothermal amplification.

In another embodiment, said amplification may comprise hot start PCR.

In yet another embodiment, the detection zone can detect electrochemical and / or fluorescent signals.

An additional modality refers to a detection zone that can detect a drop's capacitance.

In another embodiment, said detection zone can be a fixed location.

In another embodiment, said detection zone can comprise any location within the system.

In another embodiment, the target nucleic acid can be delivered to the system within at least three drops.

In another embodiment, said droplets can each comprise the same target nucleic acid.

In yet another embodiment, said drops may comprise a mixture of drops containing the same target nucleic acid and different target nucleic acids.

In another embodiment, each drop can also comprise a detection agent.

In an additional embodiment, each drop can contain the same detection agent.

In another embodiment, each drop can contain a different detection agent.

In addition, in another embodiment, the gums may comprise a labeled drop subset that each contains an agent for detecting the target nucleic acid and an unlabeled drop subset that each does not contain said agent to detect the target nucleic acid. target nucleic acid.

In an additional embodiment, each drop within the subset containing a detection agent can comprise a different detection agent.

In another embodiment, each drop within the subset containing a detection agent can comprise the same detection agent.

In an additional embodiment, each subset of drops can comprise 1 or more, 2 or more, 10 or more, 100 or more, 1,000 or more, or 10,000 or more drops.

Additionally or alternatively, the detection of a genetic biomarker may include an automated amplification method that may comprise (a) providing an electro-wetting device with a biplane configuration of parallel electrode arrays to perform manipulations of drops mediated by electro-wetting, and further wherein said device contains at least one inductive heating element and at least one detection zone; (b) providing on said device droplets comprising a target nucleic acid, wherein said droplets comprise a subset of droplets containing a target nucleic acid detection agent and a subset of droplets that do not contain said droplet agent for detecting target nucleic acid; (c) amplifying the target nucleic acid in each said drop in parallel; (d) quantifying the amplified target nucleic acid in said subset of droplets containing said agent by detecting said agent; and (e) after obtaining a desired amount of said target nucleic acid in said subset of drops containing an agent, recovering at least one drop of said subset of drops that does not contain an agent for further analysis or processing.

[694] In some embodiments, the detection of a genetic biomarker (for example, one or more genetic biomarkers) may include any of the various forms described in PCT Publication WO 2017/123316, which is incorporated herein by reference in its entirety . For example, the detection of a genetic biomarker may include a targeted sequencing workflow in which an input sample comprising a sufficient amount of genetic material is provided so that minimal or nonexistent amplification processes are required prior to sequencing. In some embodiments, the incoming sample is derived from an intact tumor or lymph nodes. In some embodiments, the incoming sample is obtained by homogenizing an intact tumor sample (total or partial) and / or one or more lymph nodes obtained from a patient or mammal. In some embodiments, the incoming sample is derived from a sufficient amount of blood, including whole blood or any fraction thereof. In some embodiments, the incoming sample is derived from cancerous tissue. In some embodiments, the incoming sample is derived from precancerous tissue. In some modalities, the targeted sequencing workflow comprises one or more amplification steps (for example, a pre-capture amplification step, a post-capture amplification step) before sequencing, where each amplification step before sequencing comprises 0 to 3 amplification cycles, and in which an aggregate of amplification cycles before sequencing does not exceed 4. In other embodiments, the targeted sequencing workflow comprises one or more amplification steps (for example, a pre-capture amplification step, a post-capture amplification step) before sequencing, where each amplification step before sequencing comprises 0 to 2 amplification cycles, and in which an aggregate of amplification cycles before sequencing does not exceed 3. In still other embodiments, the targeted sequencing workflow comprises an amplification step before sequencing (p for example, a pre-capture amplification step or a post-capture amplification step), where the only amplification step before sequencing comprises 0 to 3 amplification cycles.

In other modalities, the targeted sequencing workflow comprises an amplification step before sequencing, where the only amplification step before sequencing comprises 1 to 3 cycles.

In yet other modalities, the targeted sequencing workflow comprises an amplification step before sequencing, where the only amplification step before sequencing comprises 1 cycle.

In even other modalities, the targeted sequencing workflow comprises an amplification step before sequencing, in which the only amplification step before sequencing comprises 2 cycles.

In some modalities, one or both of the pre-capture amplification stage or the post-capture amplification stage, but before sequencing, use LM-PCR.

In addition or alternatively, the detection of a genetic biomarker may include a method for sequencing genomic material within a sample that comprises: homogenizing a tumor sample and / or lymph node sample to provide a homogenized sample; isolate at least 0.5 microgram of genomic material from the homogenized sample; prepare at least 0.5 micrograms of isolated genomic material for sequencing; and sequence the prepared genomic material.

In some

but modalities, the method does not include any step of amplification before sequencing.

In some modalities, the method comprises at least one pre-capture or post-capture amplification step, in which an aggregate number of amplification cycles performed during at least one pre-capture or post-capture amplification step is a maximum of 4 cycles.

In some modalities, the aggregate number of amplification cycles is 3. In some modalities, the aggregate number of amplification cycles is 2. In some modalities, the preparation of at least 0.5 micrograms of genetic material isolated for sequencing comprises hybridizing at least 0.5 micrograms of isolated genomics to capture probes and capture the hybridized genomic material.

In some modalities, the amount of genomic material captured varies from about 90ng to about 900ng.

In some modalities, 1 or 2 amplification cycles are performed on the captured genomic material.

In some modalities, the homogenized sample comprises a representative sample of cells.

In some embodiments, at least 1 microgram of genomic material is isolated from homogenized samples.

In some modalities, at least 5 micrograms of genomic material are isolated from homogenized samples.

In some embodiments, at least 10 micrograms of genomic material are isolated from homogenized samples.

In addition or alternatively, the detection of a genetic biomarker may include a method of DNA sequencing within a sample comprising isolating at least 0.5 micrograms of DNA from a blood sample; prepare at least 0.5 microgram of isolated DNA for sequencing and sequence the prepared DNA.

In some embodiments, the method comprises the amplification step before sequencing.

In some embodiments, the preparation of at least 0.5 micrograms of isolated DNA for sequencing comprises hybridizing at least 0.5 micrograms of isolated genomics to capture probes and capture the hybridized genomic material.

In some modalities, the amount of genomic material captured varies from about 90ng to about 900ng.

In some modalities, 1 or 2 amplification cycles are performed on the captured genomic material.

In some modalities, at least 1 microgram of DNA is isolated from the blood sample.

In addition or alternatively, the detection of a genetic biomarker may include a targeted representational sequencing method comprising: (i) homogenizing at least a portion of a tumor, one or more whole or partial lymph nodes, or any combination of them to provide a homogenized sample; (ii) extracting genomic material from the homogenized sample; (iii) capture the genomic material extracted in spheres; and (iv) sequencing the captured genomic material; where the targeted representational sequencing comprises performing a maximum of 4 amplification cycles before sequencing the captured genomic material.

In some modalities, a maximum of 3 amplification cycles can be carried out before the capture of the extracted genomic material or after the capture of the extracted genomic material, or any combination thereof.

In some modalities, no pre-capture amplification cycle is conducted.

In some modalities, the amount of captured genetic material varies from about 90ng to about 900ng.

In some modalities, 1 to 3 amplification cycles are performed after the capture of the extracted genomic material, but before sequencing.

In some modalities, at least 0.5 microgram of genomic material is extracted from the homogenized sample.

In some embodiments, at least 100 times more genomic material is derived from the homogenized sample compared to an amount of input material used in a sequencing method that requires more than 4 amplification cycles.

Additionally or alternatively, the detection of a genetic biomarker may include a method of DNA sequencing within a sample comprising: providing at least 0.5 micrograms of input genomic material, at least 0.5 micrograms of derived genomic material of a tumor sample, a lymph node sample, or a blood sample, isolate the DNA from the incoming genomic sample, preparing the isolated DNA for sequencing and sequencing the prepared DNA, in which the method does not comprise any amplification step .

In some modalities, at least 0.5 micrograms of incoming genomic material is derived from multiple histological and / or biopsy samples.

In some embodiments, the at least 0.5 microgram of incoming genomic material is derived from a homogenized tumor sample.

In some embodiments, the at least 0.5 microgram of input genomic material is derived from a sample from a homogenized lymph node sample.

In some embodiments, the at least 0.5 microgram of input genomic material is a representative sample of the tumor sample, lymph node sample or blood sample from which it is derived.

In some embodiments, sequencing is performed using a next generation sequencing method.

In some embodiments, sequencing is performed using a synthetic sequencing methodology.

In addition or alternatively, the detection of a genetic biomarker may include a method for reducing mutations introduced by PCR during sequencing comprising isolating DNA from a sample comprising a sufficient amount of genomic material; prepare the isolated DNA for sequencing; and sequencing the prepared DNA, in which the method comprises a maximum of 3 amplification cycles before sequencing.

In some modalities, the method comprises 1 or

2 amplification cycles before sequencing.

In some modes, a sufficient amount of input genomic material is such that no pre-capture amplification cycle is used.

In some modalities, the sample is derived from a patient with suspected cancer.

In some modalities, the sample is derived from a patient diagnosed with cancer.

In some modalities, the sample is derived from a patient at risk of developing cancer.

In some embodiments, the sample is derived from healthy tissue samples.

In some embodiments, 0.5 micrograms of DNA are isolated from the sample.

In some modalities, at least 1 microgram of genomic material is isolated from homogenized samples.

In some embodiments, at least 5 micrograms of genomic material is extracted from the homogenized sample.

In some modalities, at least 10 micrograms of genomic material are isolated from the sample.

In addition or alternatively, the detection of a genetic biomarker may include a sequencing method in which the mutations introduced by PCR are reduced, comprising capturing at least 0.05 micrograms of genomic material and performing between 0 and 2 cycles amplification before sequencing.

In some modalities, 0 amplification cycles are performed.

In other ways, 1 amplification cycle is performed.

In still other modalities, 2 amplification cycles are performed.

Additionally or alternatively, the detection of a genetic biomarker may include a sequence capture method in which the bias introduced by PCR in the proportional representation of the genome content is reduced, the sequencing method comprises providing an input sample comprising at least 0.5 microgram of genomic material and where the sequence capture method comprises performing between 0 and 2 amplification cycles before sequencing.

In some modalities, 0 amplification cycles are performed.

In other modalities, 1 amplification cycle is performed.

In still other modes, 2 amplification cycles are performed.

In some ways, the input sample comprises at least 1 microgram of genomic material.

In some embodiments, the input sample comprises at least 5 micrograms of genomic material.

In some modalities, the input sample comprises at least 10 micrograms of genomic material.

Additionally or alternatively, the detection of a genetic biomarker may include a sequence capture method in which mutations introduced by PCR are eliminated, the sequence capture method comprising preparing an input sample comprising at least 0.5 microgram of genomic material.

In some embodiments, the input sample comprises at least 1 microgram of genomic material.

In some modalities, the input sample comprises at least 5 micrograms of genomic material.

In some modalities, the input sample comprises at least 10 micrograms of genomic material.

In addition or alternatively, the detection of a genetic biomarker may include a sequence capture method in which a step of removing duplicate PCR readings is eliminated prior to sequencing, the sequence capture method comprising providing an input sample comprising at least 0.5 microgram of genomic material.

In some embodiments, the input sample comprises at least 1 microgram of genomic material.

In some modalities, the input sample comprises at least 5 micrograms of genomic material.

In some embodiments, the input sample comprises at least 10 micrograms of genomic material.

In addition or alternatively, the detection of a genetic biomarker may include a sequencing method in which mutations introduced by PCR are practiced.

eliminated, the sequencing method comprising capturing at least 0.05 micrograms of genomic material. In some modalities, about 0.05 micrograms of genomic material is provided after the capture of the genomic material. In some modalities, 1 or 2 post-capture amplification cycles are performed before sequencing.

[695] Examples of genetic biomarkers that can be detected using any of the various techniques described here include, without limitation, ABCA7, ABL1, ABL2, ACVR1B, ACVR2A, AJUBA, AKT1, AKT2, ALB, ALDOB, ALK, AMBRA1, AMER1, AMOT, ANKRD46, APC, AR, ARHGAP35, ARHGEF12, ARID1A, ARID1B, ARID2, ARID4B, ARL15, ARMCX1, ASXL1, ASXL2, ATAD2, ATF1, ATG14, ATG5, ATM, ATX, ATX, ATX, ATX BAP1, BCL11A, BCL11B, BCL2, BCL3, BCL6, BCL9, BCLAF1, BCOR, BCR, BIRC6, BIRC8, BLM, BLVRA, BMPR1A, BRAF, BRCA1, BRCA2, BRD7, BRE, BRWD3, BTBD7, BTRC, Corf57 C2CD5, C3orf62, C8orf34, CAMKV, CAPG, CARD11, CARS, CASP8, CBFA2T3, CBFB, CBLC, CBX4, CCAR1, CCDC117, CCDC88A, CCM2, CCNC, CCND1, CCND2, CCND3, CCR3, CD1D, CD79CP, CD173 CDH1, CDH11, CDK12, CDK4, CDK6, CDKN1A, CDKN1B, CDKN2A, CDX2, CEBPA, CELF1, CENPB, CEP128, CHD2, CHD4, CHD8, CHEK2, CHRDL1, CHUK, CIC, CLEC4C, CNOT, CNEC, CN2 COL11A1, COPS4, COX7B2, CREB1, CREBBP, CSDE1, CSMD3, CTCF, CTDNEP1, CTNNB1, CUL1, CUL2, CYB5B, CYLD, DACH1, DCHS1, DCUN1D1, DDB2, DDIT3, DDX3X, DDX5, DDX6, DEK, DHX15, DHX16, DICER1, DIRC2, DIS3, DIXDC1, DKK2, DNAJB5, DNER, DNM1L, DNM1, EIFA EIF2AK3, EIF2S2, EIF4A1, EIF4A2, ELF3, ELK4, EMG1, EMR3, EP300, EPB41L4A, EPHA2, EPS8, ERBB2, ERBB3, ERRFI1, ETV4, ETV6, EVI1, EWSR1, F5, EXT2, F2, E2 FAT1, FBN2, FBXW7,

FCER1G, FEV, FGF2, FGFR1, FGFR1OP, FGFR2, FGFR3, FH, FLT3, FN1, FOXA1, FOXP1, FUBP1, FUS, GALNTL5, GATA3, GGCT, GIGYF2, GK2, GLIPR2, GAS, GNH, GNAS, GNP, GH GOPC, GOT2, GPC3, GPS2, GPX7, GRK1, GSE1, GZMA, HDAC1, HERC1, HERC4, HGF, HIST1H2BO, HLA-A, HLA-B, HMCN1, HMGA1, HMGA2, HNRNPA1, HRAS, ID3901 IDH2, IFNGR2, IFT88, IKZF2, IL2, INO80C, INPP4A, INPPL1, IRF4, IWS1, JAK1, JAK2, JUN, KANSL1, KAT8, KATNAL1, KBTBD7, KCNMB4, KDM5C, KDM6A, KIT, KDM6A, KE KMT2B, KMT2C, KMT2D, KMT2E, KRAS, KRT15, LAMTOR1, LARP4B, LCK, LMO2, LPAR2, LYN, MAF, MAFB, MAML2, MAP2K1, MAP2K2, MAP2K4, MAP3K1, MAP4K3, MAPDK1, MBD, MBD, MBD, MBD MBNL1, MBNL3, MDM2, MDM4, MED12, MED23, MEN1, MET, MGA, MITF, MKLN1, MLH1, MLL, MLLT4, MOAP1, MORC4, MPL, MS4A1, MSH2, MSI1, MTOR, MYB, MYC, MYCL1, MYCN, MYD88, MYL6, MYO1B, MYO6, NAA15, NAA25, NAP1L2, NAP1L4, NCOA2, NCOA4, NCOR1, NEK9, NF1, NF2, NFE2L2, NFE2L3, NFKB2, NIPBL, NIT1, NKXCH1-1, NKXCH1-1, NR4A3, NRAS , NSD1, NTRK1, NUP214, NUP98, PALB2, PAX8, PBRM1, PCBP1, PCOLCE2, PDGFB, PHF6, PIK3CA, PIK3CB, PIK3R1, PIM1, PLAG1, PML, POLA2, POT1, PPARD, PPAR, PPA, PPAR, PPAR, PPAR , PRKCI, PRPF40A, PSIP1, PTEN, PTH2, PTMS, PTN, PTPN11, RAB18, RAC1, RAF1, RANBP3L, RAPGEF6, RASA1, RB1, RBBP6, RBM10, RBM26, RC3H2, REL, RERE, RETB, RFC4 , RIMS2, RIT1, RNF111, RNF43, ROS1, RPL11, RPL5, RQCD1, RRAS2, RUNX1, RXRA, SARM1, SCAF11, SDHB, SDHD, SEC22A, SENP3, SENP8, SETD1B, SETD2, SF3A3, SFQA , SMAD2, SMAD3, SMAD4, SMARCA4, SMARCB1, SMARCC2, SMO, SNCB, SOCS1, SOS1, SOX4, SOX9, SP3, SPEN, SPOP, SPSB2, SS18, STAG2, STK11, STK31, SUFU, SUFU, SUF1 , TARDBP,

TAS2R30, TBL1XR1, TBX3, TCF12, TCF3, TCF7L2, TCL1A, TET2, TEX11, TFDP2, TFG, TGFBR2, THRAP3, TLX1, TM9SF1, TMCO2, TMED10, TMEM107, TMEM30A, TMPO, TNF, TNT, TNF TPR, TRAF3, TRIM8, TRIP12, TSC1, TSC2, TTK, TTR, TUBA3C, U2AF1, UBE2D3, UBR5, UNC13C, UNKL, UPP1, USO1, USP28, USP6, USP9X, VHL, VN1R2, VPS33B, WAC, WDR33 WRN, WT1, WWP1, XPO1, YOD1, ZC3H13, ZDHHC4, ZFHX3, ZFP36L1, ZFP36L2, ZGRF1, ZMYM3, ZMYM4, ZNF234, ZNF268, ZNF292, ZNF318, ZNF318, ZNF318, ZNF318, ZNF318, ZNF318, ZNF,

[696] As used herein, "TP53" refers to the gene and / or the protein encoded by the gene, which is the p53 tumor suppressor protein involved in regulating cell proliferation. The TP53 gene plays a crucial role in preventing the formation of cancer. The TP53 gene encodes proteins that bind to DNA and regulate gene expression to prevent mutations in the genome. Human TP53 sequences are known in the art (for example, GenBank accession numbers NM_001276761, NM_000546, NM_001126112, NM_001126113 and NM_001126114). One skilled in the art can identify additional TP53 sequences and variants thereof.

[697] As used herein, "PIK3CA" refers to the gene and / or the protein encoded by the gene, which is the catalytic subunit of Phosphoinositol-3 kinase (PI3K), alpha isoform, also referred to as p110alpha. PIK3CA has been found to be oncogenic and is implicated in a variety of cancers. Human PIK3CA sequences are known in the art (for example, GenBank accession number NM_006218). One skilled in the art can identify additional PIK3CA sequences and variants thereof.

[698] As used herein, the term "FGFR3" refers to the gene and / or the protein encoded by the gene, which is the receptor for fibroblast growth factor 3 (FGFR3), which belongs to a family of receptors for structurally related tyrosine kinase (FGFRs 1-4) encoded by four different genes. The extracellular portion of the protein interacts with fibroblast growth factors, triggering a cascade of signals downstream that ultimately influence mitogenesis and cell differentiation. Human FGFR3 sequences are known in the art (for example, Gen Bank accession numbers NM_000142, NM_001163213, NM_022965, NM_001354809 and NM_001354810). One skilled in the art can identify additional FGFR3 sequences and variants thereof.

[699] As used in this document, the term "KRAS" refers to the gene and / or protein encoded by the gene known as K-ras or Ki-ras, which is a proto-oncogene corresponding to the oncogene first identified in the virus of Kirsten rat sarcoma and the gene product was first found as a p21 GTPase. Human KRAS sequences are known in the art (for example, GenBank accession numbers NM_004985 and NM_033360). One skilled in the art can identify additional and varied KRAS sequences.

[700] As used herein, the term "ErbB2" refers to the gene and / or protein encoded by the gene, also known as homologue 2 of the viral oncogene of avian erythroblastic leukemia v-erb-b2, c-erbB2 / neu , her2 / neu or Her2. ErbB2 is a member of the epidermal tyrosine kinase growth factor receptor family. It is amplified and / or overexpressed in several types of cancer, including breast and ovarian cancer. Human ErbB2 sequences are known in the art (for example, GenBank accession numbers NM_001005862, NM_001289936, NM_001289937, NM_001289938 and NM_004448). One skilled in the art can identify additional ErbB2 sequences and variants thereof.

[701] As used in this document, "CDKN2A" refers to the gene and / or the protein encoded by the gene, which is known as cyclin-dependent 2A kinase inhibitor, acts as tumor suppressors by regulating the cell cycle. Human CDKN2A sequences are known in the art (for example, GenBank accession numbers NM_000077, NM_001195132, NM_058195, NM_058196 and NM_058197). One skilled in the art can identify additional CDKN2A sequences and variants thereof.

[702] As used in this document, the term "MLL" refers to the gene and / or protein encoded by the gene, which is lymphoid or mixed lineage 2 leukemia. MLL is a major histone H3 lysine mono-methyltransferase 4 (H3K4) of mammals. The MLL protein is co-localized with determinants of lineage transcription in transcription enhancers and is essential for cell differentiation and embryonic development. MLL also plays critical roles in regulating the transition of cellular fate, metabolism and suppression of tumors. Mutations in the MLL have been associated with Ka- buki syndrome, congenital heart disease and various forms of cancer. Human MLL sequences are known in the art (for example, GenBank accession number NM_003482). One skilled in the art can identify additional MLL sequences and variants thereof.

[703] As used in this document, the term "HRAS" refers to the gene and / or the protein encoded by the gene, which is the homologue of the viral oncogene of harvey rat sarcoma, is a small G protein, activating the MAP kinase. HRAS is involved in the regulation of cell division in response to stimulation of the growth factor. HRAS has been shown to be a proto-oncogene. When mutated, proto-oncogenes have the potential to cause normal cells to become cancerous. Human HRAS sequences are known in the art (eg GenBank accession numbers

NM_001130442, NM_005343, NM_176795 and NM_001318054). A person skilled in the art can identify additional and varied HRAS sequences.

[704] As used herein, the term "MET" refers to the gene and / or the protein encoded by the gene. The MET gene encodes c-Met, also called Met tyrosine protein kinase or hepatocyte growth factor receptor (HGFR). MET is a single-pass tyrosine kinase receptor essential for embryonic development, organogenesis and wound healing. The hepatocyte growth factor / dispersion factor (HGF / SF) and its binding isoform (NK1, NK2) are the only known ligands of the MET receptor. MET is normally expressed by cells of epithelial origin, whereas HGF / SF expression is restricted to cells of mesenchymal origin. When HGF / SF binds its cognate receptor MET, it induces its dimerization through a mechanism not yet fully understood, leading to its activation. Human MET sequences are known in the art (for example, GenBank accession numbers NM_000245, NM_001127500, NM_001324401 and NM_001324402). One skilled in the art can identify additional MET sequences and variants thereof.

[705] As used herein, the term "VHL" refers to the gene and / or the protein encoded by the gene. The VHL gene is the Von Hippel Lindau tumor suppressor gene. A mutation in the germ line of the VHL gene is the basis of the family inheritance of Von Hippel-Lindau syndrome, an inherited inherited cancer syndrome that predisposes to a variety of malignant and benign tumors of the eyes, brain, spinal cord spinal, kidney, pancreas and adrenal glands. Human VHL sequences are known in the art (for example, GenBank accession numbers NM_000551, NM_198156 and NM_001354723). One skilled in the art can identify additional VHL sequences and variants thereof. Score for genetic mutations

[706] The present disclosure provides methods for identifying the presence of cancer in an individual with high sensitivity and specificity based, at least in part, on the presence of one or more genetic biomarkers. Several methods are provided here to determine whether the individual has cancer and / or the likelihood that the individual will have cancer. In some modalities, these methods involve various types of statistical techniques and methods, including, for example, scoring methods, regression analysis, grouping, principal component analysis, classifying analysis of nearest neighbors (eg, k- neighbors), linear discriminating analysis, neural networks and support vector machines, etc.

[707] In some modalities, one or more genetic biomarkers can be used to generate a score. The score may indicate the likelihood of the individual having cancer or not. In some modalities, the probability is generated by comparing the frequency of the mutation allele of each mutation in one or more genetic biomarkers with a reference distribution of the frequency of the mutation allele. As described here, genomic segments containing one or more biomarkers can be amplified by a set of initiators. The primer set can have one or more pairs of primers that amplify one or more non-overlapping genomic segments. The same set of primers can be used to amplify model DNA collected from an individual in one or more wells (for example, equal to or more than 1, 2, 3, 4, 5, 6, 7, 8, 9 , 10, 11, 12, 20 or 30 wells), so the amplification process can provide duplicate signals for mutants (eg, rare mutations) that are detectable in multiple wells. In some modalities, PCR products can

individuals may be at one or more additional amplification cycles before sequencing. In some embodiments, readings from a common model molecule can be grouped, for example, based on unique identifier sequences (UIDs) that are incorporated as molecular bar codes. In some modalities, artifact changes that are introduced during the sample preparation or sequencing steps can be reduced by requiring that a mutation be present in, for example, greater than 80%, 85%, 90%, 91%, 92 %, 93%, 94%, 95%, 96%, 97%, 98% or 99% of the readings in each UID family. In some embodiments, redundant readings resulting from optical duplication can be eliminated by requiring that readings with the same UID and sample index be at least 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000 or 9000 pixels in size. when located in the same block.

[708] In some modalities, mutations that meet one or two of the following two criteria are considered (i) present in the database of the Somatic Mutations in Cancer Catalog (COSMIC) or (ii) predicted as inactivating gene suppressor genes tumors (senseless mutations, insertions or deletions outside the frame, mutations in the canonical splice site) In some modalities, synonymous mutations, except for those at the ends of the exon, and intronic mutations, except for those at the splice sites, are excluded. These selected mutations are referred to as supermutants. Thus, in some modalities, supermutants include, for example, mutations present in the database of the Somatic Mutations in Cancer Catalog (COSMIC), mutations predicted to inactivate tumor suppressor genes (non-sense mutations, insertions or out-of-frame deletions, mutations in the canonical splice site), non-synonymous mutations, mutations that can affect the splice and / or mutations that can affect expression etc.

[709] Thus, as used in this document, the term "mutant allele frequency" or "MAF" within a sample (for example, a well, a test sample) refers to the proportion of UIDs in the sample that have that mutation. The MAF reflects the mutant fraction within each sample (for example, each well) and represents an independent sample of the frequency of the mutant allele in the sample of interest. In some embodiments, the MAF of a mutation in a sample (instead of the well) can be calculated by the total number of mutants present in all wells in the sample (for example, a sample collected from an individual) divided by the total number of UIDs .

[710] In some modalities, MAF normalization is carried out. In some modalities, all mutations that do not have at least one supermutant in at least one well are excluded from the analysis. For example, the frequency of mutant allele (MAF) may reflect the ratio between the total number of supermutants in each well in this sample and the total number of UIDs in the same well in this sample. In some modalities, MAF is first normalized based on the MAFs observed for each mutation in a set of normal controls comprising normal plasmas in the training set. In some modalities, mutations with <100 UIDs are excluded. Normalization can be performed by standardization ie, subtracting the mean and dividing by the standard deviation) or multiplying the MAF for a predetermined reason. In some modalities, normalization is performed by first calculating the mean MAF (ave_i) for each change i = 1,… n, found among the normal controls. Using the 25th percentile of the distribution generated by these averages as the reference value (ave_ref), each MAF can be normalized by multiplying it by the ave_ref / ave_i ratio. For example, if the average observed MAF of a mutation in a set of controls is 10 times greater than ave_ref, each MAF for that mutation can be multiplied by 1/10.

[711] The classification of the status of the genetic biomarker of a sample can be obtained, for example, from a statistical test comparing the MAF of one or more mutations in the selected genetic biomarkers to a reference distribution of the frequency of the allele of mutation for mutations in a group of control samples, comparing the mutation allele frequency of one or more mutations in the selected genetic biomarkers with a first mutation allele frequency reference distribution in control samples and a second reference distribution of frequency of mutation allele in samples collected from individuals with cancer or comparing the frequency of mutation allele of one or more mutations in the selected genetic biomarkers up to the maximum frequency of mutation allele in samples of control.

[712] The control reference distribution and the maximum frequency of the mutation allele can be determined from control samples. In some embodiments, the control sample group has at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100 , 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 or more control individuals. In some modalities, the control subjects are healthy individuals, or at least do not have cancer, or are not suspected of having cancer. Control samples collected from these control individuals can be amplified and sequenced. Changes in one or more selected genetic biomarkers can be determined. Thus, an MAF for a specific mutation in an individual can be determined and the distribution of MAF in control samples can be determined from the group of control individuals. Likewise, the maximum frequency of the mutation allele can also be determined in control samples.

[713] In some modalities, the MAF of one or more mutations in the selected genetic biomarkers can be compared with the reference distribution, thus obtaining a score that indicates the possibility or probability that the individual will have cancer. In some modalities, if the score (for example, possibility or probability) is equal to or higher than a reference threshold, it can be determined that the individual is likely to have cancer, otherwise it can be determined that the individual you probably don't have cancer. In some modalities, the comparison may provide a score that indicates the possibility or probability that the individual does not have cancer. In some modalities, if the score (for example, possibility or probability) is equal to or less than a reference threshold, it is possible to determine that the individual is likely to have cancer, otherwise it can be determined that it is not likely that the individual has cancer.

[714] In some modalities, MAF is first normalized based on the MAFs observed in a set of normal controls for each mutation. After this specific mutation normalization, the MAF of each mutation in each well is compared to a reference distribution of MAFs constructed from normal controls with all mutations included, and a p-value is calculated from that distribution. In some modalities, the lowest p-value among all mutations detected in a given sample was considered the "superior mutation". The classification of a sample's ctDNA status is based on the p-value of that higher mutation being below or above a certain threshold. The threshold can be selected based on the desired specificity observed among an independent set of normal controls.

[715] In some modalities, Stouffer's Z score is used to combine hypothesis test results from two or more independent tests (for example, test results of 2, 3, 4, 5, 6, 7, 8, 9 , 10, 11 or 12 wells). For example, MAF results for two or mutations can be combined into a single test. In some modalities, a sample is scored as positive when Stouffer's Z score is greater than a reference threshold. In some modalities, a sample is scored as positive if the ratio between Stouffer's Z score and the average of the first (for example, 2, 3, 4, 5, 6 7, 8, 9 or 10) higher Z scores Stouffer values in the controls are greater than a reference threshold.

[716] In some embodiments, the MAF of one or more mutations in the selected genetic biomarkers is compared to the maximum frequency of the mutation allele in control samples. If one or more mutations (for example, equal to or at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190 or 200) have an MAF that is greater than the maximum frequency of the mutation mutation allele in control samples; it can be determined that the individual has cancer. In some modes, a score can be obtained from the comparison. In some modalities, the score is the total number of mutations that have an MAF greater than the maximum frequency of the mutation allele in control samples. If the score is greater than a reference threshold, it can be determined that the individual is likely to have cancer. In some modalities, the mean MAF of one or more mutations in the selected genetic biomarkers is calculated.

[717] In some embodiments, the MAF of one or more mutations in the selected genetic biomarkers can be compared to a first reference distribution of the frequency of mutation alleles in control samples and a second reference distribution of the frequency of mutation alleles in samples collected from individual with cancer. In some modalities, a score is obtained by comparing the mutation frequencies of the sample of interest with the distribution of the mutation frequencies of, respectively, normal and cancer samples in the training set.

[718] In some modalities, the UID interval for each change is divided into 10 intervals (for example, <1,000, 1,000 - 2,000, ...,

8,000-9,000,> 9,000). Depending on the number of UIDs, the MAF of each mutation in each well can be compared to two reference MAF distributions constructed from samples in the corresponding UID range: 1) a distribution constructed from all normal control samples in the training set; and 2) a distribution constructed from samples of cancer patients in the training set. In some modalities, the cancer training set includes only those in which the same mutation is present in the sample (for example, plasma) and in the corresponding primary tumor, with MAF> 5% in the tumor. Corresponding p values, pN and pC, can be obtained. The reference distributions for normal and cancer samples can be constructed independently, from the training sets, in each round and at each 10-fold cross-validation iteration, that is, 90% of the samples in each iteration are used for training and 10% of the samples are used for testing.

[719] For each mutation, an omega score can be obtained. The log ratio of these two p values, pC / pN, can be calculated. In some modalities, the minimum and maximum of these log relationships in the replicated wells can be eliminated so that the results are less sensitive to extreme values. In some embodiments, a log likelihood ratio is used. Compared to the log-likelihood ratio, the log ratio of p-values can provide some additional advantages, because the relatively low number of available data points does not allow a robust estimate of the densities of MAF distributions (particularly for pC). Like this,

in some modalities, an "omega" score was then determined according to the following formula: where wi is the number of UIDs in well i divided by the total number of UIDs for this mutation in the wells included in the analysis. In some modalities, the total number of wells included in the analysis is 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 20, 30 or more. In some modalities, wells with the maximum log ratio and the minimum log ratio may be excluded from the analysis. In some modalities, the total number of wells included for analysis is 1, therefore, the omega score can be obtained by the following formula:

[720] The log ratio of p values can be weighted so that wells containing more model molecules have a greater impact on the final statistic (the omega score). The justification for this weighting was that the greater the number of model molecules in a well, the greater the confidence in the result.

[721] In some modalities, a score for each change can be determined. In some modalities, mutations with scores Ω greater than a reference score (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9 or 10) are selected. The total number of mutations with a score Ω greater than a reference result reflects the mutation burden in an individual. In some modalities, if the total number of mutations with Ω scores that is greater than a reference score is greater than a reference threshold, it is determined that the individual is likely to have cancer; otherwise, it can be determined that the individual is unlikely to have cancer.

[722] In some modalities, the mutation with the highest score is considered the "higher mutation". The score Ω for the maximum mutation can be used to determine whether an individual is likely to have cancer. For example, the Ω score for the top mutation can be compared to a reference threshold or combined with other information (eg protein biomarkers) in various methods (eg regression analysis) to determine the probability of a individual having cancer. In some modalities, if the Ω score for the higher mutation is greater than a reference threshold, it is determined that the individual is likely to have cancer; otherwise, it can be determined that the individual is unlikely to have cancer. In some modalities, the score Ω is used in a regression analysis (for example, logistic regression). Detecting protein biomarkers

[723] In some embodiments, the presence of a protein biomarker can be detected in any of a variety of biological samples isolated or obtained from an individual (eg, a human individual) including, but not limited to, blood, plasma, serum , urine, cerebrospinal fluid, saliva, sputum, bronchoalveolar lavage, bile, lymphatic fluid, cyst fluid, feces, ascites and combinations thereof. Any protein biomarker known in the art can be detected when a threshold value is obtained above which normal, healthy human individuals do not fall, but human individuals with cancer do fall.

[724] Any appropriate method can be used to detect the level of one or more protein biomarkers, as described here. In some embodiments, the level of one or more protein biomarkers is compared to a predetermined threshold. In some modes, the predetermined threshold is a general or global threshold. In other cases, the predetermined threshold is relevant for a specific protein biomarker. In some embodiments, the level of one or more protein biomarkers is compared to an absolute amount of a reference protein biomarker. In some modalities, the level of one or more protein biomarkers is relative to an amount of a reference protein biomarker. In some embodiments, the level of one or more protein biomarkers is high. In some modalities, the level of one or more protein biomarkers is above a predetermined threshold. In other cases, the level of one or more protein biomarkers is within a predetermined threshold range. In some embodiments, the level of one or more protein biomarkers is or approaches a predetermined threshold. In some modalities, the level of one or more protein biomarkers is below a predetermined threshold. In some modalities, the level of one or more protein biomarkers in a biological sample is lower than a certain threshold. In some embodiments, the level of one or more protein biomarkers in a biological sample is reduced compared to a predetermined threshold.

[725] In some embodiments, the methods provided here to select an individual for further diagnostic testing and / or increased monitoring include detecting a protein biomarker in the biological sample and comparing the amount of protein biomarker in the biological sample with a level reference in a reference sample. In some modalities, methods for selecting an individual for further diagnostic testing and / or increased monitoring include detecting a protein biomarker in the biological sample and comparing the amount of protein biomarker in the biological sample with a reference level, where the reference level is a composite number derived from multiple reference samples. In some modalities, the protein biomarker in the biological sample is at least 5% higher than a reference level.

In some modalities, the protein biomarker in the biological sample is at least 10% higher than a reference level.

In some modalities, the protein biomarker in the biological sample is at least 15% higher than a reference level.

In some embodiments, the protein biomarker in the biological sample is at least 20% greater than a reference level.

In some embodiments, the protein biomarker in the biological sample is at least 25% greater than a reference level.

In some modalities, the protein biomarker in the biological sample is at least 30% greater than a reference level.

In some embodiments, the protein biomarker in the biological sample is at least 40% greater than a reference level.

In some modalities, the protein biomarker in the biological sample is at least 50% higher than a reference level.

In some modalities, the protein biomarker in the biological sample is at least 60% greater than a reference level.

In some embodiments, the protein biomarker in the biological sample is at least 70% greater than a reference level.

In some embodiments, the protein biomarker in the biological sample is at least 80% greater than a reference level.

In some modalities, the protein biomarker in the biological sample is at least 90% greater than a reference level.

In some embodiments, the protein biomarker in the biological sample is at least 100% greater than a reference level.

In some modalities, the protein biomarker in the biological sample is at least 200% higher than a reference level.

In some embodiments, the protein biomarker in the biological sample is at least 300% greater than a reference level.

In some modalities, the protein biomarker in the biological sample is at least 400% higher than a reference level.

In some embodiments, the protein biomarker in the biological sample is at least 500% greater than a reference level.

In some embodiments, the protein biomarker in the biological sample is at least 600% greater than a reference level.

In some modalities, the protein biomarker in the biological sample is at least 700% higher than a reference level.

In some modalities, the protein biomarker in the biological sample is at least 800% higher than a reference level.

In some modes, the protein biomarker in the biological sample is at least 900% higher than a reference level.

In some embodiments, the protein biomarker in the biological sample is at least 1000% greater than a reference level.

In some modalities, the protein biomarker in the biological sample is between about 5% and about 1000% higher than a reference level.

In some modalities, the protein biomarker in the biological sample is between about 10% and about 1000% higher than a reference level.

In some embodiments, the protein biomarker in the biological sample is between about 15% and about 1000% higher than a reference level.

In some embodiments, the protein biomarker in the biological sample is between about 20% and about 1000% higher than a reference level.

In some embodiments, the protein biomarker in the biological sample is between about 25% and about 1000% higher than a reference level.

In some embodiments, the protein biomarker in the biological sample is between about 30% and about 1000% higher than a reference level.

In some embodiments, the protein biomarker in the biological sample is between about 10% and about 100% higher than a reference level.

In some embodiments, the protein biomarker in the biological sample is between about 15% and about 100% higher than a reference level.

In some embodiments, the protein biomarker in the biological sample is between about 20% and about 100% higher than a reference level.

In some modalities,

the protein biomarker in the biological sample is between about 25% and about 100% higher than a reference level. In some modalities, the protein biomarker in the biological sample is between about 30% and about 100% higher than a reference level.

[726] In some embodiments, the protein biomarker is at least 5% less than a reference level. In some embodiments, the protein biomarker is at least 10% less than a reference level. In some embodiments, the protein biomarker is at least 15% less than a reference level. In some modalities, the protein biomarker is at least 20% less than a reference level. In some embodiments, the protein biomarker is at least 25% less than a reference level. In some modes, the protein biomarker is at least 30% less than a reference level. In some modalities, the protein biomarker in the biological sample is at least 40% less than a reference level. In some embodiments, the protein biomarker in the biological sample is at least 50% less than a reference level. In some modalities, the protein biomarker in the biological sample is at least 60% less than a reference level. In some modalities, the protein biomarker in the biological sample is at least 70% less than a reference level. In some ways, the protein biomarker in the biological sample is at least 80% less than a reference level. In some modalities, the protein biomarker in the biological sample is at least 90% less than a reference level. In some modalities, the protein biomarker in the biological sample is between about 5% and about 100% lower than a reference level. In some modes, the protein biomarker in the biological sample is between about 10% and about 100% lower than a reference level. In some embodiments, the protein biomarker in the biological sample is between about 15% and about 100% lower than a reference level. In some embodiments, the protein biomarker in the biological sample is between about 20% and about 100% lower than a reference level. In some modalities, the protein biomarker in the biological sample is between about 25% and about 100% lower than a reference level. In some embodiments, the protein biomarker in the biological sample is between about 30% and about 100% lower than a reference level.

[727] In some embodiments, the protein biomarker is a cytokine biomarker. In some embodiments, the protein biomarker is a chemokine biomarker. In some embodiments, the protein biomarker is a growth factor biomarker. In some modalities, the protein biomarker is associated with inflammation. In some modalities, the protein biomarker is associated with cancer. In some modalities, the protein biomarker is associated with a particular type of cancer. Any appropriate cancer can be identified and / or treated as described here. In some modalities, cancer is a common cancer. In some modalities, cancer is a cancer in which no blood-based test is available. In some modalities, cancer is a cancer in which no test for early detection is available. In some modalities, cancer is Stage I cancer. In some modalities, cancer is Stage II cancer. In some modalities, cancer is a Stage III cancer. In some modalities, the cancer is Stage IV cancer. In some modalities, cancer is a surgically resectable cancer. In some modalities, cancer is a surgically unresectable cancer. Examples of cancers that are identified as described here (for example, based, at least in part, on the presence or absence of one or more first biomarkers (for example, genetic biomarkers) and / or a high level of one or more second biomarkers (for example, peptide biomarkers)) and / or the presence of aneuploidy includes, without limitation, liver cancer, ovarian cancer, esophageal cancer, stomach cancer, pancreatic cancer, colorectal cancer, lung cancer, breast cancer and prostate cancer.

[728] In some embodiments, the levels of one or more protein biomarkers can be detected independently (for example, using singleplex peptide tools). Examples of methods for detecting protein levels include, without limitation, spectrometry methods (for example, high performance liquid chromatography (HPLC) and liquid chromatography-mass spectrometry (LC / MS)), antibody-dependent methods (eg enzyme linked immunosorbent assay (ELISA), protein immunoprecipitation, immunoelectrophoresis, western blotting and protein immunostaining) and aptamer-dependent methods. In some embodiments, the level of one or more protein biomarkers can be detected as described in the Examples.

[729] Many of the singleplex peptide tools, such as, but not limited to ELISAs or Western blotting, can be used sequentially or simultaneously to analyze multiple peptide biomarkers. Multiplex peptide tools may include combining singleplex peptide tools or elements from singleplex peptide tools. Additionally or alternatively, the detection of the levels of one or more peptide biomarkers can occur by means of multiplex peptide tools, such as "chips", microarrays or immunoassay systems. In a non-limiting example, several analytes can be probed by multiple capture antibodies detected in microarrays and analyzed using an antibody / chemiluminescence system conjugated to horseradish peroxidase (HRP). In this method, each point captures a specific target protein and a second target specific detector antibody is used for quantification. In addition to membrane antibody arrays, glass slides can be used for quantitative antibody arrays. The commercial modalities of multiplexed ELISAs include, but are not limited to, Q-Plex available from Quansys Biosciences, Mosaic ™ available from R&D Systems, Ciraplex® available from Aushon Biosystems, MULTI-ARRAY available from Meso Scale Disco- very , FAST Quant available from Whatman Schleicher & Schuell BioScience, A2 available from Beckman Coulter and Quantibody® available from RayBiotech. Additionally or alternatively, multiplex assays can be used spheres or particles to detect one or more protein biomarkers simultaneously. In a non-limiting example, polystyrene or paramagnetic beads are impregnated with dyes of different wavelengths, to detect multiple target antibodies simultaneously, where each combination of dye or dye corresponds to a different target antibody. Sandwich assays are used to measure protein levels. The commercial modalities of this technique use the Luminex® and FirePlex® technology platforms, for example, Bio-Plex® Multiplex Immunoassay System available from Bio-Rad, FlowCytomix available from eBioscience, ProcartalPlex Immunoassay System available from ThermoFisher Scientific , Novex® Multiplex assays available from Invitogen.

[730] In some embodiments, an assay includes the detection of threshold biomarkers of proteins in a biological sample (for example, any biological sample described herein, such as, without limitation, blood or plasma) without detection of genetic biomarkers (for example, example, mutations in the circulating tumor DNA (ctDNA)) and / or aneuploidy and / or an additional class of biomarker.

In some modalities, an assay includes the detection of threshold protein biomarkers in a biological sample (for example, any biological sample described here, such as, without limitation, blood or plasma) with detection of genetic biomarkers ( for example, mutations in circulating tumor DNA (ctDNA)) and / or aneuploidy and / or an additional class of biomarker.

For example, an assay may include detecting one or more of (for example, each) CA19-9, CEA, HGF, OPN, CA125, prolactin, TIMP-1 and / or myeloperoxidase (MPO) in a biological sample.

In some embodiments, an assay may include detecting one or more of (for example, each) CA19-9, CEA, HGF, OPN, CA125, prolactin, TIMP-1 and / or myeloperoxidase (MPO) in a biological sample any of the thresholds disclosed here.

As another example, a test may include detecting one or more of (for example, each of) CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1, follistatin, G-CSF and / or CA15-3 in a biological sample.

In some modalities, an assay may include detecting one or more of (for example, each) CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1, folistatin, G-CSF, and / or CA15-3 in a biological sample any of the thresholds disclosed herein.

As another example, an assay may include detecting one or more of (for example, each of) CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1, and / or CA15-3 in a sample biological.

In some embodiments, an assay may include detecting one or more of (for example, each) CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1, and / or CA15-3 in a biological sample any of the thresholds disclosed here.

As another example, an assay may include detecting one or more of (for example, each) KRAS (for example, codons 12 and 61), TP53, CDKN2A and / or SMAD4 in a biological sample.

In some embodiments, an assay may include detecting one or more of (for example, each) KRAS (for example, codons 12 and 61), TP53, CDKN2A and / or SMAD4 in a biological sample at any of the levels of threshold disclosed here. In some embodiments, once an assay that includes detecting threshold biomarkers of proteins in a biological sample is performed, subsequent tests or monitoring are performed (for example, any of the varieties of additional diagnostic tests or monitoring techniques). increased investment disclosed here). In some embodiments, since an assay that includes detecting protein biomarkers with a threshold in a biological sample is performed, a second assay that includes detecting the presence of one or more genetic biomarkers present in DNA without cells (for example , ctDNA, for example, any of the variety of genetic changes that are present in cellless DNA or ctDNA, as described here), the presence of one or more protein biomarkers (for example, any of the variety of biomarkers of proteins described herein), the presence of aneuploidy and / or the presence of one or more additional classes of biomarkers can be performed.

[731] Examples of protein biomarkers that can be detected using any of the various techniques described here include, without limitation, Actin gamma (ACTG1), AFP, Alpha-2-HS glycol protein, Angiopoietin-2, protein apolipo 1, AXL, CA125, CA15-3, carbohydrate antigen 19-9 (CA19-9), carcinoembryonic antigen (CEA), Catenin, Caveolin-1, CD44, member of class B 1 (HSP90AB1), complement c3a , Cyclin D, CYFRA 21-1, Defensin α 6, DKK1, EAFP, Endoglin, eukaryotic translation elongation factor 1 gamma (EEF1G), ferritin, FGF2, follistatin, galectin-3, G-CSF, GDF15, protein regulated by glucose-8, glyceraldehyde-3-phosphate dehydrogenase (GAPDH), HE4, 90 kDa alpha heat shock protein (cytosolic), heavy polypeptide 1 (FTH1), hepatocyte growth factor

(HGF), IL-6, IL-8, kallikrein 6, Lamina A / C filament protein, large subunit, leptin, light polypeptide (FTL), LRG-1, mesothelin, midkine, MMPs, muscle (PKM2) , myeloperoxidase, NSE, OPG, osteopontine (OPN), P0 (RPLP0), PAR, prolactin, pyruvate kinase, ribosomal protein, ribosomal protein L3 (RPL3), large subunit of ribosomal protein P0 (RPLP0), S100 P, sEGFR , serum C peptide, sFas, SHBG, sHER2 / sEGFR2 / sErbB2, sPECAM-1, TGFa, thioredoxin as protein-2, Thrombospondin-2, TIMP-1, TIMP-2, Transferrin, translation extension factor (EEF1A1 ), Txl-2 (thioredoxin-like protein 2), Vitronectin, α -1, -2, -3 and / or α-1 antitrypsin defense. Exemplary protein biomarkers detected in various types of cancer are shown in Example 2. Detecting aneuploidy

[732] Aneuploidy is the presence of an abnormal number of chromosomes in a cell. Aneuploidy usually originates during cell division, when chromosomes do not separate properly between the two cells. Aneuploidy occurs as a result of a weakened mitotic checkpoint, as these checkpoints tend to interrupt or delay cell division until all components of the cell are ready to enter the next phase. If a checkpoint is weakened, the cell may not notice that a pair of chromosomes is not aligned on the mythotic plate. In this case, most chromosomes would normally separate (with a chromatid ending in each cell), while others could fail to separate. This would generate a child cell without a copy and a child cell with an extra copy. Aneuploidy has been consistently seen in many cancers.

[733] Aneuploidy can be detected through karyotyping, a process in which a sample of cells is fixed and stained to create the typical pattern of light and dark chromosomal bands and an image of the chromosomes is analyzed. Other non-limiting techniques for detecting aneuploidy include, for example, Fluorescent In Situ Hybridization (FISH), Quantitative PCR for Short Tandem Repeats, Quantitative Fluorescence PCR (QF-PCR), Quantitative PCR dosage analysis , Quantitative Mass Spectrometry of Single Nucleotide Polymorphisms, Comparative Genomic Hybridization (CGH), microarrays, Sanger sequencing and massively parallel sequencing, etc.

[734] The present disclosure provides methods for detecting aneuploidy. For example, the present disclosure provides methods and materials for evaluating sequencing data to identify a mammal as having a disease associated with one or more chromosomal abnormalities (for example, cancer). Sequencing data can be processed to identify significant gains or losses in the single chromosome arm, as well as allelic imbalance in the chromosome arms, using the Within-Sample AneupLoidy Detection (WALDO) method. WALDO incorporates a support vector machine (SVM) to discriminate between aneuploid and euploid samples. SVM can be trained using aneuploid samples (for example, synthetic aneuploid samples) and euploid samples (for example, peripheral white blood cell (WBC)). A sample can be classified as positive (aneuploid), if the SVM's discrimination score exceeds a certain threshold. In some embodiments, a single pair of primers is used to amplify ~ 38,000 loci of long intercalated nucleotide elements (LINEs) across the genome. Massively parallel sequencing is then carried out. In some modalities, one of the initiators includes a UID as a molecular bar code, which can be used to reduce the error rates associated with PCR and sequencing. WALDO overview

[735] In euploid samples, the number of LINE readings within each 500 kb genomic range should accompany the number of readings in certain other genomic regions. Genomic intervals that go together do so because the amplicons within them amplify to similar lengths. Here, these accompanying genomic regions are called "clusters". Clusters can be used for sequencing data in euploid samples. In a test sample, it is determined whether the number of readings at each genomic interval in each predefined cluster is within the expected limit of the other clusters in the same sample. If the readings within a genomic range are outside the statistically expected limit and there are many outsiders on the same chromosomal arm, that chromosomal arm will be classified as aneuploidy

[736] In summary, although the number of readings on each LINE is not randomly distributed across the genome, the distribution of scale readings within each cluster is approximately normal. A convenient property of normal distributions is that the sum of several normal distributions is also a normal distribution. The theoretical average and the variation of the summed readings in each chromosome arm can be calculated simply by adding the means and the variations of all the clusters represented in that chromosome arm.

[737] WALDO employs several methods that make it applicable to the analysis of amplicons generated by PCR from clinical samples. One of these methods is to control the amplification bias due to the strong dependence of the data on the size of the initial model. Another is the use of a Support Vector Machine (SVM) to allow the detection of aneuploidy in samples containing low neoplastic fractions.

[738] As shown in FIG. 36, a single pair of primers is used to amplify LINEs. A test sample is then combined with several euploid samples with genomic DNA of similar size. The genome is divided into multiple ranges and each range is similar in size (for example, 100, 200, 300, 400, 500, 600, 700, 800, 900 Kb or 1 Mb, 2 Mb, 3 Mb, 4 Mb, or 5 Mb). The readings within these genomic intervals in the euploid samples are grouped into clusters. All genomic intervals in clusters have similar reading depths. The readings for each of the genomic ranges in the test sample are placed in the predefined clusters. Statistical tests, for example, an SVM-based algorithm, are used to determine whether the total readings for all genomic intervals on each chromosomal arm are distributed as expected, if the sample is euploid. The statistical tests are based on the observed distribution of readings in the sample clusters, and not in comparison with the readings on euploid samples. The germline sequence variants at sites of common polymorphisms known in LINEs provide information about the allelic imbalance at the arm level that can also be used to assess the aneuploidy of individual chromosomal arms. These same polymorphisms can be used to determine whether any two samples are derived from the same individual. When there is a corresponding normal sample from the same individual available, the methods described here can detect the number and nature of substitutions and insertions and exclusions from a single base in the LINEs. Fast-SeqS

[739] For each DNA sample evaluated, FAST-SeqS can be used to amplify approximately 38,000 amplicons with a single pair of primers (Kinde I, Papadopoulos N, Kinzler KW, & Voelselstein B (2012) FAST-SeqS: a simple and efficient method for the detection of aneuploidy by massively parallel sequencing.

7 (7): e41162). Massively parallel sequencing can be performed. In some embodiments, the degenerate bases at the 5 'end of the primer are used as molecular bar codes to uniquely mark each model DNA molecule. Thus, each model DNA molecule will be counted only once. In some modalities, each single reading can be sequenced between 1 and 20 times (for example, about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , 15, 16, 17, 18, 19 or 20 times). Sample alignment and genomic interval grouping

[740] Alignment programs (for example, Bowtie2) can be used to align readings with the human reference genome set (for example, GRC37). Exact matches for the reference genome can be identified. These exact matches allow for the inclusion of common polymorphisms. In light of the experimental and stochastic variation, it is expected that the number of mapped readings for each genomic region of any euploid sample will be variable. In some modalities, to minimize this variability, clusters of genomic intervals with similar reading depth are identified on all chromosomes in multiple euploid samples. This step can estimate the expected variability in the depth of reading in a sample when there is no aneuploidy. In some modalities, the genomic range has a size of 100, 200, 300, 400, 500, 600, 700, 800, 900 Kb or 1 Mb, 2 Mb, 3 Mb, 4 Mb or 5 Mb. In some modalities, the genomic range has a size of 500 kb.

[741] The grouping of genomic intervals can be performed as follows. Each test sample is compared to euploid samples that have similar amplicon sizes. This is important because smaller amplicons can be over-represented in amplicons generated from DNA that is small in size before amplification. Euploid samples can be derived from WBC or plasma DNA from normal individuals, collectively called "euploid reference set". Euploid samples can include at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120 , 130, 140, 150, 160, 170, 180, 190 or 200 samples. In some embodiments, euploid samples do not include more than 5, 6, 7, 8, 9 or 10 samples.

[742] For each test sample p, samples with the shortest Euclidean distance to p, defined as where, pn and qn are the fraction of amplicons of size n in samples p and q, and the sum is over all sizes of amplicons in two samples. In some modalities, before calculating the Euclidean distances between the test samples of the samples from the euploid reference set, the following amplicons are excluded: (i) Using maximum likelihood estimates, amplicons are classified by variation between the euploid samples and the first 1% excluded. (ii), any amplicons with <10 readings in one sample, but> 50 readings in any of the other samples are removed. In each sample, the genomic intervals are scaled by subtracting the mean and dividing by the standard deviation of the readings in each sample.

[743] The scale genomic ranges are grouped into the selected normal samples. First, each genomic interval i is assigned to a primary cluster Ci. Then, the readings in the genomic interval i in all samples are compared to the average number of readings in the samples in all other geographic intervals i 'that occurred in the remaining autosomal chromosomes. If the average number of readings in the genomic range i 'is not significantly different from the number of readings in the genomic range i, it will be added to the Ci cluster. This process is repeated for each genomic range, generating several clusters (for example, more 1000, 2000, 3000, 4000 or 5000 clusters). Each interval i belongs to its primary cluster, but the same interval can also belong to some other clusters. In some modalities, there are about 4300 clusters.

[744] In some modalities, scale readings are not randomly distributed. In some modalities, the distribution of readings at scale in the genomic intervals in each cluster follows an approximately normal distribution. Identifying gains or losses of the chromosome arm in a test sample

[745] The methods described here can use only a few euploid samples (for example, about 2, 3, 4, 5 6, 7, 8, 9 or 10 samples) to define groups of genomic intervals with similar amplification properties . The statistical tests for aneuploidy are based on the reading distributions within the test sample and independently of the reading distributions in any euploid sample. In some modalities, maximum likelihood is used to estimate the μ means and δ2 variations of the genomic intervals in each cluster. In some modalities, the robustness of these estimates can be improved by iteratively removing the peripheral genomic intervals within the test sample from the clusters. In some modalities, clusters containing less than 10 genomic intervals are not included in the analysis. In some modalities, for each cluster, any genomic range that meets the criteria is removed from all clusters. Then, the parameters of μ and δ2 variances of each cluster are re-estimated by maximum likelihood. The two steps are repeated until no external genomic intervals remain. The statistical significance of the total readings for all genomic intervals in the arm is estimated. Since the sums of normally distributed random variables are also normally distributed random variables, the calculation is straightforward. For each chromosome arm, it can be calculated where Ri is the scale reading and i is the number of groups in the arm. Z scores can be produced using the quantile function Positive Z scores> α represent gains and negative Z scores <-α represent losses. The α value is the selected significance threshold. Allelic imbalance at arm level

[746] Common polymorphisms of 1000 genomes (including, for example, 24,720 single nucleotides and 1,500 indels, MAF> 1%) can be used as candidate heterozygous sites. For each of the normal samples, the polymorphic sites can be safely called as heterozygous and diploid. Polymorphisms can be defined as those with varying allelic frequencies (VAF) (0.4 <VAF <0.6), where VAF = # readings without reference / total readings. VAFs can be modeled at these sites as random variables taken from a normal distribution with μ = 0.5; and the δ2 variances can be estimated by the maximum probability as a function of the reading depth.

[747] To determine whether the alleles on a chromosomal arm in a test sample are unbalanced, the subset of polymorphic sites in which the two alleles are present and where the sum of the readings on both alleles is> 25 is identified.

[748] The VAF observed with the normal distribution, using the expected variation for the observed reading depth, producing a two-sided P-value. All p values in a chromosomal arm can be transformed into Z and combined with the weighted Stouffer method, with the reading depth observed at each site used as its weight. The formula used for this calculation is where wi is the depth of the UID in variant i, Zi is the Z score of variant i, and k is the number of variants observed in the chromosome arm. A chromosomal arm is classified as having an allelic imbalance if the resulting Z score is greater than the selected statistically significant threshold α (for example, by unilateral testing). Generation of synthetic aneuploid samples

[749] Synthetic aneuploid samples can be created by adding (or subtracting) readings from various chromosomal arms to the readings from these normal DNA samples. Readings of 1, 5, 10, 15, 20 or 25 chromosomal arms selected at random are added or subtracted from each sample. Additions and subtractions are designed to represent fractions of neoplastic cells ranging from 0.5% to 10% and result in synthetic samples containing exactly nine million readings. The readings for each chromosome arm can be added or subtracted uniformly. These synthetically generated samples in which readings from just a single chromosomal arm are added or subtracted can be used to estimate WALDO's performance. Detection of aneuploidy throughout the genome

[750] The present disclosure provides detections of aneuploidy across the genome. In some embodiments, a two-class support vector machine (SVM) can be trained to discriminate between euploid samples and synthetic samples. The training set can contain samples of white blood cells (WBC) and samples with aneuploidy (for example, all synthetic samples).

[751] SVM training can be performed by various statistical software (for example, in R, using the radial base kernel and standard parameters).

[752] The number of readings of the data in experimental samples can vary widely, especially when the samples are derived from sources with limited amounts of DNA, such as plasma. In some modalities, samples with low readings can generate artificially high SVM scores if the depth of the reading is not taken into account. Thus, the reading depth can be controlled by modeling the change in SVM scores as a function of the reading depth in normal samples. The average ratio r at each depth decreased monotonically due to the increase in reading depth. The relationship between reading depth and SVM score can be modeled using the following equation. Thus, the raw SVM scores can be corrected by dividing by the ratio r, using the formula.

[753] To classify a sample as aneuploid, it is determined whether a single arm on a chromosome is lost or gained in a statistically significant way. A statistically significant gain from a single chromosomal arm is defined as one whose Z score is above the maximum Z score observed in normal samples (for example, 1, 2, 3, 4 or 5 above). Likewise, a statistically significant loss of a single chromosomal arm is defined as one whose Z score is below the minimum Z score observed in normal samples (for example, 1, 2, 3, 4 or 5 below). The allelic imbalance based on SNPs can be defined for a chromosomal arm whose Z score is above the maximum Z score observed in normal samples (for example, 1, 2, 3, 4 or 5 above). Only samples in which no chromosomal arm is gained or lost when defined in this way are subjected to SVM analysis. The justification for this process is that SVM was designed to identify samples with a large number of gains or losses in the chromosomal arm, but with relatively low neoplastic cell fractions. In some modalities, SVM is not designed to detect aneuploidy in samples with fractions of neoplastic cells> 10%, which are easily identified by evaluating their Z scores and comparing them with normal samples. Somatic sequence mutations and microsatellite instability (MSI)

[754] The present disclosure also provides methods for detecting somatic sequence mutations and microsatellite instability. When corresponding normal samples are available, somatic changes of single base substitution (SBS), insertion and exclusion (indel) can be detected based on the sequences and alignments of LINE amplicons. In some embodiments, the molecular bar code approach for error reduction is used. In some modalities, mutations in the SBS can be identified by directly comparing amplicons from the test sample with amplicons of the corresponding normal and do not require any alignment with the reference genome.

[755] Indels can be called in a similar way. The amplicons are aligned with the test sample and correspond to the normal sample to the reference genome (GRc37). In some modalities, a somatic indel may have at least ten readings from the test sample that differ from any normal reading due to the same insertion or exclusion.

[756] The instability of microsatellites in a test sample can be determined by counting the number of somatic indels in mononucleotide tracts of> 3 nucleotides. Somatic indels in monotracts can be rare in a normal sample. Therefore, the null distribution of counts can be modeled as Poisson (= 1), where is the average number of somatic indels in a monotract in a normal sample. A sample is determined to contain MSI if the number of somatic indels is statistically significant. To assess how often normal samples can be scored as MSI using this process, the total readings on normal samples can be randomly divided into two equal partitions. The first partition can be used as a reference sample and the second partition can be used as a test sample.

[757] This document provides methods and materials for identifying one or more chromosomal abnormalities (for example, aneuploidies) in a sample. For example, a mammal (for example, a sample obtained from a mammal) can be assessed for the presence or absence of one or more chromosomal abnormalities. In some cases, this document provides methods and materials for using amplicon-based sequencing data to identify a mammal as having a disease associated with one or more chromosomal abnormalities (eg, cancer). For example, the methods and materials described herein can be applied to a sample obtained from a mammal to identify the mammal as having one or more chromosomal abnormalities. For example, the methods and materials described herein can be applied to a sample obtained from a mammal to identify the mammal as having a disease associated with one or more chromosomal abnormalities (for example, cancer). This document also provides methods and materials for identifying and / or treating a disease or disorder associated with one or more chromosomal abnormalities (for example, one or more chromosomal abnormalities identified as described herein). In some cases, one or more chromosomal abnormalities can be identified in DNA (for example, genomic DNA) obtained from a sample obtained from a mammal. For example, a prenatal mammal (eg, prenatal human) can be identified as having a disease or disorder based, at least in part, on the presence of one or more chromosomal abnormalities and, optionally, can be treated with one or more treatments for the disease or disorder. For example, a mammal identified as having cancer based, at least in part, on the presence of one or more chromosomal abnormalities can be treated with one or more cancer treatments.

[758] Any suitable mammal can be assessed and / or treated as described herein. A mammal can be a prenatal mammal (for example, a prenatal human). A mammal can be a mammal suspected of having a disease associated with one or more chromosomal abnormalities (for example, cancer). In some cases, humans or other primates, such as monkeys, can be assessed for the presence of one or more chromosomal abnormalities, as described here. In some cases, dogs, cats, horses, cows, pigs, sheep, mice and rats can be assessed for the presence of one or more chromosomal abnormalities, as described here. For example, a human being can be assessed for the presence of one or more chromosomal anomalies, as described here, and, optionally, can be treated with one or more cancer treatments, as described here.

[759] Any appropriate sample of a mammal can be assessed as described here (for example, assessed for the presence of one or more chromosomal abnormalities). A sample can include

Genomic DNA. In some cases, a sample may include circulating cellless DNA (for example, circulating fetal DNA without cells). In some cases, a sample may include circulating tumor DNA (ctDNA). Examples of samples that may contain DNA include, but are not limited to, blood (eg whole blood, serum or plasma), anion, tissue, urine, cerebrospinal fluid, saliva, sputum, bronchoalveolar lavage, bile, lymphatic fluid, cyst, feces, ascites, Pap tests, cerebral spinal fluid, endocervical, endometrial and fallopian samples. For example, a sample can be a plasma sample. For example, a sample can be a urine sample. For example, a sample can be a sample of saliva. For example, a sample can be a sample of cyst fluid. For example, a sample can be a sputum sample. In some cases, a sample may include a fraction of a neoplastic cell (for example, a low neoplastic cell fraction). In cases where a sample includes a low neoplastic cell fraction, the neoplastic cell fraction can be from about 0.01% to about 10% (for example, from about 0.05% to about 10%, from about 0.5% to about 10%, from about 1% to about 10%, from about 3% to about 10%, from about 5% to about 10%, about 7 % to about 10%, from about 0.01% to about 8%, from about 0.01% to about 5%, from about 0.01% to about 2%, from about 0 , 01% to about 1%, from about 0.01% to about 0.5%, from about 0.05% to about 8%, from about 0.1% to about 4% or from about 0.5% to about 1%) of the cell content of the entire sample. For example, a sample that includes a low neoplastic cell fraction can be about 1% neoplastic cells. For example, a sample that includes a low neoplastic cell fraction can be about 0.5% of neoplastic cells.

[760] In some cases, a sample can be processed to isolate and / or purify the sample's DNA. In some cases, DNA isolation and / or purification may include cell lysis (for example, using detergents and / or surfactants). In some cases, DNA isolation and / or purification may include removing proteins (for example, using a protease). In some cases, DNA isolation and / or purification may include removing RNA (for example, using an RNase).

[761] Methods and materials for identifying one or more chromosomal abnormalities may include assessing a genome (for example, a genome of a mammal) for the presence or absence of one or more chromosomal anomalies (for example, aneuploidy). The presence or absence of one or more chromosomal abnormalities in a mammal's genome can, for example, be determined by sequencing a plurality of amplicons obtained from a sample obtained from the mammal to obtain sequencing readings and pooling the readings of sequencing in clusters of genomic intervals. In some cases, the reading counts of the genomic intervals can be compared to the reading counts of other genomic intervals in the same sample. In some cases where the counts of genomic intervals are compared with the counts of other genomic intervals in the same sample, a second sample (for example, control or reference) is not analyzed. For example, when using methods and materials described here to identify numerical disorders (eg, aneuploidy) and / or structural abnormalities, genomic intervals can be compared to the reading counts of other genomic intervals in the same sample . In some cases, the reading counts of genomic intervals can be compared to the reading counts of genomic intervals in another sample. For example, when using the methods and materials described here to identify genetic relationships, polymorphisms (eg, somatic mutations) and / or microsatellite instability, genomic intervals can be compared to interval reading counts genomics in a reference sample. A reference sample can be a synthetic sample. A reference sample can be from a database. In some cases where the methods and materials described here are used to identify genetic relatedness, a reference sample can be a forensic sample. In some cases where the methods and materials described here are used to identify genetic relatedness, a reference sample can be obtained from a suspicious relationship. In some cases where the methods and materials described here are used to identify anomalies (eg, aneuploidies), one or more polymorphisms (eg, somatic mutations) and / or microsatellite instability, a reference sample may be a normal sample obtained from the same cancer patient (for example, a sample from the cancer patient that does not harbor cancer cells) or a normal sample from another source (for example, a patient who does not have cancer).

[762] In some cases, the methods and materials described here can be used to detect aneuploidy in a mammalian genome. For example, a plurality of amplicons obtained from a sample obtained from a mammal can be sequenced, the sequencing readings can be grouped into groups of genomic intervals, the sums of the distributions of the sequencing readings at each genomic interval can be calculated, a Z score of a chromosome arm can be calculated and the presence or absence of an aneuploidy in the mammalian genome can be identified. The distributions of the sequencing readings in each genomic interval can be added together. For example, sums of distributions of sequencing readings in each genomic interval can be calculated using the equation ∑ ~ (∑, ∑), where Ri is the number of sequencing readings, I is the number of clusters in a chromosomal arm , N is a Gaussian distribution with parameters ie i2, i is the average number of sequencing readings in each genomic interval and i2 is the variation in the sequencing readings in each genomic interval. a Z score of a chromosomal arm can be calculated using any appropriate technique. For example, a Z score of a chromosome arm can be calculated using the quantile function 1-CDF (∑, ∑). The presence of an aneuploidy in the mammalian genome can be identified in the mammalian genome when the Z score is outside a predetermined threshold of significance, and the absence of an aneuploidy in the mammalian genome can be identified in the mammalian genome when the the Z score is within a predetermined significance threshold. The predetermined threshold can correspond to the confidence in the test and the acceptable number of false positives. For example, a threshold of significance can be ± 1.96, ± 3 or ± 5.

[763] In some cases, the methods and materials described here can be used to detect one or more polymorphisms in a mammalian genome. For example, a plurality of amplicons obtained from a sample obtained from a first mammal (for example, a test mammal or a mammal suspected of harboring one or more polymorphisms) can be sequenced, a plurality of amplicons obtained of a sample obtained from a second mammal (for example, a reference mammal) can be sequenced, variant sequencing readings from the sample obtained from the first mammal can be grouped into groups of genomic intervals, sequencing readings from sample reference obtained from the second mammal can be grouped into groups of genomic intervals, a chromosomal arm with a sum of the variant sequencing readings and the reference sequencing readings in both alleles that is greater than about 3 (for For example, greater than about 4, greater than about 5, greater than about 6, greater than about 7, greater than about 8, greater than about 9, greater than about 10, greater than about 12, greater than about 15, greater than about 18, greater than about 18, greater than about 20, greater than about 22, greater than about 25 or greater than about 30) can be selected, a frequency variant allele (VAF) of the selected chromosomal arm can be determined and the presence or absence of one or more polymorphisms in the selected chromosomal arm can be identified. A VAF of the selected chromosome arm can be determined using any appropriate technique. For example, a VAF of the selected chromosome arm can be the number of variance sequencing readings / total number of sequencing readings. The presence of one or more polymorphisms in the mammalian genome can be identified in the mammalian genome when the VAF is between about 0.2 and about 0.8 (for example, between about 0.3 and about 0.8 , between about 0.4 and about 0.8, between about 0.5 and about 0.8, between about 0.6 and about 0.8, between about 0.2 and about 0 , 7, between about 0.2 and about 0.6, between about 0.2 and about 0.5 or between about 0.2 and about 0.4) and the absence of one or more polymorphisms in the mammalian genome it can be identified in the mammalian genome when the VAF is within a predetermined threshold of significance. As a non-limiting example, the presence of one or more polymorphisms in the mammalian genome can be identified in the mammalian genome when the VAF is between about 0.4 and 0.6.

[764] Methods and materials for identifying one or more chromosomal abnormalities, as described in this document, may include the use of amplicon-based sequencing readings. For example, a plurality of amplicons (for example, amplicons obtained from a sample obtained from the mammal) can be sequenced. In some cases, each amplicon can be sequenced between about 1 and about 20 (for example, between about 1 and about 15, between about 1 and about 12, between about 1 and about 10, between about from about 1 to about 8, between about 1 and about 5, between about 5 and about 20, between about 7 and about 20, between about 10 and about 20, between about 13 and about 20 , between about 3 and about 18, between about 5 and about 16, or between about 8 and about 12) times. In some cases, amplicon-based sequencing readings may include continuous sequencing readings. In some cases, amplicons may include interspersed long nucleotide elements (LINEs). In some cases, amplicon-based sequencing readings can include from about 100,000 to about 25 million (for example, from about 100,000 to about 20 million, from about

100,000 to about 15 million, from about 100,000 to about 12 million, from about 100,000 to about 10 million, from about 100,000 to about 5 million, from about 100,000 to about 1 million, about 100,000 to about 750,000, from about 100,000 to about

500,000, from about 100,000 to about 250,000, from about 250,000 to about 25 million, from about 500,000 to about 25 million,

750,000 to 25 million, from 1 to 25 million, from 5 to 25 million, from 10 to 25 million, from 15 to 25 million, from about 200,000 to about 20 million, from about 250,000 to about 15 million, from about 500,000 to about 10 million, about 750,000 to about 5 million or about 1 million to about 2 million) sequencing readings. In some cases, amplicon sequencing methods include, without limitation, an Aneuploidy Rapid Screening Test-Sequencing System (FAST-SeqS). For example, sequencing a plurality of amplicons may include assigning a unique identifier (UID) to each model molecule (for example, each amplicon), amplifying each uniquely labeled model molecule to create UID families and redundantly supply amplification products. For example,

sequencing a plurality of amplicons may include calculating a Z score of a variant on that selected chromosome arm ∑ using the equation ~, where wi is the depth of the UID in a ∑ variant i, Zi is the Z score of variant i, ek is the number of variants seen in the chromosome arm. In some cases, the amplicon sequencing methods may be those described in Example 6. In some cases, the amplicon sequencing methods may be those described elsewhere (see, for example, US 2015/0051085; and Kinde et al 2012 PloS ONE 7: e41162).

[765] In some cases, methods and materials for identifying one or more chromosomal abnormalities (for example, aneuploidies) as described herein may include amplification of a plurality of amplifications. For example, amplification of a plurality of amplicons can be performed using a single pair of primers. Methods of amplifying multiple amplicons include, without limitation, polymerase chain reaction (PCR) assays.

[766] A plurality of amplicons can include any appropriate number of amplicons. In some cases, a plurality of amplifiers may include 10,000 to 1,000,000 (for example, 15,000 to

1,000,000, from 25,000 to 1,000,000, from 35,000 to 1,000,000, from 50,000 to

1,000,000, from 75,000 to 1,000,000, from 75,000 to 75,000). for about

1,000,000, from 100,000 to 1,000,000, from 125,000 to 1,000,000, from 160,000 to 1,000,000, from 180,000 to 1,000,000, from 200,000 to 1,000,000, from

300,000 to 1,000,000, from 500,000 to 1,000,000, from about 750,000 to about 1,000,000, from about 10,000 to about 800,000, from about 10,000 to about 500,000, from about 10,000 to about 250,000 , from about 10,000 to about 150,000, from about 10,000 to about

100,000, from about 10,000 to about 75,000, from about 10,000 to about 50,000, from about 10,000 to about 40,000, from about

10,000 to about 30,000 or about 10,000 to about 20,000) amplicons. As a non-limiting example, a plurality of amplifiers can include about 38,000 amplicons. Amplicons in a plurality of amplicons can be of any appropriate length. In some cases, an amplicon may include from about 50 to about 140 (for example, from about 60 to about 140, from about 76 to about 140, from about 90 to about 140, from about 100 to about 140, about 130 to about 140, about 50 to about 130, about 50 to about 120, about 50 to about 110, about 50 to about 100 , about 50 to about 90, about 50 to about 80, about 60 to about 130, about 70 to about 125, about 80 to about 120 or about 90 to about 100) nucleotides. As a non-limiting example, an amplicon can include about 100 nucleotides.

[767] Methods and materials for identifying one or more chromosomal abnormalities, as described here may include grouping sequencing readings (for example, from a plurality of amplicons) into clusters (for example, single clusters) of genomic intervals. A genomic range can be included in one or more clusters. In some cases, a genomic range can be from about 100 to about 252 (for example, from about 125 to about 252, from about 150 to about 252, from about 175 to about 252, about from 200 to about 252, from about 225 to about 252, from about 100 to about 250, from about 100 to about 225, from about 100 to about 200, from about 100 to about 175, about 100 to about 150, about 125 to about 225, about 150 to about 200 or about 160 to about 180) clusters. As a non-limiting example, a genomic range can belong to about 176 clusters. Each cluster can include any number

appropriate number of genomic intervals. In some cases, each cluster may include the same number of genomic intervals. In some cases, the cluster may include several numbers of genomic clusters. As a non-limiting example, each cluster can include about 200 genomic ranges.

[768] A cluster of genomic ranges can include any appropriate number of genomic ranges. In some cases, a cluster of genomic ranges can include from about 4000 to about 4500 (for example, from about 4100 to about 4500, from about 4200 to about 4500, from about 4300 to about 4500, from about 4400 to about 4500, from about 4000 to about 4400, from about 4000 to about 4300, from about 4000 to about 4200, from about 4000 to about 4100, from about 4100 to about 4400 or about 4200 to about 4300) genomic intervals. As a non-limiting example, a cluster of genomic ranges can include about 4361 genomic ranges. A genomic range can be any appropriate length. For example, a genomic range can be the length of an amplicon sequenced as described here. For example, a genomic range can be the length of a chromosome arm. In some cases, a genomic range can include from about 100 to about 125,000,000 (for example, from about 250 to about 125,000,000, from about 500 to about 125,000,000, from about 750 to about 125,000,000, from 1,000 to about 125,000,000, from 1,500 to about 125,000,000, from about 2,000 to about

125,000,000, from 5,000 to about 125,000,000, from 7,500 to about

125,000,000, from about 10,000 to about 125,000,000, from about

25,000 to about 125,000,000, from about 50,000 to about

125,000,000, from about 100,000 to about 125,000,000, from about

250,000 to 125,000,000, from about 500,000 to about 125,000,000, from about 100 to about 1,000,000, from about 100 to about

750,000, about 100 to about 500,000, about 100 to about 250,000, about 100 to about 100,000, about 100 to about 50,000, about 100 to about 25,000, about 100 to about 10,000, about 100 to about 5,000, about 100 to about 2,500, about 100 to about 1,000, about 100 to about 750, about 100 to about 500 , from about 100 to about 250, from about 500 to 1,000,000, from about 5,000 to 900,000, from about 50,000 to 800,000 or from 100,000 to 750,000) nucleotides. As a non-limiting example, a genomic range can include about 500,000 nucleotides. Groups of genomic intervals can be formed using any appropriate method. For example, amplifiers of similar size can be agglomerated. In some cases, groups of genomic intervals can be formed as described in Example 6.

[769] The methods and materials described here can also employ supervised machine learning. In some cases, supervised machine learning can detect small changes in one or more chromosomal arms. For example, supervised machine learning can detect changes, such as gains or losses in the chromosomal arm, often present in a disease or disorder associated with chromosomal abnormalities, such as cancer. In some cases, supervised machine learning can be used to classify samples according to aneuploidy status. For example, supervised machine learning can be employed to make aneuploidy calls across the genome. In some cases, a support vector machine model may include obtaining an SVM score. An SVM score can be obtained using any appropriate technique. In some cases, an SVM score can be obtained as described elsewhere (see, for example, Cortes 1995 Machine learning 20: 273-297;

and Meyer et al. 2015 R package version: 1.6-3). At lower read depths, a sample will normally have a higher gross SVM score. Thus, in some cases, the gross probabilities of SVM can be corrected based on the reading depth of a sample using the equation log 1 -! = # $ + &, where r is the ratio of the SVM score at a specific reading depth / minimum SVM score for a particular sample, given sufficient reading depth. A and B can be determined as described in Example 6. For example, A = -7.076 * 10 ^ -7, x = the number of unique model molecules for the sample provided and B = -1.946 * 10 ^ -1.

[770] The methods and materials described here can be used to identify any appropriate chromosomal abnormalities. Examples of chromosomal anomalies include, without limitation, numerical disorders, structural abnormalities, allelic imbalances and microsatellite instabilities. A chromosomal abnormality can include a numerical disorder. For example, a chromosomal abnormality can include an aneuploidy (for example, an abnormal number of chromosomes). In some cases, an aneuploidy can include an entire chromosome. In some cases, an aneuploidy may include part of a chromosome (for example, a gain in the chromosome arm or a loss in the chromosome arm). Examples of aneuploidies include, without limitation, monosomy, trisomy, tetrasomy and pentasomy. A chromosomal abnormality can include a structural abnormality. Examples of structural abnormalities include, without limitation, deletions, duplications, translocations (for example, reciprocal translocations and Robertsonian translocations), inversions, insertions, rings and isochromosomes. Chromosomal anomalies can occur on any pair of chromosomes (for example, chromosome 1, chromosome 2, chromosome 3, chromosome 4, chromosome 5, chromosome 6, chromosome 7, chromosome

mossome 8, chromosome 9, chromosome 10, chromosome 11, chromosome 12, chromosome 13, chromosome 14, chromosome 15, chromosome 16, chromosome 17, chromosome 18, chromosome 19, chromosome 20, chromosome 21 and chromosome 22 and / or one of the sex chromosomes (for example, an X chromosome or a Y chromosome). For example, aneuploidy can occur, without limitation, on chromosome 13 (for example, trisomy 13), chromosome 16 (for example, trisomy 16), chromosome 18 (for example, trisomy 18), chromosome 21 (for example, trisomy 21 ) and / or sex chromosomes (for example, X chromosome monosomy; sex chromosome trisomy, such as XXX, XXY and XYY; sex chromosome tetrasomy, such as XXXX and XXYY; and sex chromosome pentassomy, such as XXXXX, XXXXY and XYYYY). For example, structural abnormalities can occur, without limitation, on chromosome 4 (for example, partial deletion of the short arm of chromosome 4), chromosome 11 (for example, a terminal deletion 11q), chromosome 13 (for example, Robertsonian translocation on chromosome 13), chromosome 14 (for example, Robertsonian translocation on chromosome 14), chromosome 15 (for example, Robertsonian translocation on chromosome 15), chromosome 17 (for example, duplication of the gene encoding peripheral myelin protein 22), chromosome 21 (for example, Robertsonian translocation on chromosome 21) and chromosome 22 (for example, Robertsonian translocation on chromosome 22).

[771] The methods and materials described herein can be used to identify and / or treat a disease associated with one or more chromosomal anomalies (for example, one or more chromosomal abnormalities identified as described herein, such as, without limitation, an aneuploidy). In some cases, a DNA sample (for example, a genomic DNA sample) obtained from a mammal can be evaluated

assessed as to the presence or absence of one or more chromosomal abnormalities. For example, a prenatal mammal (eg, prenatal human) can be identified as having a disease based, at least in part, on the presence of one or more chromosomal abnormalities, which can be treated with a or more cancer treatments. As another example, a mammal identified as having cancer based, at least in part, on the presence of one or more chromosomal abnormalities can be treated with one or more cancer treatments.

[772] In some cases, a mammal identified as having a disease associated with one or more chromosomal abnormalities, as described here (for example, based at least in part on the presence of one or more chromosomal abnormalities, such as, without limitation, a aneuploidy) may have the diagnosis of the disease been confirmed using any appropriate method. Examples of methods that can be used to confirm the presence of one or more chromosomal abnormalities include, without limitation, karyotyping, fluorescence in situ hybridization (FISH), quantitative short tandem repeat PCR, quantitative fluorescence PCR ( QF-PCR), quantitative analysis of the dosage by PCR, quantitative mass spectrometry of SNPs, comparative genomic hybridization (CGH), complete genome sequencing and exome sequencing.

[773] Once identified as having a disease associated with one or more chromosomal abnormalities, as described here (for example, based at least in part on the presence of one or more chromosomal abnormalities, such as, without limitation, an aneuploidy), one mammal can be treated accordingly. For example, when a mammal is identified as having cancer associated with one or more chromosomal anomalies, as described here, the mammal can be treated with one or more cancer treatments. The one or more cancer treatments may include any treatments appropriate for cancer. Cancer treatment may include surgery. A cancer treatment can include radiation therapy. Cancer treatment may include administering pharmacotherapy, such as chemotherapy, hormonal therapy, targeted therapy and / or cytotoxic therapy. Examples of cancer treatments include, without limitation, platinum compounds (such as cisplatin or carboplatin), taxanes (such as paclitaxel or docetaxel), albumin-bound paclitaxel (nab-paclitaxel), altretamine, capecitabine, cyclophosphamide, etoposide (vp -16), gemcitabine, ifosfahamide, irinotecan (cpt-11), liposomal doxorubicin, melphalan, pemetrexed, topotecan, vinorelbine, luteinizing hormone releasing hormone (LHRH) agonists (as therapy with goserelin and leuproline) , anti-estrogen aromatase inhibitors (such as letrozole, anastrozole and exemestane), angiogenesis inhibitors (such as bevacizumab), poly (ADP) -ribose polymerase (PARP) inhibitors (such as olaparib, ruca-paribe and niraparib), radiotherapy by external beam, brachytherapy, radioactive phosphorus and combinations thereof.

[774] Any appropriate disease associated with one or more chromosomal anomalies, as described here (for example, based at least in part on the presence of one or more chromosomal abnormalities, such as, without limitation, an aneuploidy) can be identified and / or treated as described here. Examples of diseases and conditions that can be associated with one or more chromosomal abnormalities include, without limitation, lung cancer (for example, small cell lung carcinoma or non-small cell lung carcinoma), thyroid cancer papillary, medullary thyroid cancer, differentiated thyroid cancer, recurrent thyroid cancer, refractory differentiated thyroid cancer, lung adenocarcinoma, bronchioles lung cell carcinoma, type 2A or 2B multiple endocrine cancer (MEN2A or MEN2B, respectively), pheochromocytoma, parathyroid hyperplasia, breast cancer, colorectal cancer (for example, metastatic colorectal cancer), papillary renal cell carcinoma, gastroenteric mucosal ganglio-neuromatosis, inflammatory myofibroblastic tumor or cervical cancer, acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML), cancer in adolescents, adrenal cancer, adrenocortical carcinoma, anal cancer, appendix cancer, astroc itoma, atypical teratoid / rhabdoid tumor, basal cell carcinoma, bile duct cancer, bladder cancer, bone cancer, brain stem glioma, brain tumor, breast cancer, bronchial tumor, Burkitt lymphoma, carcinoid tumor , unknown primary carcinoma, cardiac tumors, cervical cancer, childhood cancer, chordoma, chronic lymphocytic leukemia (CLL), chronic myeloid leukemia (CML), chronic myeloproliferative neoplasms, colon cancer, colorectal cancer, craniopharyngioma, lineal cutaneous T-cell form, bile duct cancer, ductal carcinoma in situ, embryonic tumors, endometrial cancer, ependymoma, esophageal cancer, esthesioneuroblastoma, Ewing's sarcoma, germ cell extracranial tumor, germ cell extragonadal tumor, cancer of extrahepatic bile duct, eye cancer, fallopian tube cancer, fibrous bone histiocytoma, gallbladder cancer, gastric cancer, gastrointestinal carcinoid tumor, tumors gastrointestinal stroma (GIST), germ cell tumor, gestational trophoblastic disease, glioma, hairy cell tumor, hairy cell leukemia, head and neck cancer, heart cancer, hepatocellular cancer, histiocytosis, Hodgkin's lymphoma, hypopharyngeal cancer - geo, intraocular melanoma, intraocular melanoma, islet cell tumors, pancreatic neuroendocrine tumors, Kaposi's sarcoma, kidney cancer, Langerhans cell histiocytosis, laryngeal cancer, leukemia, lip and oral cavity cancer, liver cancer, cancer of lung, lymphoma, macroglobulinemia, malignant fibrous histiocytoma of the bones, osteocarcinoma, melanoma, Merkel cell carcinoma,

mesothelioma, metastatic squamous neck cancer, carcinoma of the middle tract, mouth cancer, multiple endocrine neoplasms syndromes, multiple myeloma, mycosis fungoides, myelodysplastic syndromes, myelodysplastic / myeloproliferative neoplasms, myeloid leukemia, myeloid leukemia, myeloid leukemia multiple, myeloproliferative neoplasms, cancer of the nasal cavity and paranasal sinus, nasopharyngeal cancer, neuroblastoma, non-Hodgkin oral cavity cancer, small cell oral lymphoma, lip cancer, oropharyngeal cancer, osteosarcoma, ovarian cancer, cancer pancreatic cancer, hepatobiliary cancer, upper urinary tract cancer, papillomatosis, paraganglioma, cancer of the paranasal sinuses and nasal cavity, parathyroid cancer, penis cancer, pharynx cancer, pheochromositoma, pituitary cancer, plasma cell neoplasia , pleuropulmonary blastoma, pregnancy and breast cancer, primary lymphoma of the central nervous system, primary peritoneal cancer, cancer of the prostate, rectal cancer, renal cell cancer, retinoblastoma, rhabdomyosarcoma, salivary gland cancer, sarcoma, Sezary syndrome, skin cancer, small cell lung cancer, small intestine cancer, soft tissue sarcoma, carcinoma squamous cell cyinoma, squamous neck cancer, stomach cancer, T-cell lymphoma, testicular cancer, throat cancer, thymoma and thymic carcinoma, thyroid cancer, renal cell cancer of the renal pelvis and ureter, unknown primary carcinoma , urethral cancer, uterine cancer, uterine sarcoma, vaginal cancer, vulvar cancer, Waldenstrom macroglobulinemia, Wilms tumor, 1p36 deletion syndrome, 1q21.1 deletion syndrome, 2q37 deletion syndrome, Wolf-Hirschhorn syndrome, Cri du chat , 5q deletion syndrome, Williams syndrome, Monosomy 8p, Monosomy 8q, Alfi syndrome, Kleefstra syndrome, Monosomy 10p, Monosomy 10q, Jacobsen syndrome, Patau syndrome, Angelman syndrome, Prader syndrome - Willi, Miller-Dieker syndrome, Smith-

Magenis, Edwards syndrome, Down syndrome, DiGeorgian syndrome, Phelan-McDermid syndrome, 22q11.2 distal deletion syndrome, cat's eye syndrome, XYY syndrome, Triple X syndrome, Klinefelter syndrome, Wolf-Hirschhorn, Jacobsen syndrome, Charcot-Marie-Tooth disease type 1A and Lynch syndrome.

[775] In some embodiments, the methods provided here may be used to detect aneuploidy (for example, monosomy or trisomy) in a sample (for example, a cervical sample, an endometrial sample or a urine sample) obtained from a individual. Aneuploidy can be detected in any region of the genome that is known to be associated with cancer (for example, endometrial or ovarian cancer). In some modalities, aneuploidy can be detected in arms 4p, 7q, 8q and / or 9q. Each of these arms houses oncogens and tumor suppressor genes that have been shown to undergo copy number changes in many types of cancer, including endometrial or ovarian cancer. In some modalities, aneuploidy can be detected in the 5q, 8q and / or 9p arms. Each of these arms houses oncogen genes and tumor suppressors that have been shown to undergo copy number changes in many cancers, including bladder cancer. Other regions suitable for the detection of aneuploidy, whose regions of aneuploidy are associated with the presence of cancer in an individual, will be known to those skilled in the art.

[776] In some embodiments, aneuploidy can be detected by amplifying interspersed nucleotide elements. For example, aneuploidy can be detected by amplifying long interspersed nucleotide elements (LINEs). Additionally or alternatively, aneuploidy can be detected by amplifying short intercalated nucleotide elements (SINEs). In some embodiments, aneuploidy can be detected using a technique in which a single PCR is used to co-amplify a plurality of members (for example, ~

38,000) of a subfamily of the long-1 interleaved nucleotide element (L1 retrotransposons, also called LINEs). L1 retrotransposons, like other human replicates, have spread throughout the genome via retrotransposition and are found in all 39 autosomal non-acrocentric arms. In some embodiments, aneuploidy can be detected by any of the several methods disclosed in the publication number of application WO2013148496 of the Patent Cooperation Treaty, the content of which is incorporated herein by reference in its entirety. Those skilled in the art will be aware of other methods suitable for detecting aneuploidy. In some ways, the sample to detect aneuploidy is collected using a Pap brush. In some modalities, the sample to detect aneuploidy is collected using a Tao brush.

[777] In some embodiments, the methods provided here to detect aneuploidy can be combined with methods to detect the presence of one or more genetic biomarkers (for example, mutations) in one or more genes selected from the group consisting of: NRAS , PTEN, FGFR2, KRAS, POLE, AKT1, TP53, RNF43, PPP2R1A, MAPK1, CTNNB1, PIK3CA, FBXW7, PIK3R1, APC, EGFR, BRAF and the detection of genetic biomarkers (for example, mutations) present in the ctDNA or in both (for example, to determine the presence of ovarian or endometrial cancer). In some embodiments, the methods provided here to detect aneuploidy can be combined with methods to detect the presence of one or more genetic biomarkers (for example, mutations) in one or more genes selected from the group consisting of: PTEN, TP53, PIK3CA, PIK3R1, CTNNB1, KRAS, FGFR2, POLE, APC, FBXW7, RNF43 and PPP2R1A and the detection of genetic biomarkers (for example, mutations) present in the ctDNA, or both (for example, to determine the presence of a cancer endometrial). In some embodiments, the methods provided here to detect aneuploidy can be combined with methods to detect the presence of one or more genetic biomarkers (eg, mutations) in TP53 and the detection of genetic biomarkers (eg, mutations) present in the ctDNA, or both (for example, to determine the presence of ovarian cancer). In some modalities, combining the detection of aneuploidy with the detection of one or more genetic biomarkers (for example, mutations) in any of the genes described here, the detection of genetic biomarkers (for example, mutations) present in the ctDNA, or both can increase the specificity and / or sensitivity of the detection of ovarian or endometrial cancer. In some embodiments, the sample is collected using a Pap brush. In some embodiments, the sample is collected using a Tao brush. Cancers

[778] In some embodiments, the methods provided here can be used to detect the presence of cancer (for example, the presence of a cancer cell) in an individual. In some modalities, the methods provided here can be used to detect the presence of cancer at an early stage. In some modalities, the methods provided here to identify the presence of cancer in an individual with high sensitivity and specificity are performed before determining that the individual already suffers from cancer, before determining that the individual is harboring a cancer cell and / or before the individual exhibits symptoms associated with cancer. In some modalities, the methods provided here can be used to detect the presence of a genetic biomarker, a protein biomarker and / or aneuploidy, whose genetic biomarker, a protein biomarker and / or aneuploidy is indicative that the individual has cancer (for example, it houses a cancer cell).

[779] The types of cancer that can be detected by any of the various forms described here include, without limitation, acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML), adrenal cancer, adrenocortical carcinoma, AIDS-related cancers , AIDS-related lymphoma, amyotrophic lateral sclerosis or ALS, anal cancer, appendix cancer, astrocytoma, astrocytoma, cerebellum or childhood brain, atypical teratoid / rhabdoid tumor, basal cell carcinoma, bile duct cancer, bile duct cancer, extra -hepatic (see cholangiocarcinoma), bladder cancer, bone cancer, bone tumor, malignant fibrous osteosarcoma / histiocytoma, brain cancer, brain stem glioma, brain tumor, brain tumor, cerebellar astrocytoma, brain tumor, cerebral astrocytoma / malignant glioma , brain tumor, ependymoma, brain tumor, medulloblastoma, brain tumor, supratentorial primitive neuroectodermal tumors, brain tumor, visual glioma and hypothalamic brain stem glioma, breast cancer, bronchial adenomas / carcinoids, bronchial tumor, bronchioles lung cell carcinoma, Burkitt's lymphoma, cancer in adolescents, carcinoid tumor, carcinoid tumor, childhood, carcinoid tumor, gastroin - testinal, carcinoma of unknown primary cardiac tumors, lymphoma of the central nervous system, primary cerebellar astrocytoma, childhood, cerebral astrocytoma / malignant glioma, childhood, cervical cancer, childhood cancer, chondrosarcoma, chordoma, chronic lymphocytic leukemia (CLL), chronic myeloid leukemia (CML), chronic myeloproliferative disorders, chronic myeloproliferative neoplasms, colon cancer, colorectal cancer, colorectal cancer (eg, metastatic colorectal cancer), craniopharyngioma, cutaneous T-cell lymphoma, tumor small round cell desmoplastic, differentiated thyroid cancer, ductal carcinoma in situ, embryonic tumors, endometrial cancer, ependymoma , epithelioid hemangioendothelioma (EHE), esophageal cancer,

esthesioneuroblastoma, Ewing's sarcoma in the Ewing tumor family, extracranial germ cell tumor, extracranial germ cell tumor, childhood, extragadal germ cell tumor, extrahepatic bile duct cancer, eye cancer, eye cancer , intraocular melanoma, eye cancer, retinoblastoma, cancer of the fallopian tubes, fibrous histiocytoma of the bone, gallbladder cancer, ganglioneuromatosis of the gastroenteric mucosa, gastric cancer (stomach), gastric cancer (stomach), gastric carcinoid, carcinoid tumor gastrointestinal, gastrointestinal stromal tumors (GIST), germ cell tumor, germ cell tumor: extracranial, extragonadal or ovarian trophoblastic disease, gestational trophoblastic tumor, glioma, brain stem glioma, glioma, infantile cerebral astrocytoma, glioma, infantile and hypothalamic pathway, hairy cell leukemia, hairy cell tumor, head and neck cancer, c heart cancer, hepatocellular (liver) cancer, histiocytosis, Hodgkin's lymphoma, hypopharyngeal cancer, glioma of the visual pathway and hypothalamus, childhood, inflammatory myofibroblastic tumor, intraocular melanoma, intraocular melanoma, islet cell carcinoma (endocrine pancreas) - crino), islet tumors, Kaposi's sarcoma, kidney cancer (renal cell cancer), Langerhans cell histiocytosis, laryngeal cancer, leukemia, acute lymphoblastic leukemia (also called acute lymphocytic leukemia), acute myeloid leukemia (also called acute myeloid leukemia), leukemia, chronic lymphocytic (also called chronic lymphocytic leukemia), leukemia, leukemia, chronic myeloid (also called chronic myeloid leukemia), leukemia, hair cell cancer, oral and lip cavity, liposarcoma, cancer liver disease (eg, primary), lung adenocarcinoma, lung cancer, lung cancer (eg, small cell lung carcinoma or lung cell carcinoma small), lymphoma, lymphoma, related to

AIDS, lymphoma, Burkitt, lymphoma, cutaneous T cell, lymphoma, Hodgkin, lymphoma, primary central nervous system, lymphomas, non-Hodgkin (an old classification of all lymphomas except Hodgkin's), macroglobulinemia, cancer of male breast, malignant fibrous histiocytoma of the bones, malignant fibrous histiocytoma of the bones / osteosarcoma, medullary thyroid cancer, medulloblastoma, childhood, melanoma, melanoma, intraocular (eye), melanoma, intraocular (eye), cannula - Merkel cell cancer, Merkel cell carcinoma, mesothelioma, mesothelioma, malignant adult, mesothelioma, childhood, metastatic squamous neck cancer, metastatic squamous neck cancer with hidden primary, middle tract carcinoma, mouth cancer, multiple endocrine neoplasia, childhood, multiple endocrine neoplasms syndromes, type 2A or 2B multiple endocrine neoplasms (MEN2A or MEN2B, respectively), multiple myeloma, multiple myeloma / plasma cell neoplasia as, mycosis fungoides, myelodysplastic syndromes, myelodysplastic / myeloproliferative diseases, myelodysplastic / myeloproliferative neoplasms, myelogenous leukemia, myelogenous leukemia, myeloid leukemia, myeloid leukemia, acute in myeloid adults, leukemia , multiple cancer (bone marrow cancer), myeloproliferative disorders, chronic neoplasms, myoproliferative, myxoma, nasal cavity and paranasal sinus cancer, nasopharyngeal cancer, nasopharyngeal carcinoma, neuroblastoma, oligo-dendroglioma, oral cancer, oral cancer, oral cancer oral cavity cancer, osteosarcinoma, osteosarcoma / malignant fibrous bone histiocytoma, ovarian cancer, ovarian epithelial cancer (surface epithelial-stromal tumor), ovarian germ cell tumor, low potential ovarian tumor malignant, pancreatic cancer, pancreatic cancer, islet cell, pancreatic neuroendocrine tumors, pancreatic renal cell carcinoma, c papillary, papillary thyroid cancer

mattosis, paraganglioma paranasal sinus and nasal cavity cancer, parathyroid cancer, parathyroid hyperplasia, penis cancer, pharynx cancer, pheochromocytoma, Phyllodes breast tumors, pineal astrocyte, pineal germinoma, pinoblastoma and neuroectal tumors - primitive supratentorial dermals, childhood, pituitary adenoma, pituitary cancer, plasma cell neoplasms / multiple myeloma, plasma cell neoplasms, pleuropulmonary blastoma, pregnancy and breast cancer, primary central nervous system lymphoma, primary peritoneal cancer, prostate cancer, rectal cancer, recurrent thyroid cancer, differentiated refractory thyroid cancer, renal cell cancer, renal cell carcinoma (renal cancer), renal pelvis and ureter, transient cell cancer, retinoblastoma, rhabdomyosarcoma, rab- domiosarcoma, childhood, salivary gland cancer, sarcoma, sarcoma, Ewing tumor family, Sarcoma, Kaposi, Sezary syndrome, cancer skin cancer, skin cancer (melanoma), skin cancer (not melanoma), skin carcinoma, Merkel cell, small intestine cancer, soft tissue sarcoma, squamous cell carcinoma, squamous cell carcinoma - see cancer skin (not melanoma), scaly neck cancer, squamous neck cancer with primary occult, metastatic, stomach cancer, supratentorial primitive neuroectodermal tumor, childhood, T cell lymphoma, T cell lymphoma, cutaneous, cancer of testicle, throat cancer, thymoma and thymic carcinoma, thymoma, childhood, thyroid cancer, thyroid cancer, childhood, transitional cell cancer of the pelvis and renal ureter, trophoblastic tumor, unknown primary gestational carcinoma, primary site unknown, cancer of, childhood, unknown primary site, adult carcinoma, ureter and renal pelvis, transitional cell cancer, urethral cancer, uterine cancer, uterine cancer, endometrial, uterine sarcoma, vagin cancer al, visual and hypothalamic glioma, childhood, vulvar cancer, Waldenstrom's macroglobulinemia and Wilms' tumor (kidney cancer).

[780] In some embodiments, the methods described here are used to detect the presence of a single type of cancer. In some modalities, the methods described here are able to detect two or more (for example, 2, 3, 4, 5, 6, 7, 8 or more) types of cancer. For example, the methods described here can be used to detect the presence of liver cancer, ovarian cancer, esophageal cancer, stomach cancer, pancreatic cancer, colorectal cancer, lung cancer or lung cancer. breast. As another example, the methods described here may be able to detect the presence of each of the liver cancers, ovarian cancer, esophageal cancer, stomach cancer, pancreatic cancer, colorectal cancer, lung cancer and breast cancer ( for example, the methods described here are capable of detecting the presence of each of these types of cancer in an individual, although only one type of cancer may be present in the individual). In some embodiments, several methods described here can be used to detect cancers selected from the group consisting of: pancreatic cancer, colon cancer, esophageal cancer, stomach cancer, ovarian cancer, liver cancer, lung and canker cancer - breast cancer and combinations thereof. As another example, the methods described here can be used to detect the presence of cancer of the cervical, endometrial, ovarian, or fallopian tubes. As another example, the methods described here may be able to detect the presence of each of the cervical, endometrial, ovarian and tubal cancers (for example, the methods described here are able to detect the presence of each of these types cancer in an individual, although only one type of cancer may be present in the individual). As another example, the methods described here can be used to detect the presence of bladder cancer or cancer.

urothelial noma of the upper tract (UTUC). As another example, the methods described here may be able to detect the presence of each of the bladder cancers and an upper end urothelial carcinoma (UTUC) (for example, the methods described here are able to detect the presence of each of these types of cancer in an individual, although only one type of cancer may be present in the individual). Additional diagnostic test

[781] In some modalities to diagnose or identify the presence of a disease (for example, cancer) in an individual (for example, using any of the variety of methods described here), the individual is also identified as a candidate for further testing of diagnosis. Methods are provided here for selecting an individual for further diagnostic testing. In some embodiments, methods for selecting an individual for additional diagnostic tests include detecting the presence of one or more genetic biomarkers in a biological sample isolated from the individual, detecting the presence of one or more protein biomarkers in a biological sample isolated from the individual. individual, and / or detect the presence of aneuploidy in a biological sample isolated from the individual and select an individual for additional diagnostic tests when the presence of one or more genetic biomarkers, one or more protein biomarkers or aneuploidy is identified. In some embodiments, methods for selecting an individual for additional diagnostic tests further include detecting the presence of one or more members of one or more other classes of biomarkers. In some modalities, the detection step is performed before it has been determined that the individual already has cancer (for example, when the individual is not known to harbor a cancer cell).

[782] In some embodiments, the biological sample is isolated from an individual. Any suitable biological sample that contains one or more genetic biomarkers, protein biomarkers and / or aneuploidy can be used according to any of the various forms described here. For example, the biological sample may include blood, plasma, urine, cerebrospinal fluid, saliva, sputum, bronchoalveolar lavage, bile, lymphatic fluid, cyst fluid, feces, ascites and combinations thereof. Methods for isolating biological samples from an individual are known to those skilled in the art.

[783] In some modalities, the individual may be selected for additional diagnostic tests. In some modalities, the methods provided here can be used to select an individual for increased monitoring in a period prior to the period when conventional techniques are able to diagnose the individual with early-stage cancer. For example, the methods provided here to select an individual for additional diagnostic tests can be used when an individual has not been diagnosed with cancer by conventional methods and / or when an individual is not known to harbor cancer. In some modalities, an individual selected for additional diagnostic tests can be administered a diagnostic test (for example, any of the diagnostic tests described here) with an increased frequency compared to an individual who was not selected for additional diagnostic tests. For example, an individual selected for additional diagnostic tests can be administered with a diagnostic test twice a day, daily, biweekly, weekly, bi-monthly, monthly, quarterly, semi-annually, or any frequency. in the same. In some modalities, an individual selected for additional diagnostic tests may be administered with one or more additional diagnostic tests compared to an individual who was not selected for additional diagnostic tests. For example, an individual selected for additional diagnostic tests can receive two or more diagnostic tests, while an individual who was not selected for additional diagnostic tests receives only a single diagnostic test (or no diagnostic test) . In some modalities, the diagnostic test method can determine the presence of the same type of cancer as the cancer originally detected. Additionally or alternatively, the diagnostic test method can determine the presence of a different type of cancer than the cancer originally detected.

[784] In some embodiments, the diagnostic test method is a scan. In some modalities, the scan is a bone scan, a computed tomography (CT), an angiotomography (CTA), an esophagogram (a barium swallow), a barium enema, a gallium scan, an MRI scan (MRI), a mammogram, monoclonal antibody scan (eg ProstaScint® scan for prostate cancer, OncoScint® scan for ovarian cancer and CEA-Scan® for colon cancer), multiport acquisition scan (MUGA) , PET scan, PET / CT, a thyroid exam, an ultrasound (for example, a breast ultrasound, an endobronchial ultrasound, an endoscopic ultrasound, a transvaginal ultrasound), an X-ray, a DEXA exam.

[785] In some modalities, the diagnostic test method is a physical examination, such as, without limitation, an anoscopy, a biopsy, a bronchoscopy (for example, an autofluorescence bronchoscopy, a white light bronchoscopy, a bronchoscopy navigation), a digital tomosynthesis of the breast, a digital rectal exam, an endoscopy, including, among others, a capsule endoscopy, virtual endoscopy, an arthroscopy, a bronchoscopy, a colonoscopy, a colposcopy, a cystoscopy, an esophagoscopy, a gastroscopy, a laparoscopy, a laryngoscopy, a neuroendoscopy, a proctoscopy, a sigmoidoscopy, a skin cancer test, a thoracoscopy, a retrograde endoscopic cholangiopancreatography (ERCP), an ophthalmoscopic examination.

[786] In some embodiments, the diagnostic test method is a biopsy (for example, bone marrow aspiration, a tissue biopsy). In some modalities, biopsy is performed by fine needle aspiration or surgical excision. In some modalities, the diagnostic test method (s) includes (s) still obtaining a biological sample (for example, a tissue sample, a urine sample, a blood sample, a swab, a sample of saliva, a sample of mucosa (eg, sputum, bronchial secretion), a nipple aspirate, a secretion or excretion). In some embodiments, diagnostic test methods include the determination of exosomal proteins (eg, an exosomal surface protein (eg, CD24, CD147, PCA-3)) (Soung et al. (2017) Cancers 9 (1 ): pii: E8). In some modalities, the diagnostic test method is an oncotype DX® test (Baehner (2016) Eccancermedicalscience 10: 675).

[787] In some modalities, the diagnostic test method is a test, such as, without limitation, an alpha-fetoprotein blood test, a bone marrow test, a blood test hidden in the stool, a papillomavirus test human, low-dose helical computed tomography, a lumbar puncture, a prostate-specific antigen (PSA) test, a Pap test, or a tumor marker test.

[788] In some embodiments, the diagnostic test method includes determining the level of a known protein biomarker (eg, CA-125 or prostate specific antigen (PSA)). For example, a high amount of CA-125 can be found in the blood of the individual, who has ovarian cancer, endometrial cancer, fallopian tube cancer, pancreatic cancer, stomach cancer, esophageal cancer, colon cancer , liver cancer, breast cancer or lung cancer. The term "biomarker", as used in this document, refers to "a biological molecule found in blood, other body fluids or tissues that is a sign of a normal or abnormal process, or of a condition or disease" , for example, as defined by the National Cancer Institute. (see, for example, the URL www.cancer.gov/publications/dictionaries/cancer-terms?CdrID=45618). A biomarker can include a genetic biomarker, such as, without limitation, a nucleic acid (for example, a DNA molecule, an RNA molecule (for example, a microRNA, a long non-coding RNA (lncRNA) or other Non-coding RNA)) A biomarker can include a protein biomarker, such as, without limitation, a peptide, a protein or a fragment thereof.

[789] In some embodiments, the biomarker is FLT3, NPM1, CEBPA, PRAM1, ALK, BRAF, KRAS, EGFR, Kit, NRAS, JAK2, KRAS, HPV virus, ERBB2, BCR-ABL, BRCA1, BRCA2, CEA, AFP , and / or LDH. See, for example, Easton et al. (1995) J. Hum. Genet. 56: 265-271, Hall et al. (1990) Science 250: 1684-1689, Lin et al. (2008) Ann. Intern. Med. 149: 192-199, Allegra et al. (2009) (2009) J. Clin. Oncol. 27: 2091-2096, Paik et al. (2004) N. Engl. J. Med. 351: 2817-2826, Bang et al. (2010) Lancet 376: 687-697, Piccart-Gebhart et al. (2005) N. Engl. J. Med. 353: 1659-1672, Romond et al. (2005) N. Engl. J. Med. 353: 1673-1684, Locker et al. (2006) J. Clin. Oncol. 24: 5313-5327, Giligan et al. (2010) J. Clin. Oncol. 28: 3388-3404, Harris et al. (2007) J. Clin. Oncol. 25: 5287-5312; Henry and Hayes (2012) Mol. Oncol. 6: 140-146. In some modalities, the biomarker is a biomarker for detecting breast cancer in an individual, such as, without limitation, MUC-1, CEA, p53, urokinase plasminogen activator, BRCA1, BRCA2 and / or

HER2 (Gam (2012) World J.

Exp.

Med. 2 (5): 86-91). In some modalities, the biomarker is a biomarker for detecting lung cancer in an individual, such as, without limitation, KRAS, EGFR, ALK, MET and / or ROS1 (Mao (2002) Oncogene 21: 6960-6969; Korpanty et al. (2014) Front Oncol. 4: 204). In some modalities, the biomarker is a biomarker for detecting ovarian cancer in an individual, such as, without limitation, HPV, CA-125, HE4, CEA, VCAM-1, KLK6 / 7, GST1, PRSS8, FOLR1, ALDH1 ( Nolen and Lokshin (2012) Future Oncol. 8 (1): 55-71; Sarojini et al. (2012) J.

Oncol. 2012: 709049). In some modalities, the biomarker is a biomarker for detecting colorectal cancer in an individual, such as, without limitation, MLH1, MSH2, MSH6, PMS2, KRAS and BRAF (Gonzalez-Pons and Cruz-Correa (2015) Bi-omed .

Res.

Int. 2015: 149014; Alvarez-Chaver et al. (2014) World J.

Gastroenterol. 20 (14): 3804-3824). In some embodiments, the diagnostic test method determines the presence and / or the level of expression of a nucleic acid (for example, microRNA (Sethi et al. (2011) J.

Carcinog.

Mutag.

S1-005), RNA, a SNP (Hosein et al. (2013) Lab.

Invest doi: 10.1038 / labinvest.2013.54; Falzoi et al. (2010) Pharmacogenomics 11: 559-571), methylation status (Castelo-Branco et al. (2013) Lancet Oncol 14: 534-542), a cancer mutation in the hotspot (You- sem et al. (2013 ) Chest 143: 1679-1684)). Non-limiting examples of methods for detecting a nucleic acid in a sample include: PCR, RT-PCR, sequencing (for example, next generation sequencing methods, deep sequencing), a DNA microarray, a microRNA microarray , a SNP microarray, fluorescent in situ hybridization (FISH), restriction fragment length polymorphism (RFLP), gel electrophoresis, Northern blot analysis, Southern blot analysis, chromogenic in situ hybridization (CISH), chromatin immunoprecipitation (ChIP), SNP genotyping and DNA methylation assay. See, for example, Meldrum et al. (2011) Clin. Biochem. Rev. 32 (4): 177-195; Sidranksy (1997) Science 278 (5340): 1054-9.

[790] In some embodiments, the diagnostic test method includes determining the presence of a protein biomarker in a sample (for example, a plasma biomarker (Mirus et al. (2015) Clin. Cancer Res. 21 (7) : 1764-1771)). Non-limiting examples of methods for determining the presence of a protein biomarker include: western blot analysis, immunohistochemistry (IHC), immunofluorescence, mass spectrometry (EM) (eg, desorption / ionization) matrix assisted laser (MALDI) -EM, desorption flight time / enhanced surface laser ionization (SELDI-TOF) -EM), enzyme linked immunosorbent assay (ELISA), flow cytometry, assay of proximity (for example, VeraTag proximity test (Shi et al. (2009) Diagnostic molecular pathology: the American journal of surgical pathology, part B: 18: 11-21, Huang et al. (2010) AM. J. Clin Patol. 134: 303-11)), a microarray of proteins (for example, a microarray of antibodies (Ingvarsson et al. (2008) Proteomics 8: 2211-9, Woodbury et al. (2002) J. Proteome Res. 1: 233-237), an IHC-based microarray (Stromberg et al. (2007) Proteomics 7: 2142-50), a microarray ELISA (Schroder et al. (2010) Mol. Cell. Proteomics 9: 1271-80). In some modalities, the method for determining the presence of a protein biomarker is a functional assay. In some modalities, the functional assay is a kinase assay (Ghosh et al. (2010) Biosensors & Bioelectronics 26: 424-31, Mizutani et al. (2010) Clin. Cancer Res. 16: 3964-75, Lee et al. (2012) Biomed. Micro-devices 14: 247-57), a protease assay (Lowe et al. (2012) ACS nano. 6: 851-7, Fujiwara et al. (2006) Breast cancer 13: 272-8, Darragh et al. (2010) Cancer Res 70: 1505-12). See, for example, Powers and

Palecek (2015) J. Heathc Eng. 3 (4): 503-534, for a review of analytical protein assays for the diagnosis of cancer patients.

[791] In some embodiments, the diagnostic test method includes detecting the presence of aneuploidy in a biological sample (for example, detecting whether the biological sample contains cells with an abnormal number of chromosomes). Non-limiting examples of methods to detect the presence of aneuploidy include karyotype, digital karyotype, fluorescence in situ hybridization (FISH), quantitative short tandem repeat PCR, quantitative fluorescence PCR (QF-PCR), quantitative PCR dosage analysis, quantitative mass spectrometry of single nucleotide polymorphisms and comparative genomic hybridization (CGH).

[792] In some modalities, an individual who has been selected for further diagnostic tests can also be selected for increased monitoring. Once the presence of a cancer cell is identified (for example, by any of the methods described here), it may be beneficial for the individual to undergo increased monitoring (for example, to assess the progression of the tumor or cancer in the individual and / or assess the development of additional mutations in the cancer cells) and further diagnostic tests (for example, to determine the size and / or the exact location of the tumor that houses the cancer cell).

[793] In some modalities, an individual who is selected for further diagnostic tests may also be selected for therapeutic intervention. Any of the therapeutic interventions described herein or known in the art can be administered. For example, an individual who has been selected for additional diagnostic tests can be administered with an additional diagnostic test and a therapeutic intervention can be administered if the presence of the cancer cell is confirmed. In addition or alternatively, an individual who has been selected for additional diagnostic tests can be administered with a therapeutic intervention and can be monitored additionally as the therapeutic intervention progresses. In some modalities, after an individual who has been selected for additional diagnostic testing has been administered a therapeutic intervention, the additional test will reveal the presence of one or more additional genetic biomarkers, the presence of one or more biomarkers of additional proteins and / or the presence of aneuploidy. In some modalities, the presence of one or more additional genetic biomarkers, the presence of one or more additional protein biomarkers and / or the presence of aneuploidy will provide cause to administer a different therapeutic intervention (for example, a resistance mutation can appear in a cancer cell during therapeutic intervention, whose cell carrying the resistance mutation is resistance to the original therapeutic intervention). Increased monitoring

[794] Methods are also provided in this document to select an individual for increased monitoring. In some modes, methods for selecting an individual for increased monitoring include detecting the presence of one or more genetic biomarkers in an individual's biological sample, detecting the presence of one or more protein biomarkers in an individual's biological sample, and / or detecting the presence of aneuploidy in a biological sample isolated from the individual and selecting an individual for increased monitoring when the presence of one or more genetic biomarkers, one or more protein biomarkers or aneuploidy is identified. In some embodiments, methods for selecting an individual for increased monitoring further include detecting the presence of one or more members of one or more other classes of biomarkers. In some modalities, the detection step is performed when the individual is not known to harbor a cancer cell (for example, when the individual is not known to harbor a cancer cell).

[795] In some embodiments, the biological sample is isolated from an individual. Any suitable biological sample that contains one or more genetic biomarkers, protein biomarkers and / or aneuploidy can be used according to any of the various forms disclosed here. For example, the biological sample may include blood, plasma, urine, cerebrospinal fluid, saliva, sputum, bronchoalveolar lavage, bile, lymphatic fluid, cyst fluid, feces, ascites and combinations thereof. Methods for isolating biological samples from an individual are known to those skilled in the art.

[796] In some modalities, once an individual has been determined to have cancer, the individual can be selected for increased or additional monitoring. In some ways, the methods provided here can be used to select an individual for increased monitoring in a period before the period in which conventional techniques are able to diagnose the individual with early cancer. For example, the methods provided here to select an individual for increased monitoring can be used when an individual has not been diagnosed with cancer by conventional methods and / or when an individual is not known to harbor cancer. In some modalities, an individual selected for increased monitoring can be administered a diagnostic test (for example, any of the diagnostic tests disclosed here) at an increased frequency compared to an individual who was not selected for increased monitoring. . For example, an individual selected to move

increased monitoring can be administered with a diagnostic test with a frequency of twice a day, daily, fortnightly, weekly, bimonthly, monthly, quarterly, semiannually annually, or any frequency thereafter. In some modalities, an individual selected for increased monitoring can be administered with one or more additional diagnostic tests compared to an individual who was not selected for increased monitoring. For example, an individual selected for increased monitoring can receive two diagnostic tests, while an individual who was not selected for increased monitoring receives only a single diagnostic test (or no diagnostic test).

[797] In some modalities, an individual who has been selected for increased monitoring can also be selected for additional diagnostic tests. Once the presence of a cancer cell has been identified (for example, by any of the methods described here), it may be beneficial for the individual to undergo increased monitoring (for example, to assess the progression of the tumor or cancer in the individual and / or assess the development of additional mutations in cancer cells) and further diagnostic tests (for example, to determine the size and / or exact location of the tumor that houses the cancer cell).

[798] In some modalities, an individual who is selected for increased monitoring may also be selected for therapeutic intervention. Any of the therapeutic interventions described herein or known in the art can be administered. For example, an individual who was selected for increased monitoring can be monitored later and a therapeutic intervention can be administered if the presence of the cancer cell is maintained throughout the increased monitoring period. In addition or alternatively, an individual who has been selected for increased monitoring can be administered with a therapeutic intervention and monitored additionally as the therapeutic intervention progresses. In some modalities, after an individual who has been selected for increased monitoring has been given a therapeutic intervention, increased monitoring will reveal the presence of one or more additional genetic biomarkers, the presence of one or more additional protein biomarkers and / or the presence of aneuploidy. In some embodiments, the presence of one or more additional genetic biomarkers, the presence of one or more additional protein biomarkers and / or the presence of aneuploidy will provide a cause for administering a different therapeutic intervention (for example, a resistance mutation may appear in a cancer cell during therapeutic intervention, whose cell carrying the resistance mutation is resistance to the original therapeutic intervention). Therapeutic interventions

[799] In some modalities, once an individual has been determined to have cancer (eg, ancestral, endometrial, ovarian, or fallopian tube cancer) or is suspected of having cancer, the individual may receive an intervention therapeutic or be selected for therapeutic intervention. In some modalities, in which the presence of cancer (for example, cervical, endometrial, ovarian or fallopian tube cancer) was detected in an individual, the individual receives a therapeutic intervention that specifically targets the individual's cancer (for example, genetic modifications present in cervical, endometrial, ovarian, or fallopian tube cancer). For example, when an individual is determined to have

ovary, an appropriate therapeutic intervention for ovarian cancer can be administered.

As another example, when an individual is determined to have endometrial cancer, an appropriate therapeutic intervention for endometrial cancer can be administered.

In some embodiments, the therapeutic intervention is chemotherapy (for example, any of the platinum-based chemotherapeutic agents described herein (for example, cisplatin, carboplatin) or a taxane (for example, placitaxel (Taxol®) or docetaxel (Taxotere®) In some embodiments, the chemotherapeutic agent is a paclitaxel bound to albumin (nap-paclitaxel, Abraxane®), altretamine (Hexalen®), capecitabine (Xeloda®), cyclophosphamide (Cytoxan®), etoposide (VP-16), gemcitabine (Gemzar®), ifosfamide (Ifex®), irinotecan (CPT-11, Camptosar®), liposomal doxorubicin (doxil®), melphalan, pemetrexed (alimta®), topotecan or vinorelbine (navelbine®). in some modalities, therapeutic intervention is a combination of chemotherapeutic agents (eg, paclitaxel, ifosfamide and cisplatin; vinblastine, ifosfamide and cisplatin; etoposide, ifosfamide and cisplatin). In some modalities, therapeutic intervention is an epigenetic therapy (see, for example, Smith et al. (2017) Gynecol.

Oncol.

Rep. 20: 81-86). In some embodiments, epigenetic therapy is a DNA methyl transferase (DNMT) inhibitor (for example, 5-azacytidine (5-AZA), decitabine (5-aza-2'-deoxycytidine) (Fu et al. ( 2011) Cancer 117 (8): 1661-1669; Falchook et al. (2013) Investig.

New Drugs 31 (5): 1192-1200; Matei et al. (2012) Cancer Res. 72 (9): 2197-2205). In some embodiments, the DNMT1 inhibitor is NY-ESO-1 (Odunsi et al. (2014) Cancer Immunol.

Res. 2 (1): 37-49). In some embodiments, epigenetic therapy is a histone deacetylase (HDAC) inhibitor. In some embodiments, the HDAC inhibitor is vorinostat (Modesitt (2008) 109 (2): 182-186) or belinostat (Mackay et al. (2010) Eur.

J.

Cancer 46 (9): 1573-1579). In some embodiments, the HDAC inhibitor is given in combination with a chemotherapeutic agent (for example, carboplatin (paraplatin), cisplatin, paclitaxel or docetaxel (taxotere)) (Mendivil (2013) Int.

J.

Gynecol.

Cancer 23 (3): 533-539; Dizon (2012) Gynecol.

Oncol. 125 (2): 367-371; Dizon (2012) Int J.

Gynecol.

Cancer 23 (3): 533-539). In some modalities, therapeutic intervention is an antiangiogenic agent (for example, bevacizumab). In some modalities, the therapeutic intervention is a poly (ADP-ribose) polymerase (PARP) -1 and / or PARP-2 inhibitor. In some modalities, the inhibitor of PARP-1 and PARP-2 is niraparib (zejula) (Scott (2017) Drugs doiL10.1007 / s40265-017-0752). In some embodiments, the PARP inhibitor is olaparib (lynparza) or rucaparib (rubraca). In some modalities, therapeutic intervention is a hormone (for example, an agonist of the luteinizing hormone releasing hormone (LHRH)). In some embodiments, the LHRH agonist is goserelin (Zoladex®) or leuprolide (Lupron®). In some modalities, the therapeutic intervention is an anti-estrogen compound (for example, tamoxifen). In some modalities, the therapeutic intervention is an aromatase inhibitor (for example, letrozole (Femara®), anastrozole (Arimidex®) or exemestane (Aromasin®). In some modalities, the therapeutic intervention is surgery (for example, clearance of the tumor mass, a hysterectomy, a bilateral salpingo-oophorectomy, an omentectomy). The term "debulking" refers to the surgical removal of almost the entire tumor ("optimally cleared"). modalities, clearance may include removing a portion of the bladder, spleen, gallbladder, stomach, liver and / or pancreas.

In some modalities, adjuvant chemotherapy is also administered to the individual after surgery (for example, clearance of the tumor mass, a hysterectomy, a bilateral salpingo-oophorectomy, an omentectomy). In some modalities, adjuvant chemotherapy is administered intra-abdominally (intraperitoneally). In some modalities, therapeutic intervention is prophylactic surgery (for example, a hysterectomy). In some embodiments, paracentesis is performed to remove ascites.

[800] In some embodiments, once an individual has been determined to have cancer (for example, bladder cancer or UTUC) according to any of the various forms of methods provided herein, the individual can be administered with therapeutic intervention or selected for therapeutic intervention. For example, when an individual is determined to have bladder cancer, an appropriate therapeutic intervention for bladder cancer can be administered. Examples of appropriate therapeutic interventions for bladder cancer include, without limitation, trans-surreal bladder resection (TURB), intravesical BCG (Bacillus Calmette-Guerin), intravesical chemotherapy, adjuvant chemotherapy, neoadjuvant chemotherapy, cystectomy or cystoprostatectomy , radiotherapy, immunotherapy, immune checkpoint inhibitors, or any combination of the above. As another example, when an individual is determined to have a UTUC, an appropriate therapeutic intervention for a UTUC can be administered. Examples of appropriate therapeutic interventions for a UTUC include, without limitation, trans-surethral resection (TURB), intravesical BCG (Bacillus Calmette-Guerin), intravesical chemotherapy, adjuvant chemotherapy, neoadjuvant chemotherapy, ureterectomy or nephroureterectomy, radiotherapy, immunotherapy , immune checkpoint inhibitors, or any combination of the above.

[801] In some embodiments, the cancer detected is a low-grade tumor (for example, a low-malignant potential neoplasm (PUNLMP) or a low-grade noninvasive papillary carcinoma). In some modalities, once an individual has been determined to have a low-grade tumor, the individual can be administered with a therapeutic intervention or selected for therapeutic intervention that includes transurethral resection of the bladder (TURB).

[802] In some modalities, in which the presence of colorectal cancer has been detected in an individual, the individual receives a therapeutic intervention that specifically targets the individual's colorectal cancer (for example, genetic modifications present in cancer with - lorretal). In some embodiments, the individual is administered an anti-EGFR monoclonal antibody (for example, cetuximab or panitu-mumab) (Cunningham et al. (2004) N. Engl. J. Med. 351 (4): 337-345) . In some embodiments, the therapeutic invention is an antigenic agent. In some modalities, the antiangiogenic agent is be- vacizumab (Avastin) (Hurwitz et al. (2004) N. Engl. J. Med. 350: 2335-2342). In some embodiments, the antiangiogenic agent is a VEGF inhibitor (eg, aflibercept (Tang et al. (2008) J. Clin. Oncol 26 (May 20 suppl; abstr 4027); vatalanibe (PTK / ZK222584; Hecht et al. (2005) ASCO Annual Meeting Proceedings J. Clin. Oncol. 23: 16S (abbr. LBA3)); sunitinib (Saltz et al. (2007) J. Clin. Oncol. 25: 4793-4799); AZD2171 (Rosen et al. (2007) J. Clin. Oncol. 25: 2369-76); AMG 706 (Drevis et al. (2007) 25: 3045-2054)). In some modalities, the vaccine is administered with a chemotherapy treatment (see, for example, Hurwitz et al. (2004) N. Engl. J. Med. 350: 2335-2342; Gruenerger et al. (2008) J. Clin. Oncol. 26: 1830- 1835) Non-limiting examples of chemotherapy treatments that can be used in patients with colorectal cancer include: 5-FU, leucovorin, oxaliplatin (eloxatin), capecitabine, celecoxib and sulindac. a combination of chemotherapeutic agents, for example, FOLFOX (5-FU, leucov orin and oxaliplatin), FOLFIRI (leukovorin, 5-FU and irinotecan (Camptosar), CapeOx (capecitabine (Xeloda) and oxaliplatin). In some embodiments, therapeutic intervention is a mammalian target of the rapamycin inhibitor (mTOR) (for example, a rapamycin analogue (Kesmodel et al. (2007) Gastrointestinal Cancer Symposium (abstr 234)); RAD-001 ( Tabernero et al. (2008) J. Clin. Oncol. 26: 1603-1610. In some embodiments, therapeutic intervention is a protein kinase C antagonist (eg, enzastaurine (Camidge et al. (2008 ) Anticancer Drugs 19: 77-84, Resta et al. (2008) J. Clin. Oncol. 26 (May 20 suppl) (abstr 3529). In some modalities, therapeutic intervention is an inhibitor of Src non-receptor tyrosine kinase (eg AZ0530 (Tabernero et al. (2007) J. Clin. Oncol. 25: 18S (abstr 3520))). In some embodiments, therapeutic intervention is a kinesin spindle protein (KSP) inhibitor (for example, example, ispinesib (SB-715992) (Chu et al. (2004) J. Clin. Oncol. 22: 14S (abstr 2078), Burris et al. (2004) J. Clin. Oncol. 22: 128 (abstr 2004) )).

[803] In some modalities, in which the presence of lung cancer has been detected in an individual, the individual receives a therapeutic intervention that specifically targets the individual's lung cancer (for example, genetic modifications present in lung cancer) . In some embodiments, the therapeutic intervention is chemotherapy (for example, any of the platinum-based chemotherapeutic agents described here (for example, cisplatin, carboplatin) or a taxane (for example, placitaxel (Taxol®) or docetaxel (Taxotere ®) In some embodiments, the chemotherapeutic agent is a paclitaxel bound to albumin (nap-paclitaxel, Abraxane®), altretamine (Hexalen®), capecitabine (Xeloda®), cyclophosphamide (Cytoxan®), etoposide (VP-16 ), gemcitabine (Gemzar®), ifosfamide (Ifex®), irinotecan (CPT-11, Camptosar®), liposomal doxorubicin (doxil®), melphalan, pemetrexedo (alimta®), topotecan or vinorelbine (navelbine®). in some modalities, the therapeutic intervention is a combination of chemotherapeutic agents (for example, paclitaxel, ifosfamide and cisplatin; vinblastine, ifosfamide and cisplatin; etoposide, ifosfamide and cisplatin). In some modalities, the therapeutic intervention is an epigenetic therapy (see, for example, Smith et al. (2017) Gynecol.

Oncol.

Rep. 20: 81-86). In some embodiments, epigenetic therapy is a DNA methyltransferase (DNMT) inhibitor (eg, 5-azacytidine (5-AZA), decitabine (5-aza-2'-deoxycytidine) (Fu et al. (2011) Cancer 117 (8): 1661-1669; Falchook et al. (2013) Investig.

New Drugs 31 (5): 1192-1200; Matei et al. (2012) Cancer Res. 72 (9): 2197-2205). In some embodiments, the DNMT1 inhibitor is NY-ESO-1 (Odunsi et al. (2014) Cancer Immunol.

Res. 2 (1): 37-49). In some embodiments, epigenetic therapy is a histone deacetylase (HDAC) inhibitor. In some embodiments, the HDAC inhibitor is vorinostat (Modesitt (2008) 109 (2): 182-186) or belinostat (Mackay et al. (2010) Eur.

J.

Cancer 46 (9): 1573-1579). In some embodiments, the HDAC inhibitor is given in combination with a chemotherapeutic agent (for example, carboplatin (paraplatin), cisplatin, paclitaxel or docetaxel (taxotere)) (Mendivil (2013) Int.

J.

Gynecol.

Cancer 23 (3): 533-539; Dizon (2012) Gynecol.

Oncol. 125 (2): 367-371; Dizon (2012) Int J.

Gynecol.

Cancer 23 (3): 533-539). In some modalities, therapeutic intervention is an antiangiogenic agent (for example, bevacizumab). In some modalities, the therapeutic intervention is a poly (ADP-ribose) polymerase (PARP) -1 and / or a PARP-2 inhibitor. In some modalities, the inhibitor of PARP-1 and PARP-2 is niraparib (zejula) (Scott (2017) Drugs doiL10.1007 / s40265-017-0752). In some embodiments, the PARP inhibitor is olaparib (lynparza) or rucaparib (rubraca). In some modalities, therapeutic intervention is a hormone (for example, an agonist of the luteinizing hormone releasing hormone (LHRH)). In some embodiments, the LHRH agonist is goserelin (Zoladex®) or leuprolide (Lupron®). In some modalities, the therapeutic intervention is an anti-estrogen compound (for example, tamoxifen). In some modalities, the therapeutic intervention is an aromatase inhibitor (for example, letrozole (Femara®), anastrozole (Arimidex®) or exemestane (Aromasin®). In some modalities, the therapeutic intervention is surgery (for example, clearance of the tumor mass, a hysterectomy, a bilateral salpingo-oophorectomy, an omentectomy). The term "debulking" refers to the surgical removal of almost the entire tumor ("optimally cleared"). modalities, clearance may include removing a portion of the bladder, spleen, gallbladder, stomach, liver and / or pancreas In some modalities, adjuvant chemotherapy is even administered to the individual after surgery (for example , clearance of the tumor mass, a hysterectomy, a bilateral salpingo-oophorectomy, an omentectomy. In some modalities, adjuvant chemotherapy is administered intra-abdominally (intraperitoneally). In some modalities, therapeutic intervention is a prophylactic surgery (for example, a hysterectomy). In some embodiments, paracentesis is performed to remove ascites.

[804] In some modalities, in which the presence of breast cancer has been detected in an individual, the individual receives a therapeutic intervention that specifically targets the individual's breast cancer (for example, genetic modifications present in breast cancer) . In some modalities, targeted drug therapy is an HER2 inhibitor (eg, trastuzumab (Herceptin), pertuzu- mab (perjecta); ado-trastuzumab emtansin (T-DM1; Kadcyla); lapatininib (Tykerb), neratinib ). See, for example, Baselga et al. (2012) N Engl J Med 366: 109-119; Konecny et al. (2006) Cancer Res 66: 1630-1639, Xia et al. (2007) Cancer Res. 67: 1170-1175; Gomez et al. (2008) J Clin Oncol 26: 2999-30005; Wong et al. (2009) Clin. Cancer Res. 15: 2552-

2558; Agus et al. (2002) Cancer Cell 2: 127-137; Lewis Philips et al. (2008) Cancer Res 68: 9280-9290. In some embodiments, targeted drug therapy is a cyclin-dependent kinase inhibitor (eg, a CDK4 / 6 inhibitor (eg, palbocyclib (Ibrance®), ribocycline (Kisqali®), abemaciclib)) (Turner et al. (2015) N Engl J Med 373: 209-219; Finn et al. (2016) N Eng J Med 375: 1925-1936; Ehab and Elbaz (2016) Breast Cancer 8: 83-91; Xu et al. (2017 ) J Hematol.

Oncol. 10 (1): 97; Corona et al. (2017) Cri Rev Oncol Hematol 112: 208-214; Barroso-Sousa et al. (2016) Breast Care 11 (3): 167-173)). In some embodiments, targeted drug therapy is a PARP inhibitor (for example, olaparib (AZD2281), veliparibe (ABT-888), ni-raparibe (MK-4827), talazoparibe (BMN-673), rucaparib (AG- 14699), CEP-9722) See, for example, Audeh et al. (2010) Lancet 376: 245-251; Fong et al. (2009) N Engl J Med 361: 123-134; Livrahi and Garber (2015) BMC Medicine 13: 188; Kaufamn et al. (2015) J Clin.

Oncol. 33: 244-250; Gelmon et al. (2011) Lancet Oncol. 12: 852-61; Isakoff et al. (2011) Cancer Res 71: P3-16-05; Sandhu et al. (2013) Lancet Oncol 14: 882-92; Tutt et al. (2010) Lancet 376: 235-44; Somlo et al. (2013) J.

Clin.

Oncol. 31: 1024; Shen et al. (2013) CLin.

Cancer Res. 19 (18): 5003-15; Awada et al. (2016) Anticancer Drugs 27 (4): 342-8. In some modalities, targeted drug therapy is an mTOR inhibitor (for example, everolimus (affinitor)). See, for example, Gong et al. (2017) On-cotarget doi: 10.18632 / oncotarget.16336; Louseberg et al. (2017) Breast Cancer 10: 239-252; Hare and Harvey (2017) Am J Cancer Res 7 (3): 383-404. In some embodiments, targeted drug therapy is an inhibitor of heat shock protein 90 (eg, tanespimicin) (Modi et al. (2008) J.

Clin Oncol. 26: s1027; Miller et al. (2007) J.

Clin.

Oncol. 25: s1115; Schulz et al. (2012) J Exp Med 209 (2): 275-89). In some modalities, targeted drug therapy also includes a bone-modifying drug (for example, a bisphosphonate or demosumab (Xgeva)). See, for example, Ethier et al. (2017) Curr Oncol Rep 19 (3): 15; Abdel-Rahman (2016) Expert Rev Anticancer Ther 16 (8): 885-91. In some modalities, the therapeutic intervention is a hormone (for example, an agonist of the luteinizing hormone releasing hormone (LHRH)). In some embodiments, the LHRH agonist is goserelin (Zoladex®) or leuprolide (Lupron®). In some modalities, therapeutic intervention is an anti-estrogen compound (eg, tamoxifen, fulvestrant (faslodex)). In some modalities, the therapeutic intervention is an aromatase inhibitor (for example, leitozol (Femara®), anastrozole (Arimidex®) or exemestane (Aromasin®). In some modalities, the therapeutic intervention is surgery (for example, a mastectomy, a single mastectomy, a double mastectomy, a total mastectomy, a modified radical mastectomy, a sentinel lymph node biopsy, an axillary lymph node dissection, breast conservation surgery). The extent of surgical removal will depend on the stage of the breast cancer and the overall prognosis.

In some modalities, therapeutic intervention is radiation therapy.

In some modalities, radiation therapy is partial irradiation of the breast or radiation therapy with modulated intensity.

In some modalities, therapeutic intervention is chemotherapy (for example, capecitabine (xeloda), carboplatin (paraplatin), cisplatin (platinol), cyclophosphamide (neosar), docetaxel (docefrez, taxotere), doxorubicin (adriamycin), pegylated liposomal doxorubicin (doxorubicin), epirubicin (ellence), fluorouracil (5-FU, adrucil), gemcitabine (gemzar), methotrexate, paclitaxel (taxol), paclitaxel linked to protein (abraxane), vinorelbine (navelbine), eribulin (halaven) ixabepilone (ixabepilone)). In some modalities, the therapeutic intervention is a combination of at least two chemotherapeutic agents (for example, doxorubicin and cyclophosphamide (CA); epirubicin and cyclophosphamide (EC);

cyclophosphamide, doxorubicin and 5-FU (CAF); cyclophosphamide, epirubicin and 5-FU (CEF); cyclophosphamide, methotrexate and 5-FU (CMF); epirubicin and cyclophosphamide (CE); docetaxel, doxorubicin and cyclophosphamide (TAC); docetaxel and cyclophosphamide (CT).

[805] In some modalities, a therapeutic intervention is administered to the individual after a cancer is detected or identified. Any of the therapeutic interventions disclosed herein or known in the art can be administered. Exemplary therapeutic interventions include, without limitation, a kinase inhibitor, an immune checkpoint inhibitor (for example, a PD-1, a PD-L1 and / or a CTLA-4 immune checkpoint inhibitor) , a chemotherapeutic agent, adoptive T cell therapy (for example, chimeric antigen receptors and / or T cells with wild-type or modified T cell receptors), an antibody, a bispecific antibody or fragments thereof (for example, BiTEs ), chemotherapy, adjuvant chemotherapy, neoadjuvant chemotherapy, cytotoxic therapy, hormonal therapy, immunotherapy, monoclonal antibody, radiation therapy, signal transduction inhibitors, surgery (for example, surgical resection), targeted therapy, such as administration of kinase inhibitors (for example, kinase inhibitors that target a specific genetic lesion, such as a translocation or mutation), or any combination thereof. Such therapeutic interventions can be administered alone or in combination. In some embodiments of any of the methods described in this document, one or more therapeutic interventions are administered sequentially or simultaneously to the individual after the detection of the cancer cell. In some modalities, therapeutic intervention can be administered at a time when the individual has cancer at an early stage and when the therapeutic intervention is more effective than if the therapeutic intervention were administered to an individual later. In some modalities, a therapeutic intervention can reduce the severity of the cancer, reduce a symptom of the cancer and / or reduce the number of cancer cells present in the individual.

[806] In some modalities, therapeutic intervention may include an immune checkpoint inhibitor. Non-limiting examples of immune checkpoint inhibitors include nivolumab (Optional), pembrolizumab (Keytruda), atezolizumab (tecentriq), avelu- mabe (bavencio), durvalumab (imfinzi), ipilimumab (yervoy). See, for example, Pardoll (2012) Nat. Rev Cancer 12: 252-264; Sun et al. (2017) Eur Rev Med Pharmacol Sci 21 (6): 1198-1205; Hamanishi et al. (2015) J. Clin. Oncol. 33 (34): 4015-22; Brahmer et al. (2012) N Engl J Med 366 (26): 2455-65; Ricciuti et al. (2017) J. Thorac Oncol. 12 (5): e51-e55; Ellis et al. (2017) Clin Lung Cancer pii: S1525-7304 (17) 30043-8; Zou and Awad (2017) Ann Oncol 28 (4): 685-687; Sorscher (2017) N Engl J Med 376 (10: 996-7; Hui et al. (2017) Ann Oncol 28 (4): 874-881; Vansteenkiste et al. (2017) Expert Opin Biol Ther 17 (6): 781-789; Hellmann et al. (2017) Lancet Oncol. 18 (1): 31-41; Chen (2017) J. Chin Med Assoc 80 (1): 7-14.

[807] In some modalities, therapeutic intervention is adoptive T cell therapy (for example, chimeric antigen receptors and / or T cells with wild-type or modified T cell receptors). See, for example, Rosenberg and Restifo (2015) Science 348 (6230): 62-68; Chang and Chen (2017) Trends Mol Med 23 (5): 430-450; Yee and Lizee (2016) Cancer J. 23 (2): 144-148; Chen et al. (2016) Oncoimmunology 6 (2): e1273302; US 2016/0194404; US 2014/0050788; US 2014/0271635; US 9,233,125; incorporated by reference in their entirety here.

[808] In some modalities, a therapeutic intervention is a chemotherapeutic agent. Non-limiting examples of chemotherapeutic agents

therapeutics include: amsacrine, azacitidine, axathioprine, bevacizumab (or a fragment of antigen binding thereof), bleomycin, busulfan, carboplatin, capecitabine, chlorambucil, cisplatin, cyclophosphamide, cytarabine, dacarbazine, daunorubicin, daunorubicin, eorububir, cine, erlotinib hydrochlorides, etoposide, fiudarabine, floxuridine, fludaburine, fluorouracil, gemcitabine, hydroxyurea, idarubicin, ifosfamide, irinotecan, lomustine, mecloretamine, oxalamine, chlorfenyetamine-methazine, methoxymethaxine, methoxyamine, methazine, methoxyamine tafluposide, temozolomide, teniposide, thioguanine, topotecan, ura-mustine, valrubicin, vinblastine, vincristine, vindesine, vinorelbine and combinations thereof. Additional examples of anticancer therapies are known in the art; see, for example, the guidelines for therapy of the American Society of Clinical Oncology (ASCO), European Society of Medical Oncology (ESMO) or National Network of Comprehensive Cancer (NCCN).

[809] In some modalities, when an individual is identified as having a genetic biomarker (for example, a mutation) in the NRAS, the individual is administered with a therapeutic intervention. In some embodiments, an individual identified as having a genetic biomarker (for example, a mutation) in the NRAS is identified as having a cancer (for example, based on the presence of the genetic biomarker, alone or in combination with the presence other genetic biomarkers and / or the presence of one or more members of other classes of biomarkers and / or the presence of aneuploidy as described herein). In some modalities, the therapeutic intervention administered to the individual with a genetic biomarker in the NRAS is one or more of a therapeutic targeting RAS, an inhibitor of the receptor's tyrosine kinase, an inhibitor of the Ras-Raf-MEK-ERK pathway , an inhibitor of the PI3K-Akt-mTOR pathway and a farnesyl transferase inhibitor. In some embodiments, the Ras-Raf-MEK-

ERK is one or more of a BRAF inhibitor, a MEK inhibitor and an ERK inhibitor.

In some embodiments, the BRAF inhibitor is one or more of vemurafenib (ZELBORAF®), dabrafenib (TAFINLAR®) and en- corafenib (BRAFTOVITM), BMS-908662 (XL281), sorafenib, LGX818, PLX3603, RAF265, RO51854 , ARQ736, GDC-0879, PLX-4720, AZ304, PLX-8394, HM95573, RO5126766 and LXH254. In some embodiments, the MEK inhibitor is one or more of trametinib (MEKINIST®, GSK1120212), cobimetinib (COTELLIC®), binimetinib (MEKTOVI®, MEK162), selumetinib (AZD6244), PD0325901, MSC1936369B, SH127300 WX-554, PD98059, CI1040 (PD184352) and hypotomycin.

In some modalities, the ERK inhibitor is one or more of FRI-20 (ON-01060), VTX-11e, 25-OH-D3-3-BE (B3CD, bromoacetoxialcidiol), FR-180204, AEZ- 131 (AEZS-131), AEZS-136, AZ-13767370, BL-EI-001, LY-3214996, LTT-462, KO-947, KO-947, MK-8353 (SCH900353), SCH772984, uli- xertinibe (BVD -523), CC -90003, GDC-0994 (RG-7482), ASN007, FR148083, 5-7-Oxozeaenol, 5-iodotubercidine, GDC0994 and ONC201. In some embodiments, the PI3K-Akt-mTOR pathway inhibitor is one or more of a PI3K inhibitor, an AKT inhibitor and an mTOR inhibitor.

In some embodiments, the PI3K inhibitor is one or more of buparlisib (BKM120), alpelisib (BYL719), WX-037, copanlisib (ALIQO-PATM, BAY80-6946), dactolisib (NVP-BEZ235, BEZ-235) , taselisib (GDC-0032, RG7604), sonolisib (PX-866), CUDC-907, PQR309, ZSTK474, SF1126, AZD8835, GDC-0077, ASN003, pictilisib (GDC-0941), pilaralisib (XL147, SAR245408) (PF-05212384, PKI-587), serabelisib (TAK-117, MLN1117, INK 1117), BGT-226 (NVP- BGT226), PF-04691502, apitolisib (GDC-0980), omipalisib (GSK2126458, GSK45is), voxtal (XL756, SAR245409), AMG 511, CH5132799, GSK1059615, GDC-0084 (RG7666), VS-5584 (SB2343), PKI-402, wortmannin, LY294002, PI-103, rigosertibe, XL-765,

LY2023414, SAR260301, KIN-193 (AZD-6428), GS-9820, AMG319 and GSK2636771. In some embodiments, the AKT inhibitor is one or more of miltefosine (IMPADIVO®), wortmannin, NL-71-101, H-89, GSK690693, CCT128930, AZD5363, ipatasertib (GDC-0068, RG7440), A-674563, A -443654, AT7867, AT13148, uprosertib, afuresertib, DC120, 2- [4- (2-aminoprop-2-yl) phenyl] -3-phenylquinoxaline, MK-2206, edelphosine, miltefosine, perifosina, erucilfofocolina, erufosina , SR13668, OSU-A9, PH-316, PHT-427, PIT-1, DM-PIT-1, triciribine (trichlorin phosphate monohydrate), API-1, N- (4- (5- (3- acetamidophenyl) -2- (2-aminopyridin-3-yl) -3H-imidazo [4,5-b] pyridin-3-yl) benzyl) -3-fluorobenzamide, ARQ092, BAY 1125976, 3-oxo- tirucálico, lactoquinomycin, boc- Phe-vinyl ketone, Perifosine (D-21266), TCN, TCN-P, GSK2141795 and ONC201. In some embodiments, the mTOR inhibitor is one or more of MLN0128, AZD-2014, CC-223, AZD2014, CC-115, everolimus (RAD001), temsirolimus (CCI-779), ridaforolimus (AP-23573) and sirolimus ( rapamycin). In some embodiments, the farnesyl transferase inhibitor is one or more of telafarnib, tipifarnib, BMS-214662, L778123, L744832 and FTI-277. In some modalities, the therapeutic intervention administered to the individual with a genetic biomarker in NRAS is a MEK inhibitor and an PI3K inhibitor.

In some modalities, the therapeutic intervention administered to the individual with a genetic biomarker in NRAS is a MEK inhibitor and an ERK inhibitor.

Other effective therapeutic interventions for the treatment of an individual with a genetic biomarker in NRAS are known in the art.

In some modalities, a therapeutic intervention administered to the individual with a genetic biomarker in NRAS is effective in the treatment of cancer in the individual.

For example, after administering a therapeutic intervention that is effective in treating an individual with a genetic biomarker in the NRAS, the number of cancer cells in the individual can be reduced, the size of one or more tumors in the individual can be reduced. , the rate or extent of metastasis may be reduced, the symptoms associated with the disease or disorder or condition may be totally or partially relieved, the condition of the disease may be stabilized (that is, not worsened) and / or survival it can be prolonged compared to expected survival if not receiving treatment.

[810] In some modalities, when an individual is identified as having a genetic biomarker (for example, a mutation) in CTNNB1, the individual is administered with a therapeutic intervention. In some embodiments, an individual identified as having a genetic biomarker (for example, a mutation) in CTNNB1 is identified as having cancer (for example, based on the presence of the genetic biomarker, alone or in combination with the presence of other genetic biomarkers) and / or the presence of one or more members of other classes of biomarkers and / or the presence of aneuploidy as described herein). In some modalities, the therapeutic intervention administered to the individual with a genetic biomarker in CTNNB1 is one or more of an β-catenin inhibitor, a WNT / β-catenin signaling inhibitor and an inhibitor of TTK kinase verification (MPS1). In some embodiments, the β-catenin inhibitor is one or more of PRI-724, CWP232291, PNU74654, 2.4 diamino-quinazoline, PKF115-584, PKF118-744, PKF118-310, PFK222-815, CGP 049090, ZTM000990 , BC21, vitamin D, retinoid acid, aspirin, sulindac (CLINORIL®, Aflodac), 2,4-diaminoquinazoline derivatives, 3 - {[(4-methylphenyl) sulfonyl] amino} benzoate (MSAB), AV65, iCRT3, iCRT5 and iCRT14. In some embodiments, the WNT / β-catenin signaling inhibitor is one or more of PRI-724, CWP232291, PNU74654, 2.4 diamino-quinazoline, PKF115-584, PKF118-744, PKF118-310, PFK222-815, CGP 049090, ZTM000990,

BC21, vitamin D, retinoid acid, aspirin, sulindac (CLINORIL®, Aflo- dac), 2,4-diamino-quinazoline derivatives, methyl 3 - {[((4-methylphenyl) sulphenyl] amino} benzoate (MSAB) , AV65, iCRT3, iCRT5, iCRT14, SM04554, LGK 974, XAV939, curcumin (eg Meriva®), quercetin, epigallocatechin gallate (EGCC), resveratrol, DIF, genistein, celecoxib (CELEBREX®), CWP232291, NSC668036, FJ9, BML-286 (3289- 8625), IWP, IWP-1, IWP-2, JW55, G007-LK, pyrvinium, foxy-5, Wnt-5a, ipafricept (OMP-54F28), vantictumabe ( OMP-18R5), OTSA 101, OTSA101 -DTPA-90Y, SM04690, SM04755, nutlin-3a, XAV939, IWR1, JW74, ocadaic acid, tautomycin, SB239063, SB203580, adenosine diphosphate (hydroxymethyl) pyrrolidinediol (ADH) pyrrolidinediol 2- [4- (4-fluorophenyl) p-perazin-1-yl] -6-methylpyrimidin-4 (3H) -one, PJ34, niclosamide (NICLOCI- DETM), cambinol, sulindac (CLINORIL®, Aflodac), J01 -017a, NSC668036, philippine, IC261, PF670462, bosutinib (BOSULIF®), PHA665752, imatinib (GLEEVEC®), ICG-001, ethacrinic acid, eta acid chynic (ethacrylic derivative) -0941), Rp-8-Br-cAMP, SDX-308, WNT974, CGX1321, ETC-1922159, AD-REIC / Dkk3, WIKI4 and windor-phen.

In some embodiments, the TTK kinase checkpoint inhibitor (MPS1) of the shaft assembly is one or more of NTRC 0066-0, CFI-402257, one (5,6-dihydro) pyrimido [4,5- and] indolizine and BOS172722. Other effective therapeutic interventions for treating an individual with a genetic biomarker in CTNNB1 are known in the art.

In some modalities, a therapeutic intervention administered to the individual with a genetic biomarker in CTNNB1 is effective in the treatment of cancer in the individual.

For example, after administering a therapeutic intervention that is effective in treating an individual with a genetic biomarker on CTNNB1, the number of cancer cells in the individual can be reduced, the size of one or more tumors in the individual can be reduced. be reduced, the rate or extent of metastasis can be reduced, the symptoms associated with the disease or disorder or condition can be totally or partially relieved, the state of the disease can be stabilized (that is, not worsened) and / or survival may be prolonged compared to expected survival if you do not receive treatment.

[811] In some modalities, when an individual is identified as having a genetic biomarker (for example, a mutation) in PIK3CA, the individual is administered with a therapeutic intervention. In some embodiments, an individual identified as having a genetic biomarker (for example, a mutation) in PIK3CA is identified as having a cancer (for example, based on the presence of the genetic biomarker, alone or in combination with the presence of other genetic biomarkers and / or the presence of one or more members of other classes of biomarkers and / or the presence of aneuploidy as described herein). In some embodiments, the therapeutic intervention administered to the individual with a PIK3CA genetic biomarker is one or more of a PI3K-alpha inhibitor, a panPI3K inhibitor and a double PI3K and mTOR inhibitor. In some modalities, the PI3K alpha inhibitor is taselisib (GDC-0032, RG7604), GDC-0077, serabelisib (TAK-117, MLN1117, INK 1117), alpelisib (BYL719) and CH5132799. In some embodiments, the panPI3K inhibitor is bu-parlisib (BKM120), copanlisib (ALIQOPATM, BAY80-6946), sonolisib (PX-866), ZSTK474, pictilisib (GDC-0941), pilaralisib (XL147, SAR245408), AMG 511, PKI-402, wortmannin, LY294002 and WX-037. In some embodiments, the dual inhibitor PI3K and mTOR is dactolisib (NVP-BEZ235, BEZ-235), PQR309, SF1126, gedatolisib (PF-05212384, PKI-587), BGT-226 (NVP-BGT226), PF-04691502, apitolisib (GDC-0980), omipalisib (GSK2126458, GSK458), voxtalisib (XL756, SAR245409), GSK1059615, GDC-0084 (RG7666), VS-5584 (SB2343) and PI-103. In some modalities, the therapeutic intervention administered to the individual with a genetic biomarker in PIK3CA is one or more of buparlisib (BKM120), alpelisib (BYL719), WX-037, co-panlisib (ALIQOPATM, BAY80-6946), dactolisib (NVP- BEZ235, BEZ-235), taselisib (GDC-0032, RG7604), sonolisib (PX-866), CUDC-907, PQR309, ZSTK474, SF1126, AZD8835, GDC-0077, ASN003, pictilisibe (GDC-0941), pilaralisib ( XL147, SAR245408), gedatolisib (PF-05212384, PKI-587), serabelisib (TAK-117, MLN1117, INK 1117), BGT-226 (NVP-BGT226), PF-04691502, apitolisib (GDC-0980), omi- palisib (GSK2126458, GSK458), voxtalisib (XL756, SAR245409), AMG 511, CH5132799, GSK1059615, GDC-0084 (RG7666), VS-5584 (SB2343), PKI-402, wortmannin, LY294002, PI-103, r -765, LY2023414, SAR260301, KIN-193 (AZD-6428), GS-9820, AMG319 and GSK2636771. Other effective therapeutic interventions for treating an individual with a PIK3CA genetic biomarker are known in the art. In some modalities, a therapeutic intervention administered to the individual with a genetic biomarker in PIK3CA is effective in treating cancer in the individual. For example, after administering a therapeutic intervention that is effective in treating an individual with a genetic biomarker on PIK3CA, the number of cancer cells in the individual can be reduced, the size of one or more tumors in the individual can be reduced, rate or extent of metastasis may be reduced, symptoms associated with the disease or disorder or condition may be alleviated or partially alleviated, the state of the disease may be stabilized (ie, not worsened) and / or survival may be prolonged in compared to expected survival if not receiving treatment.

[812] In some modalities, when an individual is identified as having a genetic biomarker (for example, a mutation) in FBXW7, the individual is administered with a therapeutic intervention. In some embodiments, an individual identified as having a genetic biomarker (for example, a mutation) in FBXW7 is identified as having a cancer (for example, based on the presence of the genetic biomarker, alone or in combination with the presence other genetic biomarkers and / or the presence of one or more members of other classes of biomarkers and / or the presence of aneuploidy as described herein). In some modalities, the therapeutic intervention administered to the individual with a genetic biomarker in FBXW7 is one or more of an mTOR inhibitor and an MCL-1 inhibitor. In some embodiments, the mTOR inhibitor is one or more of MLN0128, AZD-2014, CC-223, AZD2014, CC-115, everolimus (RAD001), temsirolimus (CCI-779), ridaforolimus (AP-23573) and sirolimus ( rapamycin). In some embodiments, the MCL-1 inhibitor is S63845, AZD5991, AMG 176, 483-LM and MIK665. Other effective therapeutic interventions for treating an individual with a genetic biomarker in FBXW7 are known in the art. In some modalities, a therapeutic intervention administered to the individual with a genetic biomarker in FBXW7 is effective in treating cancer in the individual. For example, after administering a therapeutic intervention that is effective in treating an individual with a genetic biomarker in FBXW7, the number of cancer cells in the individual may be reduced, the size of one or more tumors in the individual may be reduced. be reduced, the rate or extent of metastasis can be reduced, the symptoms associated with the disease or disorder or condition can be totally or partially relieved, the state of the disease can be stabilized (ie, not worsened) and / or survival may be prolonged compared to expected survival if not treated.

[813] In some modalities, when an individual is identified as having a genetic biomarker (for example, a mutation) in the APC, the individual is administered with a therapeutic intervention. In some modalities, an individual identified as having a genetic biomarker (for example, a mutation) in the APC is identified as having a cancer (for example, based on the presence of the genetic biomarker, alone or in combination with the presence of others genetic biomarkers and / or the presence of one or more members of other classes of biomarkers and / or the presence of aneuploidy as described here). In some modalities, the therapeutic intervention administered to the individual who has a genetic biomarker in APC is one or more of TASIN-1 (selective inhibitor of truncated APC) and an inhibitor of WNT / β-catenin signaling.

In some modalities, the WNT / β-catenin signaling inhibitor is one or more of PRI-724, CWP232291, PNU74654, 2.4 diamino-quinazoline, PKF115- 584, PKF118-744, PKF118-310, PFK222- 815, CGP 049090, ZTM000990, BC21, vitamin D, retinoid acid, aspirin, sulindac (CLI-NORIL®, Aflodac), 2,4-diamino-quinazoline derivatives, methyl 3 - {[(4-methylphenyl) sulfonyl] amino } benzoate (MSAB), AV65, iCRT3, iCRT5, iCRT14, PRI-724, CWP232291, PNU74654, 2.4 diamino-quinazoline, PKF115-584, PKF118-744, PKF118-310, PFK222-815, CGP 049090, ZTM000 BC21, vitamin D, retinoid acid, aspirin, sulindac (CLI-NORIL®, Aflodac), 2,4-diamino-quinazoline derivatives, methyl 3 - {[(4-methylphenyl) sulfonyl] amino} benzoate (MSAB), AV65, iCRT3, iCRT5, iCRT14, SM04554, LGK 974, XAV939, curcumin (e.g., Meriva®), quercetin, epigallocatechin gallate (EGCC), resveratrol, DIF, genistein, celecoxib (CELEBREX®), CWP980362291, , BML-286 (3289-8625), IWP, IWP-1, IWP-2, JW55, G007-LK, pyrvinium, f oxy-5, Wnt-5a, ipafricept (OMP-54F28), vantictumab (OMP-18R5), OTSA 101, OTSA101 -DTPA-90Y, SM04690, SM04755, nutlin-3a, XAV939, IWR1, JW74, ocadaic acid, tautomycin, SB239063, SB203580, adenosine diphosphate (hydroxymethyl) pyrrolidinediol (ADP-HPD), 2- [4- (4-fluorophenyl) piperazin-1-yl] -6-methylpyrimidin-4 (3H) -one, PJ34, niclosamide (NICLOCIDETM ), cambinol, sulindac (CLINORIL®,

Aflodac), J01-017a, NSC668036, philippine, IC261, PF670462, bosutinib (BOSULIF®), PHA665752, imatinib (GLEEVEC®), ICG-001, ethacrylic acid, ethacrylic acid (ethacrylic derivative) -0941, -0941 8-Br-cAMP, SDX-308, WNT974, CGX1321, ETC-1922159, AD-REIC / Dkk3, WIKI4 and windorphen. Other effective therapeutic interventions for treating an individual with a genetic biomarker in APC are known in the art. In some modalities, a therapeutic intervention administered to the individual with a genetic biomarker in APC is effective in treating cancer in the individual. For example, after administering a therapeutic intervention that is effective in treating an individual with a genetic biomarker in the APC, the number of cancer cells in the individual can be reduced, the size of one or more tumors in the individual can be reduced. , the rate or extent of metastasis may be reduced, symptoms associated with the disease or disorder or condition may be alleviated or partially alleviated, the state of the disease may be stabilized (ie, not worsened) and / or survival may be prolonged compared to expected survival if not receiving treatment.

[814] In some modalities, when an individual is identified as having a genetic biomarker (for example, a mutation) in the EGFR, the individual is administered with a therapeutic intervention. In some embodiments, an individual identified as having a genetic biomarker (for example, a mutation) in the EGFR is identified as having cancer (for example, based on the presence of the genetic biomarker, alone or in combination with the presence other genetic biomarkers and / or the presence of one or more members of other classes of biomarkers and / or the presence of aneuploidy as described herein). In some modalities, the therapeutic intervention administered to the individual with a genetic biomarker

toxic to EGFR is one or more of a selective inhibitor for EGFR, a panHER inhibitor and an anti-EGFR antibody.

In some embodiments, the EGFR inhibitor is a covalent inhibitor.

In some embodiments, the EGFR inhibitor is a non-covalent inhibitor.

In some modalities, the therapeutic intervention administered to the individual with a genetic biomarker in the EGFR is one or more of osimertinib (AZD9291, merelectinib, TAGRISSOTM), erlotinib (TARCEVA®), gefitinib (IRESSA®), cetuximab (ERBITUX® ), necitumumab (PORTRAZ-ZATM, IMC-11F8), neratinib (HKI-272, NERLYNX®), lapatinib (TYKERB®), panitumumab (ABX-EGF, VECTIBIX®), vandetanib (CAPRELSA®), rociletinibe (CO-16 ), olmutinib (OLTM), HM61713, BI-1482694), nasotinib (ASP8273), nazartinib (EGF816, NVS-816), PF-06747775, icotinib (BPI-2009H), afatinib (BIBW 2992, GILO-TRIF®), dacomitinib (PF-00299804, PF-804, PF-299, PF-299804), avininib (AC0010), AC0010MA EAI045, matuzumab (EMD-7200), nimo-tuzumab (h-R3, BIOMAb EGFR®), zalutumab , MDX447, depatuxizumab (humanized mAb 806, ABT-806)), depatuxizumab mafodotin (ABT-414), ABT-806, mAb 806, canertinib (CI-1033), shikonin, shikonin derivatives (for example, deoxysikonin, isobutyrylshikonin, acetylshikonin , β, β-dimethylacrylshikonine and acetylalcanine), poziotinib (NOV120101, HM781-36B), AV-412, ibrutinib, WZ4002, brigatinib (AP26113, ALUNBRIG®), pelitinib (EKB-569), tarloxotinibe (TH10), PRLoxotinib (TH10) , BPI-15086, Hemay022 ZN-e4, tesevatinib (KD019, XL647), YH25448, epitinib (HMPL-813), CK-101, MM-151, AZD3759, ZD6474, PF-06459988, varlintinib (ASLAN001, ARRY-334543) , AP32788, HLX07, D -0316, AEE788, HS-10296, avitinib, GW572016, pyrotinib (SHR1258), SCT200, CPGJ602, Sym004, MAb-425, modotuximab (TAB-H49), futuximabe (992 DS), zalutumbe -140, RO5083945, IMGN289, JNJ-61186372, LY3164530, Sym013, AMG 595, auto T cells

tologists armed with EGFRBi and therapy with EGFR CAR-T. Other effective therapeutic interventions for treating an individual with a genetic biomarker in EGFR are known in the art. In some modalities, a therapeutic intervention administered to the individual with a genetic biomarker in EGFR is effective in the treatment of cancer in the individual. For example, after administering a therapeutic intervention that is effective in treating an individual with a genetic biomarker on the EGFR, the number of cancer cells in the individual can be reduced, the size of one or more tumors in the individual can be reduced, rate or extent of metastasis may be reduced, symptoms associated with the disease or disorder or condition may be totally or partially relieved, the state of the disease may be stabilized (ie, not worsened) and / or survival may be prolonged in comparison with expected survival if not receiving treatment.

[815] In some modalities, when an individual is identified as having a genetic biomarker (for example, a mutation) in BRAF, the individual is administered with a therapeutic intervention. In some modalities, an individual identified as having a genetic biomarker (for example, a mutation) in BRAF is identified as having cancer (for example, based on the presence of the genetic biomarker, alone or in combination with the presence other genetic biomarkers and / or the presence of one or more members of other classes of biomarkers and / or the presence of aneuploidy as described herein). In some modalities, the therapeutic intervention administered to the individual with a genetic biomarker in BRAF is one or more of vemurafenib (ZELBORAF®), dabrafenib (TAFINLAR®) and encouragefenib (BRAFTOVITM), BMS-908662 (XL281) , sorafenib, LGX818, PLX3603, RAF265, RO5185426, GSK2118436, ARQ 736, GDC-0879, PLX-4720, AZ304, PLX-8394,

HM95573, RO5126766 and LXH254. In some modalities, the therapeutic intervention administered to the individual with a genetic biomarker in BRAF is a BRAF inhibitor and a MEK inhibitor.

In some embodiments, the BRAF inhibitor is vemurafenib (ZELBO-RAF®), dabrafenib (TAFINLAR®) and encouragefenib (BRAFTOVITM), BMS-908662 (XL281), sorafenib, LGX818, PLX3603, RAF265, AR5185426, 736, GDC-0879, PLX-4720, AZ304, PLX-8394, HM95573, RO5126766 or LXH254 and the MEK inhibitor is trametinib (MEKINIST®, GSK1120212), cobimetinib (COTELLIC®), binimetinibe (MEKTOVI®, MEKTOVI®, MEKTOVI®, MEKTOVI®, MEKTOVI®, MEKTOVI®, MEKTOVI®, MEKTOVI®, SELK2 (AZD6244) PD0325901, MSC1936369B, SHR7390, TAK-733, RO5126766, CS3006, WX-554, PD98059, CI1040 (PD184352) or hypotomycin.

In some modalities, the therapeutic intervention administered to the individual with a genetic biomarker in BRAF is a BRAF inhibitor and the ERK inhibitor.

In some embodiments, the BRAF inhibitor is vemurafenib (ZELBO- RAF®), dabrafenib (TAFINLAR®) and encouragefenib (BRAFTOVITM), BMS-908662 (XL281), sorafenib, LGX818, PLX3603, RAF265, RO5185484, GK2 -0879, PLX-4720, AZ304, PLX-8394, HM95573, RO5126766 or LXH254 and the ERK inhibitor is FRI-20 (ON-01060), VTX-11e, 25-OH-D3-3-BE (B3CD, bromoacetoxial - cidiol), FR-180204, AEZ-131 (AEZS-131), AEZS-136, AZ-13767370, BL-EI-001, LY-3214996, LTT-462, KO-947, KO-947, MK-8353 (SCH900353), SCH772984, ulixertinib (BVD-523), CC-90003, GDC-0994 (RG-7482), ASN007, FR148083, 5-7-Oxozeaenol, 5-iodotubercidine, GDC0994 or ONC201. Other effective therapeutic interventions for treating an individual with a BRAF genetic biomarker are known in the art.

In some modalities, a therapeutic intervention administered to the individual with a genetic biomarker in BRAF is effective in treating cancer in the individual.

For example, after administering a therapeutic intervention that is effective in treating an individual with a genetic biomarker in BRAF, the number of cancer cells in the individual can be reduced, the size of one or more tumors in the individual can be reduced. , the rate or extent of metastasis may be reduced, symptoms associated with the disease or disorder or condition may be totally or partially relieved, the state of the disease may be stabilized (ie, not worsened) and / or survival may be prolonged compared to expected survival if not treated.

[816] In some modalities, when an individual is identified as having a genetic biomarker (for example, a mutation) in the CDNK2A, the individual receives a therapeutic intervention. In some embodiments, an individual identified as having a genetic biomarker (eg, a mutation) on CDNK2A is identified as having cancer (for example, based on the presence of the genetic biomarker, alone or in combination with the presence of other genetic biomarkers and / or the presence of one or more members of other classes of biomarkers and / or the presence of aneuploidy as described herein). In some modalities, the therapeutic intervention administered to the individual with a genetic biomarker in CDNK2A is a CDK4 / 6 inhibitor. In some modalities, the CDK4 / 6 inhibitor is one or more of palbocyclib, ribocyclib and abemaciclib. Other effective therapeutic interventions for treating an individual with a genetic biomarker on CDNK2A are known in the art. In some modalities, a therapeutic intervention administered to the individual with a genetic biomarker in CDNK2A is effective in the treatment of cancer in the individual. For example, after administering a therapeutic intervention that is effective in treating an individual with a genetic biomarker on CDNK2A, the number of cancer cells in the individual can be reduced, the size of one or more tumors in the individual can be reduced. , the rate or extent of metastasis may be reduced, the symptoms associated with the disease or disorder or condition may be totally or partially relieved, the condition of the disease may be stabilized (that is, not worsened) and / or survival it can be prolonged compared to expected survival if not receiving treatment.

[817] In some modalities, when an individual is identified as having a genetic biomarker (for example, a mutation) in CDNK2, the individual is administered with a therapeutic intervention. In some embodiments, an individual identified as having a genetic biomarker (for example, a mutation) in CDKN2 is identified as having a cancer (for example, based on the presence of the genetic biomarker, alone or in combination with the presence other genetic biomarkers and / or the presence of one or more members of other classes of biomarkers and / or the presence of aneuploidy as described herein). In some modalities, the therapeutic intervention administered to the individual with a genetic biomarker on CDKN2 is a CDK4 / 6 inhibitor. In some modalities, the CDK4 / 6 inhibitor is one or more of palbocyclib, ribocyclib and abema-cyclib . Other effective therapeutic interventions for treating an individual with a genetic biomarker on CDKN2 are known in the art. In some modalities, a therapeutic intervention administered to the individual with a genetic biomarker in CDKN2 is effective in the treatment of cancer in the individual. For example, after administering a therapeutic intervention that is effective in treating an individual with a genetic biomarker on CDKN2, the number of cancer cells in the individual can be reduced, the size of one or more tumors in the individual can be reduced. , the rate or extent of metastasis may be reduced, symptoms associated with the disease or disorder or condition may be totally or partially relieved, the state of the disease may be stabilized (ie, not worsened) and / or survival may be prolonged compared to expected survival if not receiving treatment.

[818] In some modalities, when an individual is identified as having a genetic biomarker (for example, a mutation) in PTEN, the individual is administered with a therapeutic intervention. In some modalities, an individual identified as having a genetic biomarker (for example, a mutation) in PTEN is identified as having a cancer (for example, based on the presence of the genetic biomarker, alone or in combination with the presence other genetic biomarkers and / or the presence of one or more members of other classes of biomarkers and / or the presence of aneuploidy as described herein). In some modalities, the therapeutic intervention administered to the individual with a genetic biomarker in PTEN is one or more of an inhibitor of the DE PI3K / AKT / mTOR signaling pathway and a PARP inhibitor. In some embodiments, the PI3K-Akt-mTOR pathway inhibitor is one or more of a PI3K inhibitor, an AKT inhibitor and an mTOR inhibitor. In some embodiments, the PI3K inhibitor is one or more of buparlisib (BKM120), alpelisib (BYL719), WX-037, copanlisib (ALIQOPATM, BAY80-6946), dactolisib (NVP-BEZ235, BEZ-235), taselisibe (GDC-0032, RG7604), sono-lysib (PX-866), CUDC-907, PQR309, ZSTK474, SF1126, AZD8835, GDC-0077, ASN003, pictilisib (GDC-0941), pilaralisib (XL147, SAR245408), gedatolisibe (PF-05212384, PKI-587), serabelisib (TAK-117, MLN1117, INK 1117), BGT-226 (NVP-BGT226), PF-04691502, apitolisib (GDC-0980), omipalisib (GSK2126458, GSK458), voxt - sibe (XL756, SAR245409), AMG 511, CH5132799, GSK1059615, GDC- 0084 (RG7666), VS-5584 (SB2343), PKI-402, wortmannin, LY294002, PI-103, rigosertibe, XL-765, LY2023414, SAR260 , KIN-193 (AZD-6428), GS-9820, AMG319 and GSK2636771. In some embodiments, the AKT inhibitor is one or more of miltefosine (IMPADIVO®), wortmannin, NL-71-101, H-89, GSK690693, CCT128930, AZD5363, ipatasertib (GDC-0068, RG7440), A- 674563, A -443654, AT7867, AT13148, upro-sertib, afuresertib, DC120, 2- [4- (2-aminoprop-2-yl) phenyl] -3-phenylquinoxaline, MK-2206, edelfosine, miltefosine, perifosine , erucylfofocolina, erucosine, SR13668, OSU-A9, PH-316, PHT-427, PIT-1, DM-PIT-1, trichiribin (triciribin phosphate monohydrate), API-1, N- (4- (5- (3-acetamidophenyl) -2- (2-aminopyridin-3-yl) -3H-imidazo [4,5-b] pyridin-3-yl) benzyl) -3-fluoro-benzamide, ARQ092, BAY 1125976, 3-oxo-tirucálico acid, lactoquino-micina, boc-Phe-vinyl ketone, Perifosina (D-21266), TCN, TCN-P, GSK2141795 and ONC201. In some embodiments, the mTOR inhibitor is one or more of MLN0128, AZD-2014, CC-223, AZD2014, CC-115, everolimus (RAD001), temsirolimus (CCI-779), ridaforolimus (AP-23573) and sirolimus ( rapamycin). In some embodiments, the farnesyl transferase inhibitor is one or more of telafarnib, tipifarnib, BMS-214662, L778123, L744832 and FTI-277. In some embodiments, the PARP inhibitor is one or more of olaparib, veliparib, iniparib, rucaparib, CEP-9722, E7016 or E7449. Other effective therapeutic interventions for treating an individual with a genetic biomarker in PTEN are known in the art.

In some modalities, a therapeutic intervention administered to the individual with a genetic biomarker in PTEN is effective in treating cancer in the individual.

For example, after administering a therapeutic intervention that is effective in treating an individual with a genetic biomarker in PTEN, the number of cancer cells in the individual can be reduced, the size of one or more tumors in the individual can be reduced. - the rate or extent of metastasis may be reduced, the symptoms associated with the disease or disorder or condition may be totally or partially alleviated, the state of the disease may be stabilized (that is, not worsened) and / or survival may be prolonged compared to expected survival if not treated.

[819] In some modalities, when an individual is identified as having a genetic biomarker (for example, a mutation) in FGFR2, the individual is administered with a therapeutic intervention. In some embodiments, an individual identified as having a genetic biomarker (for example, a mutation) in FGFR2 is identified as having a cancer (for example, based on the presence of the genetic biomarker, alone or in combination with the presence other genetic biomarkers and / or the presence of one or more members of other classes of biomarkers and / or the presence of aneuploidy as described herein). In some embodiments, the therapeutic intervention administered to the individual with a genetic biomarker in FGFR2 is one or more of an anti-FGFR2 antibody, a selective inhibitor of FGFR2 and an inhibitor of pan-FGFR. In some modalities, therapeutic intervention is a covalent inhibitor of FGFR2 (for example, PRN1371, BLU9931, FIIN-4, H3B-6527 and FIIN-2). In some embodiments, therapeutic interventions are a non-covalent inhibitor of FGFR2 (for example, AZD4547, BGJ398, Debio-1347, dovitinib, JNJ-42756493 and LY2874455). In some embodiments, the anti-FGFR2 antibody is GP369, BAY1187982 or FPA144 (bemarituzumab). In some modalities, the therapeutic intervention is one or more of PRN1371, BLU9931, FIIN-4, H3B-6527, NVP-BGJ398, ARQ087, TAS-120, JNJ-42756493, CH5183284 / Debio 1347, INCB054828, GP369, BAY1187982 or FPA144 (bemarituzumab), NVP-BGJ398, JNJ-42756493 (erdafitinib), rogaratinib (BAY1163877), FIIN-2, JNJ-42756493, LY2874455, lenvatinib (E7080), ponatinib (AP24534), regorafine (AP24534), regorafine (AP24534), regorafine (AP24534), regorafina (AP24534), regorafina (AP24534), regorafina (AP24534) regorafine (AP44534), regorafine (AP44534), regorafine (AP44534), regorafine (AP44534), regorafine (AP44534) (TKI258), lucitanib (E3810), cedira-nibe (AZD2171), intedanib (BIBF 1120) 540215),

ASP5878, AZD4547, BGJ398 (infigratinib), E7090, HMPL-453, ninte-danibe (OFEV®, BIBF 1120), MAX-40279, XL999, orantinib (SU6668), pazopanib (VOTRIENT®), anlotinib, AL3818. Other effective therapeutic interventions for treating an individual with a FGFR2 genetic biomarker are known in the art. In some modalities, a therapeutic intervention administered to the individual with a genetic biomarker in FGFR2 is effective in treating an individual's cancer. For example, after administering a therapeutic intervention that is effective in treating an individual with a genetic biomarker in FGFR2, the number of cancer cells in the individual may be reduced, the size of one or more tumors in the individual can be reduced, the rate or extent of metastasis can be reduced, the symptoms associated with the disease or disorder or condition can be totally or partially relieved, the state of the disease can be stabilized (ie not worsened) and / or survival it can be prolonged compared to expected survival if not treated.

[820] In some modalities, when an individual is identified as having a genetic biomarker (for example, a mutation) in HRAS, the individual is administered with a therapeutic intervention. In some embodiments, an individual identified as having a genetic biomarker (for example, a mutation) in HRAS is identified as having a cancer (for example, based on the presence of the genetic biomarker, alone or in combination with the presence other genetic biomarkers and / or the presence of one or more members of other classes of biomarkers and / or the presence of aneuploidy as described herein). In some modalities, the therapeutic intervention administered to the individual with a genetic biomarker in HRAS is one or more of a therapeutic directed to RAS, an inhibitor of the receptor's tyrosine kinase, an inhibitor of the Ras-Raf-

MEK-ERK, an inhibitor of the PI3K-Akt-mTOR pathway and a farnesyl transferase inhibitor.

In some embodiments, the Ras-Raf-MEK-ERK pathway inhibitor is one or more of a BRAF inhibitor, a MEK inhibitor and an ERK inhibitor.

In some embodiments, the BRAF inhibitor is one or more of vemurafenib (ZELBORAF®), dabrafenib (TAFINLAR®) and en- corafenib (BRAFTOVITM), BMS-908662 (XL281), sorafenib, LGX818, PLX3603, RAF265, RO51854 , ARQ736, GDC-0879, PLX-4720, AZ304, PLX-8394, HM95573, RO5126766 and LXH254. In some embodiments, the MEK inhibitor is one or more of trametinib (MEKINIST®, GSK1120212), cobimetinib (COTELLIC®), binimetinib (MEKTOVI®, MEK162), selumetinib (AZD6244), PD0325901, MSC1936369B, SHR3390, TA RO5126766, CS3006, WX-554, PD98059, CI1040 (PD184352) and hypotomycin.

In some embodiments, the ERK inhibitor is one or more of FRI-20 (ON-01060), VTX-11e, 25-OH-D3-3-BE (B3CD, bromoacetoxialcidiol), FR-180204, AEZ-131 (AEZS-131), AEZS-136, AZ-13767370, BL-EI-001, LY-3214996, LTT-462, KO-947, KO-947, MK-8353 (SCH900353), SCH772984, ulixertinibe (BVD-523 ), CC-90003, GDC-0994 (RG-7482), ASN007, FR148083, 5-7-Oxozeaenol, 5-iodotubercidine, GDC0994 and ONC201. In some embodiments, the PI3K-Akt-mTOR pathway inhibitor is one or more of a PI3K inhibitor, an AKT inhibitor and an mTOR inhibitor.

In some embodiments, the PI3K inhibitor is one or more of buparlisib (BKM120), alpelisib (BYL719), WX-037, copansisib (ALIQOPATM, BAY80-6946), dactolisib (NVP-BEZ235, BEZ-235), taselisibe (GDC-0032, RG7604), sonolisib (PX-866), CUDC-907, PQR309, ZSTK474, SF1126, AZD8835, GDC-0077, ASN003, pictilisib (GDC-0941), pilaralisib (XL147, SAR245408), gedatolisibe - 05212384, PKI-587), serabelisibe (TAK-117, MLN1117, INK 1117), BGT-226 (NVP-BGT226), PF-04691502, apitolisib (GDC-0980), ompalisib (GSK2126458, GSK458), voxtalisibe (XL756, SAR245409),

AMG 511, CH5132799, GSK1059615, GDC-0084 (RG7666), VS-5584 (SB2343), PKI-402, wortmannin, LY294002, PI-103, rigosertibe, XL-765, LY2023414, SAR260301, KIN-193 (AZD- 6428 ), GS-9820, AMG319 and GSK2636771. In some embodiments, the AKT inhibitor is one or more of miltefosine (IMPADIVO®), wortmannin, NL-71-101, H-89, GSK690693, CCT128930, AZD5363, ipatasertib (GDC-0068, RG7440), A-674563, A -443654, AT7867, AT13148, uprosertib, afuresertib, DC120, 2- [4- (2-aminoprop-2-yl) phenyl] -3-phenylquinoxaline, MK-2206, edelphosine, miltefosine, perifosina, erucilfofocolina, erufosina , SR13668, OSU-A9, PH-316, PHT-427, PIT-1, DM-PIT-1, triciribine (trichlorin phosphate monohydrate), API-1, N- (4- (5- (3- acetamidophenyl) -2- (2-aminopyridin-3-yl) -3H-imidazo [4,5-b] pyridin-3-yl) benzyl) -3-fluorobenzamide, ARQ092, BAY 1125976, 3-oxo- tirucálico, lactoquinomycin, boc- Phe-vinyl ketone, Perifosine (D-21266), TCN, TCN-P, GSK2141795 and ONC201. In some embodiments, the mTOR inhibitor is one or more of MLN0128, AZD-2014, CC-223, AZD2014, CC-115, everolimus (RAD001), temsirolimus (CCI-779), ridaforolimus (AP-23573) and sirolimus ( rapamycin). In some embodiments, the farnesyl transferase inhibitor is one or more of telafarnib, tipifarnib, BMS-214662, L778123, L744832 and FTI-277. In some modalities, the therapeutic intervention administered to the individual with a genetic biomarker in HRAS is an inhibitor of MEK and an inhibitor of PI3K.

In some modalities, the therapeutic intervention administered to the individual with a genetic marker in HRAS is a MEK inhibitor and an ERK inhibitor.

Other effective therapeutic interventions for the treatment of an individual with a genetic biomarker in HRAS are known in the art.

In some modalities, a therapeutic intervention administered to the individual with a genetic biomarker in HRAS is effective in the treatment of cancer in the individual.

For example, after administering a therapeutic intervention that is effective in treating an individual with a genetic biomarker in HRAS, the number of cancer cells in the individual can be reduced, the size of one or more tumors in the individual can be reduced. , the rate or extent of metastasis may be reduced, the symptoms associated with the disease or disorder or condition may be totally or partially relieved, the condition of the disease may be stabilized (that is, not worsened) and / or survival it can be prolonged compared to expected survival if not receiving treatment.

[821] In some modalities, when an individual is identified as having a genetic biomarker (for example, a mutation) in KRAS, the individual is administered with a therapeutic intervention. In some embodiments, an individual identified as having a genetic biomarker (for example, a mutation) in KRAS is identified as having cancer (for example, based on the presence of the genetic biomarker, alone or in combination with the presence other genetic biomarkers and / or the presence of one or more members of other classes of biomarkers and / or the presence of aneuploidy as described herein). In some modalities, the therapeutic intervention administered to the individual with a genetic biomarker in KRAS is one or more of a therapeutic directed to RAS, an inhibitor of the receptor's tyrosine kinase, an inhibitor of the Ras-Raf-MEK-ERK pathway , an inhibitor of the PI3K-Akt-mTOR pathway and a farnesyl transferase inhibitor. In some modalities, the therapeutic targeting RAS is one or more of SML-10-70-4 and AA12. In some embodiments, the Ras-Raf-MEK-ERK pathway inhibitor is one or more of a BRAF inhibitor, a MEK inhibitor and an ERK inhibitor. In some methods of making the BRAF inhibitor is one or more Vemurafenib (ZELBO- RAF®) dabrafenibe (TAFINLAR®) and encorafenibe (BRAFTOVITM), BMS-908662 (XL281), sorafenib LGX818, PLX3603, RAF265, RO5185426 , GSK2118436, ARQ736, GDC-0879, PLX-4720, AZ304,

PLX-8394, HM95573, RO5126766 and LXH254. In some modalities, the MEK inhibitor is one or more of trametinib (MEKINIST®, GSK1120212), cobimetinib (COTELLIC®), binimetinib (MEKTOVI®, MEK162), selumetinib (AZD6244), PD0325901, MSC1973369B, SHR1973369B, SHR1973369B, SHR 733, RO5126766, CS3006, WX-554, PD98059, CI1040 (PD184352) and hypotomycin.

In some embodiments, the ERK inhibitor is one or more of FRI-20 (ON-01060), VTX-11e, 25-OH-D3-3-BE (B3CD, bromoacetoxialcidiol), FR-180204, AEZ-131 (AEZS - 131), AEZS-136, AZ-13767370, BL-EI-001, LY-3214996, LTT-462, KO-947, KO-947, MK-8353 (SCH900353), SCH772984, ulixertinib (BVD-523), CC -90003, GDC-0994 (RG-7482), ASN007, FR148083, 5-7-Oxozeaenol, 5-iodotubercidine, GDC0994 and ONC201. In some embodiments, the PI3K-Akt-mTOR pathway inhibitor is one or more of a PI3K inhibitor, an AKT inhibitor and an mTOR inhibitor.

In some embodiments, the PI3K inhibitor is one or more of buparlisib (BKM120), alpelisib (BYL719), WX-037, copanlisib (ALIQOPATM, BAY80-6946), dactolisib (NVP-BEZ235, BEZ-235), taselisibe (GDC-0032, RG7604), sono-lysib (PX-866), CUDC-907, PQR309, ZSTK474, SF1126, AZD8835, GDC-0077, ASN003, pictilisib (GDC-0941), pilaralisib (XL147, SAR245408), gedatolisibe (PF-05212384, PKI-587), serabelisib (TAK-117, MLN1117, INK 1117), BGT-226 (NVP-BGT226), PF-04691502, apitolisib (GDC-0980), omipalisib (GSK2126458, GSK458), voxt - sibe (XL756, SAR245409), AMG 511, CH5132799, GSK1059615, GDC- 0084 (RG7666), VS-5584 (SB2343), PKI-402, wortmannin, LY294002, PI-103, rigosertibe, XL-765, LY2023414, SAR260 , KIN-193 (AZD-6428), GS-9820, AMG319 and GSK2636771. In some embodiments, the AKT inhibitor is one or more of miltefosine (IMPADIVO®), wortmannin, NL-71-101, H-89, GSK690693, CCT128930, AZD5363, ipatasertib (GDC-0068, RG7440), A- 674563, A -443654, AT7867, AT13148, upro-

sertib, afuresertibe, DC120, 2- [4- (2-aminoprop-2-yl) phenyl] -3-phenylquinoxaline, MK-2206, edelfosine, miltefosine, perifosine, erucilofofocolina, eruphosine, SR13668, OSU-A9 , PH-316, PHT-427, PIT-1, DM-PIT-1, triciribin (triciribin phosphate monohydrate), API-1, N- (4- (5- (3-acetamidophenyl) -2 - (2-aminopyridin-3-yl) -3H-imidazo [4,5-b] pyridin-3-yl) benzyl) -3-fluoro-benzamide, ARQ092, BAY 1125976, 3-oxo-tyrucalic acid, lactoquino- mycine, boc-Phe-vinyl ketone, Perifosine (D-21266), TCN, TCN-P, GSK2141795 and ONC201. In some embodiments, the mTOR inhibitor is one or more of MLN0128, AZD-2014, CC-223, AZD2014, CC-115, everolimus (RAD001), temsirolimus (CCI-779), ridaforolimus (AP-23573) and sirolimus ( rapamycin). In some embodiments, the farnesyl transferase inhibitor is one or more of telafarnib, tipifarnib, BMS-214662, L778123, L744832 and FTI-277. In some modalities, the therapeutic intervention administered to the individual with a genetic biomarker in KRAS is an MEK inhibitor and an PI3K inhibitor.

In some modalities, the therapeutic intervention administered to the individual with a genetic biomarker in KRAS is an MEK inhibitor and an ERK inhibitor.

Other effective therapeutic interventions for treating an individual with a KRAS genetic biomarker are known in the art.

In some modalities, a therapeutic intervention administered to the individual with a genetic biomarker in KRAS is effective in treating cancer in the individual.

For example, after administering a therapeutic intervention that is effective in treating an individual with a genetic biomarker in KRAS, the number of cancer cells in the individual can be reduced, the size of one or more tumors in the individual can be reduced. , the rate or extent of metastasis may be reduced, symptoms associated with the disease or disorder or condition may be alleviated or partially alleviated, the state of the disease may be stabilized (ie, not worsened) and / or survival may be prolonged compared to expected survival if not receiving treatment.

[822] In some modalities, when an individual is identified as having a genetic biomarker (for example, a mutation) in AKT1, the individual is administered with a therapeutic intervention. In some embodiments, an individual identified as having a genetic biomarker (for example, a mutation) in AKT1 is identified as having cancer (for example, based on the presence of the genetic biomarker, alone or in combination with the presence other genetic biomarkers and / or the presence of one or more members of other classes of biomarkers and / or the presence of aneuploidy as described herein). In some modalities, the therapeutic intervention administered to the individual with a genetic biomarker in AKT1 is one or more of miltefosine (IMPADIVO®), wortmannin, NL-71-101, H-89, GSK690693, CCT128930, AZD5363, ipatasertib (GDC-0068, RG7440), A-674563, A -443654, AT7867, AT13148, upro-sertib, afuresertib, DC120, 2- [4- (2-aminoprop-2-yl) phenyl] -3-phenylquinoxamine , MK-2206, edelfosine, miltefosina, perifosina, erucilfofocolina, erunosphine, SR13668, OSU-A9, PH-316, PHT-427, PIT-1, DM-PIT-1, triciribbin (triciribin phosphate monohydrate) , API-1, N- (4- (5- (3-acetamidophenyl) -2- (2-aminopyridin-3-yl) -3H-imidazo [4,5-b] pyridin-3-yl) benzyl ) -3-fluoro-benzamide, ARQ092, BAY 1125976, 3-oxo-tyrucalic acid, lactoquino-mycine, boc-Phe-vinyl ketone, Perifosine (D-21266), TCN, TCN-P, GSK2141795 and ONC201. Other effective therapeutic interventions for treating an individual with an AKT1 genetic biomarker are known in the art. In some modalities, a therapeutic intervention administered to the individual with a genetic biomarker in AKT1 is effective in the treatment of cancer in the individual. For example, after administering a therapeutic intervention that is effective in treating an individual with a genetic biomarker in the

AKT1, the number of cancer cells in the individual can be reduced, the size of one or more tumors in the individual can be reduced, the rate or extent of metastasis can be reduced, the symptoms associated with the disease or disorder or condition can be reduced. be totally or partially relieved, the state of the disease can be stabilized (that is, not worsened) and / or the survival can be prolonged in comparison with the expected survival if not receiving treatment.

[823] In some modalities, when an individual is identified as having a genetic biomarker (for example, a mutation) in TP53, the individual is administered with a therapeutic intervention. In some embodiments, an individual identified as having a genetic biomarker (for example, a mutation) in TP53 is identified as having cancer (for example, based on the presence of the genetic biomarker, alone or in combination with the presence of others genetic biomarkers and / or the presence of one or more members of other classes of biomarkers and / or the presence of aneuploidy as described here). In some modalities, the therapeutic intervention administered to the individual with a genetic biomarker in TP53 is one or more of reactivation and induction of p53 of massive apoptosis-1 (PRIMA-1), APR-246 (PRIMA-1MET), 2-sulfonylpyrimidines like PK11007, pyrazoles like PK7088, metallochaperone-zinc-1 (ZMC1; NSC319726 / ZMC 1), a thiosemicarbazone (eg COTI-2), CP-31398, STIMA-1 (SH group targeting compound that induces massive apoptosis), MIRA-1 (NSC19630) and its analogues MIRA-2 and -3, RITA (NSC652287), Chetomin (CTM), PK7088, adherent acid (NSC87511), p53R3, SCH529074, WR-1065, inhibitors Hsp90 (eg 17-AAG, geldanamycin, ganetespibe, AUY922, IPI-504), HDAC inhibitors (eg, vorinostat / SAHA, romidepsin / depipeptide, HBI-8000), arsenic compounds, acid gambogic, spangin-1, YK-3-237, NSC59984, disulfiram (DSF), gentamicin, G418 and amikamycin, reactivate transcriptional activity (RETRA), PD0166285, MDM2 inhibitors (for example, RG 7112 (RO5045337), RO5503781, MI ‑ 773 (SAR405838), DS ‑ 3032b, AM-8553, AMG 232, MI-219, MI-713, MI-888, TDP521252, NSC279287, PXN822, SAH-8 (staple peptides) ATSP-7041, spiroligomer, PK083, PK5174, PK5196, PK7088, nutlin 3a, RG7388, Ro-2443, adherent acid and NSC319726), and MDM4 inhibitors. Other effective therapeutic interventions for treating an individual with a TP53 genetic biomarker are known in the art. In some modalities, a therapeutic intervention administered to the individual with a genetic biomarker in TP53 is effective in treating cancer in the individual. For example, after the administration of a therapeutic intervention that is effective in treating an individual with a genetic biomarker in TP53, the number of cancer cells in the individual can be reduced, the size of one or more tumors in the individual can be reduced. , the rate or extent of metastasis may be reduced, symptoms associated with the disease or disorder or condition may be totally or partially relieved, the state of the disease may be stabilized (ie, not worsened) and / or survival may be prolonged compared to expected survival if not receiving treatment.

[824] In some modalities, when an individual is identified as having a genetic biomarker (for example, a mutation) in PPP2R1A, the individual is administered with a therapeutic intervention. In some embodiments, an individual identified as having a genetic biomarker (for example, a mutation) in PPP2R1A is identified as having a cancer (for example, based on the presence of the genetic biomarker, alone or in combination with the presence of other genetic biomarkers and / or the presence of one or more members of other classes of biomarkers and / or the presence of aneuploidy as described herein). In some modalities, the therapeutic intervention administered to the individual with a genetic biomarker in PPP2R1A is one or more PP2A activators, such as SET inhibitors (for example, FTY-720, ceramide and OP449). Other effective therapeutic interventions for treating an individual with a genetic biomarker in PPP2R1A are known in the art. In some modalities, a therapeutic intervention administered to the individual with a genetic biomarker in PPP2R1A is effective in the treatment of cancer in the individual. For example, after administering a therapeutic intervention that is effective in treating an individual with a genetic biomarker in PPP2R1A, the number of cancer cells in the individual can be reduced, the size of one or more tumors in the individual can be reduced. , the rate or extent of metastasis may be reduced, the symptoms associated with the disease or disorder or condition may be totally or partially relieved, the condition of the disease may be stabilized (that is, not worsened) and / or survival it can be prolonged compared to expected survival if not receiving treatment.

[825] In some modalities, when an individual is identified as having a genetic biomarker (for example, a mutation) in GNAS, the individual is administered with a therapeutic intervention. In some modalities, an individual identified as having a genetic biomarker (for example, a mutation) in GNAS is identified as having a cancer (for example, based on the presence of the genetic biomarker, alone or in combination with the presence other genetic biomarkers and / or the presence of one or more members of other classes of biomarkers and / or the presence of aneuploidy as described herein). In some modalities, the therapeutic intervention administered to the individual with a genetic biomarker in the GNAS is one or more of an inhibitor of the Ras-Raf-MEK-ERK pathway and an inhibitor of WNT / β-catenin signaling.

In some modalities, the Ras-Raf-MEK-ERK pathway inhibitor is one or more of a BRAF inhibitor, a MEK inhibitor and an ERK inhibitor.

In some modalities, the BRAF inhibitor is one or more of vemurafenib (ZELBO-RAF®), dabrafenib (TAFINLAR®) and encouragefenib (BRAFTOVITM), BMS-908662 (XL281), sorafenib, LGX818, PLX3603, RAF265, RO , GSK2118436, ARQ736, GDC-0879, PLX-4720, AZ304, PLX-8394, HM95573, RO5126766 and LXH254. In some modalities, the MEK inhibitor is one or more of trametinib (MEKINIST®, GSK1120212), cobimetinib (COTELLIC®), binimetinib (MEKTOVI®, MEK162), selumetinib (AZD6244), PD0325901, MSC1973369B, SHR1973369B, SHR1973369B, SHR 733, RO5126766, CS3006, WX-554, PD98059, CI1040 (PD184352) and hypotomycin.

In some embodiments, the ERK inhibitor is one or more of FRI-20 (ON-01060), VTX-11e, 25-OH-D3-3-BE (B3CD, bromoacetoxialcidiol), FR-180204, AEZ-131 (AEZS - 131), AEZS-136, AZ-13767370, BL-EI-001, LY-3214996, LTT-462, KO-947, KO-947, MK-8353 (SCH900353), SCH772984, ulixertinib (BVD-523), CC -90003, GDC-0994 (RG-7482), ASN007, FR148083, 5-7-Oxozeaenol, 5-iodotubercidine, GDC0994 and ONC201. In some embodiments, the WNT / β-catenin signaling inhibitor is one or more of PRI-724, CWP232291, PNU74654, 2.4 diamino-quinazoline, PKF115-584, PKF118-744, PKF118-310, PFK222-815, CGP 049090, ZTM000990, BC21, vitamin D, retinoid acid, aspirin, sulindac (CLINORIL®, Aflodac), 2,4-diamino-quinazoline derivatives, methyl 3 - {[(4-methylphenyl) sulphanil] amino } benzoate (MSAB), AV65, iCRT3, iCRT5, iCRT14, PRI-724, CWP232291, PNU74654, 2.4 diamino-quinazoline, PKF115-584, PKF118-744, PKF118-310, PFK222-815, CGP 049090, ZTM000 BC21, vitamin D, retinoid acid, aspirin, sulindac (CLINORIL®, Aflodac), 2,4-diamino-quinazoline derivatives, methyl 3 - {[((4-methylphenyl) sulphenyl] amino} benzoate (MSAB), AV65, iCRT3, iCRT5, iCRT14, SM04554,

LGK 974, XAV939, curcumin (eg Meriva®), quercetin, epigallocatechin gallate (EGCC), resveratrol, DIF, genistein, celecoxib (CELEBREX®), CWP232291, NSC668036, FJ9, BML-286 (3289 - 8625), IWP, IWP-1, IWP-2, JW55, G007-LK, pyrvinium, foxy-5, Wnt-5a, ipafricept (OMP-54F28), vantictumab (OMP-18R5), OTSA 101, OTSA101 -DTPA -90Y, SM04690, SM04755, nutlin-3a, XAV939, IWR1, JW74, ocadaic acid, tautomycin, SB239063, SB203580, adenosine diphosphate (hydroxymethyl) pyrrolidinediole (ADP-HPD), 2- [4- (4-fluorophenyl) pi - perazin-1-yl] -6-methylpyrimidin-4 (3H) -one, PJ34, niclosamide (NICLOCI- DETM), cambinol, sulindac (CLINORIL®, Aflodac), J01-017a, NSC668036, philippine, IC261, PF670462, bosutinib (BOSULIF®), PHA665752, imatinib (GLEEVEC®), ICG-001, ethacrylic acid, ethacrylic acid (ethacrylic derivative) -0941), Rp-8-Br-cAMP, SDX-308, WNT974, CGX1321, ETC-192215 , AD-REIC / Dkk3, WIKI4 and windorphen. Other effective therapeutic interventions for treating an individual with a genetic biomarker in GNAS are known in the art. In some modalities, a therapeutic intervention administered to the individual with a genetic biomarker in GNAS is effective in the treatment of cancer in the individual. For example, after administering a therapeutic intervention that is effective in treating an individual with a genetic biomarker in GNAS, the number of cancer cells in the individual can be reduced, the size of one or more tumors in the individual can be reduced. , the rate or extent of metastasis may be reduced, symptoms associated with the disease or disorder or condition may be totally or partially relieved, the state of the disease may be stabilized (ie, not worsened) and / or survival may be prolonged compared to expected survival if not receiving treatment.

[826] In some modalities, when an individual is identified

cated as having a genetic biomarker (for example, a mutation) in SMAD4, the individual is administered with a therapeutic intervention.

In some embodiments, an individual identified as having a genetic biomarker (for example, a mutation) in SMAD4 is identified as having cancer (for example, based on the presence of the genetic biomarker, alone or in combination with the presence other genetic biomarkers and / or the presence of one or more members of other classes of biomarkers and / or the presence of aneuploidy as described herein). In some modalities, the therapeutic intervention administered to the individual with a genetic biomarker in SMAD4 is one or more of a PI3K inhibitor, antiangiogenic therapy and chemotherapy based on 5-FU.

In some embodiments, the PI3K inhibitor is one or more of buparlisib (BKM120), alpelisib (BYL719), WX-037, copanlisib (ALIQOPATM, BAY80-6946), dactolisib (NVP-BEZ235, BEZ-235), taselisibe (GDC-0032, RG7604), sono-lysib (PX-866), CUDC-907, PQR309, ZSTK474, SF1126, AZD8835, GDC-0077, ASN003, pictilisib (GDC-0941), pilaralisib (XL147, SAR245408), gedatolisibe (PF-05212384, PKI-587), serabelisib (TAK-117, MLN1117, INK 1117), BGT-226 (NVP-BGT226), PF-04691502, apitolisib (GDC-0980), omipalisib (GSK2126458, GSK458), voxt - sibe (XL756, SAR245409), AMG 511, CH5132799, GSK1059615, GDC- 0084 (RG7666), VS-5584 (SB2343), PKI-402, wortmannin, LY294002, PI-103, rigosertibe, XL-765, LY2023414, SAR260 , KIN-193 (AZD-6428), GS-9820, AMG319 and GSK2636771. In some modalities, antiangiogenic therapy is an inhibitor of one or more of VEGFR1, VEGFR, VEGFR2, VEGFA, CDH5, EDNRA, ANGPT2, CD34 and ANGPT.

In some embodiments, antiangiogenic therapy is one or more of vatalanib (PTK787 / ZK222584), TKI-538, sunitinib (SU11248, SUTENT®), pazopanib (VOTRIENT®), bevacizumab (AVASTIN®), talidamide, lenalidomide (REVLIMID®), ranibizumab, EYE001 and axitinib

(AG013736, INLYTA®). Other effective therapeutic interventions for treating an individual with a SMAD4 genetic biomarker are known in the art. In some modalities, a therapeutic intervention administered to the individual with a genetic biomarker in SMAD4 is effective in the treatment of cancer in the individual. For example, after administering a therapeutic intervention that is effective in treating an individual with a genetic biomarker on SMAD4, the number of cancer cells in the individual can be reduced, the size of one or more tumors in the individual can be reduced, rate or extent of metastasis may be reduced, symptoms associated with the disease or disorder or condition may be alleviated or partially alleviated, the state of the disease may be stabilized (ie, not worsened) and / or survival may be prolonged in compared to expected survival if not receiving treatment.

[827] In some modalities, when an individual is identified as having a genetic biomarker (for example, a mutation) in the POLE, the individual is administered with a therapeutic intervention. In some embodiments, an individual identified as having a genetic biomarker (for example, a mutation) in POLE is identified as having a cancer (for example, based on the presence of the genetic biomarker, alone or in combination with the presence other genetic biomarkers and / or the presence of one or more members of other classes of biomarkers and / or the presence of aneuploidy as described herein). In some modalities, the therapeutic intervention administered to the individual with a genetic biomarker in POLE is one or more immunotherapy and an immune checkpoint inhibitor. Other effective therapeutic interventions for treating an individual with a POLE genetic biomarker are known in the art. In some modalities, a therapeutic intervention administered to the individual with a genetic biomarker in

POLE is effective in treating cancer in the individual. For example, after administering a therapeutic intervention that is effective in treating an individual with a genetic biomarker in POLE, the number of cancer cells in the individual can be reduced, the size of one or more tumors in the individual can be reduced. , the rate or extent of metastasis may be reduced, symptoms associated with the disease or disorder or condition may be alleviated or partially alleviated, the state of the disease may be stabilized (ie, not worsened) and / or survival may be prolonged compared to expected survival if not receiving treatment.

[828] In some modalities, when an individual is identified as having a genetic biomarker (for example, a mutation) in the RNF43, the individual is administered with a therapeutic intervention. In some modalities, an individual identified as having a genetic biomarker (for example, a mutation) in the RNF43 is identified as having a cancer (for example, based on the presence of the genetic biomarker, alone or in combination with the presence other genetic biomarkers and / or the presence of one or more members of other classes of biomarkers and / or the presence of aneuploidy as described herein). In some modalities, the therapeutic intervention administered to the individual with a genetic biomarker in RNF43 is one or more dendritic cells pulsed by autologous RNF43 peptide (DCs), DCs pulsed with RNF43 peptide, low systemic dose of interleukin- 2 and PORCN inhibitors. In some embodiments, the PORCN inhibitor is one or more of RXC0004, ETC-1922159, ETC-159, IWP-2, LGK974 and WNT-C59. Other effective therapeutic interventions for treating an individual with a genetic biomarker in RNF43 are known in the art. In some modalities, a therapeutic intervention administered to the individual with a genetic biomarker in RNF43 is effective in the treatment of cancer in the individual. For example, after administering a therapeutic intervention that is effective in treating an individual with a genetic biomarker in RNF43, the number of cancer cells in the individual can be reduced, the size of one or more tumors in the individual can be reduced , the rate or extent of metastasis may be reduced, the symptoms associated with the disease or disorder or condition may be totally or partially relieved, the state of the disease may be stabilized (ie, not worsened) and / or survival may be prolonged. compared to expected survival if not receiving treatment.

[829] In some modalities, when an individual is identified as having a genetic biomarker (for example, a mutation) in MAPK1, the individual is administered with a therapeutic intervention. In some embodiments, an individual identified as having a genetic biomarker (for example, a mutation) in MAPK1 is identified as having a cancer (for example, based on the presence of the genetic biomarker, alone or in combination with the presence other genetic biomarkers and / or the presence of one or more members of other classes of biomarkers and / or the presence of aneuploidy as described herein). In some embodiments, the therapeutic intervention administered to the individual with a genetic biomarker in MAPK1 is one or more of an ERK inhibitor, a MEK inhibitor, an ERBB receptor inhibitor (for example, an EGFR inhibitor or an HER2 inhibitor) or inhibitor of the PI3K-Akt-mTOR pathway. In some embodiments, the ERK inhibitor is one or more of FRI-20 (ON-01060), VTX-11e, 25-OH-D3-3-BE (B3CD, bromoacetoxialcidiol), FR-180204, AEZ-131 (AEZS - 131), AEZS-136, AZ-13767370, BL-EI-001, LY-3214996, LTT-462, KO-947, KO-947, MK-8353 (SCH900353), SCH772984, ulixertinib (BVD-523), CC -90003, GDC-0994 (RG-7482), ASN007, FR148083, 5-7-Oxozeaenol, 5-iodotubercidine, GDC0994,

ONC201. In some embodiments, the MEK inhibitor is one or more of trametinib (MEKINIST®, GSK1120212), cobimetinib (COTELLIC®), binimetinib (MEKTOVI®, MEK162), selumetinib (AZD6244), PD0325901, MSC1936369B, SHR3390, TA RO5126766, CS3006, WX-554, PD98059, CI1040 (PD184352) and hypotomycin.

In some embodiments, the PI3K-Akt-mTOR pathway inhibitor is one or more of a PI3K inhibitor, an AKT inhibitor and an mTOR inhibitor.

In some embodiments, the PI3K inhibitor is one or more of buparlisib (BKM120), alpelisib (BYL719), WX-037, copanlisib (ALIQOPATM, BAY80-6946), dactolisib (NVP-BEZ235, BEZ-235), taselisib (GDC - 0032, RG7604), sonolisib (PX-866), CUDC-907, PQR309, ZSTK474, SF1126, AZD8835, GDC-0077, ASN003, pictilisib (GDC-0941), pilaris-lysib (XL147, SAR245408), gedatolisib (PF -05212384, PKI-587), rabelisib (TAK-117, MLN1117, INK 1117), BGT-226 (NVP-BGT226), PF-04691502, apitolisib (GDC-0980), omipalisib (GSK2126458, GSK45is), voxtal (XL756, SAR245409), AMG 511, CH5132799, GSK1059615, GDC-0084 (RG7666), VS-5584 (SB2343), PKI-402, wortmann, LY294002, PI-103, rigosertibe, XL-765, LY2023414, SAR260301 , KIN-193 (AZD-6428), GS-9820, AMG319 and GSK2636771. In some embodiments, the AKT inhibitor is one or more of miltephosine (IMPADIVO®), wortmannin, NL-71-101, H-89, GSK690693, CCT128930, AZD5363, ipatasertib (GDC-0068, RG7440), A- 674563, A -443654, AT7867, AT13148, uprosertib, afuresertib, DC120, 2- [4- (2-aminoprop-2-yl) phenyl] -3-phenylquinoxaline, MK-2206, edelfosine, miltephosine, perifosine, erucylfofocoline , erufosin, SR13668, OSU-A9, PH-316, PHT-427, PIT-1, DM-PIT-1, triciribine (trichiribine phosphate monohydrate), API-1, N- (4- (5- ( 3-acetamidophenyl) -2- (2-aminopyridin-3-yl) -3H-imidazzo [4,5-b] pyridin-3-yl) benzyl) -3-fluorobenzamide, ARQ092, BAY 1125976, acid 3- oxo-tirucálico, lactoquinomycin, boc-Phe-vinyl ketone,

Perifosine (D-21266), TCN, TCN-P, GSK2141795 and ONC201. In some embodiments, the mTOR inhibitor is one or more of MLN0128, AZD-2014, CC-223, AZD2014, CC-115, everolimus (RAD001), temsirolimus (CCI-779), ridaforolimus (AP-23573) and sirolimus (rapamycin). In some embodiments, the HER2 inhibitor is one or more of AZD8931, AST1306, AEE788, CP724714, CUDC101, TAK285, dacomitinib, peli- tinib, AC480, trastuzumab (HERCEPTIN®), pertuzumab (PER- JETA®), trastuz (DR) DXL-702, E-75, PX-104.1, ZW25, CP-724714, irbinitinib (ARRY-380, ONT-380), TAS0728, lapatinib (TYKERB®, TYVERB®), AST-1306, AEE-788 , perlitinib (EKB-569), afatinib (BIBW 2992, GILOTRIF®), neratinib (HKI-272, NERLYNX®, PKI-166, D-69491, HKI-357, AP32788, GW572016, canertinib (CI-1033), AC -480 (BMS-599626), dacomitinib (PF299804, PF299), RB-200h, ARRY-334543 (ARRY-543, ASLAN001), poziotinib (NOV120101), CUDC-101, emodine, IDM-1, ado-trastuzumab emtan- sign (KADCYLA®), Zemab, DS-8201a, T-DM1, anti-HER2 CART, HER2-Peptid-Vakzine and activated T cells armed with HER2Bi.

In some embodiments, the EGFR inhibitor is osimertinib (AZD9291, merelectinib, TAGRISSOTM), erlotinib (TARCEVA®), gefitinib (IRESSA®), cetuximab (ERBITUX®), necitumumab (PORTRAZ-ZATM, IMC-11F8) -272, NERLYNX®), lapatinib (TYKERB®), panitumumab (ABX-EGF, VECTIBIX®), vandetanib (CAPRELSA®), rociletinib (CO-1686), olmutinib (OLTM), HM61713, BI-1482694), naotinib ( ASP8273), nazartinib (EGF816, NVS-816), PF-06747775, icotinib (BPI-2009H), afatinib (BIBW 2992, GILO-TRIF®), dacomitinib (PF-00299804, PF-804, PF-299, PF- 299804), avininib (AC0010), AC0010MA EAI045, matuzumab (EMD-7200), nimo-tuzumab (h-R3, BIOMAb EGFR®), zalutumab, MDX447, depatuxizumab (humanized mAb 806, ABT-806)) , depatuxizumab mafodotina

(ABT-414), ABT-806, mAb 806, canertinib (CI-1033), shikonin, shikonin derivatives (for example, deoxysikonin, isobutyrylshikonin, acetylshikonin, β, β-dimethylacrilshikonina and acetylalinine, 2010) HM781-36B), AV-412, ibrutinib, WZ4002, brigatinib (AP26113, ALUNBRIG®), pelitinib (EKB-569), tarloxotinib (TH-4000, PR610), BPI-15086, Hemay022 ZN-e4, tesevatinibe (KD019, XL647), YH25448, epitinib (HMPL-813), CK-101, MM-151, AZD3759, ZD6474, PF-06459988, varlintinib (ASLAN001, ARRY-334543), AP32788, HLX07, DL -0316, AEE788, HS-10296 , avitinib, GW572016, pyrotinib (SHR1258), SCT200, CPGJ602, Sym004, MAb-425, modotuximab (TAB-H49), futuximab (992 DS), zalutumumabe, KL-140, RO5083945, JNJ-61186372, Sym LY316 595, autologous T cells armed with EGFRBi and therapy with EGFR CAR-T. Other effective therapeutic interventions for treating an individual with a MAPK1 genetic biomarker are known in the art. In some embodiments, a therapeutic intervention administered to the individual with a MAPK1 genetic biomarker is effective in treating cancer in the individual. For example, after administering a therapeutic intervention that is effective in treating an individual with a genetic biomarker in MAPK1, the number of cancer cells in the individual may be reduced, the size of one or more tumors in the individual may be reduced. be reduced, the rate or extent of metastasis may be reduced, symptoms associated with the disease or disorder or condition may be totally or partially relieved, the state of the disease may be stabilized (ie, not worsened) and / or survival may be prolonged compared to expected survival if not treated.

[830] In some modalities, when an individual is identified as having a genetic biomarker (for example, a mutation)

tion) in PI3KR1, the individual is administered with a therapeutic intervention.

In some embodiments, an individual identified as having a genetic biomarker (for example, a mutation) in PI3KR1 is identified as having a cancer (for example, based on the presence of the genetic biomarker, alone or in combination with the presence other genetic biomarkers and / or the presence of one or more members of other classes of biomarkers and / or the presence of aneuploidy as described herein). In some embodiments, the therapeutic intervention administered to the individual with a PI3KR1 genetic biomarker is one or more than one or more of a panPI3K inhibitor, a double PI3K and mTOR inhibitor and an Ras-Raf- MEK- ERK.

In some embodiments, the panPI3K inhibitor is buparlisib (BKM120), copanlisib (ALIQOPATM, BAY80-6946), sonolisib (PX-866), ZSTK474, pictilisib (GDC-0941), pilaralisib (XL147, SAR245408), AMG 511, 402 , wortmannin, LY294002 and WX-037. In some embodiments, the dual inhibitor PI3K and mTOR is dactolisib (NVP-BEZ235, BEZ-235), PQR309, SF1126, gedatolisib (PF-05212384, PKI- 587), BGT-226 (NVP-BGT226), PF- 04691502, apitolisib (GDC-0980), omipalisib (GSK2126458, GSK458), voxtalisib (XL756, SAR245409), GSK1059615, GDC-0084 (RG7666), VS-5584 (SB2343) and PI-103. In some modalities, the therapeutic intervention administered to the individual with a genetic biomarker in PIK3CA is one or more of buparisysib (BKM120), alpelisib (BYL719), WX-037, copanlisib (ALIQO- PATM, BAY80-6946), dactolisib (NVP-BEZ235, BEZ-235), taselisib (GDC-0032, RG7604), sonolisib (PX-866), CUDC-907, PQR309, ZSTK474, SF1126, AZD8835, GDC-0077, ASN003, pictilisibe (GDC-0941 ), pilaralisib (XL147, SAR245408), gedatolisib (PF-05) PKI-587), serabelisib (TAK-117, MLN1117, INK 1117), BGT-226 (NVP-BGT226), PF-04691502, apitolisib (GDC-0980 ), omipalisib (GSK2126458, GSK458), voxtalisib (XL756, SAR245409), AMG 511, CH5132799,

GSK1059615, GDC-0084 (RG7666), VS-5584 (SB2343), PKI-402, wortmann, LY294002, PI-103, rigosertibe, XL-765, LY2023414, SAR260301, KIN-193 (AZD- 6428), GS -9820, AMG319 and GSK2636771. In some embodiments, the Ras-Raf-MEK-ERK pathway inhibitor is one or more of a BRAF inhibitor, a MEK inhibitor and an ERK inhibitor.

In some embodiments, the BRAF inhibitor is one or more of vemurafenib (ZELBORAF®), dabrafenib (TAFINLAR®) and encouragefenib (BRAFTOVITM), BMS-908662 (XL281), sorafenib, LGX818, PLX3603, RAF265, GS58454 , ARQ736, GDC-0879, PLX-4720, AZ304, PLX-8394, HM95573, RO5126766 and LXH254. In some modalities, the MEK inhibitor is one or more of trametinib (MEKINIST®, GSK1120212), cobimetinib (COTELLIC®), binimetinib (MEKTOVI®, MEK162), selumetinib (AZD6244), PD0325901, MSC1973909, 733, RO5126766, CS3006, WX-554, PD98059, CI1040 (PD184352) and hypotomycin.

In some embodiments, the ERK inhibitor is one or more of FRI-20 (ON-01060), VTX-11e, 25-OH-D3-3-BE (B3CD, bromoacetoxialcidiol), FR-180204, AEZ-131 (AEZS - 131), AEZS-136, AZ-13767370, BL-EI-001, LY-3214996, LTT-462, KO-947, KO-947, MK-8353 (SCH900353), SCH772984, ulixertinib (BVD-523), CC -90003, GDC-0994 (RG-7482), ASN007, FR148083, 5-7-Oxozeaenol, 5-iodotubercidine, GDC0994 and ONC201. Other effective therapeutic interventions for treating an individual with a PI3KR1 genetic biomarker are known in the art.

In some embodiments, a therapeutic intervention administered to the individual with a PI3KR1 genetic biomarker is effective in treating cancer in the individual.

For example, after administering a therapeutic intervention that is effective in treating an individual with a genetic biomarker in PI3KR1, the number of cancer cells in the individual can be reduced, the size of one or more tumors in the individual can be reduced.

reduced, the rate or extent of metastasis may be reduced, symptoms associated with the disease or disorder or condition may be totally or partially relieved, the state of the disease may be stabilized (that is, not worsened) and / or survival may be prolonged compared to expected survival if not receiving treatment.

[831] In some modalities, when an individual is identified as having a genetic biomarker (for example, a mutation) in FGFR3, the individual is administered with a therapeutic intervention. In some embodiments, an individual identified as having a genetic biomarker (for example, a mutation) in FGFR3 is identified as having a cancer (for example, based on the presence of the genetic biomarker, alone or in combination with the presence other genetic biomarkers and / or the presence of one or more members of other classes of biomarkers and / or the presence of aneuploidy as described herein). In some modalities, the therapeutic intervention administered to the individual with a genetic biomarker in FGFR3 is one or more of an anti-FGFR3 antibody, a selective inhibitor of FGFR3 and an inhibitor of pan-FGFR. In some modalities, therapeutic intervention is a covalent inhibitor of FGFR (for example, PRN1371, BLU9931, FIIN-4, H3B-6527 and FIIN-2). In some embodiments, therapeutic interventions are a non-covalent inhibitor of FGFR (for example, AZD4547, BGJ398, Debio-1347, dovitinib, JNJ-42756493 and LY2874455). In some embodiments, the anti-FGFR3 antibody is), MFGR1877S or B-701. In some modalities, the therapeutic intervention is one or more of MFGR1877S, B-701, FP-1039 (GSK230), NVP-BGJ398, JNJ-42756493 (erdafitinib), rogaraintinibe (BAY1163877), FIIN-2, JNJ -42756493, LY2874455, lenvatinib (E7080), ponatinib (AP24534), regorafenib (BAY 73-4506), dovitinib (TKI258), lucitanib (E3810), cediranib (AZD2171), intedanib (BIBF

1120), brivanibe (BMS-540215), ASP5878, AZD4547, BGJ398 (infigratinib), Debio-1347, dovitinib, E7090, HMPL-453, nintedanib (OFEV®, BIBF 1120), MAX-40279, XL999, orantinib ( SU6668), pazopanib (VO-TRIENT®), anlotinib and AL3818. Other effective therapeutic interventions for treating an individual with a genetic biomarker in FGFR3 are known in the art. In some modalities, a therapeutic intervention administered to the individual with a genetic biomarker in FGFR3 is effective in the treatment of cancer in the individual. For example, after administering a therapeutic intervention that is effective in treating an individual with a genetic biomarker in FGFR3, the number of cancer cells in the individual can be reduced, the size of one or more tumors in the individual can be reduced , the rate or extent of metastasis may be reduced, symptoms associated with the disease or disorder or condition may be totally or partially relieved, the state of the disease may be stabilized (ie, not worsened) and / or survival may be prolonged compared to expected survival if not receiving treatment.

[832] In some modalities, when an individual is identified as having a genetic biomarker (for example, a mutation) in ERBB2, the individual is administered with a therapeutic intervention. In some modalities, an individual identified as having a genetic biomarker (for example, a mutation) in ERBB2 is identified as having a cancer (for example, based on the presence of the genetic biomarker, alone or in combination with the presence other genetic biomarkers and / or the presence of one or more members of other classes of biomarkers and / or the presence of aneuploidy as described herein). In some modalities, the therapeutic intervention administered to the individual with an ERBB2 genetic biomarker is one or more of an anti-ERBB2 antibody, a selective ERBB2 inhibitor and a pan-ERBB inhibitor.

In some modalities, therapeutic intervention is a covalent ERBB2 inhibitor. In some modalities, therapeutic intervention is a non-covalent ERBB2 inhibitor. In some modalities, the therapeutic intervention administered to the individual with a genetic biomarker in ERBB2 is one or more of AZD8931, AST1306, AEE788, CP724714, CUDC101, TAK285, dacomitinib, pelitinib, AC480, trastuzumab (HERCEP- TIN®), pertains ®), trastuzumab (DR) DXL-702, E-75, PX-104.1, ZW25, CP-724714, irbinitinib (ARRY-380, ONT-380), TAS0728, lapatinib (TYKERB®, TYVERB®), AST-1306 , AEE-788, perillinib (EKB-569), afatinib (BIBW 2992, GILOTRIF®), neratinib (HKI- 272, NERLYNX®, PKI-166, D-69491, HKI-357, AP32788, GW572016, canertinibe ( CI-1033), AC-480 (BMS-599626), dacomitinib (PF299804, PF299), RB-200h, ARRY-334543 (ARRY-543, ASLAN001), poziotinib (NOV120101), CUDC-101, emodine, IDM-1 , ado-trastuzumab emtanesine (KADCYLA®), Zemab, DS-8201a, T-DM1, anti-HER2 CART, HER2-Peptid-Vakzine and activated T cells armed with HER2Bi.

Other effective therapeutic interventions for the treatment of an individual with a genetic biomarker in ERBB2 are known in the art.

In some modalities, a therapeutic intervention administered to the individual with a genetic biomarker in ERBB2 is effective in the treatment of cancer in the individual.

For example, after administering a therapeutic intervention that is effective in treating an individual with a genetic biomarker in ERBB2, the number of cancer cells in the individual can be reduced, the size of one or more tumors in the individual can be reduced. , the rate or extent of metastasis may be reduced, symptoms associated with the disease or disorder or condition may be totally or partially relieved, the state of the disease may be stabilized (ie, not worsened) and / or survival may be prolonged compared to expected survival if not receiving treatment.

[833] In some modalities, when an individual is identified as having a genetic biomarker (for example, a mutation) in the MLL, the individual is administered with a therapeutic intervention. In some modalities, an individual identified as having a genetic biomarker (for example, a mutation) in the MLL is identified as having a cancer (for example, based on the presence of the genetic biomarker, alone or in combination with the presence of others genetic biomarkers and / or the presence of one or more members of other classes of biomarkers and / or the presence of aneuploidy as described here). In some modalities, the therapeutic intervention administered to the individual with a genetic biomarker in MLL is one or more of cytosine arabinoside, all-trans retinoic acid (ATRA), an HDAC inhibitor (for example, valproic acid and HBI-8000) , a DNA methyltransferase inhibitor (eg, decitabine), an LSD1 inhibitor (eg, ORY1001 (RG6016), ORY1001 (RG6016), GSK2879552, GSK2879552, INCB059872, IMG7289 and CC90011), girl 1 inhibitors (for example, MI1, MI2, MI3, Mi2-2 (MI-2-2), MI463, MI503, MIV-6R), DOLT1 inhibitors (histone-lysine KMT) (e.g. EPZ004777, EPZ-5676, SGC0946, CN-SAH , SYC-522, SAH and SYC-534) and WDR5-MLL antagonists (for example, MM-101, MM-102, MM-103, MM-401, WDR5-0101, WDR5-0102, WDR5-0103 and OICR -9429). Other effective therapeutic interventions for treating an individual with an MLL genetic biomarker are known in the art. In some modalities, a therapeutic intervention administered to the individual with a genetic biomarker in MLL is effective in the treatment of cancer in the individual. For example, after administering a therapeutic intervention that is effective in treating an individual with a genetic biomarker in the MLL, the number of cancer cells in the individual can be reduced, the size of one or more tumors in the individual can be reduced. , the rate or extent of metastasis may be reduced, symptoms associated with the disease or disorder or condition may be alleviated or partially alleviated, the state of the disease may be stabilized (ie, not worsened) and / or survival may be prolonged compared to expected survival if not receiving treatment.

[834] In some modalities, when an individual is identified as having a genetic biomarker (for example, a mutation) in the MET, the individual is administered with a therapeutic intervention. In some modalities, an individual identified as having a genetic biomarker (for example, a mutation) in the MET is identified as having a cancer (for example, based on the presence of the genetic biomarker, alone or in combination with the presence of others genetic biomarkers and / or the presence of one or more members of other classes of biomarkers and / or the presence of aneuploidy as described here). In some modalities, the therapeutic intervention administered to the individual with a genetic biomarker on the MET is a MET inhibitor, an HGF antagonist, an anti-HGF antibody (for example, rilotumumab (AMG102), ficlatuzumab (AV-299) and TAK701, YYB101) and a multiquinase inhibitor (for example, tivantinib (ARQ 197), golvatinib (E7050), cabozantinib (XL 184, BMS-907351), foretinib (GSK1363089), crizotinib (PF-02341066), MK- 2461, BPI-9016M, BPI-9016M, TQ-B3139, MGCD265 and MK-8033). In some embodiments, the MET inhibitor is one or more of capmatinib (INC280, INCB28060), onartuzumab (MetMAb), savolitinib, tepotinib (MSC2156119J, EMD1214063), CE-35562, AMG-337, AMG-458, fore-tinib, PHA-665725, MK-2461, PF-04217903 and SU11274, SU11274 and PHA-665752, SAIT301, HS-10241, ARGX-111, MSC2156119J, glume-tinib (SCC244), EMD 1204831, AZD6094 (savolitinibe, volitinibe, volitinibe, volitinibe, volitinibe,

HMPL-504), PLB1001, ABT-700, AMG 208, INCB028060, AL2846 and PF-

04217903. In some modalities, the therapeutic intervention administered to the individual with a genetic biomarker in the MET is one or more of capmatinib (INC280, INCB28060), onartuzumab (MetMAb), savolitinib, tepotinib (MSC2156119J, EMD1214063), CE-35562, AMG-337, AMG-458, Foretinib, PHA-665725, MK-2461, PF-04217903 and SU11274, SU11274 and PHA-665752, SAIT301, HS-10241, ARGX-111, MSC2156119J, glumetinib (SCC244), EMD 12031, AZD6094 (savolithinib, volitinib, HMPL-504), PLB1001, ABT-700, AMG 208, INCB028060, AL2846, PF-04217903, rilotumumab (AMG102), ficlatu-zumab (AV-299), and TAK701, YYB101, tivantinibe (ARQ 197), Golvatinib (E7050), Cabozantinib (XL 184, BMS-907351), Foretinib (GSK1363089), Crizotinib (PF-02341066), MK-2461, BPI-9016M, BPI-9016M, TQ-B3139, MGCD265, MK-8033, ABBV-399, HTI-1066, and JNJ-

61186372. Other effective therapeutic interventions for treating an individual with a genetic biomarker in MET are known in the art. In some modalities, a therapeutic intervention administered to the individual with a genetic biomarker in MET is effective in the treatment of cancer in the individual. For example, after administering a therapeutic intervention that is effective in treating an individual with a genetic biomarker on the MET, the number of cancer cells in the individual may be reduced, the size of one or more tumors in the individual may be reduced. be reduced, the rate or extent of metastasis may be reduced, symptoms associated with the disease or disorder or condition may be totally or partially relieved, the state of the disease may be stabilized (ie, not worsened) and / or survival may be prolonged compared to expected survival if not treated.

[835] In some modalities, when an individual is identified

cated as having a genetic biomarker (for example, a mutation) in VHL, the individual is administered with a therapeutic intervention.

In some modalities, an individual identified as having a genetic biomarker (for example, a mutation) in VHL is identified as having a cancer (for example, based on the presence of the genetic biomarker, alone or in combination with the presence of others genetic biomarkers and / or the presence of one or more members of other classes of biomarkers and / or the presence of aneuploidy as described here). In some modalities, the therapeutic intervention administered to the individual with a genetic biomarker in VHL is one or more of an antiangiogenic therapy (for example, inhibitors of one or more of VEGFR1, VEGFR, VEGFR2, VEGFA, CDH5, EDNRA, ANGPT2, CD34, and ANGPT) vatalanib (PTK787 / ZK222584), TKI-538, sunitinib (SU11248, SUTENT®), pazopanib (VOTRIENT®), bevacizumab (AVASTIN®), thalidomide, lenalidomide (REVLIMIDuma), ra- nib EYE001, 007 INLYTA®), a c-KIT inhibitor (eg, dovitinib (TKI258)), an HDAC inhibitor (eg, vorinostat and HBI-8000), an HIF-2alpha inhibitor (eg PT2385 and PT2977), an Hsp90 inhibitor (for example, 17 allylamino-17-desmethoxigeldanamycin, AUY922 and IPI-504) and growth factor and receptor inhibitors (for example, E10030). Other effective therapeutic interventions for treating an individual with a genetic biomarker in VHL are known in the art.

In some modalities, a therapeutic intervention administered to the individual with a genetic biomarker in VHL is effective in the treatment of cancer in the individual.

For example, after administering a therapeutic intervention that is effective in treating an individual with a genetic biomarker in the VHL, the number of cancer cells in the individual can be reduced, the size of one or more tumors in the individual can be reduced, rate or extent of metastasis may be reduced, symptoms associated with the disease or disorder or condition may be totally or partially relieved, the state of the disease may be stabilized (ie, not worsened) and / or survival may be prolonged compared to expected survival if you do not receive treatment.

[836] In some modalities, when an individual is identified as having a genetic biomarker (for example, a mutation) in TERT (for example, in a TERT promoter), the individual is administered with a therapeutic intervention. In some modalities, an individual identified as having a genetic biomarker (for example, a mutation) in TERT (for example, in a TERT promoter) is identified as having cancer (for example, based on the presence of the genetic biomarker) , alone or in combination with the presence of other genetic biomarkers and / or the presence of one or more members of other classes of biomarkers and / or the presence of aneuploidy as described herein). In some embodiments, the therapeutic intervention administered to the individual with a genetic biomarker in TERT (for example, in a TERT promoter) is one or more eribulin, a dendritic cell vaccine transfected with hTERT mRNA (for example, AST-VAC1 (hTERT-DC, GRNVAC1)), INO-1400, INO-1401, GX301, dendritic cells transfected with hTERT mRNA, survivin and tumor cells and arsenic trioxide. Other effective therapeutic interventions for treating an individual with a genetic biomarker on TERT (for example, on a TERT promoter) are known in the art. In some embodiments, a therapeutic intervention administered to the individual with a genetic biomarker in TERT (for example, in a TERT promoter) is effective in treating cancer in the individual. For example, after administering a therapeutic intervention that is effective in treating an individual with a genetic biomarker in TERT (for example, in a TERT promoter), the number of cancer cells in the individual may be reduced, the size of one or more tumors in the individual can be reduced, the rate or extent of metastasis can be reduced, the symptoms associated with the disease or disorder or condition can be totally or partially relieved, the state of the disease can be stabilized (that is, not worsened ) and / or survival may be prolonged compared to expected survival if not receiving treatment.

[837] In some modalities, when an individual is identified as being at risk (for example, increased risk) of developing a disease (for example, using any of the various ways described here), the individual is administered with a therapeutic intervention. In some embodiments, an individual identified as being at risk (for example, increased risk) of developing a disease (for example, using any of the various ways described here) is identified as being at risk of developing cancer (for example, based on in the presence of one or more genetic biomarkers, the presence of one or more protein biomarkers, the presence of one or more other biomarkers and / or the presence of aneuploidy, as described here). In some modalities, the therapeutic intervention administered to the individual identified as being at risk of developing cancer is a chemopreventive. Non-limiting examples of chemopreventives include a non-steroidal anti-inflammatory drug (eg, aspirin, tolfenamic acid, indomethacin, celecoxib, sulindac sulfide, diclofenac, indomethacin, ibuprofen, flurbiprophen, piroxicam, diflunisal, etodolaco, ketoprofen ketorolac, nabumeton, naproxen, oxaprozine, salsalate and, tolmetin), a selective estrogen receptor modulator (eg, tamoxifen (NOLVA-DEX®, SOLTAMOXTM) and raloxifene (EVISTA®)), instilational BCG, val - rubicin, finasteride, dutasteride, curcumin, bisdemetoxicurcumin,

metformin, an aromatase inhibitor (eg exemestane), resveratrol, lunasin, vitamin A, isothiocyanate, green tea, luteolin, genistein, bitter melon lysine, comferin A, guggulsterone, selenides, diselenides, crocetine, piperine, statin (a 3-hydroxy-3-methylglutaryl-coenzyme A reductase inhibitor), a carotenoid, vitamin A, a retinoid, folic acid, vitamin C, vitamin D, vitamin E, calcium, a flavonoid and an anticancer vaccine. In some modalities, tamoxifen and / or raloxifene is administered to an individual identified as being at risk of developing breast cancer. In some modalities, BCG and / or instillational valrubicin are administered to an individual identified as being at risk of developing bladder cancer. In some modalities, finasteride and / or dutasteride are administered to an individual identified as being at risk of developing prostate cancer. In some modalities, celecoxib is administered to an individual identified as being at risk of developing colorectal neoplasia.

[838] In some modalities, therapeutic intervention can result in an early onset of cancer remission in an individual. In some modalities, therapeutic intervention can result in an increase in the time of cancer remission in an individual. In some modalities, therapeutic intervention can result in an increase in an individual's survival time. In some modalities, therapeutic intervention may result in the reduction of the size of a solid primary tumor in an individual. In some modalities, therapeutic intervention may result in a decrease in the volume of a solid primary tumor in an individual. In some modalities, therapeutic intervention can result in the reduction of the size of a metastasis in an individual. In some modalities, therapeutic intervention may result in the reduction of the volume of a metastasis in an individual. In some modalities, therapeutic intervention may result in the reduction of the tumor burden on an individual.

[839] In some modalities, therapeutic intervention can result in improving an individual's prognosis. In some modalities, therapeutic intervention may result in a reduced risk of developing a metastasis in an individual. In some modalities, therapeutic intervention can result in decreased risk of developing additional metastasis in an individual. In some modalities, therapeutic intervention can result in a decrease in the migration of cancer cells in an individual. In some modalities, therapeutic intervention can result in a decrease in the invasion of cancer cells in an individual. In some ways, therapeutic intervention can result in a decrease in an individual's hospitalization time. In some modalities, therapeutic intervention can result in a decrease in the presence of cancer stem cells within a tumor in an individual.

[840] In some modalities, therapeutic intervention may result in an increase in the infiltration of immune cells within the tumor microenvironment in an individual. In some modalities, therapeutic intervention may result in altering the immune cell composition within the tumor microenvironment of a tumor in an individual. In some modalities, therapeutic intervention may result in the modulation of a previously immunosuppressive tumor microenvironment into an immunogenic inflammatory tumor microenvironment. In some modalities, therapeutic intervention can result in a reversal of the immunosuppressive tumor microenvironment in an individual.

[841] In some modalities, therapeutic intervention can interrupt tumor progression in an individual. In some ways, therapeutic intervention can slow the progression of the tumor in an individual. In some modalities, therapeutic intervention can inhibit tumor progression in an individual. In some ways, therapeutic intervention can inhibit the pathways of the immune checkpoint of a tumor in an individual. In some modalities, therapeutic intervention can immuno-modulate the tumor microenvironment of a tumor in an individual. In some modalities, therapeutic intervention can immuno-modulate the tumor macroenvironment of a tumor in an individual.

[842] In some modalities, a therapeutic intervention can reduce the number of cancer cells present in an individual. For example, a therapeutic intervention can reduce the number of cancer cells present in an individual by 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95% or more. In some modalities, a therapeutic intervention can reduce the number of cancer cells present in an individual, so that no cancer cell is observable. In some modalities, a therapeutic intervention can reduce the observable tumors present in an individual.

[843] In some embodiments, one or more therapeutic interventions (for example, chemotherapy or any of the other appropriate therapeutic interventions disclosed herein) may be administered to an individual once or several times over a period of time which varies from days to weeks. In some embodiments, one or more therapeutic interventions can be formulated in a pharmaceutically acceptable composition for administration to an individual with cancer. For example, a therapeutically effective amount of a therapeutic intervention (for example, a chemotherapeutic or immunotherapeutic agent) can be formulated in conjunction with one or more pharmaceutically acceptable carriers (additives) and / or diluents. A pharmaceutical composition can be formulated for administration

traction in solid or liquid form, including, without limitation, sterile solutions, suspensions, sustained release formulations, tablets, capsules, pills, powders and granules.

[844] Pharmaceutically acceptable carriers, fillers and vehicles that can be used in a pharmaceutical composition described herein include, without limitation, ion exchangers, aluminum, aluminum stearate, lecithin, serum proteins, such as serum albumin buffer substances such as phosphates, glycine, sorbic acid, potassium sorbate, partial mixtures of saturated vegetable fatty acid glycerides, water, salts or electrolytes, such as protamine sulfate, disodium hydrogen phosphate, potassium hydrogen phosphate, chloride sodium, zinc salts, colloidal silica, magnesium trisilicate, polyvinyl pyrrolidone, cellulose-based substances, polyethylene glycol, sodium carboxymethylcellulose, polyacrylates, waxes, polyethylene-polyoxypropylene block polymers, polyethylene glycol and wool fat.

[845] A pharmaceutical composition containing one or more therapeutic interventions can be designed for oral or parenteral administration (including subcutaneous, intramuscular, intravenous and intradermal). When administered orally, a pharmaceutical composition can be in the form of a tablet, tablet or capsule. Compositions suitable for parenteral administration include sterile aqueous and non-aqueous injection solutions that may contain antioxidants, buffers, bacteriostats and solutes that make the formulation isotonic with the intended recipient's blood. The formulations can be established in single-dose or multi-dose containers, for example, sealed ampoules and bottles, and can be stored in a freeze-dried (lyophilized) condition requiring only the addition of the sterile liquid carrier, for example, water for injections, just before use. Solutions and suspensions for extemporaneous injection can be prepared from sterile powders, granules and tablets.

[846] In some embodiments, a pharmaceutically acceptable composition, including one or more therapeutic interventions, can be administered locally or systemically. For example, a composition provided here can be administered locally by injection into tumors. In some embodiments, a composition provided in this document can be administered systemically, orally or by injection to an individual (for example, a human).

[847] Effective doses may vary depending on the severity of the cancer, the route of administration, the age and general health of the individual, the use of excipients, the possibility of co-use with other therapeutic treatments, such as use of other agents and the judgment of the attending physician.

[848] An effective amount of a composition containing one or more therapeutic interventions can be any amount that reduces the number of cancer cells present in the individual without producing significant toxicity for the individual. If a particular individual does not respond to a specific amount, the amount of a therapeutic intervention can be increased by, for example, twice. After receiving this higher amount, the individual can be monitored for responsiveness to treatment symptoms and toxicity, and adjustments made accordingly. The effective amount can remain constant or can be adjusted as a sliding scale or variable dose, depending on the patient's response to treatment. Several factors can influence the actual effective amount used for a specific application. For example, the frequency of administration, the duration of treatment, the use of multiple treatment agents, the route of administration and the severity of the condition (for example, cancer) may require an increase or decrease in the actual effective amount administered.

[849] The frequency of administration of one or more therapeutic interventions can be any quantity that reduces the number of cancer cells present in the individual without producing significant toxicity for the individual. For example, the frequency of administration of one or more therapeutic interventions can be from about two to about three times a week to about two to about three times a month. The frequency of administration of one or more therapeutic interventions may remain constant or may vary during the duration of treatment. A course of treatment with a composition containing one or more therapeutic interventions may include rest periods. For example, a composition containing one or more therapeutic interventions can be administered daily for a period of two weeks, followed by a rest period of two weeks, and this regimen can be repeated several times. As well as the effective amount, several factors can influence the actual frequency of administration used for a specific application. For example, the effective amount, the duration of treatment, the use of multiple treatment agents, the route of administration and the severity of the condition (for example, cancer) may require an increase or decrease in the frequency of administration.

[850] An effective duration for administering a composition containing one or more therapeutic interventions can be any duration that reduces the number of cancer cells present in the individual without producing significant toxicity for the individual. In some modes, the effective duration can vary from several days to several weeks. In general, the effective duration for reducing the number of cancer cells present in the individual can vary in duration from about one week to about four weeks. Multiple factors can influence the actual effective duration used for a specific treatment. For example, an effective duration can vary with the frequency of administration, effective amount, use of multiple treatment agents, route of administration and severity of the condition being treated. Exemplary modalities

[851] In some embodiments, methods for detecting biomarkers are provided here, whose methods include detecting the presence of one or more members of a first class of biomarkers in a sample obtained from the individual and detecting the presence of one or more more members of a second class of biomarkers in the sample obtained from the individual. In some modalities of biomarker detection methods, the methods also include detecting the presence of aneuploidy in the sample obtained from the individual. In some modalities of biomarker detection methods, the first class of biomarkers includes genetic biomarkers. In some modalities of biomarker detection methods, members of the first class of biomarkers are associated with the presence of cancer. In some types of biomarker detection methods, the second class of biomarkers includes protein biomarkers. In some modalities of biomarker detection methods, members of the second class of biomarkers are associated with the presence of cancer.

[852] In some embodiments, methods for detecting biomarkers are provided here, whose methods include detecting the presence of one or more members of a first class of biomarkers in a sample obtained from the individual; detect the presence of aneuploidy in the sample obtained from the individual. In some modalities of biomarker detection methods, the methods also include detecting the presence of one or more members of a second class of biomarkers in the sample obtained from the individual. In some modalities of biomarker detection methods, the first class of biomarkers comprises genetic biomarkers. In some modes of biomarker detection methods, the first class of biomarkers comprises protein biomarkers. In some modalities of biomarker detection methods, members of the first class of biomarkers are associated with the presence of cancer. In some modalities of biomarker detection methods in which the first class of biomarkers comprises protein biomarkers, the second class of biomarkers comprises genetic biomarkers. In some modalities of biomarker detection methods in which the first class of biomarkers comprises protein biomarkers, members of the second class of biomarkers are associated with the presence of cancer.

[853] In some embodiments, methods are provided here to identify an individual as having cancer, whose methods include: detecting the presence of one or more members of a first class of biomarkers in a sample obtained from the individual; detect the presence of one or more members of a second class of biomarkers in the sample obtained from the individual; and identifying the individual as having cancer when the presence of one or more members of the first class of biomarkers is detected in the sample, the presence of one or more members of the second class of biomarkers is detected in the sample, or both. In some modalities of methods to identify an individual as having cancer, the methods also include detecting the presence of aneuploidy in the sample obtained from the individual; in which the individual is identified as having cancer when the presence of one or more members of the first class of biomarkers is detected in the sample, the presence of one or more members of the second class of biomarkers is detected in the sample, the presence of aneuploidy is determined detected in the sample or combinations thereof. In some modalities of methods to identify an individual as having cancer, the first class of biomarkers includes genetic biomarkers. In some modalities of methods to identify an individual as having cancer, members of the first class of biomarkers are associated with the presence of cancer. In some modalities of methods for identifying an individual as having cancer, the second class of biomarkers includes protein biomarkers. In some modalities of methods to identify an individual as having cancer, members of the second class of biomarkers are associated with the presence of cancer.

[854] In some embodiments, methods are provided here to identify an individual as having cancer, whose methods include: detecting the presence of one or more members of a first class of biomarkers in a sample obtained from the individual; detect the presence of aneuploidy in the sample obtained from the individual; and to identify the individual as having cancer when the presence of one or more members of the first class of biomarkers is detected in the sample, the presence of aneuploidy is detected in the sample, or both. In some modalities of methods to identify an individual as having cancer, the methods also include detecting the presence of one or more members of a second class of biomarkers in the sample obtained from the individual; where the individual is identified as having cancer when the presence of one or more members of the first class of biomarkers is detected in the sample, the presence of one or more members of the second class of biomarkers is detected in the sample, the presence of aneuploidy is detected in the sample or combinations thereof. In some modalities of methods to identify an individual as having cancer, the first class of biomarkers comprises genetic biomarkers. In some modalities of methods to identify an individual as having cancer, the first class of biomarkers includes protein biomarkers. In some modalities of methods for identifying an individual as having cancer, members of the first class of biomarkers are associated with the presence of cancer. In some modalities of methods to identify an individual as having cancer in which the first class of biomarkers comprises protein biomarkers, the second class of biomarkers comprises genetic biomarkers. In some modalities of methods to identify an individual as having cancer, in which the first class of biomarkers comprises protein biomarkers, members of the second class of biomarkers are associated with the presence of cancer.

[855] In some modalities of methods to identify an individual as having cancer, which includes detecting the presence of one or more members of a first class of biomarkers in a sample obtained from the individual and detecting the presence of one or more members a second class of biomarkers in the sample obtained from the individual, the sensitivity of detecting the presence of cancer is increased compared to methods that include detecting the presence of one or more members of only a single class of biomarkers. In some modalities of methods to identify an individual as having cancer, which includes detecting the presence of one or more members of a first class of biomarkers in a sample obtained from the individual and detecting the presence of one or more members of a second class of biomarkers in the sample obtained from the individual, the specificity of detecting the presence of cancer is increased compared to methods that include detecting the presence of one or more members of only a single class of biomarkers. In some modalities of methods to identify an individual as having cancer, which includes detecting the presence of one or more members of a first class of biomarkers in a sample obtained from the individual and detecting the presence of aneuploidy in the sample obtained from the individual, the sensitivity to detect the presence of cancer is increased in comparison with methods that include detecting only one or more members of the class of biomarkers or only the presence of aneuploidy. In some modalities of methods to identify an individual as having cancer, which includes detecting the presence of one or more members of a first class of biomarkers in a sample obtained from the individual and detecting the presence of aneuploidy in the sample obtained from the individual, the specificity of detecting the presence of cancer is increased compared to methods that include detecting only one or more members of the class of biomarkers or just the presence of aneuploidy.

[856] In some embodiments, methods are provided here to treat an individual identified as having cancer, whose methods include: detecting the presence of one or more members of a first class of biomarkers in a sample obtained from the individual; detect the presence of one or more members of a second class of biomarkers in the sample obtained from the individual; identify the individual as having cancer when the presence of one or more members of the first class of biomarkers is detected in the sample, the presence of one or more members of the second class of biomarkers is detected in the sample, or both; and administering a therapeutic intervention to the individual. In some modalities of methods to treat an individual identified as having cancer, the methods also include detecting the presence of aneuploidy in the sample obtained from the individual; where the individual is identified as having cancer when the presence of one or more members of the first class of biomarkers is detected in the sample, the presence of one or more members of the second class of biomarkers is detected in the sample, the presence of aneuploidy is detected in the sample or combinations thereof. In some modalities of methods for treating an individual identified as having cancer, the first class of biomarkers includes genetic biomarkers. In some modalities of methods to treat an individual identified as having cancer, members of the first class of biomarkers are associated with the presence of cancer. In some modalities of methods for treating an individual identified as having cancer, the second class of biomarkers includes protein biomarkers. In some modalities of methods to treat an individual identified as having cancer, members of the second class of biomarkers are associated with the presence of cancer.

[857] In some modalities, methods are provided here to treat an individual identified as having cancer, whose methods include: detecting the presence of one or more members of a first class of biomarkers in a sample obtained from the individual; detect the presence of aneuploidy in the sample obtained from the individual; identify the individual as having cancer when the presence of one or more members of the first class of biomarkers is detected in the sample, the presence of aneuploidy is detected in the sample, or both; and administering a therapeutic intervention to the individual. In some modalities of methods for treating an individual identified as having cancer, the methods further include detecting the presence of one or more members of a second class of biomarkers in the sample obtained from the individual; in which the individual is identified as having cancer when the presence of one or more members of the first class of biomarkers is detected in the sample, the presence of one or more members of the second class of biomarkers is detected in the sample, the presence of aneuploidy day is detected in the sample or combinations thereof. In some modalities of methods to treat an individual identified as having cancer, the first class of biomarkers comprises genetic biomarkers. In some modalities of methods for treating an individual identified as having cancer, the first class of biomarkers includes protein biomarkers. In some modalities of methods to treat an individual identified as having cancer, members of the first class of biomarkers are associated with the presence of cancer. In some modalities of methods for treating an individual identified as having cancer in which the first class of biomarkers comprises protein biomarkers, the second class of biomarkers comprises genetic biomarkers. In some modalities of methods to treat an individual identified as having cancer, in which the first class of biomarkers comprises protein biomarkers, members of the second class of biomarkers are associated with the presence of cancer.

[858] In some modalities of methods for treating an individual identified as having cancer, which includes detecting the presence of one or more members of a first class of biomarkers in a sample obtained from the individual and detecting the presence of one or more members of a second class of biomarkers in the sample obtained from the individual, the sensitivity of detecting the presence of cancer is increased compared to methods that include detecting the presence of one or more members of only a single class of biomarkers. In some modalities of methods to treat an individual identified as having cancer, which includes detecting the presence of one or more members of a first class of biomarkers in a sample obtained from the individual and detecting the presence of one or more members of a second class of biomarkers in the sample obtained from the individual, the specificity of detecting the presence of cancer is increased compared to methods that include detecting the presence of one or more members of only a single class of biomarkers. In some modalities of methods to treat an individual identified as having cancer, which includes detecting the presence of one or more members of a first class of biomarkers in a sample obtained from the individual and detecting the presence of aneuploidy in the sample obtained from the individual, the sensitivity of detecting the presence of cancer is increased compared to methods that include detecting only one or more members of the class of biomarkers or just the presence of aneuploidy. In some modalities of methods to treat an individual identified as having cancer, which includes detecting the presence of one or more members of a first class of biomarkers in a sample obtained from the individual and detecting the presence of aneuploidy in the sample obtained from the individual, the specificity of detecting the presence of cancer is increased in comparison with methods that include detecting only one or more members of the class of biomarkers or only the presence of aneuploidy.

[859] In some modalities of methods to treat an individual identified as having cancer, any of the various therapeutic interventions described here (for example, surgery, chemotherapy, hormonal therapy, targeted therapy, radiation therapy and combinations of them) can be administered to the individual.

[860] In some embodiments, methods are provided here to identify an individual as having cancer which include: detecting the presence of one or more genetic biomarkers in the circulating DNA in a blood sample obtained from that individual; detecting the presence of a high level of one or more peptide biomarkers in the blood sample obtained from an individual; and identifying the individual as having cancer when the presence of one or more genetic biomarkers is detected in the circulating DNA in said blood sample, when a high level of one or more peptide biomarkers is detected in said blood sample, or both. In some modalities, methods of treating an individual with cancer are provided here which include: detecting the presence of one or more genetic biomarkers in the circulating DNA in a blood sample obtained from that individual; detecting the presence of a high level of one or more peptide biomarkers in the blood sample obtained from an individual; and administer one or more therapeutic interventions (for example, one or more surgeries, chemotherapy, hormonal therapy, targeted therapy and radiotherapy) to that individual when the presence of one or more genetic biomarkers is detected in the surrounding DNA in that sample of blood, when a high level of one or more peptide biomarkers is detected in said blood sample, or both. In some embodiments, methods are provided herein to identify the location of a cancer in an individual, said method comprising: detecting the presence of one or more genetic biomarkers in the circulating DNA in a blood sample obtained from said individual; detecting the presence of a high level of one or more peptide biomarkers in the blood sample obtained from an individual; and identify the location of the cancer in the individual when the presence of one or more genetic biomarkers is detected in the circulating DNA in that blood sample, when a high level of one or more peptide biomarkers is detected in the said blood sample, or in both.

[861] In some modalities to identify an individual as having cancer, treat an individual with cancer or identify the location of a cancer in an individual (for example, based on the presence of one or more genetic biomarkers in the circulating DNA in a blood sample obtained from said individual and / or the presence of a high level of one or more peptide biomarkers in the blood sample obtained from an individual), the individual is human. In some ways to identify an individual as having cancer, treat an individual with cancer or identify the location of a cancer in an individual, the aforementioned blood sample is a plasma sample. In some modalities to identify an individual as having cancer, treat an individual with cancer or identify the location of a cancer in an individual, cancer is a Stage I cancer. In some modalities to identify an individual as having cancer, treat an individual having cancer or identifying the location of a cancer in an individual, the one or more genetic biomarkers comprise one or more modifications in one or more genes. In some modalities to identify an individual as having cancer, treat an individual with cancer or identify the location of a cancer in an individual, the one or more modifications comprise inactivation modifications and in which said one or more genes comprise suppressor genes of tumors. In some modalities to identify an individual as having cancer, treat an individual with cancer or identify the location of a cancer in an individual, the one or more modifications include a modification selected independently from substitutions, insertions or deletions, single-base translocations, merges, breaks, duplications or amplifications.

[862] In some modalities to identify an individual as having cancer, treat an individual with cancer or identify the location of a cancer in an individual (for example, based on the presence of one or more genetic biomarkers in the circulating DNA in a blood sample obtained from said individual and / or the presence of a high level of one or more peptide biomarkers in the blood sample obtained from an individual), the cancer is liver cancer, ovarian cancer, esophageal cancer, stomach cancer, pancreatic cancer, colorectal cancer, lung cancer, breast cancer or prostate cancer. In some ways to identify an individual as having cancer, treat an individual with cancer or identify the location of a cancer in an individual in which the cancer is liver cancer, ovarian cancer, esophageal cancer, stomach cancer, cancer pancreas, colorectal cancer, lung cancer, breast cancer or prostate cancer and where one or more genetic biomarkers are genes, one or more genes are one or more of: NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A or GNAS. In some modalities to identify an individual as having cancer, treat an individual with cancer or identify the location of a cancer in an individual in which one or more genes one or more of: NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR , BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A or GNAS, the one or more protein biomarkers are one or more of CA19-9, CEA, HGF, OPN, CA125, prolactin, TIMP-1 or myeloperoxidase (MPO). In some modalities to identify an individual as having cancer, treat an individual with cancer or identify the location of a cancer in an individual in which one or more genes one or more of: NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR , BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A or GNAS, the one or more protein biomarkers are one or more of CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP -1, folista- tina, G-CSF or CA15-3. In some modalities to identify an individual as having cancer, treat an individual with cancer or identify the location of a cancer in an individual in which one or more genes one or more of: NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR , BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A or GNAS, the one or more protein biomarkers are one or more of CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP -1 or CA15-

3. In some ways to identify an individual as having cancer, treat an individual with cancer or identify the location of a cancer in an individual in which one or more genes one or more of: NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR , BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A or GNAS, one or more modifications are selected regardless of the modifications established in Table 3.

[863] In some modalities to identify an individual as having cancer, treat an individual with cancer or identify the location of a cancer in an individual (for example, based on the presence of one or more genetic biomarkers in the circulating DNA in a blood sample obtained from said individual and / or the presence of a high level of one or more peptide biomarkers in the blood sample obtained from an individual), cancer is pancreatic cancer. In some ways to identify an individual as having cancer, treat an individual with cancer or identify the location of a cancer in an individual in which cancer is pancreatic cancer and in which one or more genetic biomarkers are genes, the one or more genes are one or more of KRAS, TP53, CDKN2A or SMAD4. In some modalities to identify an individual as having cancer, treat an individual having cancer or identify the location of a cancer in an individual in which the one or more genes one or more of: KRAS, TP53, CDKN2A or SMAD4, the one or more protein biomarkers are one or more of CA19-9, CEA, HGF or OPN.

[864] In some modalities to identify an individual as having cancer, treat an individual with cancer or identify the location of a cancer in an individual (for example, based on the presence of one or more genetic biomarkers in the circulating DNA in a blood sample obtained from said individual and / or the presence of a high level of one or more peptide biomarkers in the blood sample obtained from an individual), the step of detecting the presence of one or more genetic biomarkers is performed using a method that includes a PCR-based multiplex assay, using a PCR-based singleplex assay, a digital PCR assay, a digital droplet PCR assay (ddPCR), a microarray assay, a next-step sequencing assay generation, a Sanger sequencing assay, a quantitative PCR assay or a binding assay. In some embodiments, the multiplex PCR-based sequencing assay includes: a. assign a unique identifier (UID) to each of a plurality of model molecules present in the sample; B. amplify each uniquely labeled model molecule to create UID families; and C. redundant sequencing of the amplification products.

[865] In some modalities to identify an individual as having cancer, treat an individual with cancer or identify the location of a cancer in an individual (for example, based on the presence of one or more genetic biomarkers in the circulating DNA in a blood sample obtained from said individual and / or the presence of a high level of one or more peptide biomarkers in the blood sample obtained from an individual), the high level step of one or more protein biomarkers is performed using a system of multiplex immunoassay.

[866] In some modalities for identifying an individual as having cancer, the methods further include identifying a cancer site. In some modalities to identify an individual as having cancer, the methods also include administering to the individual one or more therapeutic interventions. In some modalities, one or more therapeutic interventions are one or more of: surgery, chemotherapy, hormonal therapy, targeted therapy or radiation therapy.

[867] In some modalities to identify an individual as having cancer, treat an individual with cancer or identify the location of a cancer in an individual (for example, based on the presence of one or more genetic biomarkers in the circulating DNA in a blood sample obtained from said individual and / or the presence of a high level of one or more peptide biomarkers in the blood sample obtained from an individual), the methods further include: a) determining a mutation allele frequency in the blood sample for two or more genetic biomarkers; b) obtain a score that indicates the probability of the individual having cancer by comparing the frequency of the mutation allele of each mutation in the selected genetic biomarkers with a first reference distribution of the frequency of the mutation allele in the control samples and a second reference distribution of the frequency of the mutation allele in samples collected from individuals with cancer; and c) identify the individual as having cancer when the score is greater than a reference value for the score.

In some modalities, obtaining the score includes calculating the probability ratio of the frequency of the mutation allele in the first reference distribution to the probability of the frequency of the mutation allele in the second reference distribution for each mutation in the selected genetic biomarkers - From.

In some modalities, the score is determined by calculating the weighted average of the logarithmic ratio of the probability of the frequency of the mutation allele in the first reference distribution with the probability of the frequency of the mutation allele in the second reference distribution for each mutation in the genetic biomarkers selected.

In some embodiments, detecting the presence of one or more genetic biomarkers is performed on two or more test samples using an assay comprising: a. assigning a unique identifier (UID) to each of a plurality of model molecules present in the sample; B. amplify each uniquely labeled model molecule to create UID families; and C. redundant sequencing of the amplification products. In some modalities, the score is calculated by the following formula:, where wi is the number of unique identifier strings (UIDs) in a test sample i divided by the total number of UIDs for that mutation in all test samples, piN is the probability of the frequency of the mutation allele in the first reference distribution, and piC is the probability of the frequency of the mutation allele in the second reference distribution.

[868] In some modalities to identify an individual as having cancer, treat an individual with cancer or identify the location of a cancer in an individual (for example, based on the presence of one or more genetic biomarkers in the circulating DNA in a blood sample obtained from said individual and / or the presence of a high level of one or more peptide biomarkers in the blood sample obtained from an individual), the sensitivity of identifying an individual as having cancer is increased compared to : 1) the sensitivity obtained when the presence of one or more members of only a single class of biomarkers in the sample obtained from the individual is detected. In some modalities for identifying an individual as having cancer, treating an individual with cancer or identifying the location of a cancer in an individual, the specificity of identifying an individual as having cancer is increased compared to: 1) the specificity obtained when the presence of one or more members of only a single class of biomarkers is detected in the sample obtained from the individual.

[869] In some modalities to identify an individual as having cancer, treat an individual with cancer or identify the location of a cancer in an individual (for example, based on the presence of one or more genetic biomarkers in the circulating DNA in a blood sample obtained from said individual and / or the presence of a high level of one or more peptide biomarkers in the blood sample obtained from an individual), methods also include: detecting the presence of aneuploidy in the sample obtained of the individual, in which the individual is identified as having cancer when the presence of one or more members of the first class of biomarkers is detected in the sample, the presence of one or more members of the second class of biomarkers is detected in the sample, the presence of aneuploidy is detected in the sample, or combinations thereof.

[870] In some embodiments, a method is provided here to identify a patient as having cancer that includes: a) determining a mutation allele frequency in a sample collected from the patient for each mutation in one or more of the following genes : NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A or GNAS; b) obtain a score that indicates the probability that the patient will have cancer by comparing the frequency of the mutation allele of each mutation in the selected genes with a first reference distribution of the frequency of the mutation allele in control samples and a second reference distribution of the frequency of the mutation allele in samples collected from cancer patients; and c) identifying the patient as having cancer when the score is greater than a reference value for the score is greater than a reference value for the score. In some modalities, these methods also include measuring the concentration of one or more of the following proteins: CA19-9, CEA, HGF, OPN, CA125, prolactin, TIMP-1 or myeloperoxidase (MPO) and determining that the hair concentration least one protein is greater than a reference value.

In some modalities, these methods also include measuring the concentration of one or more of the following proteins: CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1, folistatin, G-CSF or CA15-3 and determine that the concentration of at least one protein is greater than a reference value.

In some modes, these methods also include measuring the concentration of one or more of the following proteins: CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1 or CA15-3 and determining that the concentration of at least one protein is greater than a reference value.

In some modalities, obtaining the score involves calculating the reason for the probability of the frequency of the mutation allele in the first reference distribution with the probability of the frequency of the mutation allele in the second reference distribution for each mutation in the genes. selected.

In some modalities, the score is determined by calculating the weighted average of the logarithmic ratio of the probability of the frequency of the mutation allele in the first reference distribution with the probability of the frequency of the mutation allele in the second reference distribution. for each mutation in the selected genes.

In some embodiments, the sample is analyzed on two or more test samples using a method that includes: a. assign a unique identifier (UID) to each of a plurality of model molecules present in the sample; B. amplify each uniquely labeled model molecule to create UID families; and C. redundant sequencing of the amplification products.

In some modalities, the score is calculated using the following formula:

, where wi is the number of unique identifier strings (UIDs) in a test sample i divided by the total number of UIDs for that mutation in all test samples, piN is the probability of the frequency of the mutation allele in the first reference distribution, and piC is the probability of the frequency of the mutation allele in the second reference distribution. In some embodiments, the blood sample is a plasma sample. In some modalities, the cancer is Stage I cancer. In some modalities, the cancer is liver cancer, ovarian cancer, esophageal cancer, stomach cancer, pancreatic cancer, colorectal cancer, lung cancer, breast cancer or prostate cancer. In some embodiments, the at least one mutation comprises inactivating modifications and where the at least one mutation is in a tumor suppressor gene. In some modalities, the at least one mutation comprises a mutation that is a single base substitution, an insertion or an exclusion. In some embodiments, the at least one mutation is a mutation set out in Table 3. In some embodiments, the step of detecting the level of one or more proteins is performed using a multiplex immunoassay system. In some modalities, the methods also include identifying a cancer site. In some embodiments, the methods further include understanding how to administer to the mammal one or more therapeutic interventions. In some modalities, one or more therapeutic interventions are one or more of: surgery, chemotherapy, hormonal therapy, targeted therapy or radiotherapy.

[871] In some embodiments, a method is provided here to identify a patient as having cancer that includes: a) determining a mutation allele frequency in a sample collected from the patient for each mutation in one or more of the following genes : KRAS, TP53, CDKN2A, or SMAD4; b) obtain a score that indicates the probability of the patient having cancer by comparing the frequency of the mutation allele of each mutation in the selected genes with a first reference distribution of the frequency of the mutation allele in control samples and a second reference distribution of the frequency of the mutation allele in samples collected from cancer patients; and c) identifying the patient as having cancer when the score is greater than a reference value for the score is greater than a reference value for the score.

In some embodiments, these methods also include measuring the concentration of one or more of the following proteins: CA19-9, CEA, HGF, or OPN and determining that the concentration of at least one protein is greater than a reference value.

In some modalities, obtaining the score includes calculating the ratio of the probability of the frequency of the mutation allele in the first reference distribution to the probability of the frequency of the mutation allele in the second reference distribution for each mutation in the genes. selected.

In some modalities, the score is determined by calculating the weighted average of the logarithmic ratio of the probability of the frequency of the mutation allele in the first reference distribution with the probability of the frequency of the mutation allele in the second reference distribution for each mutation in the selected genes. .

In some embodiments, the sample is analyzed on two or more test samples using a method that includes: a. assigning a unique identifier (UID) to each of a plurality of model molecules present in the sample; B. amplify each uniquely labeled model molecule to create UID families; and C. redundant sequencing of amplification products.

In some modalities, the score is calculated using the following formula:

, where wi is the number of unique identifier strings (UIDs) in a test sample i divided by the total number of UIDs for that mutation in all test samples, piN is the probability of the frequency of the mutation allele in the first reference distribution, and piC is the probability of the frequency of the mutation allele in the second reference distribution. In some embodiments, the blood sample is a plasma sample. In some modalities, the cancer is Stage I cancer. In some modalities, the cancer is liver cancer, ovarian cancer, esophageal cancer, stomach cancer, pancreatic cancer, colorectal cancer, lung cancer, breast cancer or prostate cancer. In some embodiments, the at least one mutation comprises inactivating modifications and where the at least one mutation is in a tumor suppressor gene. In some modalities, the at least one mutation comprises a mutation that is a single base substitution, an insertion or an exclusion. In some embodiments, the step of detecting the level of one or more proteins is performed using a multiplex immunoassay system. In some modalities, the methods also include identifying a cancer site. In some modalities, the methods also include understanding how to give the mammal one or more therapeutic interventions. In some modalities, one or more therapeutic interventions are one or more of: surgery, chemotherapy, hormonal therapy, targeted therapy or radiation therapy.

[872] In some embodiments, systems are provided in this document to generate a report for a patient that includes: at least one device configured to analyze a set of genes in a biological sample to determine the frequency of the mutation allele of each mutation in the gene pool in a sample collected from the patient, where the gene pool comprises one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , 13, 14, 16 or 16) of the following genes: NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A and / or

GNAS; b) at least one computer database, including: i) a first reference distribution of the frequency of the mutation allele in control samples for each mutation in the gene pool; and ii) a second reference distribution of the frequency of mutation alleles in samples collected from cancer patients for each mutation in the gene pool; c) a computer-readable program code comprising instructions for performing the following: i) insert the frequency of the mutation allele into the biological sample of each mutation in the gene pool; ii) compare the frequency of the mutation allele with the first reference distribution; iii) to compare the frequency of the mutation allele with the second reference distribution; and iv) calculate a score that indicates the probability that the patient will have cancer; d) a computer-readable program code comprising instructions for generating a report that indicates that the patient has cancer if the score is greater than a reference value; or a report indicating that the patient does not have cancer if the score is not higher than the reference value.

[873] In some embodiments, systems are provided in this document to generate a report for a patient that includes: at least one device configured to analyze a set of genes in a biological sample to determine the frequency of the mutation allele of each mutation in the gene pool in a sample collected from the patient, where the gene pool comprises one or more (for example, 1, 2, 3 or 4) of the following genes: KRAS, TP53, CDKN2A and / or SMAD4; b) at least one computer database, including: i) a first reference distribution of the frequency of the mutation allele in control samples for each mutation in the gene pool; and ii) a second reference distribution of the frequency of mutation alleles in samples collected from cancer patients for each mutation in the gene pool; c) a computer-readable program code comprising instructions for performing the following: i) insert the frequency of the mutation allele into the biological sample of each mutation in the gene pool; ii) compare the frequency of the mutation allele with the first reference distribution; iii) to compare the frequency of the mutation allele with the second reference distribution; and iv) calculate a score that indicates the probability that the patient will have cancer; d) a computer-readable program code, comprising instructions for generating a report that indicates that the patient has cancer if the score is greater than a reference value; or a report indicating that the patient does not have cancer if the score is not higher than the reference value.

[874] In some embodiments, systems are provided here to generate a report to identify a cancer treatment for a patient that includes: a) at least one device configured to test a set of genes in a biological sample to determine the frequency of the allele mutation of each mutation in the gene pool in a sample collected from the patient, where the gene pool comprises one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16 or 16) of the following genes: NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A and / or GNAS; b) at least one computer database comprising: i) a first reference distribution of the frequency of the mutation allele in control samples for each mutation in the gene pool; ii) a second reference distribution of the frequency of mutation alleles in samples collected from cancer patients for each mutation in the gene pool; and iii) an effective cancer treatment list linked to a biological state of at least one member of the gene pool; c) a computer-readable program code comprising instructions for performing the following: i) introducing the frequency of the mutation allele into the biological sample of each mutation in the gene pool; ii) compare the frequency of the mutation allele with the first reference distribution; iii) compare the frequency of the mutation allele with the second reference distribution; and iv) calculate a score that indicates the probability that the patient will have cancer; d) a computer-readable program code, comprising instructions for generating a report that indicates that the patient has cancer if the score is greater than a reference value; or a report that indicates that the patient does not have cancer if the score is not higher than the reference value; and e) a computer-readable program code that comprises instructions for identifying at least one cancer treatment for the patient from the list of cancer treatments in (b) (iii) when the patient is identified as having a cancer in step (d), in which the calculated score provides an indication that the treatment of the identified cancer will be effective in the patient.

[875] In some embodiments, systems are provided in this document to generate a report to identify a cancer treatment for a patient that includes: a) at least one device configured to analyze a set of genes in a biological sample for determine the frequency of the mutation allele of each mutation in the gene pool in a sample taken from the patient, where the gene pool comprises one or more (for example, 1, 2, 3 or 4) of the following genes: KRAS, TP53 , CDKN2A and / or SMAD4; b) at least one computer database, comprising: i) a first reference distribution of the frequency of the mutation allele in control samples for each mutation in the gene pool; ii) a second reference distribution of the frequency of mutation alleles in samples collected from cancer patients for each mutation in the gene pool; and iii) an effective cancer treatment list linked to a biological state of at least one member of the gene pool; c) a computer-readable program code comprising instructions to perform the following: i) introduce the frequency of the mutation allele into the biological sample of each mutation in the gene pool; ii) compare the frequency of the mutation allele with the first reference distribution; iii) compare the frequency of the mutation allele with the second reference distribution; and iv) calculate a score that indicates the probability that the patient will have cancer; d) a computer-readable program code, comprising instructions for generating a report that indicates that the patient has cancer if the score is greater than a reference value; or a report indicating that the patient does not have cancer if the score is not higher than the reference value; and e) a computer-readable program code comprising instructions for identifying at least one cancer treatment for the patient from the list of cancer treatments in (b) (iii) when the patient is identified as having cancer in step (d), where the calculated score provides an indication that the identified cancer treatment will be effective for the patient.

[876] In some embodiments, systems are provided in this document to generate a report for a patient to identify him as a candidate for increased monitoring, additional diagnostic tests, or both that include: a) at least one device configured to test a set of genes in a biological sample to determine the frequency of the mutation allele of each mutation in the set of genes in a sample collected from the patient, where the set of genes comprises one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16 or 16) of the following genes: NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A and / or GNAS; b) at least one computer database comprising: i) a first reference distribution of the mutation allele frequency in control samples for each mutation in the gene pool; ii) a second reference distribution of the frequency of mutation alleles in samples collected from cancer patients for each mutation in the gene pool; and iii) an effective cancer treatment list linked to a biological state of at least one member of the gene pool; c) a computer-readable program code comprising instructions to perform the following: i) introduce the frequency of the mutation allele into the biological sample of each mutation in the gene pool; ii) compare the frequency of the mutation allele with the first reference distribution; iii) compare the frequency of the mutation allele with the second reference distribution; and iv) calculate a score that indicates the probability that the patient will have cancer; d) a computer-readable program code, comprising instructions for generating a report that indicates that the patient has cancer if the score is greater than a reference value; or a report indicating that the patient does not have cancer if the score is not higher than the reference value; and e) a computer-readable program code that comprises instructions for identifying at least one cancer treatment for the patient from the list of cancer treatments in (b) (iii) when the patient is identified as having a cancer in step (d), where the calculated score provides an indication that the identified cancer treatment will be effective in the patient.

[877] In some embodiments, systems are provided in this document to generate a report for a patient to identify him or her as a candidate for increased monitoring, additional diagnostic tests or both that include: a) at least one device configured to analyze a set of genes in a biological sample to determine the frequency of the mutation allele of each mutation in the set of genes in a sample collected from the patient, where the set of genes comprises one or more (for example, 1, 2, 3 or 4)

of the following genes: KRAS, TP53, CDKN2A and / or SMAD4; b) at least one computer database, comprising: i) a first reference distribution of the frequency of the mutation allele in control samples for each mutation in the gene pool; ii) a second reference distribution of the frequency of mutation alleles in samples collected from cancer patients for each mutation in the gene pool; and iii) an effective cancer treatment list linked to a biological state of at least one member of the gene pool; c) a computer-readable program code comprising instructions to perform the following: i) introduce the frequency of the mutation allele into the biological sample of each mutation in the gene pool; ii) compare the frequency of the mutation allele with the first reference distribution; iii) compare the frequency of the mutation allele with the second reference distribution; and iv) calculate a score that indicates the probability that the patient will have cancer; d) a computer-readable program code, comprising instructions for generating a report that indicates that the patient has cancer if the score is greater than a reference value; or a report indicating that the patient does not have cancer if the score is not higher than the reference value; and e) a computer-readable program code comprising instructions for identifying at least one cancer treatment for the patient from the list of cancer treatments in (b) (iii) when the patient is identified as having cancer in step (d), where the calculated score provides an indication that the identified cancer treatment will be effective for the patient.

[878] In some modalities of systems for generating a report for a patient, systems for generating a report to identify a cancer treatment for a patient, or systems for generating a report for a patient to identify the patient as a candidate for a increased monitoring, additional diagnostic tests, or both, calculating the score comprises calculating the ratio between the probability of the frequency of the mutation allele in the first reference distribution and the probability of the frequency of the mutation allele in the second distribution reference for each mutation in the selected genes.

In some modalities of systems to generate a report for a patient, systems to generate a report to identify a cancer treatment for a patient, or systems to generate a report for a patient to identify the patient as a candidate for increased monitoring, additional diagnostic tests, or both, the score is calculated by calculating the weighted average of the logarithmic ratio of the probability of the frequency of the mutation allele in the first reference distribution with the probability of the frequency of the mutation allele in the second reference distribution for each mutation in the selected genes.

In some modalities of systems to generate a report for a patient, systems to generate a report to identify a cancer treatment for a patient, or systems to generate a report for a patient to identify the patient as a candidate for increased monitoring, additional diagnostic tests, or both, the computer database comprises test sample data, the test sample data includes assigning a unique identifier (UID) to each of a plurality of model molecules present in the sample.

In some modes of systems to generate a report for a patient, systems to generate a report to identify a cancer treatment for a patient or systems to generate a report for a patient to identify the patient as a candidate for increased monitoring. - tested, additional diagnostic tests, or both, the score is calculated using the following formula:

, where wi is the number of unique identifier strings (UIDs) in a test sample i divided by the total number of UIDs for that mutation in all test samples, piN is the probability of the frequency of the mutation allele in the first reference distribution, and piC is the probability of the frequency of the mutation allele in the second reference distribution.

In some modalities of systems to generate a report for a patient, systems to generate a report to identify a cancer treatment for a patient or systems to generate a report for a patient to identify the patient as a candidate for increased monitoring, diagnostic tests additional, or both, the device configured to test a set of genes comprises a device employing a includes a PCR-based multiplex assay, using a PCR-based singleplex assay, a digital PCR assay, a PCR assay digital droplet (ddPCR), a microarray assay, next generation sequencing assay, Sanger sequencing assay, quantitative PCR assay or a binding assay.

In some modalities of systems to generate a report for a patient, systems to generate a report to identify a cancer treatment for a patient or systems to generate a report for a patient to identify the patient as a candidate for a enhanced monitoring, additional diagnostic tests, or both, the multiplex sequencing assay based on PCR comprises: a. assign a unique identifier (UID) to each of a plurality of model molecules present in the sample; B. amplify each uniquely labeled model molecule to create UID families; and C. redundant sequencing of the amplification products.

In some modalities of systems for generating a report for a patient, systems for generating a report to identify a cancer treatment for a patient, or systems for generating a report for a patient to identify the patient as a candidate. increased monitoring, additional diagnostic tests, or both, the system also includes a device (for example, a device that includes a multiplex immunoassay system) configured to detect a level of one or more protein biomarkers in a biological sample , where the protein biomarkers are one or more (for example, 1, 2, 3, 4, 5, 6, 7 or 8) from: CA19-9, CEA, HGF, OPN, CA125, prolactin, TIMP-1 and / or myeloperoxidase (MPO). In some modalities of systems to generate a report for a patient, systems to generate a report to identify a cancer treatment for a patient, or systems to generate a report for a patient to identify the patient as a candidate for a monitor - augmented ment, additional diagnostic tests, or both, the system also includes a device (for example, a device that includes a multiplex immunoassay system) configured to detect a level of one or more protein biomarkers in a biological sample, in which the protein biomarkers are one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or 11) from: CA19-9, CEA , HGF, OPN, CA125, AFP, prolactin, TIMP-1, follistatin, G-CSF, and / or CA15-3. In some modalities of systems to generate a report for a patient, systems to generate a report to identify a cancer treatment for a patient, or systems to generate a report for a patient to identify the patient as a candidate for monitoring augmented, additional diagnostic tests, or both, the system further includes a device (for example, a device that includes a multiplex immunoassay system) configured to detect a

of one or more protein biomarkers in a biological sample, where the protein biomarkers are one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8 or 9) of : CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1 and / or CA15-3. In some modalities of systems to generate a report for a patient, systems to generate a report to identify a cancer treatment for a patient, or systems to generate a report for a patient to identify the patient as a candidate for increased monitoring, additional diagnostic tests, or both, which include at least one device configured to test one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16 or 16) of the following genes: NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A and / or GNAS, the system also includes a device (for example, a device that includes a multiplex immunoassay system) configured to detect a level of one or more protein biomarkers in a biological sample, where the protein biomarkers are one or more more (for example, 1, 2, 3, 4, 5, 6, 7 or 8) from: CA19-9, CEA, HGF, OPN, CA125, prolactin, TIMP-1 and / or myelope roxidase (MPO). In some modalities of systems to generate a report for a patient, systems to generate a report to identify a cancer treatment for a patient, or systems to generate a report for a patient to identify the patient as a candidate for increased monitoring, additional diagnostics, or both, that include at least one device configured to test one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16 or 16) of the following genes: NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A and / or GNAS, the system also includes a - active (for example, a device that includes a multiplex immunoassay system) configured to detect a level of one or more protein biomarkers in a biological sample, where the protein biomarkers are one or more (for example , 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or 11) from: CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP- 1 , follistatin, G-CSF, and / or CA15-3. In some modalities of systems for generating a report for a patient, systems for generating a report to identify a cancer treatment for a patient, or systems for generating a report for a patient to identify the patient as a candidate for monitoring larger, more diagnostic tests, or both that include at least one device configured to test one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16 or 16) of the following genes: NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A and / or GNAS, the system also includes a device (for example, a device that includes a multiplexed immunoassay system configured to detect a level of one or more protein biomarkers in a biological sample, where the protein biomarkers are one or more (for example, example, 1, 2, 3, 4, 5, 6, 7, 8 or 9) of: CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1 and / or CA15-3. n some modalities of systems for generating a report for a patient, systems for generating a report to identify a cancer treatment for a patient, or systems for generating a report for a patient to identify the patient as a candidate for increased monitoring, additional diagnostic tests, or both, the system further includes a device (for example, a device that includes a multiplex immunoassay system) configured to detect a level of one or more protein biomarkers in a biological sample, in that protein biomarkers are one or more (for example, 1, 2, 3 or 4) of: CA19-9, CEA, HGF and / or OPN.

In some modalities of systems to generate a report for a patient, systems to generate a report to identify a cancer treatment for a patient, or systems to generate a report for a patient to identify the patient as a candidate for monitoring increased, additional diagnostic tests, or both, which include at least one device configured to test one or more (for example, 1, 2, 3 or 4) of the following genes: KRAS, TP53, CDKN2A and / or SMAD4, the system further includes a device (for example, a device that includes a multiplex immunoassay system) configured to detect a level of one or more protein biomarkers in a biological sample, where the protein biomarkers are one or more (for example , 1, 2, 3 or 4) of: CA19-9, CEA, HGF and / or OPN.

In some modalities of systems for generating a report for a patient, systems for generating a report to identify a cancer treatment for a patient, or systems for generating a report for a patient to identify the patient as a candidate for increased monitoring , additional diagnostic tests, or both, the sensitivity of the system in identifying an individual as having cancer is enhanced compared to conventional systems for generating a report for a patient.

In some modalities of systems to generate a report for a patient, systems to generate a report to identify a cancer treatment for a patient or systems to generate a report for a patient to identify the patient as a candidate for increased monitoring, testing of di - Additional agnostics, or both, the specificity of the system in identifying an individual as having cancer is improved compared to conventional systems for generating a report for a patient.

In some modalities of systems to generate a report for a patient, systems to generate a report to identify a cancer treatment for a patient, or systems to generate a report for a patient to identify the patient as a candidate for increased monitoring, additional diagnostic tests, or both, the first and second reference distributions of the mutation allele frequency values are entered into the system from a remote location on at least one computer database.

In some modalities of systems to generate a report for a patient, systems to generate a report to identify a cancer treatment for a patient or systems to generate a report for a patient to identify the patient as a candidate for increased monitoring , additional diagnostic tests, or both, the first and the second reference distribution of the mutation allele frequency values are entered into the system via an Internet connection.

In some modalities of systems to generate a report for a patient, systems to generate a report to identify a cancer treatment for a patient or systems to generate a report for a patient to identify the patient as a candidate for increased monitoring, testing additional diagnostics, or both, the report is in electronic or paper format.

In some modalities of systems to generate a report for a patient, systems to generate a report to identify a cancer treatment for a patient or systems to generate a report for a patient to identify the patient as a candidate for increased monitoring , additional diagnostic tests, or both, cancer is one of the types of cancer described here (see, for example, the section entitled "Cancers"). In some modalities of systems for generating a report for a patient, systems for generating a report to identify a cancer treatment for a patient, or systems for generating a report for a patient to identify the patient as a candidate for increased monitoring, additional diagnostic tests, or both, that include at least one device configured to test one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16 or 16) of the following genes: NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A and / or GNAS, and at least one device (for example, a device that includes a multiplex immunoassay system) configured to detect a level of one or more protein biomarkers in a biological sample, where the protein biomarkers are one or more (for example, 1, 2, 3, 4, 5, 6, 7 or 8) from: CA19-9, CEA, HGF, OPN, CA125, prolactin, TIMP-1 and / or myeloperoxidase (MPO), the cancer is liver cancer, ovarian cancer, esophagus, stomach cancer, pancreatic cancer, colorectal cancer, lung cancer, breast cancer or prostate cancer.

In some modalities of systems to generate a report for a patient, systems to generate a report to identify a cancer treatment for a patient, or systems to generate a report for a patient to identify the patient as a candidate for increased monitoring, additional diagnostic tests, or both, which include at least one device configured to test one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16 or 16) of the following genes: NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A and / or GNAS, and at least one device (for example, a device that includes a multiplex immunoassay system) configured to detect a level of one or more protein biomarkers in a biological sample, where the protein biomarkers are one or more (for example, example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or 11) from: CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1, leaves tatin, G-CSF, and / or CA15-3, the cancer is liver cancer, ovarian cancer, esophagus, stomach cancer, pancreatic cancer, colorectal cancer, lung cancer, breast cancer or prostate cancer .

In some modalities of systems to generate a report for a patient, systems to generate a report to identify a cancer treatment for a patient, or systems to generate a report for a patient to identify the patient as a candidate for monitoring larger, more diagnostic tests, or both that include at least one device configured to test one or more (e.g. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16 or 16) of the following genes: NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A and / or GNAS, the system also includes a device (for example, a device that includes a multiplexed immunoassay system configured to detect a level of one or more protein biomarkers in a biological sample, where the protein biomarkers are one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8 or 9) of: CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1 and / or CA15-3, the cance r is liver cancer, ovarian cancer, esophagus, stomach cancer, pancreatic cancer, colorectal cancer, lung cancer, breast cancer or prostate cancer.

In some modalities of systems for generating a report for a patient, systems for generating a report to identify a cancer treatment for a patient, or systems for generating a report for a patient to identify the patient as a candidate for monitoring increased, additional diagnostic tests, or both, which include at least one device configured to test one or more (for example, 1, 2, 3 or 4) of the following genes: KRAS, TP53, CDKN2A and / or SMAD4, and at least one device (for example, a device that includes a multiplex immunoassay system) configured to detect a level of one or more protein biomarkers in a biological sample, where the protein biomarkers are one or more (for example, 1, 2, 3 or 4) of: CA19-9, CEA, HGF and / or OPN, cancer is pancreatic cancer. In some modalities of systems to generate a report for a patient or systems to generate a report to identify a cancer treatment for a patient, the patient is identified as a candidate for increased monitoring, additional diagnostic tests or both (for example , any of the enhanced monitoring types or other diagnostic methods described here).

[879] In some modalities, methods are provided here to identify an individual as having cancer which include: detecting the presence of one or more genetic biomarkers in a first sample obtained from that individual; detect the presence of aneuploidy in a second sample obtained from that individual; and to identify the individual as having cancer when the presence of one or more genetic biomarkers is detected in the first sample, when the presence of aneuploidy is detected in the second sample, or both. In some modalities, methods of treating an individual with cancer are provided here, which include: detecting the presence of one or more genetic biomarkers in a first sample obtained from that individual; detect the presence of aneuploidy in a second sample obtained from that individual; and administer one or more therapeutic interventions to that individual when the presence of one or more genetic biomarkers is detected in the first sample, when the presence of aneuploidy is detected in the second sample, or both.

[880] In some modalities to identify an individual as having cancer or treatment of an individual with cancer (for example, based on the presence of one or more genetic biomarkers in a first sample obtained from that individual and / or the presence of aneu- ploidy in a second sample obtained from the individual), the step of detecting the presence of one or more genetic biomarkers comprises a method that includes a multiplex assay based on PCR, using a singleplex assay based on PCR, a digital PCR assay, a digital droplet PCR assay (ddPCR), a microarray assay, a next generation sequencing assay, a Sanger sequencing assay, a quantitative PCR assay or a binding assay. In some modalities, the step of detecting the presence of one or more genetic biomarkers comprises a method that increases the sensitivity of massively parallel sequencing instruments with an error reduction technique that comprises: a. assign a unique identifier (UID) to each of a plurality of model molecules present in the sample; B. amplify each uniquely labeled model molecule to create UID families; and C. redundant sequencing of the amplification products.

[881] In some modalities to identify an individual as having cancer or treatment of an individual with cancer (for example, based on the presence of one or more genetic biomarkers in a first sample obtained from that individual and / or the presence of aneu- ploidy in a second sample obtained from the individual), the step of detecting the presence of aneuploidy includes: amplifying long, interspersed nucleotide elements (LINEs) through the genome of the second sample, thus obtaining a plurality of amplicons; sequencing the plurality of amplicons to obtain sequencing readings; placing the sequencing readings into predefined groups of genomic intervals; and determining the presence of aneuploidy in the second sample when the number of sequencing readings from a geographic region within a predefined cluster is significantly different from the expected number of sequencing readings from the geographic region within the predefined cluster . In some embodiments, predefined clusters of genomic intervals are created by grouping genomic intervals based on the reading depths of the sequencing readings of two or more euploid samples. In some embodiments, determining that the number of sequencing readings from a genomic region within a predefined cluster is significantly different from the expected number of sequencing readings from the genomic region within the predefined cluster includes: calculating the distribution of readings from sequencing of all genomic intervals in the predefined cluster, where the sequence readings of all genomic intervals in the predefined cluster are obtained by sequencing the amplicons derived from the second sample; and determining the number of sequencing readings in the genomic region is outside a threshold of significance of the distribution. In some embodiments, determining that the number of sequencing readings from a genomic region within a predefined cluster is significantly different from the expected number of sequencing readings from the genomic region within the predefined cluster includes: calculating sums of distributions of sequencing readings in each genomic interval using the equation ∑ ~ (∑, ∑), where Ri is the number of sequencing readings, I is the number of clusters in a chromosome arm, N is a Gaussian distribution with parameters ie i2, where i is the average number of sequencing readings in each genomic interval and where i2 is the variation of sequencing readings in each genomic interval; calculate a Z score of a chromosome arm using the quantile function 1- CDF (∑, ∑); and to identify the presence of an aneuploidy in the mammalian tissue when the Z score is outside a threshold of significance.

[882] In some modalities to identify an individual as having cancer or treatment of an individual with cancer (for example, based on the presence of one or more genetic biomarkers in a first sample obtained from that individual and / or the presence of aneu- ploidy in a second sample obtained from the individual), the step of detecting the presence of aneuploidy includes: sequencing a plurality of amplicons obtained from a second sample to obtain variant sequencing reading for a plurality of polymorphic sites; selecting a chromosomal arm with the variant sequencing readings and the reference sequencing readings on the two alleles which is greater than about 3; determine a variant allele frequency (VAF) for each polymorphic site on the selected chromosomal arm, where said VAF is the number of variant sequencing readings / total number of sequencing readings; and to identify the presence of aneuploidy in the selected chromosomal arm if the AVF of one or more polymorphic sites is outside a normal distribution significance threshold, where the expected AVF is 0.5. In some modalities, the sequencing step includes: a. assign a unique identifier (UID) to each of a plurality of amplicons, b. amplify each amplicon with a unique tag to create UID families, and c. redundant sequencing of amplification products. In some modalities, the step to identify the presence of aneuploidy in the selected chromosomal arm includes: calculating a Z score for one or more polymorphic sites in that selected chroosomal arm using the equation ~, where wi is the depth of the UID in a variant i, Zi is the VAF Z score for variant i, and k is the number of variants observed in the chromosomal arm.

[883] In some modalities to identify an individual as having cancer or to treat an individual as having cancer (for example, based on the presence of one or more genetic biomarkers in a first

first sample obtained from said individual and / or in the presence of aneuploidy in a second sample obtained from the individual), the methods also include performing cytology on the first sample, the second sample or both, and identifying the individual as having cancer when the presence of one or more genetic biomarkers is detected in the first sample, when the presence of aneuploidy is detected in the second sample, a positive cytology indicates that the individual has cancer or combinations of them.

[884] In some modalities to identify an individual as having cancer or to treat an individual having cancer (for example, based on the presence of one or more genetic biomarkers in a first sample obtained from that individual and / or the presence of aneu - ploidy in a second sample obtained from the individual), the cancer is a bladder cancer or an urothelial carcinoma of the upper tract; the one or more genetic biomarkers are one or more of: TP53, PIK3CA, FGFR3, KRAS, ERBB2, CDKN2A, MLL, HRAS, MET or VHL; the method further includes detecting the presence of at least one genetic biomarker (for example, a mutation) in a TERT promoter; and the presence of one or more genetic biomarkers in one or more of TP53, PIK3CA, FGFR3, KRAS, ERBB2, CDKN2A, MLL, HRAS, MET and VHL, the presence of at least one genetic biomarker (for example, a mutation) in TERT promoter, or the presence of aneuploidy indicates that the individual has bladder cancer. In some modalities, the presence of one or more genetic biomarkers in one or more of TP53, PIK3CA, FGFR3, KRAS, ERBB2, CDKN2A, MLL, HRAS, MET and VHL, the presence of at least one genetic biomarker (for example , a mutation) in the TERT promoter, and the presence of aneuploidy indicates that the individual has bladder cancer. In some embodiments, the one or more genetic biomarkers are TP53, FGFR3 or both. In some

but modalities, the step of detecting the presence of aneuploidy comprises detecting the presence of aneuploidy in one or more of the chromosomal arms 5q, 8q and 9p. In some embodiments, one or more genetic biomarkers, one or more genetic biomarkers (for example, mutations) in the TERT promoter, or both, are present in 0.03% or less of the urinary cells in the sample. In some modalities, the step of detecting the presence of at least one genetic biomarker (for example, a mutation) in the TERT promoter is performed using a PCR-based multiplex assay, a Sanger Sequencing assay or a next generation sequencing. In some embodiments, the step of detecting the presence of at least one genetic biomarker (for example, a mutation) in the TERT promoter is performed by increasing the sensitivity of massively parallel sequencing instruments with an error reduction technique, including: The. assign a unique identifier (UID) to each of a plurality of model molecules present in the sample; B. amplify each uniquely labeled model molecule to create UID families; and C. redundant sequencing of the amplification products.

[885] In some modalities to identify an individual as having cancer or to treat an individual as having cancer (for example, based on the presence of one or more genetic biomarkers in a first sample obtained from that individual and / or the presence of aneu - ploidy in a second sample obtained from the individual), the method includes detecting bladder cancer and also includes administering transurethral resection of the bladder (TURB), intravesical BCG (Bacillus Calmette-Guerin), intravesical chemotherapy, adjuvant chemotherapy, chemotherapy neoadjuvant, cystectomy or cystprostatectomy, radiation therapy, immunotherapy, immune checkpoint inhibitors or any combination thereof.

[886] In some modalities to identify an individual as having cancer or to treat an individual having cancer (for example, based on the presence of one or more genetic biomarkers in a first sample obtained from that individual and / or the presence of aneu - ploidy in a second sample obtained from the individual), the method includes detecting a urothelial carcinoma of the upper tract and also includes administering transurethral resection, intravesical BCG (Bacillus Calmette-Guerin), intravesical chemotherapy, adjuvant chemotherapy, neoadjuvant chemotherapy, ureterectomy or nephroureterectomy, radiation therapy, immunotherapy, immune control inhibitors or any combination thereof.

[887] In some modalities to identify an individual as having cancer or to treat an individual having cancer (for example, based on the presence of one or more genetic biomarkers in a first sample obtained from that individual and / or the presence of aneu - ploidy in a second sample obtained from the individual), the cancer is an ovarian or endometrial cancer; the one or more genetic biomarkers are one or more of NRAS, PTEN, FGFR2, KRAS, POLE, AKT1, TP53, RNF43, PPP2R1A, MAPK1, CTNNB1, PIK3CA, FBXW7, PIK3R1, APC, EGFR, BRAF or CDKN2A; and the presence of one or more mutations in one or more of NRAS, PTEN, FGFR2, KRAS, POLE, AKT1, TP53, RNF43, PPP2R1A, MAPK1, CTNNB1, PIK3CA, FBXW7, PIK3R1, APC, EGFR, BRAF or CDKN2A , the presence of aneuploidy, or both, indicates that the individual has ovarian or endometrial cancer. In some modalities to identify an individual as having cancer or to treat an individual having cancer (for example, based on the presence of one or more genetic biomarkers in a first sample obtained from that individual and / or the presence of aneuploidy in a second sample obtained of the individual), cancer is an endometrial cancer; the one or more genetic biomarkers are one or more of PTEN, TP53,

PIK3CA, PIK3R1, CTNNB1, KRAS, FGFR2, POLE, APC, FBXW7, RNF43 or PPP2R1A; and the presence of one or more mutations in one or more of PTEN, TP53, PIK3CA, PIK3R1, CTNNB1, KRAS, FGFR2, POLE, APC, FBXW7, RNF43 or PPP2R1A, the presence of aneuploidy or both indicate that the individual has endometrial cancer . In some modalities to identify an individual as having cancer or to treat an individual having cancer (for example, based on the presence of one or more genetic biomarkers in a first sample obtained from that individual and / or the presence of aneuploidy in a second sample obtained of the individual), cancer is a high-grade serous carcinoma; the one or more genetic biomarkers in TP53; and the presence of one or more genetic biomarkers in TP53, the presence of aneuploidy, or both indicate that the individual has a high-grade serous carcinoma. In some modalities, the step of detecting the presence of aneuploidy includes detecting the presence of aneuploidy in one or more of the chromosomal arms 4p, 7q, 8q, and 9q. In some modalities, the first sample, the second sample, or both are collected by intrauterine sampling. In some modalities, the first sample, the second sample or both are collected with a Tao brush. In some modalities, the methods also include detecting in a sample of circulating tumor DNA (ctDNA) obtained from the individual the presence of at least one genetic biomarker in one or more of the following genes: AKT1, APC, BRAF, CDKN2A , CTNNB1, EGFR, FBXW7, FGFR2, GNAS, HRAS, KRAS, NRAS, PIK3CA, PPP2R1A, PTEN or TP53. In some modalities, the methods also include administering to the individual a therapy, in which the therapy includes: surgery, adjuvant chemotherapy, neoadjuvant chemotherapy, radiation therapy, immunotherapy, targeted therapy, immune checkpoint inhibitors or combinations thereof.

[888] In some modalities to identify an individual as having cancer or treatment of an individual having cancer (for example, based on the presence of one or more genetic biomarkers in a first sample obtained from that individual and / or the presence of aneu- ploidy in a second sample obtained from the individual), the genetic biomarker is a mutation in a gene.

[889] In some modalities to identify an individual as having cancer or treatment of an individual having cancer (for example, based on the presence of one or more genetic biomarkers in a first sample obtained from that individual and / or the presence of aneu- ploidy in a second sample obtained from the individual), the first sample and the second sample are the same. In some modalities to identify an individual as having cancer or treatment of an individual having cancer (for example, based on the presence of one or more genetic biomarkers in a first sample obtained from that individual and / or the presence of aneuploidy in a second sample obtained from the individual), the first sample and the second sample are different. In some modalities to identify an individual as having cancer or treatment of an individual having cancer (for example, based on the presence of one or more genetic biomarkers in a first sample obtained from that individual and / or the presence of aneuploidy in a second sample obtained from the individual), the first sample is a blood sample.

[890] In some modalities to identify an individual as having cancer or treatment of an individual having cancer (for example, based on the presence of one or more genetic biomarkers in a first sample obtained from that individual and / or the presence of aneu- ploidy in a second sample obtained from the individual), the methods also include a) determining a frequency of mutation allele in the sample for two or more of the genetic biomarkers; b) obtain a score that indicates the probability of the individual having a cancer by comparing the frequency of the mutation allele of each mutation in the selected genetic biomarkers with a reference distribution of the frequency of the mutation allele for each mutation in samples of control; and c) identify the individual as having cancer when the score is greater than a reference value for the score. In some modalities, the methods also include selecting a genetic biomarker with the highest score that indicates the probability that the individual will have cancer from two or more genetic biomarkers; and compare the score of the selected genetic biomarker with the reference value. In some modalities, the score is Stouffer's Z score.

[891] In some modalities to identify an individual as having cancer or treatment of an individual having cancer (for example, based on the presence of one or more genetic biomarkers in a first sample obtained from that individual and / or the presence of aneu- ploidy in a second sample obtained from the individual), the methods also include a) determining a frequency of mutation allele in the sample for two or more of the genetic biomarkers; and b) comparing the mutation allele frequency of each mutation in the selected genetic biomarkers with the maximum mutation allele frequency of each mutation for each mutation in control samples.

[892] In some modalities to identify an individual as having cancer or treatment of an individual having cancer (for example, based on the presence of one or more genetic biomarkers in a first sample obtained from that individual and / or the presence of aneu- ploidy in a second sample obtained from the individual), the sensitivity to identify an individual as having cancer is increased in comparison with: 1) the sensitivity obtained when only the presence of one or more genetic biomarkers in the sample obtained from the individual is detected, or 2) the sensitivity obtained when the presence of cancer only aneuploidy in the sample obtained from the individual is detected. In some modalities to identify an individual as having cancer or treatment of an individual having cancer (for example, based on the presence of one or more genetic biomarkers in a first sample obtained from that individual and / or the presence of aneuploidy in a second sample obtained from the individual), the specificity to identify an individual as having cancer is increased compared to: 1) the specificity obtained when only the presence of one or more genetic biomarkers in the sample obtained from the individual is detected, or 2) the specificity obtained when the presence of cancer only aneuploidy in the sample obtained from the individual is detected.

[893] In some modalities to identify an individual as having cancer or treatment of an individual having cancer (for example, based on the presence of one or more genetic biomarkers in a first sample obtained from that individual and / or the presence of aneu- ploidy in a second sample obtained from the individual), the methods also include detecting the presence of one or more biomarkers of proteins in a sample obtained from the individual, where the sample is the first sample, the second sample or a third sample. In some modalities to identify an individual as having cancer or treatment of an individual having cancer (for example, based on the presence of one or more genetic biomarkers in a first sample obtained from that individual and / or the presence of aneuploidy in a second sample obtained from the individual), the individual is a human. In some modalities to identify an individual as having cancer or treatment of an individual having cancer (for example, based on the presence of one or more genetic biomarkers in a first sample obtained from that individual and / or the presence of aneuploidy in a second sample obtained from the individual), the cancer is a Stage I cancer.

[894] In some modalities to identify an individual as having cancer or treatment of an individual having cancer (for example, based on the presence of one or more genetic biomarkers in a first sample obtained from that individual and / or the presence of aneu- ploidy in a second sample obtained from the individual), the one or more genetic biomarkers comprise one or more modifications in one or more genes. In some embodiments, the one or more modifications comprise inactivation modifications and wherein said one or more genes comprise tumor suppressor genes. In some modalities, the one or more modifications include a selected modification regardless of single-base substitutions, insertions or deletions, translocations, fusions, breaks, duplications or amplifications.

[895] In some modalities to identify an individual as having cancer or treatment of an individual having cancer (for example, based on the presence of one or more genetic biomarkers in a first sample obtained from that individual and / or the presence of aneu- ploidy in a second sample obtained from the individual),

[896] In some embodiments, methods are provided here to identify a patient as having cancer that includes: a) determining a frequency of mutation allele in a sample collected from the patient for each mutation in one or more of the following genes: NRAS, PTEN, FGFR2, KRAS, POLE, AKT1, TP53, RNF43, PPP2R1A, MAPK1, CTNNB1, PIK3CA, FBXW7, PIK3R1, APC, EGFR, BRAF or CDKN2A; b) obtain a score that indicates the probability of the individual having a cancer by comparing the frequency of the mutation allele of each mutation in the selected genetic biomarkers with a reference distribution of the frequency of the mutation allele for each mutation in control samples; and c) identify the individual as having cancer when the score is greater than a reference value for the score.

In some embodiments, methods are provided here to identify a patient as having cancer that includes: a) determining a mutation allele frequency in a sample collected from the patient for each mutation in one or more of the following genes: PTEN, TP53 , PIK3CA, PIK3R1, CTNNB1, KRAS, FGFR2, POLE, APC, FBXW7, RNF43 or PPP2R1A; b) obtain a score that indicates the probability of the individual having a cancer by comparing the frequency of the mutation allele of each mutation in the selected genetic biomarkers with a reference distribution of the frequency of the mutation allele for each mutation in control samples; and c) identify the individual as having cancer when the score is greater than a reference value for the score.

In some modalities, the methods even include selecting a genetic biomarker with the highest score that indicates the probability that the individual will have cancer from two or more genetic biomarkers; and compare the score of the selected genetic biomarker with the reference value.

In some embodiments, methods are provided here to identify a patient as having cancer that includes: a) determining a frequency of mutation allele in the sample for one or more of the genetic biomarkers; b) obtain a score that indicates the probability of the individual not having cancer by comparing the frequency of the mutation allele of each mutation in the selected genetic biomarkers with a reference distribution of the frequency of the mutation allele for each mutation in control samples; and c) identify the individual as not having cancer when the score is less than a reference value for the score.

In some modalities, the methods also include selecting a genetic biomarker with the highest score that indicates the probability that the individual will have cancer from two or more genetic biomarkers; and compare the score of the selected genetic biomarker with the reference value.

In some modalities, the score is Stouffer's Z score.

In some embodiments, methods are provided here to identify a patient as having cancer that includes: a) determining a frequency of mutation allele in the blood sample for two or more of the genetic biomarkers; and b) comparing the mutation allele frequency of each mutation in the selected genetic biomarkers with the maximum mutation allele frequency of each mutation for each mutation in control samples.

In some embodiments, methods are provided here to identify a patient as having a cancer described in the previous paragraph, the sample is analyzed on two or more test samples using a method that comprises: a. assigning a unique identifier (UID) to each of a plurality of model molecules present in the sample; B. amplify each uniquely labeled model molecule to create UID families; and C. redundant sequencing of the amplification products.

In some modalities to identify a patient as having cancer, the methods also include detecting the presence of aneuploidy in the sample (for example, the presence of aneuploidy in one or more chromosomal arms 4p, 7q, 8q and 9q). In some ways to identify a patient as having cancer, the methods also include detecting in a sample of circulating tumor DNA (ctDNA) obtained from the individual the presence of at least one genetic biomarker in one or more of the following genes : AKT1, APC, BRAF, CDKN2A, CTNNB1, EGFR, FBXW7, FGFR2, GNAS, HRAS, KRAS, NRAS, PIK3CA, PPP2R1A, PTEN or TP53. In some modalities, cancer is Stage I cancer.

In some modalities, the cancer is cervical cancer, endometrial cancer, ovarian cancer or fallopian tube cancer.

In some modalities, at least one mutation comprises inactivating modifications and in which at least one mutation is in a tumor suppressor gene. In some modes, modifications are selected independently of substitutions, insertions, deletions, translocations, fusions, breaks, duplications or single-base amplifications. In some modalities, the methods also include administering to the individual one or more therapeutic interventions (for example, one or more of: surgery, adjuvant chemotherapy, neoadjuvant chemotherapy, radiation therapy, immunotherapy, targeted therapy, immune verification or combinations thereof).

[897] In some embodiments, methods are provided here to identify a patient as having cancer that includes: a) determining a frequency of mutation allele in a sample collected from the patient for each mutation in one or more of the following genes: TP53, PIK3CA, FGFR3, KRAS, ERBB2, CDKN2A, MLL, HRAS, MET, or VHL; b) obtain a score that indicates the probability of the individual having a cancer by comparing the frequency of the mutation allele of each mutation in the selected genetic biomarkers with a reference distribution of the frequency of the mutation allele for each mutation in control samples; and c) identify the individual as having cancer when the score is greater than a reference value for the score. In some modalities, the methods also include selecting a genetic biomarker with the highest score that indicates the probability that the individual will have cancer from two or more genetic biomarkers; and compare the score of the selected genetic biomarker with the reference value. In some embodiments, methods are provided here to identify a patient as having cancer that includes: a) determining a frequency of mutation allele in the sample for one or more of the genetic biomarkers; b) obtain a score that indicates the probability that the individual will not have cancer

stopping the frequency of the mutation allele of each mutation in the selected genetic biomarkers with a reference distribution of the frequency of the mutation allele for each mutation in control samples; and c) identify the individual as not having cancer when the score is less than a reference value for the score.

In some modalities, the methods also include selecting a genetic biomarker with the highest score that indicates the probability that the individual will have cancer from two or more genetic biomarkers; and compare the score of the selected genetic biomarker with the reference value.

In some modalities, the score is Stouffer's Z score.

In some modalities, methods are provided here to identify a patient as having cancer that includes: a) determining a mutation allele frequency in the blood sample for two or more of the genetic biomarkers; and b) comparing the mutation allele frequency of each mutation in the selected genetic biomarkers with the maximum mutation allele frequency of each mutation for each mutation in control samples.

In some modalities, methods are provided here to identify a patient as having a cancer described in the previous paragraph, the sample is analyzed on two or more test samples using a method comprising: a. assigning a unique identifier (UID) to each of a plurality of model molecules present in the sample; B. amplify each model molecule uniquely marked to create UID families; and C. redundant sequencing of the amplification products.

In some embodiments, the methods also include detecting the presence of at least one genetic biomarker (for example, a mutation) in a TERT promoter in the sample.

In some ways to identify a patient as having cancer, the methods also include detecting the presence of aneuploidy in the sample (for example, the presence of aneuploidy in one or more chromosomal arms 5q, 8q, and 9p. In some modalities to identify a patient as having a cancer, the methods also include detecting in a sample of circulating tumor DNA (ctDNA) obtained from the individual the presence of at least one genetic biomarker in one or more of the following genes : AKT1, APC, BRAF, CDKN2A, CTNNB1, EGFR, FBXW7, FGFR2, GNAS, HRAS, KRAS, NRAS, PIK3CA, PPP2R1A, PTEN or TP53. In some modalities, cancer is Stage I cancer. cancer is a bladder cancer or an urothelial cancer of the upper tract (UTUC) In some modalities, at least one mutation comprises inactivating modifications and in which at least one mutation is in a tumor suppressor gene. , modifications are selected independently substitutions, insertions, deletions, translocations, fusions, breaks, duplications or single-base amplifications. In some modes, the methods also include administering to the individual one or more therapeutic interventions (for example, one or more of: surgery, adjuvant chemotherapy, neoadjuvant chemotherapy, radiation therapy, immunotherapy, targeted therapy, immune verification or combinations thereof).

[898] In some embodiments, systems are provided in this document to generate a report for a patient that includes: at least one device configured to analyze a set of genes in a biological sample to determine the frequency of the mutation allele of each mutation in the gene pool in a sample collected from the patient, where the gene pool comprises one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9 or 10) of the following genes : TP53, PIK3CA, FGFR3, KRAS, ERBB2, CDKN2A, MLL, HRAS, MET and / or VHL; b) at least one computer database, including: i)

a first reference distribution of the frequency of the mutation allele in control samples for each mutation in the gene pool; and ii) a second reference distribution of the frequency of mutation alleles in samples collected from cancer patients for each mutation in the gene pool; c) a computer-readable program code comprising instructions for performing the following: i) insert the frequency of the mutation allele into the biological sample of each mutation in the gene pool; ii) compare the frequency of the mutation allele with the first reference distribution; iii) to compare the frequency of the mutation allele with the second reference distribution; and iv) calculate a score that indicates the probability that the patient will have cancer; d) a computer-readable program code, comprising instructions for generating a report that indicates that the patient has cancer if the score is greater than a reference value; or a report indicating that the patient does not have cancer if the score is not higher than the reference value. In some modalities of systems for generating a report for a patient, the systems also include at least one device configured to detect the presence of at least one genetic biomarker (for example, at least one mutation) in a TERT promoter .

[899] In some embodiments, systems are provided in this document to generate a report for a patient that includes: at least one device configured to analyze a set of genes in a biological sample to determine the frequency of the mutation allele of each mutation in the gene pool in a sample collected from the patient, where the gene pool comprises one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , 13, 14, 15, 16, 17 or 18) of the following genes: NRAS, PTEN, FGFR2, KRAS, POLE, AKT1, TP53, RNF43, PPP2R1A, MAPK1, CTNNB1, PIK3CA, FBXW7, PIK3R1, APC,

EGFR, BRAF and / or CDKN2A; b) at least one computer database, including: i) a first reference distribution of the frequency of the mutation allele in control samples for each mutation in the gene pool; and ii) a second reference distribution of the frequency of mutation alleles in samples collected from cancer patients for each mutation in the gene pool; c) a computer-readable program code comprising instructions to perform the following: i) introduce the frequency of the mutation allele into the biological sample of each mutation in the gene pool; ii) compare the frequency of the mutation allele with the first reference distribution; iii) compare the frequency of the mutation allele with the second reference distribution; and iv) calculate a score that indicates the probability that the patient will have cancer; d) a computer-readable program code comprising instructions for generating a report that indicates that the patient has cancer if the score is greater than a reference value; or a report indicating that the patient does not have cancer if the score is not higher than the reference value.

In some embodiments, systems are provided in this document to generate a report for a patient that includes: at least one device configured to analyze a set of genes in a biological sample to determine the frequency of the mutation allele of each mutation in the set of genes in a sample collected from the patient, where the set of genes comprises one or more (eg 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 or 12) of the following genes : PTEN, TP53, PIK3CA, PIK3R1, CTNNB1, KRAS, FGFR2, POLE, APC, FBXW7, RNF43 and / or PPP2R1A; b) at least one computer database, including: i) a first reference distribution of the frequency of the mutation allele in control samples for each mutation in the gene pool; and ii) a second reference distribution of the frequency of mutation alleles in samples collected from cancer patients for each mutation in the gene pool; c) a computer-readable program code comprising instructions for performing the following: i) insert the frequency of the mutation allele into the biological sample of each mutation in the gene pool; ii) compare the frequency of the mutation allele with the first reference distribution; iii) to compare the frequency of the mutation allele with the second reference distribution; and iv) calculate a score that indicates the probability that the patient will have cancer; d) a computer-readable program code, comprising instructions for generating a report that indicates that the patient has cancer if the score is greater than a reference value; or a report indicating that the patient does not have cancer if the score is not higher than the reference value.

In some embodiments, systems are provided here to generate a report for a patient that includes: at least one device configured to test a TP53 gene on a biological sample collected from the patient to determine the frequency of TP53 in the mutation allele; b) at least one computer database, including: i) a first reference distribution of the frequency of mutation alleles in control samples for TP53; and ii) a second reference distribution of the frequency of mutation alleles in samples collected from cancer patients for TP53; c) a computer-readable program code comprising instructions for performing the following: i) introducing the frequency of the mutation allele into the TP53 biological sample; ii) compare the frequency of the mutation allele with the first reference distribution; iii) compare the frequency of the mutation allele with the second reference distribution; and iv) calculate a score that indicates the probability that the patient will have cancer; d) a computer-readable program code, comprising instructions for generating a report that indicates that the patient has cancer if the score is greater than a reference value; or a report indicating that the patient does not have cancer if the score is not higher than the reference value.

[900] In some embodiments, systems are provided in this document to generate a report to identify a cancer treatment for a patient that includes: a) at least one device configured to analyze a set of genes in a biological sample for determine the frequency of the mutation allele of each mutation in the gene pool in a sample collected from the patient, where the gene pool comprises one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8 , 9 or 10) of the following genes: TP53, PIK3CA, FGFR3, KRAS, ERBB2, CDKN2A, MLL, HRAS, MET and / or VHL; b) at least one computer database, comprising: i) a first reference distribution of the frequency of the mutation allele in control samples for each mutation in the gene pool; ii) a second reference distribution of the frequency of mutation alleles in samples collected from cancer patients for each mutation in the gene pool; and iii) an effective cancer treatment list linked to a biological state of at least one member of the gene pool; c) a computer-readable program code comprising instructions to perform the following: i) introduce the frequency of the mutation allele into the biological sample of each mutation in the gene pool; ii) compare the frequency of the mutation allele with the first reference distribution; iii) compare the frequency of the mutation allele with the second reference distribution; and iv) calculating a score that indicates the probability that the patient will have cancer; d) a computer-readable program code, comprising instructions for generating a report that indicates that the patient has cancer if the score is greater than a reference value; or a report indicating that the patient does not have cancer if the score is not higher than the reference value; and e) a computer-readable program code comprising instructions for identifying at least one cancer treatment for the patient from the list of cancer treatments in (b) (iii) when the patient is identified as having cancer at the stage (d), in which the calculated score provides an indication that the treatment of the identified cancer will be effective in the patient. In some modalities of systems to generate a report to identify a cancer treatment for a patient, the systems also include at least one device configured to detect the presence of at least one genetic biomarker (for example, at least one mutation ) in a TERT promoter.

[901] In some embodiments, systems are provided here to generate a report to identify a cancer treatment for a patient that includes: a) at least one device configured to test a set of genes in a biological sample to determine the frequency of the allele of mutation of each mutation in the gene pool in a sample collected from the patient, where the gene pool comprises one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 or 18) of the following genes: NRAS, PTEN, FGFR2, KRAS, POLE, AKT1, TP53, RNF43, PPP2R1A, MAPK1, CTNNB1, PIK3CA, FBXW7, PIK3R1, APC, EGFR, BRAF and / or CDKN2A; b) at least one computer database comprising: i) a first reference distribution of the frequency of mutation allele in control samples for each mutation in the gene pool; ii) a second reference distribution of the frequency of mutation alleles in samples collected from cancer patients for each mutation in the gene pool; and iii) an effective cancer treatment list linked to a biological state of at least one member of the gene pool; c) a computer-readable program code comprising instructions for performing the following: i) introducing the frequency of the mutation allele into the biological sample of each mutation in the gene pool; ii) compare the frequency of the mutation allele with the first reference distribution; iii) compare the frequency of the mutation allele with the second reference distribution; and iv) calculate a score that indicates the probability that the patient will have cancer; d) a computer-readable program code, comprising instructions for generating a report that indicates that the patient has cancer if the score is greater than a reference value; or a report indicating that the patient does not have cancer if the score is not higher than the reference value; and e) a computer-readable program code that includes instructions for identifying at least one cancer treatment for the patient from the list of cancer treatments in (b) (iii) when the patient is identified as having a cancer. step (d), in which the calculated score provides an indication that the identified cancer treatment will be effective in the patient.

In some embodiments, systems are provided in this document to generate a report to identify a cancer treatment for a patient that includes: a) at least one device configured to analyze a set of genes in a biological sample to determine the frequency the mutation allele of each mutation in the gene pool in a sample collected from the patient, where the gene pool comprises one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 or 12) of the following genes: PTEN, TP53, PIK3CA, PIK3R1, CTNNB1, KRAS, FGFR2, POLE, APC, FBXW7, RNF43 and / or PPP2R1A; b) at least one computer database, comprising: i) a first reference distribution of the frequency of the mutation allele in control samples for each mutation in the gene pool; ii) a second reference distribution of the frequency of mutation alleles in samples collected from cancer patients for each mutation in the gene pool; and iii) an effective cancer treatment list linked to a biological state of at least one member of the gene pool; c) a computer-readable program code comprising instructions to perform the following: i) introduce the frequency of the mutation allele into the biological sample of each mutation in the gene pool; ii) compare the frequency of the mutation allele with the first reference distribution; iii) compare the frequency of the mutation allele with the second reference distribution; and iv) calculate a score that indicates the probability that the patient will have cancer; d) a computer-readable program code, comprising instructions for generating a report that indicates that the patient has cancer if the score is greater than a reference value; or a report indicating that the patient does not have cancer if the score is not higher than the reference value; and e) a computer-readable program code comprising instructions for identifying at least one cancer treatment for the patient from the list of cancer treatments in (b) (iii) when the patient is identified as having cancer in step (d), where the calculated score provides an indication that the identified cancer treatment will be effective for the patient.

In some embodiments, systems are provided here to generate a report to identify a cancer treatment for a patient that includes: a) at least one device configured to test a TP53 gene in a biological sample collected from the patient to determine the frequency of TP53 in the mutation allele; b) at least one computer database comprising: i) a first reference distribution of the frequency of the mutation allele in control samples for TP53; ii) a second reference distribution of the frequency of mutation alleles in samples collected from cancer patients for TP53; and iii) a list of cancer treatments with efficacy linked to a bio-

logical of TP53; c) a computer-readable program code comprising instructions for performing the following: i) introducing frequency of the mutation allele into the biological TP53 sample; ii) compare the frequency of the mutation allele with the first reference distribution; iii) compare the frequency of the mutation allele with the second reference distribution; and iv) calculate a score that indicates the probability that the patient will have cancer; d) a computer-readable program code, comprising instructions for generating a report that indicates that the patient has cancer if the score is greater than a reference value; or a report indicating that the patient does not have cancer if the score is not higher than the reference value; and e) a computer-readable program code comprising instructions for identifying at least one cancer treatment for the patient from the list of cancer treatments in (b) (iii) when the patient is identified as having cancer at the stage (d), where the calculated score provides an indication that the identified cancer treatment will be effective for the patient.

[902] In some embodiments, systems are provided in this document to generate a report for a patient to identify him as a candidate for increased monitoring, additional diagnostic tests, or both that include: a) at least one device configured to analyze a set of genes in a biological sample to determine the frequency of the mutation allele of each mutation in the set of genes in a sample collected from the patient, where the set of genes comprises one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9 or 10) of the following genes: TP53, PIK3CA, FGFR3, KRAS, ERBB2, CDKN2A, MLL, HRAS, MET and / or VHL; b) at least one computer database, comprising: i) a first reference distribution of the frequency of the mutation allele in control samples

trolley for each mutation in the gene pool; ii) a second reference distribution of the frequency of mutation alleles in samples collected from cancer patients for each mutation in the gene pool; and iii) an effective cancer treatment list linked to a biological state of at least one member of the gene pool; c) a computer-readable program code comprising instructions to perform the following: i) introduce the frequency of the mutation allele into the biological sample of each mutation in the gene pool; ii) compare the frequency of the mutation allele with the first reference distribution; iii) compare the frequency of the mutation allele with the second reference distribution; and iv) calculating a score that indicates the probability that the patient will have cancer; d) a computer-readable program code, comprising instructions for generating a report that indicates that the patient has cancer if the score is greater than a reference value; or a report indicating that the patient does not have cancer if the score is not higher than the reference value; and e) a computer-readable program code comprising instructions for identifying at least one cancer treatment for the patient from the list of cancer treatments in (b) (iii) when the patient is identified as having cancer at the stage (d), in which the calculated score provides an indication that the treatment of the identified cancer will be effective in the patient. In some modalities of systems to generate a report for a patient to identify him as a candidate for increased monitoring, additional diagnostic tests or both, the systems also include at least one device configured to detect the presence of at least one genetic biomarker (for example, at least one mutation) in a TERT promoter.

[903] In some embodiments, systems are provided in this document to generate a report for a patient to identify the patient as a candidate for increased monitoring, additional diagnostic tests, or both that include: a) at least one device configured to analyze a set of genes in a biological sample to determine the frequency of the mutation allele of each mutation in the set of genes in a sample collected from the patient, where the set of genes in a sample collected from the patient, in that the gene pool comprises one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 or 18) of following genes: NRAS, PTEN, FGFR2, KRAS, POLE, AKT1, TP53, RNF43, PPP2R1A, MAPK1, CTNNB1, PIK3CA, FBXW7, PIK3R1, APC, EGFR, BRAF and / or CDKN2A; b) at least one computer database comprising: i) a first reference distribution of the frequency of the mutation allele in control samples for each mutation in the gene pool; ii) a second reference distribution of the frequency of mutation alleles in samples collected from cancer patients for each mutation in the gene pool; and iii) an effective cancer treatment list linked to a biological state of at least one member of the gene pool; c) a computer-readable program code comprising instructions for performing the following: i) insert the frequency of the mutation allele into the biological sample of each mutation in the gene pool; ii) compare the frequency of the mutation allele with the first reference distribution; iii) to compare the frequency of the mutation allele with the second reference distribution; and iv) calculate a score that indicates the probability that the patient will have cancer; d) a computer-readable program code, comprising instructions for generating a report that indicates that the patient has cancer if the score is greater than a reference value; or a report indicating that the patient does not have cancer if the score is not higher than the reference value; and e) a computer-readable program code that comprises instructions for identifying at least one cancer treatment for the patient from the list of cancer treatments in (b) (iii) when the patient is identified as having cancer in the step (d), in which the calculated score provides an indication that the identified cancer treatment will be effective in the patient.

In some embodiments, systems are provided in this document to generate a report for a patient to identify the patient as a candidate for increased monitoring, additional diagnostic tests, or both that include: a) at least one device configured to analyze a set of genes in a biological sample to determine the frequency of the mutation allele for each mutation in the gene pool in a sample collected from the patient, where the gene pool comprises one or more (for example, 1, 2, 3, 4 , 5, 6, 7, 8, 9, 10, 11 or 12) of the following genes: PTEN, TP53, PIK3CA, PIK3R1, CTNNB1, KRAS, FGFR2, POLE, APC, FBXW7, RNF43 and / or PPP2R1A; b) at least one computer database, comprising: i) a first reference distribution of the frequency of the mutation allele in control samples for each mutation in the gene pool; ii) a second reference distribution of the frequency of mutation alleles in samples collected from cancer patients for each mutation in the gene pool; and iii) an effective cancer treatment list linked to a biological state of at least one member of the gene pool; c) a computer-readable program code comprising instructions to perform the following: i) introduce the frequency of the mutation allele into the biological sample of each mutation in the gene pool; ii) compare the frequency of the mutation allele with the first reference distribution; iii) compare the frequency of the mutation allele with the second reference distribution; and iv) calculate a score that indicates the probability that the patient will have cancer; d) a computer-readable program code, comprising instructions for generating a report that indicates that the patient has cancer if the score is greater than a reference value; or a report indicating that the patient does not have cancer if the score is not higher than the reference value; and e) a computer-readable program code comprising instructions for identifying at least one cancer treatment for the patient from the list of cancer treatments in (b) (iii) when the patient is identified as having cancer at the stage (d), where the calculated score provides an indication that the identified cancer treatment will be effective for the patient.

In some embodiments, systems are provided in this document to generate a report for a patient to identify him or her as a candidate for increased monitoring, additional diagnostic tests, or both that include: a) at least one device configured to test a TP53 gene in a biological sample collected from the patient to determine the frequency of TP53 in the mutation allele; b) at least one computer database comprising: i) a first reference distribution of the frequency of the mutation allele in control samples for TP53; ii) a second reference distribution of the frequency of mutation alleles in samples collected from cancer patients for TP53; and iii) a list of cancer treatments with effectiveness linked to a biological state of TP53; c) a computer-readable program code comprising instructions for performing the following: i) introducing frequency of the mutation allele into the TP53 biological sample; ii) compare the frequency of the mutation allele with the first reference distribution; iii) compare the frequency of the mutation allele with the second reference distribution; and iv) calculating a score that indicates the probability that the patient will have cancer; d) a computer-readable program code, comprising instructions for generating a report that indicates that the patient has cancer if the score is greater than a reference value; or a report indicating that the patient does not have cancer if the score is not higher than the reference value; and e) a computer-readable program code comprising instructions for identifying at least one cancer treatment for the patient from the list of cancer treatments in (b) (iii) when the patient is identified as having cancer at the stage (d), in which the calculated score provides an indication that the treatment of the identified cancer will be effective in the patient.

[904] In some modalities of systems for generating a report for a patient, systems for generating a report to identify a cancer treatment for a patient or systems for generating a report for a patient to identify the patient as a candidate for monitoring increased, additional diagnostic tests, or both, the systems also include a computer-readable program code that comprises instructions to perform the following: i) determine a mutation allele frequency in the sample for two or more genetic biomarkers; ii) obtain a score that indicates the probability of the individual having a cancer by comparing the frequency of the mutation allele of each mutation in the selected genetic biomarkers with a reference distribution of the frequency of the mutation allele for each mutation in samples of control; and ii) identify the individual as having cancer when the score is greater than a reference value for the score. In some modalities of systems to generate a report for a patient, systems to generate a report to identify a cancer treatment for a patient or systems to generate a report for a patient to identify the patient as a candidate for increased monitoring, testing of additional diagnostics, or both, systems also include a computer-readable program code that comprises instructions for executing the following instructions: i) select a genetic biomarker with the highest score that indicates the probability that the individual has a cancer from two or more genetic biomarkers; and ii) compare the score of the selected genetic biomarker with the reference value. In some modalities of systems for generating a report for a patient, systems for generating a report to identify a cancer treatment for a patient or systems for generating a report for a patient to identify the patient as a candidate for increased monitoring , additional diagnostic tests, or both, the score is Stouffer's Z score. In some modes of systems to generate a report for a patient, systems to generate a report to identify a cancer treatment for a patient or systems to generate a report for a patient to identify the patient as a candidate for increased monitoring. - test, additional diagnostic tests, or both, the systems also include a computer-readable program code that includes instructions to execute the following instructions: i) determine a frequency of mutation allele in the sample for two or more of the biomarines - genetic scavengers; and ii) compare the frequency of mutation allele of each mutation in the selected genetic biomarkers with the maximum frequency of mutation allele of each mutation for each mutation in control samples.

[905] In some modalities of systems for generating a report for a patient, systems for generating a report to identify a cancer treatment for a patient or systems for generating a report for a patient to identify the patient as a candidate for monitoring augmented, additional diagnostic tests, or both, the device configured to test a set of genes comprises a device that employs a PCR-based multiplex assay, using a PCR-based singleplex assay, an digital PCR output, a digital droplet PCR assay (ddPCR), a microarray assay, next generation sequencing assay, Sanger sequencing assay, quantitative PCR assay or a binding assay.

In some modalities of systems for generating a report for a patient, systems for generating a report to identify a cancer treatment for a patient or systems for generating a report for a patient to identify the patient as a candidate for increased monitoring , additional diagnostic tests, or both, the PCR-based multiplex sequencing assay comprises: a. assigning a unique identifier (UID) to each of a plurality of model molecules present in the sample; B. amplify each uniquely labeled model molecule to create UID families; and C. redundant sequencing of the amplification products.

In some modalities of systems to generate a report for a patient, systems to generate a report to identify a cancer treatment for a patient or systems to generate a report for a patient to identify the patient as a candidate for monitoring. increased growth, additional diagnostic tests, or both, the system also includes a device configured to detect the presence of aneuploidy in a biological sample.

In some modalities of systems for generating a report for a patient, systems for generating a report to identify a cancer treatment for a patient, or systems for generating a report for a patient to identify the patient as a candidate for increased monitoring, further testing diagnostics or both that include at least one device configured to test one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16 or 16) of the following genes: NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A and / or GNAS, the system also includes a device (for example, a device that includes an immunity system

multiplex assay) configured to detect the presence of aneuploidy in a biological sample.

In some modalities of systems to generate a report for a patient, systems to generate a report to identify a cancer treatment for a patient or systems to generate a report for a patient to identify the patient as a candidate for a enhanced monitoring, additional diagnostic tests, or both that include: 1) at least one device configured to test one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16 or 16) of the following genes: NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A and / or GNAS and 2) at least one device configured to detect the presence of a genetic biomarker (for example, at least one mutation) in a TERT promoter, the system also includes a device (for example, a device that includes a system multiplex immunoassay) configured to detect the presence of aneuploidy in a biological sample.

In some modes of systems to generate a report for a patient, systems to generate a report to identify a cancer treatment for a patient, or systems to generate a report for a patient to identify the patient as a candidate for monitoring increased, more diagnostic tests, or both that include at least one device configured to test one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 or 18) of the following genes: NRAS, PTEN, FGFR2, KRAS, POLE, AKT1, TP53, RNF43, PPP2R1A, MAPK1, CTNNB1, PIK3CA, FBXW7, PIK3R1, APC, EGFR, BRAF and / or CDKN2A, the system also includes a device (for example, a device that includes a multiplex immunoassay system) configured to detect the presence of aneuploidy in a biological sample.

In some modalities of systems to generate a report for a patient, systems to generate a report to identify a cancer treatment for a patient, or systems to generate a report for a patient to identify the patient as a candidate for a monitor - increased ment, more diagnostic tests or both that include at least one device configured to test one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 or 12) of the following genes: PTEN, TP53, PIK3CA, PIK3R1, CTNNB1, KRAS, FGFR2, POLE, APC, FBXW7, RNF43 and / or PPP2R1A, the system also includes a device (for example, a device that includes a multi-immunoassay system tiplex) configured to detect the presence of aneuploidy in a biological sample. In some modalities of systems for generating a report for a patient, systems for generating a report to identify a cancer treatment for a patient, or systems for generating a report for a patient to identify the patient as a candidate for increased monitoring, more diagnostic tests or both that include at least one device configured to test TP53, the system also includes a device (for example, a device that includes a multiplex immunoassay system) configured to detect the presence of aneuploidy in a biological sample. The presence of aneuploidy can be detected in one or more chromosomes or chromosomal arms that are associated with cancer. In some modalities, the presence of aneuploidy is detected in one or more of the chromosomal arms 5q, 8q, and / or 9p. In some modalities, the presence of aneuploidy can be detected in one or more of the 4p, 7q, 8q, and / or 9q chromosomal arms

[906] In some modalities of systems for generating a report for a patient, systems for generating a report to identify a cancer treatment for a patient, or systems for generating a report for a patient to identify the patient as a candidate due to increased monitoring, additional diagnostic tests, or both, the sensitivity of the system in identifying an individual as having cancer is enhanced compared to conventional systems for generating a report for a patient.

In some modes of systems to generate a report for a patient, systems to generate a report to identify a cancer treatment for a patient or systems to generate a report for a patient to identify the patient as a candidate for increased monitoring. - whether, additional diagnostic tests, or both, the specificity of the system in identifying an individual as having cancer is improved compared to conventional systems to generate a report for a patient.

In some modalities of systems to generate a report for a patient, systems to generate a report to identify a cancer treatment for a patient, or systems to generate a report for a patient to identify the patient as a candidate for increased monitoring, Additional diagnostics, or both, the first and second reference distribution of the mutation allele frequency values are entered into the system from a remote location on at least one computer database.

In some modalities of systems to generate a report for a patient, systems to generate a report to identify a cancer treatment for a patient or systems to generate a report for a patient to identify the patient as a candidate for increased monitoring, testing Additional diagnostics, or both, the first and second reference distributions of the mutation allele frequency values are entered into the system via an Internet connection.

In some modalities of systems to generate a report for a patient, systems to generate a report to identify a cancer treatment for a patient or systems to generate a report for a patient to identify the patient as a candidate for increased monitoring, diagnostic tests additional, or both, the report is in electronic or paper format.

[907] In some modalities of systems for generating a report for a patient, systems for generating a report to identify a cancer treatment for a patient or systems for generating a report for a patient to identify the patient as a candidate for monitoring increased, additional diagnostic tests, or both, cancer is one of the types of cancer described here (see, for example, the section entitled "Cancers"). In some modalities of systems for generating a report for a patient, systems for generating a report to identify a cancer treatment for a patient, or systems for generating a report for a patient to identify the patient as a candidate for increased monitoring, further testing Diagnostic tests or both that include at least one device configured for testing comprise one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9 or 10) of the following genes: TP53, PIK3CA , FGFR3, KRAS, ERBB2, CDKN2A, MLL, HRAS, MET and / or VHL and at least one device configured to detect the presence of aneuploidy in a biological sample, the cancer is a bladder cancer or a urothelial carcinoma of the tract higher. In some modalities of systems for generating a report for a patient, systems for generating a report to identify a cancer treatment for a patient, or systems for generating a report for a patient to identify the patient as a candidate for increased monitoring, further testing diagnostic or both that include at least one device configured to test one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 or 18) of the following genes: NRAS, PTEN, FGFR2, KRAS, POLE, AKT1, TP53, RNF43, PPP2R1A, MAPK1, CTNNB1, PIK3CA, FBXW7, PIK3R1, APC, EGFR, BRAF and / or CDKN2A, and at least one device configured to detect the presence of aneuploidy in a biological sample, the cancer is cervical cancer, endometrial cancer, ovarian cancer or fallopian tube cancer. In some modalities of systems to generate a report for a patient, systems to generate a report to identify a cancer treatment for a patient, or systems to generate a report for a patient to identify the patient as a candidate for a monitor - increased ment, more diagnostic tests or both that include at least one device configured to test one or more (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 or 12) of the following genes: PTEN, TP53, PIK3CA, PIK3R1, CTNNB1, KRAS, FGFR2, POLE, APC, FBXW7, RNF43 and / or PPP2R1A, and at least one device configured to detect the presence of aneuploidy in a biological sample , cancer is an endometrial cancer. In some modalities of systems to generate a report for a patient, systems to generate a report to identify a cancer treatment for a patient or systems to generate a report for a patient to identify the patient as a candidate for increased monitoring, testing of di - additional agnostics, or both that include at least one device configured to test TP53 and at least one device configured to detect the presence of aneuploidy in a biological sample, cancer is a high-grade serous carcinoma. In some modalities of systems to generate a report for a patient or systems to generate a report to identify cancer treatment for a patient, the patient is identified as a candidate for increased monitoring, additional diagnostic tests or both (for example , any of the enhanced monitoring types or other diagnostic methods described here).

[908] Many of the tests currently approved for early cancer detection are procedural in nature and include colonoscopy,

mammography and cervical cytological analysis. To date, the vast majority of cancer patients evaluated with liquid biopsies based on mutations have advanced disease. Yet another problem with liquid biopsies is the identification of the underlying organ. As the same genetic mutations lead to various types of tumors, liquid biopsies based on these changes generally fail to identify the location of the primary tumor, leading to a positive blood test. A noninvasive combinatorial blood test (for example, a test that combines DNA markers and protein markers) is described here for the early detection and localization of many common cancers.

[909] This document provides methods and materials to assess and / or treat mammals (eg, humans) having or suspected of having cancer. In some embodiments, this document provides methods and materials for identifying a mammal as having cancer. For example, a sample (for example, a blood sample) obtained from a mammal can be evaluated to determine whether the mammal has cancer based, at least in part, on the presence or absence of one or more biomarkers (for example , genetic biomarkers) and / or a high level of one or more biomarkers (for example, peptide biomarkers). In some embodiments, this document provides methods and materials for identifying the location (for example, the anatomical location) of a cancer in a mammal. For example, a sample (for example, a blood sample) obtained from a mammal can be evaluated to determine the location of cancer in the mammal based, at least in part, on the presence or absence of one or more biomarkers (for example, biomarkers) genetic) and / or a high level of one or more biomarkers (eg peptide biomarkers). In some modalities, this document provides methods

all and materials to identify a mammal as having cancer and administer one or more pharmacological interventions to treat the mammal. For example, a sample (for example, a blood sample) obtained from a mammal can be evaluated to determine whether the mammal has cancer based, at least in part, on the presence or absence of one or more biomarkers ( for example, genetic biomarkers) and / or a high level of one or more biomarkers (for example, peptide biomarkers), and administer one or more cancer treatments to the mammal.

[910] As demonstrated here, an analysis of just 2,001 bp of genomic DNA could detect at least one mutation in 82% of the eight common cancers. A test (Cancer-SEEK) was developed that evaluated the levels of 10 circulating proteins, as well as the mutations of these 2,001 bp in DNA without circulating cells. This test was applied to 1,005 patients with cancer of the liver, ovary, esophagus, stomach, pancreas, colorectum, lung or breast. The Cancer-SEEK tests were positive at a median of 70% of the eight types of cancer, while less than 1% of the 812 normal individuals had a positive score. Sensitivities ranged from 69% to 98% for the detection of five types of cancer (liver, ovary, esophagus, stomach and pancreas) for which there are no screening tests available for individuals at medium risk. In addition, the source of the cancer can be located in a small number of anatomical sites in a median of 84% of patients who test positive for the CancerSEEK test.

[911] The ability to use a blood test with very high specificity (for example, combining DNA markers and protein markers) can allow doctors to detect cancers in the early stages, resulting in earlier treatment with less pro - unnecessary follow-up procedures, less anxiety and / or reduction in cancer deaths.

[912] In general, one aspect of this document presents a method for identifying a mammal as having cancer.

The method may include, or consist essentially of, detecting one or more genetic biomarkers in the circulating DNA in a blood sample obtained from a mammal; detecting a high level of one or more peptide biomarkers in the blood sample obtained from a mammal; and identifying the mammal as having cancer when the presence of one or more genetic biomarkers is detected in the circulating DNA in said blood sample, when a high level of one or more peptide biomarkers is detected in said blood sample, or both.

The mammal may be a human.

The blood sample can be a plasma sample.

Cancer can be Stage I cancer.

Cancer can be cancer of the liver, ovary, esophagus, stomach, pancreas, colorectal cancer, lung, breast or prostate.

The one or more genetic biomarkers can include one or more modifications in one or more genes.

The one or more modifications may include inactivation modifications and the one or more genes may include tumor suppressor genes.

One or more modifications can be selected independently among single-base substitutions, insertions and deletions.

The one or more genes can include NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A and / or GNAS.

The one or more modifications can be selected independently of the modifications set out in Table 5. The step of detecting the presence of one or more genetic biomarkers can be performed using a multiplex PCR-based sequencing assay.

The multiplex PCR-based sequencing assay can include assigning a unique identifier (UID) to each model molecule; amplify each uniquely labeled model molecule to create UID families; and redundantly sequence the amplification products.

The one or more peptide biomarkers can include prolactin, OPN, IL-6, CEA, CA125, HGF, myeloperoxidase, CA19-9, Midkine and / or TIMP-1. The step of detecting the level of one or more peptide biomarkers can be performed using a multiplex immunoassay system. The method may also include identifying a site for that cancer. The method can also include administering one or more cancer treatments to the mammal. One or more cancer treatments may include surgery, chemotherapy, hormonal therapy, targeted therapy, radiation therapy and combinations thereof.

[913] In another aspect, this document presents a method for treating a mammal with cancer. The method may include, or consist essentially of, detecting one or more genetic biomarkers in the circulating DNA in a blood sample obtained from a mammal; detecting a high level of one or more peptide biomarkers in the blood sample obtained from a mammal; and administering one or more cancer treatments to said mammal when the presence of one or more genetic biomarkers is detected in the circulating DNA in said blood sample, when a high level of one or more peptide biomarkers is detected in said blood sample, or both. The one or more cancer treatments may include surgery, chemotherapy, hormonal therapy, targeted therapy, radiotherapy and combinations thereof. The mammal may be a human. The blood sample can be a plasma sample. The cancer can be a Stage I cancer. The cancer can be a cancer of the liver, ovary, esophagus, stomach, pancreas, colorectal cancer, lung, breast or prostate cancer. The one or more genetic biomarkers can include one or more modifications in one or more genes. The one or more modifications can include inactivation modifications and the one or more genes can include tumor suppressor genes. One or more modifications can be selected independently among single-base substitutions, insertions and deletions. The one or more genes can include NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A and / or GNAS. The one or more modifications can be selected regardless of the modifications set out in Table 5. The step of detecting the presence of one or more genetic biomarkers can be performed using a multiplex PCR-based sequencing assay. The multiplex PCR-based sequencing assay may include assigning a unique identifier (UID) to each model molecule; amplify each uniquely labeled model molecule to create UID families; and sequencing amplification products redundantly. The one or more peptide biomarkers can include pro- lactin, OPN, IL-6, CEA, CA125, HGF, myeloperoxidase, CA19-9, Mid-kine and / or TIMP-1. The step of detecting the level of one or more peptide biomarkers can be performed using a multiplex immunoassay system.

[914] In another aspect, this document presents a method for identifying the location of cancer in a mammal. The method may include, or consist essentially of, detecting one or more genetic biomarkers in the circulating DNA in a blood sample obtained from a mammal; detecting a high level of one or more peptide biomarkers in the blood sample obtained from a mammal; and identifying the cancer site in the mammal when the presence of one or more genetic biomarkers is detected in the circulating DNA in the said blood sample, when a high level of one or more peptide biomarkers is detected in the said blood sample, or both. The mammal may be a human. The blood sample can be a plasma sample. The cancer can be a Stage I cancer. The cancer can be a cancer of the liver, ovary, esophagus, stomach, pancreas, colorectal cancer, lung, breast or prostate cancer. The one or more genetic biomarkers can include one or more modifications in one or more genes. The one or more modifications can include inactivation modifications and the one or more genes can include tumor suppressor genes. One or more modifications can be selected independently among single-base substitutions, insertions and deletions. The one or more genes can include NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A and / or GNAS. The one or more modifications can be selected regardless of the modifications set out in Table 5. The step of detecting the presence of one or more genetic biomarkers can be performed using a multiplex PCR-based sequencing assay. The multiplex PCR-based sequencing assay may include assigning a unique identifier (UID) to each model molecule; amplify each uniquely labeled model molecule to create UID families; and sequencing amplification products redundantly. The one or more peptide biomarkers can include pro- lactin, OPN, IL-6, CEA, CA125, HGF, myeloperoxidase, CA19-9, Mid-kine and / or TIMP-1. The step of detecting the level of one or more peptide biomarkers can be performed using a multiplex immunoassay system.

[915] Methods are provided here to identify the presence of cancer in a human individual, comprising: detecting in a first biological sample isolated from the human individual, the presence of one or more genetic changes in the cellless DNA derived from a selected gene from the group consisting of: AKT1, APC, BRAF, CDKN2, CTNNB1, FBXW7, FGFR2, GNAS, HRAS, KRAS, PPP2R1A, TP53, PTEN, PIK3CA, EGFR and NRAS, and combinations thereof; detect a level of one or more protein biomarkers in a second biological sample isolated from the human individual, in which the protein biomarker is selected from the group consisting of: carbohydrate antigen 19-9 (CA19-9), carcinoembryonic antigen (CEA), heptocyte growth factor (HGF), osteopontin (OPN), CA125, AFP, prolactin, TIMP-1, follistatin, G-CSF and CA15-3 and combinations thereof; comparing the detected levels of one or more protein biomarkers with one or more reference levels of protein biomarkers; and to identify the presence of cancer in the human individual when the presence of one or more genetic alterations in the DNA without cells is detected, the detected levels of one or more protein biomarkers are higher than the reference levels of one or more biomarkers of proteins. proteins, or both. In some modalities, the first biological sample comprises blood, plasma, urine, cerebrospinal fluid, saliva, sputum, bronchoalveolar lavage, bile, lymphatic fluid, cyst fluid, feces, ascites and combinations thereof. In some embodiments, the first biological sample, the second biological sample, or both comprise plasma. In some embodiments, the first and second biological samples are the same.

[916] In some modalities to identify the presence of cancer in a human individual, the step of detecting the presence of one or more genetic alterations in DNA without cells in the first biological sample comprises amplifying an amplicon comprising codons and their surrounding splicing sites , in which the codons are selected from the group consisting of: AKT1 codons 16-18; codons 1304-1311 or 1450-1459 from APC; codons 591-602 of BRAF; codons 51-58 or 76-88 of CDKN2A; codons 31-39 or 38-47 of CTNNB1; codons 856-868 of EGFR; codons 361-371, 464-473, 473-483 or 498-507 of FBXW7; codons 250-256 of FGFR2; 199-208 codons from GNAS; HRAS standards 7-19; codons 7-14, 57-65 or 143-148 of KRAS; codons 3-15 or 54-63 of NRAS; codons 80-90, 343-348, 541-551 or 1038-1050 of PIK3CA; codons 175-187 of PPP2R1A; codons 90-98, 125-132, 133-146, 145-154 from PTEN; codons 10-22, 25-32, 33-40, 40-52, 52-64, 82- 94, 97-110, 112-125, 123-125, 126-132, 132-142, 150-163, 167 -177, 175-186, 187-195, 195-206, 207-219, 219-224, 226-237, 232-245, 248- 261, 261-268, 272-283, 279-290, 298-307 , 307-314, 323-331, 333-344, 344-355, 367-375 or 374-386 of TP53 and combinations thereof.

In some modalities, the step of detecting the presence of one or more genetic alterations in the DNA without cells in the first biological sample comprises genetic sequencing regions comprising codons and their surrounding splicing sites, in which the codons are selected from the group consisting of: AKT1 codons 16-18; codons 1304-1311 or 1450-1459 from APC; codons 591-602 of BRAF; codons 51-58 or 76-88 of CDKN2A; codons 31-39 or 38-47 of CTNNB1; codons 856-868 of EGFR; codons 361-371, 464-473, 473- 483 or 498-507 of FBXW7; codons 250-256 of FGFR2; codons 199- 208 of the GNAS; codons 7-19 of the HRAS; codons 7-14, 57-65 or 143-148 of KRAS; codons 3-15 or 54-63 of NRAS; codons 80-90, 343-348, 541-551 or 1038-1050 of PIK3CA; codons 175-187 of PPP2R1A; codons 90-98, 125-132, 133-146, 145-154 from PTEN; codons 10-22, 25-32, 33-40, 40-52, 52-64, 82-94, 97-110, 112-125, 123-125, 126-132, 132-142, 150-163, 167 -177, 175-186, 187-195, 195-206, 207-219, 219-224, 226- 237, 232-245, 248-261, 261-268, 272-283, 279-290, 298-307 , 307-314, 323-331, 333-344, 344-355, 367-375 or 374-386 of TP53 and combinations thereof.

In some modalities, the step of detecting the presence of one or more genetic alterations in the DNA without cells in the first biological sample comprises genetic sequencing regions comprising codons and their surrounding splice of each of: AKT1 codons 16-18 ; codons 1304-1311 and 1450-1459 from APC; codons 591-602 of BRAF; codons 51-58 and 76-88 of CDKN2A; codons 31-39 and 38-47 of CTNNB1; codons 856-868 of EGFR; codons 361-371,

464-473, 473-483 and 498-507 of FBXW7; codons 250-256 of FGFR2; 199-208 codons from GNAS; codons 7-19 of the HRAS; codons 7-14, 57-65 and 143-148 of KRAS; codons 3-15 and 54-63 of NRAS; codons 80-90, 343-348, 541-551 and 1038-1050 of PIK3CA; codons 175-187 of PPP2R1A; codons 90-98, 125-132, 133-146, 145-154 from PTEN; codons 10-22, 25-32, 33-40, 40-52, 52-64, 82-94, 97-110, 112-125, 123-125, 126-132, 132-142, 150-163, 167 -177, 175-186, 187-195, 195-206, 207-219, 219- 224, 226-237, 232-245, 248-261, 261-268, 272-283, 279-290, 298-307 , 307-314, 323-331, 333-344, 344-355, 367-375 and 374-386 of TP53 and combinations thereof.

[917] In some modalities to identify the presence of cancer in a human individual, the one or more biomarkers of proteins include CA19-9. In some embodiments, the reference level for the CA19-9 protein biomarker is 92 U / mL. In some modalities, the one or more protein biomarkers include CEA. In some embodiments, the CEA protein biomarker reference level is 7.5 ng / mL. In some embodiments, the one or more protein biomarkers include HGF. In some modalities, the reference level of the HGF protein biomarker is 0.89 ng / mL. In some embodiments, the one or more protein biomarkers include OPN. In some embodiments, the reference level for the OPN protein biomarker is 158 ng / mL. In some embodiments, the one or more protein biomarkers include CA125. In some embodiments, the reference level for the CA125 protein biomarker is 577 U / mL. In some embodiments, the one or more protein biomarkers include AFP. In some embodiments, the reference level for the AFP protein biomarker is 21 ng / mL. In some embodiments, the one or more protein biomarkers include prolactin. In some modalities, the reference level of the

The prolactin protein marker is 145 ng / mL. In some modes, the one or more protein biomarkers include TIMP-1. In some modalities, the reference level for the TIMP-1 protein biomarker is 177ng / mL. In some embodiments, the one or more protein biomarkers include follistatin. In some modalities, the reference level of the follistatin protein biomarker is 2 ng / mL In some modalities, the one or more protein biomarkers include G-CSF. In some modalities, the reference level of the G-CSF protein biomarker is 800 pg / mL. In some embodiments, the one or more protein biomarkers include CA15-3. In some modalities, the reference level of the CA15-3 protein biomarker is 98 U / mL.

[918] In some modalities to identify the presence of a cancer in a human individual, the presence of cancer in the human individual is identified when: (i) the presence of one or more genetic changes in the DNA without derived cells is detected and (ii ) the detected levels of one or more protein biomarkers are higher than the reference levels of one or more protein biomarkers.

[919] In some modalities to identify the presence of cancer in a human individual, the presence of one or more genetic alterations in DNA without cells in the first biological sample is detected by amplifying DNA without cells to form families of amplicons in which each member of a family is derived from a single model molecule in cellless DNA, where each member of a family is marked by a common oligonucleotide bar code and where each family is marked by a bar code distinct oligonucleotide. In some embodiments, the oligonucleotide bar code is introduced into the model molecule by an amplification step with a population of primers that collectively contain a plurality of

of oligonucleotide barcodes. In some modalities, the oligonucleotide bar code is endogenous to the model molecule and an adapter comprising a DNA synthesis initiation site is attached to one end of the model molecule adjacent to the oligonucleotide bar code.

[920] In some modalities to identify the presence of cancer in a human individual, a therapeutic intervention is administered to the individual when the presence of cancer is identified. In some modalities, therapeutic intervention is selected from the group consisting of: adoptive T-cell therapy, radiation therapy, surgery, administration of a chemotherapeutic agent, administration of an immune checkpoint inhibitor, administration of targeted therapy, administration of a kinase inhibitor, administration of a signal transduction inhibitor, administration of a bispecific antibody, administration of a monoclonal antibody and combinations thereof. In some modalities, cancer, and in which therapeutic intervention is more effective than if therapeutic intervention were later administered to a human individual.

[921] In some modalities to identify the presence of cancer in a human individual, the presence of cancer in the human individual is detected in a moment before the diagnosis of the human individual with cancer. In some modalities, the presence of cancer in the human individual is detected earlier than the human subject, showing symptoms associated with cancer.

[922] In some modalities to identify the presence of cancer in a human individual, the human individual is a human undergoing a radiological scan of an organ or region of the body to identify the location of the cancer. In some modalities, the human individual is subjected to radiological examination of the entire body to identify the location of the cancer. In some modalities, the scan is a computed tomography by positron emission tomography (PET-CT).

[923] In some modalities, cancer is selected from the group consisting of: pancreatic cancer, colon cancer, esophageal cancer, stomach cancer, ovarian cancer, liver cancer, lung cancer and breast cancer and combinations thereof.

[924] Methods are also provided here to identify the presence of cancer in a human individual, comprising: detecting a level of one or more protein biomarkers in a first biological sample isolated from the human individual, where the one or more biomarkers of proteins are selected from the group consisting of: carbohydrate antigen 19-9 (CA19-9), carcinoembryonic antigen (CEA), hepatocyte growth factor (HGF), osteopontin (OPN), CA125, AFP, prolactin, TIMP-1, follistatin, G-CSF and CA15-3 and combinations thereof; comparing the detected levels of one or more protein biomarkers with one or more reference levels of protein biomarkers; and to identify the presence of cancer in the human individual when the detected levels of one or more protein biomarkers are higher than the reference levels of one or more protein biomarkers.

[925] Methods are provided here to identify the presence of pancreatic cancer in a human individual, comprising: detecting in a first biological sample isolated from the human individual, the presence of one or more genetic alterations in DNA without cells derived from a gene selected from the group consisting of: KRAS, TP53, CDKN2A, SMAD4, and combinations thereof; detect a level of one or more protein biomarkers in a second biological sample isolated from the human individual, in which one or more protein biomarkers are selected from the group consisting of: carbohydrate antigen 19-9 (CA19-9 ), carcinoembryonic antigen (CEA), heptocyte growth factor (HGF), osteopontin (OPN), and combinations thereof; comparing the detected levels of one or more protein biomarkers with one or more reference levels of protein biomarkers; and to identify the presence of pancreatic cancer in the human individual when the presence of one or more genetic alterations in DNA without cells is detected, the detected levels of one or more protein biomarkers are higher than the reference levels of one or more protein biomarkers, or both. In some modalities, the first biological sample comprises blood, plasma, urine, cerebrospinal fluid, saliva, sputum, bronchoalveolar lavage, bile, lymphatic fluid, cyst fluid, feces, ascites and combinations thereof. In some embodiments, the first biological sample, the second biological sample, or both comprise plasma. In some embodiments, the first and second biological samples are the same.

[926] In some modalities of methods to identify the presence of pancreatic cancer in a human individual, one or more genetic alterations occur in codons 12 or 61 of the KRAS gene. In some modalities, the one or more protein biomarkers include CA19-9. In some embodiments, the CA19-9 protein biomarker reference level is 100 U / mL. In some embodiments, the one or more protein biomarkers include CEA. In some embodiments, the CEA protein biomarker reference level is 7.5 ng / mL. In some embodiments, the one or more protein biomarkers include HGF. In some modalities, the reference level of the HGF protein biomarker is 0.92 ng / mL. In some embodiments, the one or more protein biomarkers include OPN. In some modalities, the reference level for the OPN protein biomarker is 158 ng / mL. In some embodiments, the detection of the level of one or more protein biomarkers comprises detecting levels of each of CA19-9, CEA, HGF and OPN; and comparing the detected levels of one or more protein biomarkers with one or more reference levels of the protein biomarker comprises comparing the detected levels of each of CA19-9, CEA, HGF and OPN with a reference level of each of CA19-9, CEA, HGF and OPN.

[927] [09] In some modalities of the methods to identify the presence of pancreatic cancer in a human individual, the presence of pancreatic cancer in the human individual is identified when: (i) the presence of one or more changes Genetics in the cellless DNA derived from the KRAS gene is detected and (ii) the detected levels of one or more protein biomarkers are higher than the reference levels of one or more protein biomarkers. In some modalities of methods to identify the presence of pancreatic cancer in a human individual, one or more genetic changes are detected by amplifying the cellless DNA to form amplicon families in which each member of a family is derived from. a single model molecule in cellless DNA, where each member of a family is marked by a common oligonucleotide barcode and where each family is marked by a distinct oligonucleotide barcode. In some embodiments, the oligonucleotide bar code is introduced into the model molecule by an amplification step with a population of primers that collectively contain a plurality of oligonucleotide bar codes. In some embodiments, the oligonucleotide bar code is endogenous to the model molecule and an adapter comprising a DNA synthesis initiation site is attached to one end of the model molecule adjacent to the oligonucleotide bar code.

[928] In some modalities, a therapeutic intervention for the human individual when the presence of pancreatic cancer is identified

tified. In some modalities, therapeutic intervention is selected from the group consisting of: adoptive T-cell therapy, radiation therapy, surgery, administration of a chemotherapeutic agent, administration of an immune checkpoint inhibitor, administration of a targeted therapy, administration of a kinase inhibitor, administration of a signal transduction inhibitor, administration of a bispecific antibody, administration of a monoclonal antibody and combinations thereof. In some modalities, therapeutic intervention is administered at the time when the human individual has early pancreatic cancer and when therapeutic intervention is more effective than if therapeutic intervention were administered to an individual human later.

[929] In some modalities, the presence of pancreatic cancer in the human individual is detected before the diagnosis of the human individual with pancreatic cancer. In some modalities, the presence of pancreatic cancer in the human individual is detected at a time prior to the human individual, showing symptoms associated with pancreatic cancer.

[930] Methods are also provided here to identify the presence of pancreatic cancer in a human individual, comprising: detecting a level of one or more protein biomarkers in a first isolated biological sample from the human individual, in which the one or more protein biomarkers are selected from the group consisting of: carbohydrate antigen 19-9 (CA19-9), carcinoembryonic antigen (CEA), hepatocyte growth factor (HGF), osteopontin (OPN), and combinations of the same; comparing the detected levels of one or more protein biomarkers with one or more reference levels of protein biomarkers; and to identify the presence of pancreatic cancer in the human individual when the detected levels of one or more protein biomarkers are higher than the reference levels of one or more protein biomarkers.

[931] Methods are provided here to identify the presence of cancer in a human individual, comprising: detecting in a first isolated biological sample from the individual the presence of one or more genetic changes in the cellless DNA derived from a gene selected from the group that consists of: AKT1, APC, BRAF, CDKN2, CTNNB1, FBXW7, FGFR2, GNAS, HRAS, KRAS, PPP2R1A, TP53, PTEN, PIK3CA, EGFR and NRAS, and combinations thereof; detect in a second biological sample isolated from the individual the absence of one or more genetic alterations in the DNA derived from a gene selected in the group consisting of: AKT1, APC, BRAF, CDKN2, CTNNB1, FBXW7, FGFR2, GNAS, HRAS, KRAS, PPP2R1A, TP53, PTEN, PIK3CA, EGFR and NRAS, and combinations thereof, in which the second biological sample is isolated from the individual's white blood cells; identify the presence of cancer in the individual when one or more genetic changes detected in the first sample are not detected in the second sample. In some embodiments, the first biological sample, the second biological sample, or both comprise blood, plasma, urine, cerebrospinal fluid, saliva, sputum, bronchoalveolar lavage, bile, lymphatic fluid, cyst fluid, feces, ascites and combinations of the same. In some embodiments, the first biological sample, the second biological sample, or both comprise plasma. In some embodiments, the first and second biological samples are the same. In some modalities to identify the presence of cancer in a human individual, the methods also include detecting a level of one or more protein biomarkers in a third isolated biological sample from the individual, in which the protein biomarker is selected of the group consisting of: carbohydrate antigen 19-9 (CA19-9), carcinoembryonic antigen

(CEA), hepatocyte growth factor (HGF), osteopontin (OPN), CA125, AFP, prolactin, TIMP-1, folistatin, G-CSF and CA15-3 and combinations thereof; compare the detected levels of one or more protein biomarkers with one or more reference levels of protein biomarkers; and identify the presence of cancer in the individual when the presence of one or more genetic changes in DNA without cells is detected in the first sample, the absence of one or more genetic changes is detected in the DNA of the second sample, and the detected levels of one or more protein biomarkers are higher than the reference levels of one or more protein biomarkers. In some embodiments, the third biological sample is the same as the first biological sample.

[932] In some modalities to identify the presence of cancer in a human individual, the step of detecting the presence of one or more genetic changes in the DNA without cells in the first biological sample, the step of detecting the absence of one or more alte - DNA genetic feeds in the second biological sample, or both, comprises amplifying an amplicon comprising codons and their surrounding splicing sites, in which the codons are selected from the group consisting of: AKT1 codons 16-18; codons 1304-1311 or 1450-1459 from APC; codons 591-602 of BRAF; codons 51-58 or 76-88 of CDKN2A; codons 31-39 or 38-47 of CTNNB1; codons 856-868 of EGFR; codons 361-371, 464-473, 473-483 or 498-507 of FBXW7; 250-256 patterns of FGFR2; 199-208 codons from GNAS; codons 7-19 of the HRAS; codons 7-14, 57-65 or 143-148 of KRAS; codons 3-15 or 54-63 of NRAS; codons 80-90, 343-348, 541-551 or 1038-1050 of PIK3CA; codons 175-187 of PPP2R1A; and PTEN codons 90-98, 125-132, 133-146, 145-154; and codons 10-22, 25-32, 33-40, 40-52, 52-64, 82-94, 97- 110, 112-125, 123-125, 126-132, 132-142, 150-163, 167 -177, 175-186, 187-195, 195-206, 207-219, 219-224, 226-237, 232-245, 248-261, 261-

268, 272-283, 279-290, 298-307, 307-314, 323-331, 333-344, 344-355, 367-375 or 374-386 of TP53. In some embodiments, the step of detecting the presence of one or more genetic changes in DNA without cells in the first biological sample, the step of detecting the absence of one or more genetic changes in DNA in the second biological sample, or both comprises regions sequencing genetics comprising codons and their surrounding splice sites, in which the codons are selected from the group consisting of: AKT1 codons 16-18; codons 1304-1311 or 1450-1459 from APC; codons 591-602 of BRAF; codons 51-58 or 76-88 of CDKN2A; codons 31-39 or 38-47 of CTNNB1; codons 856-868 of EGFR; codons 361-371, 464-473, 473- 483 or 498-507 of FBXW7; codons 250-256 of FGFR2; codons 199- 208 of the GNAS; codons 7-19 of the HRAS; codons 7-14, 57-65 or 143-148 of KRAS; codons 3-15 or 54-63 of NRAS; codons 80-90, 343-348, 541-551 or 1038-1050 of PIK3CA; codons 175-187 of PPP2R1A; and PTEN codons 90-98, 125-132, 133-146, 145-154; and codons 10-22, 25-32, 33-40, 40-52, 52-64, 82-94, 97-110, 112-125, 123-125, 126-132, 132-142, 150-163, 167-177, 175-186, 187-195, 195-206, 207-219, 219-224, 226-237, 232-245, 248-261, 261-268, 272-283, 279-290, 298- 307, 307-314, 323-331, 333-344, 344-355, 367-375 or 374-386 of TP53. In some embodiments, the step of detecting the presence of one or more genetic changes in DNA without cells in the first biological sample, the step of detecting the absence of one or more genetic changes in DNA in the second biological sample, or both comprise genetic regions. sequencing models comprising codons and their surrounding splice for each of: AKT1 codons 16-18; codons 1304-1311 and 1450-1459 from APC; codons 591-602 of BRAF; codons 51-58 and 76-88 of CDKN2A; codons 31-39 and 38-47 of CTNNB1; codons 856-868 of EGFR; codons 361-371, 464-473, 473-483 and 498-507 of FBXW7; 250-256 patterns of FGFR2; 199-208 codons from GNAS; codons 7-19 of the

HRAS; codons 7-14, 57-65 and 143-148 of KRAS; codons 3-15 and 54-63 of NRAS; codons 80-90, 343-348, 541-551 and 1038-1050 of PIK3CA; codons 175-187 of PPP2R1A; and PTEN codons 90-98, 125-132, 133-146, 145-154; and codons 10-22, 25-32, 33-40, 40-52, 52-64, 82-94, 97- 110, 112-125, 123-125, 126-132, 132-142, 150-163, 167 -177, 175-186, 187-195, 195-206, 207-219, 219-224, 226-237, 232-245, 248-261, 261- 268, 272-283, 279-290, 298- 307, 307-314, 323-331, 333-344, 344-355, 367-375 and 374-386 of TP53.

[933] In some modalities to identify the presence of cancer in a human individual, the one or more biomarkers of proteins include CA19-9. In some embodiments, the reference level for the CA19-9 protein biomarker is 92 U / mL. In some modalities, the one or more protein biomarkers include CEA. In some embodiments, the CEA protein biomarker reference level is 7.5 ng / mL. In some embodiments, the one or more protein biomarkers include HGF. In some modalities, the reference level of the HGF protein biomarker is 0.89 ng / mL. In some embodiments, the one or more protein biomarkers include OPN. In some embodiments, the reference level for the OPN protein biomarker is 158 ng / mL. In some embodiments, the one or more protein biomarkers include CA125. In some embodiments, the reference level for the CA125 protein biomarker is 577 U / mL. In some embodiments, the one or more protein biomarkers include AFP. In some embodiments, the reference level for the AFP protein biomarker is 21 ng / mL. In some embodiments, the one or more protein biomarkers include prolactin. In some embodiments, the reference level for the prolactin protein biomarker is 145 ng / mL. In some modes, the one or more protein biomarkers include TIMP-1.

In some modalities, the reference level for the TIMP-1 protein biomarker is 177ng / mL. In some embodiments, the one or more protein biomarkers include follistatin. In some modalities, the reference level for the follistatin protein biomarker is 2 ng / mL. In some embodiments, the one or more protein biomarkers include G-CSF. In some modalities, the reference level of the G-CSF protein biomarker is 800 pg / mL. In some embodiments, the one or more protein biomarkers include CA15-3. In some modalities, the reference level of the CA15-3 protein biomarker is 98 U / mL.

[934] In some modalities to identify the presence of cancer in a human individual, the presence of one or more genetic changes in DNA without cells in the first biological sample, the absence of one or more genetic changes in DNA in the second sample biological, or both are detected by amplifying cellless DNA to form amplicon families in which each member of a family is derived from a single model molecule in cellless DNA, where each member of a family is marked by a bar code - ras common oligonucleotide and in which each family is marked by a distinct oligonucleotide barcode. In some embodiments, the oligonucleotide bar code is introduced into the model molecule by an amplification step with a population of primers that collectively contain a plurality of oligonucleotide bar codes. In some embodiments, the oligonucleotide bar code is endogenous to the model molecule and an adapter comprising a DNA synthesis initiation site is attached to one end of the model molecule adjacent to the oligonucleotide bar code.

[935] In some modalities, a therapeutic intervention is administered to the human individual when the presence of cancer is identified

tified. In some modalities, therapeutic intervention is selected from the group consisting of: adoptive T-cell therapy, radiation therapy, surgery, administration of a chemotherapeutic agent, administration of an immune checkpoint inhibitor, administration of a targeted therapy, administration of a kinase inhibitor, administration of a signal transduction inhibitor, administration of a bispecific antibody, administration of a monoclonal antibody and combinations thereof. In some modalities, therapeutic intervention is administered at the moment when the individual has an early-stage cancer and when the therapeutic intervention is more effective than if the therapeutic intervention were administered to an individual later.

[936] In some modalities to identify the presence of cancer in a human individual, the presence of cancer in the individual is detected at a time prior to the diagnosis of the individual with cancer. In some modalities, the presence of cancer in the individual is detected at a time before the individual, showing symptoms associated with cancer.

[937] In some modalities to identify the presence of a cancer in a human individual, the human individual is a human undergoing a radiological scan of an organ or region of the body to identify the location of the cancer. In some modalities, the human individual is subjected to radiological examination of the entire body to identify the location of the cancer. In some modalities, the scan is a computed tomography by positron emission tomography (PET-CT).

[938] In some modalities to identify the presence of a cancer in a human being, the cancer is selected from the group consisting of: pancreatic cancer, colon cancer, esophageal cancer, stomach cancer, cancer of the ovary, liver cancer, lung cancer and breast cancer and combinations thereof.

[939] As described in more detail here, it has been demonstrated that the DNA from the sampled fluid (eg, cells containing fluid sampled in the endocervical canal) can be used in an assay (eg, a PCR-based multiplex test) to evaluate simultaneously - encourage genetic changes that usually occur in enteral or ovarian cancers (Fig. 19). In addition, as described in more detail here and without limitation, two ways to increase sensitivity have been identified. First, the intra-uterine sample was tested (with a "Tao brush"), a method that allows the collection of samples closer to the anatomical site of the tumors. Second, in a recent study, it was shown that testing for mutations in the saliva and plasma of the same individual increased the sensitivity of detecting head and neck tumors (Wang et al., Detection of somatic mutations and HPV in the saliva and plasma of patients with head and neck squamous cell carcinomas. Sci Transl Med 7, 293ra104 (2015)). Based on this precedent, it has been shown that testing for mutations in the plasma and Pap test fluid can increase sensitivity to cancers (eg ovarian cancer).

[940] In some respects, methods are provided here to detect endometrial and ovarian cancer based on genetic analyzes of DNA recovered from fluids (for example, fluids obtained during a routine Pap test). In some modalities, this new test, called PapSEEK, incorporates assays for mutations in one or more of the 18 genes and / or an assay for aneuploidy. In some modality, the method provided here to detect gynecological cancers is used at a stage when cancers are more likely to be curable. In some embodiments, PapSEEK can be combined with assays for mutations in one or more genes in nucleic acids present in a plasma sample.

[941] In some embodiments, methods are provided here to detect ovarian or endometrial cancer in an individual which include detecting in a sample obtained from the individual the presence of one or more mutations in one or more genes selected from the group consisting of : NRAS, PTEN, FGFR2, KRAS, POLE, AKT1, TP53, RNF43, PPP2R1A, MAPK1, CTNNB1, PIK3CA, FBXW7, PIK3R1, APC, EGFR, BRAF and CDKN2A, detect the presence of aneuploidy or both, in which the sample presence of one or more mutations in NRAS, PTEN, FGFR2, KRAS, POLE, AKT1, TP53, RNF43, PPP2R1A, MAPK1, CTNNB1, PIK3CA, FBXW7, PIK3R1, APC, EGFR, BRAF or CDKN2A, the presence of aneuploidy or both indicates that the individual has ovarian or endometrial cancer.

In some embodiments, the step of detecting the presence of one or more mutations in NRAS, PTEN, FGFR2, KRAS, POLE, AKT1, TP53, RNF43, PPP2R1A, MAPK1, CTNNB1, PIK3CA, FBXW7, PIK3R1, APC, EGFR, BRAF, CDKN2A is performed using a PCR-based multiplex assay, using a PCR-based singleplex assay, a digital PCR assay, a digital droplet PCR assay (ddPCR), a microarray assay, a sequencing assay next generation, a Sanger sequencing assay, a quantitative PCR assay or a binding assay.

In some modes, the step of detecting the presence of one or more mutations in NRAS, PTEN, FGFR2, KRAS, POLE, AKT1, TP53, RNF43, PPP2R1A, MAPK1, CTNNB1, PIK3CA, FBXW7, PIK3R1, APC, EGFR, BRAF, CDKN2A is performed by increasing the sensitivity of massively parallel sequencing instruments with an error reduction technique that allows the detection of rare mutant alleles in an interval of 1 mutant model between 100 to 1,000,000 jungle-type models. gem.

For example, the step of detecting one or more mutations can be performed by increasing the sensitivity of massively parallel sequencing instruments with an error reduction technique that comprises: a) molecularly assigning a unique identifier (UID) to each model molecule, b) amplify each model molecule marked in a unique way to create UID families and c) redundantly sequence the amplification products. In some modalities, the methods provided here also include performing cytology in the sample, in which the presence of one or more mutations in NRAS, PTEN, FGFR2, KRAS, POLE, AKT1, TP53, RNF43, PPP2R1A, MAPK1, CTNNB1, PIK3CA , FBXW7, PIK3R1, APC, EGFR, BRAF or CDKN2A, the presence of aneuploidy and / or a positive cytology indicates that the individual has ovarian or endometrial cancer. In some modalities, the step of detecting the presence of aneuploidy in the sample involves detecting the presence of one or more changes in one or more of the 4p, 7q, 8q and 9q chromosomal arms. In some modalities, the step of detecting the presence of aneuploidy in the sample comprises amplifying interspersed nucleotide elements. In some modalities, the methods provided here also include detecting in a second sample comprising circulating tumor DNA (ctDNA) the presence of at least one mutation in one or more genes selected from the group consisting of: AKT1, APC, BRAF, CDKN2A , CTNNB1, EGFR, FBXW7, FGFR2, GNAS, HRAS, KRAS, NRAS, PIK3CA, PPP2R1A, PTEN and TP53. In some embodiments, the second sample includes plasma. In some modalities, the sample is collected via intrauterine sampling. In some embodiments, the sample is collected with a Tao brush. In some embodiments, the methods also include administering therapy to the individual (for example, adjuvant chemotherapy, neoadjuvant chemotherapy, radiation therapy, immunotherapy, targeted therapy, immune checkpoint inhibitors and combinations thereof).

[942] In some embodiments, methods are provided here to detect endometrial cancer in an individual which include detecting in a sample obtained from the individual the presence of one or more mutations in one or more genes selected from the group consisting of: PTEN, TP53, PIK3CA, PIK3R1, CTNNB1, KRAS, FGFR2, POLE, APC, FBXW7, RNF43 and PPP2R1A, detect in the sample the presence of aneuploidy, or both, in which the presence of one or more mutations in PTEN, TP53, PIK3CA, PIK3R1 , CTNNB1, KRAS, FGFR2, POLE, APC, FBXW7, RNF43 and PPP2R1A, the presence of aneuploidy, or both, indicate that the individual has endometrial cancer.

In some modalities, the step of detecting the presence of one or more mutations in PTEN, TP53, PIK3CA, PIK3R1, CTNNB1, KRAS, FGFR2, POLE, APC, FBXW7, RNF43 and PPP2R1A is performed using a multiplex assay based on PCR , using a PCR-based singleplex assay, a digital PCR assay, a digital droplet PCR assay (ddPCR), a microarray assay, a next generation sequencing assay, a Sanger sequencing assay, a quantitative PCR assay or a binding test.

In some modalities, the step of detecting the presence of one or more mutations in PTEN, TP53, PIK3CA, PIK3R1, CTNNB1, KRAS, FGFR2, POLE, APC, FBXW7, RNF43 and PPP2R1A is performed by increasing the sensitivity of sequencing instruments massively parallel with an error reduction technique that allows the detection of rare mutant alleles in a range of 1 mutant model between 100 to 1,000,000 wild-type models.

For example, the step of detecting one or more mutations can be performed by increasing the sensitivity of massively parallel sequencing instruments with an error reduction technique that comprises: a) molecularly assigning a unique identifier (UID) to each model molecule, b) amplify each model molecule uniquely marked to create UID families and c) redundantly sequence the amplification products.

In some modalities, the methods provided here also include performing cytology on the sample, in which the presence of one or more mutations in PTEN, TP53, PIK3CA, PIK3R1, CTNNB1, KRAS, FGFR2, POLE, APC, FBXW7, RNF43 and PPP2R1A , the presence of aneuploidy and / or positive cytology indicates that the individual has endometrial cancer. In some modes, the step of detecting the presence of aneuploidy in the sample comprises detecting the presence of one or more changes in one or more of the chromosomal arms 4p, 7q, 8q and 9q. In some modalities, the step of detecting the presence of aneuploidy in the sample comprises amplifying interspersed nucleotide elements. In some embodiments, the methods provided here also include detecting in a second sample comprising circulating tumor DNA (ctDNA) the presence of at least one mutation in one or more genes selected from the group consisting of: AKT1, APC, BRAF, CDKN2A , CTNNB1, EGFR, FBXW7, FGFR2, GNAS, HRAS, KRAS, NRAS, PIK3CA, PPP2R1A, PTEN and TP53. In some modalities, the second sample includes plasma. In some modalities, the sample is collected via intrauterine sampling. In some embodiments, the sample is collected with a Tao brush. In some modalities, the methods also include administering therapy to the individual (for example, adjuvant chemotherapy, neoadjuvant chemotherapy, radiation therapy, immunotherapy, targeted therapy, immune checkpoint inhibitors and combinations thereof).

[943] In some embodiments, methods are provided here to detect ovarian cancer in an individual which includes detecting in a sample obtained from the individual the presence of one or more mutations in TP53, detecting in the sample the presence of aneuploidy, or both, in that the presence of one or more mutations in TP53, the presence of aneuploidy or both indicate that the individual has ovarian cancer. In some embodiments, the step of detecting the presence of one or more mutations in TP53 is performed using a multiplex assay based on

PCR, using a PCR-based singleplex assay, a digital PCR assay, a digital droplet PCR assay (ddPCR), a microarray assay, a next generation sequencing assay, a Sanger sequencing assay, a PCR assay quantitative analysis or a binding test. In some modalities, the step of detecting the presence of one or more mutations in TP53 is performed by increasing the sensitivity of massively parallel sequencing instruments with an error reduction technique that allows the detection of rare mutant alleles in a range of 1 mutant model between 100 to

1,000,000 wild-type models. For example, the step of detecting one or mutations can be performed by increasing the sensitivity of massively parallel sequencing instruments with an error reduction technique that comprises: a) molecularly assigning a unique identifier (UID) to each model molecule , b) amplify each model molecule uniquely marked to create UID families and c) redundantly sequence the amplification products. In some modalities, the methods provided here also include performing cytology in the sample, in which the presence of one or more mutations in TP53, the presence of aneuploidy and / or a positive cytology indicate that the individual has ovarian cancer. In some modalities, the step of detecting the presence of aneuploidy in the sample involves detecting the presence of one or more changes in one or more of the 4p, 7q, 8q and 9q chromosomal arms. In some modalities, the step of detecting the presence of aneuploidy in the sample comprises amplifying interspersed nucleotide elements. In some modalities, the methods provided here also include detecting in a second sample comprising circulating tumor DNA (ctDNA) the presence of at least one mutation in one or more genes selected from the group consisting of: AKT1, APC, BRAF, CDKN2A , CTNNB1, EGFR, FBXW7, FGFR2, GNAS, HRAS, KRAS, NRAS, PIK3CA, PPP2R1A, PTEN and

TP53. In some embodiments, the second sample includes plasma. In some modalities, the sample is collected via intrauterine sampling. In some embodiments, the sample is collected with a Tao brush. In some embodiments, the methods also include administering therapy to the individual (for example, adjuvant chemotherapy, neoadjuvant chemotherapy, radiation therapy, immunotherapy, targeted therapy, immune checkpoint inhibitors and combinations thereof).

[944] Methods are provided here to detect bladder cancer or urothelial carcinoma of the upper tract in an individual that includes: detecting in a urine sample obtained from the individual the presence of one or more mutations in one or more genes selected from the group that consists of: TP53, PIK3CA, FGFR3, KRAS, ERBB2, CDKN2A, MLL, HRAS, MET and VHL, detecting in the sample the presence of at least one mutation in a TERT promoter and detecting in the sample the presence of aneuploidy, in which the presence of one or more mutations in the group consisting of: TP53, PIK3CA, FGFR3, KRAS, ERBB2, CDKN2A, MLL, HRAS, MET and VHL, the presence of at least one mutation in the TERT promoter or the presence of aneuploidy indicates that the individual has bladder cancer. In some modalities of methods to detect bladder cancer or urothelial carcinoma of the upper tract in an individual, the one or more genes are TP53, FGFR3 or both. In some modalities of methods to detect bladder cancer or urothelial carcinoma of the upper tract in an individual, to one or more mutations in TP53, PIK3CA, FGFR3, KRAS, ERBB2, CDKN2A, MLL, HRAS, MET or VHL, one or more mutations in the TERT promoter, or both, are present in 0.03% or less of the urinary cells in the sample.

[945] In some modalities of methods to detect bladder cancer or urothelial carcinoma of the upper tract in an individual, the step of detecting the presence of one or more mutations in TP53, PIK3CA, FGFR3, KRAS, ERBB2, CDKN2A, MLL, HRAS , MET, VHL, the step of detecting the presence of at least one mutation in the TERT promoter or both is performed using a PCR-based multiplex assay, a Sanger sequencing assay, a next generation sequencing assay, a next generation assay quantitative PCR, a digital droplet PCR (ddPCR), or a microarray technique. In some modalities of methods for detecting bladder cancer or urothelial carcinoma of the upper tract in an individual, the step of detecting the presence of one or more mutations in TP53, PIK3CA, FGFR3, KRAS, ERBB2, CDKN2A, MLL, HRAS, MET, VHL, the step of detecting the presence of at least one mutation in the TERT promoter or both is performed using a Sanger Sequencing assay. In some modalities of methods for detecting bladder cancer or urothelial carcinoma of the upper tract in an individual, the step of detecting the presence of one or more mutations in TP53, PIK3CA, FGFR3, KRAS, ERBB2, CDKN2A, MLL, HRAS, MET, VHL, the step of detecting the presence of at least one mutation in the TERT promoter or both is performed using a next generation sequencing assay.

[946] In some modalities of methods to detect bladder cancer or urothelial carcinoma of the upper tract in an individual, the step of detecting the presence of aneuploidy in the sample involves detecting the presence of one or more changes in one or more 5q chromosomal arms, 8q, and 9p. In some modalities of methods to detect bladder cancer or urothelial carcinoma of the upper tract in an individual, the step of detecting in the sample the presence of aneuploidy comprises amplifying long and interconnected nucleotide elements (LINESs).

[947] In some modalities of methods of detecting bladder cancer or urothelial carcinoma of the upper tract in an individual,

the step of detecting the presence of one or more mutations in TP53, PIK3CA, FGFR3, KRAS, ERBB2, CDKN2A, MLL, HRAS, MET, VHL, the step of detecting the presence of at least one mutation in the TERT promoter or both is performed by increasing the sensitivity of massively parallel sequencing instruments with an error reduction technique that allows the detection of rare mutant alleles in a range of 1 model mutant between 5,000 and 1,000,000 of wild models. In some modalities of methods to detect bladder cancer or urothelial carcinoma of the upper tract in an individual, the step of detecting the presence of one or more mutations in TP53, PIK3CA, FGFR3, KRAS, ERBB2, CDKN2A, MLL, HRAS, MET, VHL, the step of detecting the presence of at least one mutation in the TERT promoter or both is performed by increasing the sensitivity of massively parallel sequencing instruments with an error reduction technique that includes: a) assigning a unique identifier (UID) to each model molecule, b) amplify each model molecule uniquely labeled to create UID families and c) redundant sequencing of the amplification products.

[948] In some modalities of methods to detect bladder cancer or urothelial carcinoma of the upper tract in an individual, the method also includes performing cytology in the sample, in which the presence of one or more mutations in TP53, PIK3CA, FGFR3, KRAS, ERBB2, CDKN2A, MLL, HRAS, MET, VHL, the presence of at least one mutation in the TERT promoter, the presence of aneuploidy or a positive cytology indicates that the individual has bladder cancer.

[949] In some modalities of methods to detect bladder cancer in an individual, the method also includes administering transurethral resection of the bladder (TURB), intravesical BCG (Bacillus Calmette-Guerin), intravesical chemotherapy, adjuvant chemotherapy, chemotherapy

neoadjuvant font, cystectomy or cystprostatectomy, radiation therapy, immunotherapy, immune checkpoint inhibitors, or any combination thereof.

[950] In some modalities of methods to detect an urothelial carcinoma of the upper tract in an individual, the method also includes administering transurethral resection, intravesical BCG (Bacillus Calmette-Guerin), intravesical chemotherapy, adjuvant chemotherapy, neoadjuvant chemotherapy, ureterectomy or nephroureterectomy, radiation therapy, immunotherapy, immune checkpoint inhibitors, or any combination thereof.

[951] In some modalities of methods for detecting bladder cancer or urothelial carcinoma of the upper tract in an individual, the cancer is a low-grade tumor. In some modalities of methods to detect bladder cancer or urothelial carcinoma of the upper tract in an individual in which the cancer is a low grade tumor, the low grade tumor is a papillary urothelial neoplasia of low malignant potential (PUNLMP ) or a low-grade noninvasive papillary urothelial carcinoma. In some modalities of methods to detect bladder cancer or urothelial carcinoma of the upper tract in an individual in which the cancer is a low-grade tumor, the method also includes administering transurethral resection of the bladder (TURB).

[952] In some modalities of methods for detecting bladder cancer or urothelial carcinoma of the upper tract in an individual, the individual has previously undergone treatment for bladder cancer or urothelial carcinoma of the upper tract. In some modalities of methods for detecting bladder cancer or urothelial carcinoma of the upper tract in an individual in which the individual has previously undergone treatment for bladder cancer or urothelial carcinoma of the upper tract, the method includes detecting in a urine sample obtained of the presence of one or more mutations in TP53, FGFR3 or both.

In some modalities of methods to detect bladder cancer or urothelial carcinoma of the upper tract in an individual, in which the individual has previously undergone treatment for bladder cancer or urothelial carcinoma of the upper tract, one or more mutations in TP53, PIK3CA , FGFR3, KRAS, ERBB2, CDKN2A, MLL, HRAS, MET or VHL, one or more mutations in the TERT promoter, or both, are present in 0.03% or less of the urinary cells in the sample.

In some modalities of methods for detecting bladder cancer or urothelial carcinoma of the upper tract in an individual in whom the individual has previously undergone treatment for bladder cancer or urothelial carcinoma of the upper tract, the step of detecting the presence of one or more further mutations in TP53, PIK3CA, FGFR3, KRAS, ERBB2, CDKN2A, MLL, HRAS, MET, VHL, the step of detecting the presence of at least one mutation in the TERT promoter or both is performed using a PCR-based multiplex assay.

In some modalities of methods to detect bladder cancer or urothelial carcinoma of the upper tract in an individual in which the individual has previously undergone treatment for bladder cancer or urothelial carcinoma of the upper tract, the step of detecting the presence of one or more mutations in TP53, PIK3CA, FGFR3, KRAS, ERBB2, CDKN2A, MLL, HRAS, MET, VHL, the step of detecting the presence of at least one mutation in the TERT promoter or both is performed using a Sanger sequencing.

In some modalities of methods for detecting bladder cancer or urothelial carcinoma of the upper tract in an individual in whom the individual has previously undergone treatment for bladder cancer or urothelial carcinoma of the upper tract, the step of detecting the presence of one or more more mutations in TP53, PIK3CA, FGFR3, KRAS, ERBB2, CDKN2A, MLL, HRAS, MET, VHL, the step of detecting the presence of at least one mutation in the TERT promoter or both is performed using a next generation sequencing assay .

[953] In some modalities of methods for detecting bladder cancer or urothelial carcinoma of the upper tract in an individual where the individual has previously undergone treatment for bladder cancer or urothelial carcinoma of the upper tract, the step of detecting the presence in the sample aneuploidy comprises detecting the presence of one or more changes in one or more chromosomal arms 5q, 8q, and 9p. In some modalities of methods to detect bladder cancer or urothelial carcinoma of the upper tract in an individual where the individual has previously undergone treatment for bladder cancer or urothelial carcinoma of the upper tract, the step of detecting the presence of aneuploidy comprises amplifying long, interspersed nucleotide elements (LINESs).

[954] In some modalities of methods of detecting bladder cancer or urothelial carcinoma of the upper tract in an individual where the individual has previously undergone treatment for bladder cancer or urothelial carcinoma of the upper tract, the detect the presence of one or more mutations in TP53, PIK3CA, FGFR3, KRAS, ERBB2, CDKN2A, MLL, HRAS, MET, VHL, the step of detecting the presence of at least one mutation in the TERT promoter or both it is performed by increasing the sensitivity of massively parallel sequencing instruments with an error reduction technique that allows the detection of rare mutant alleles in a range of 1 model mutant between 5,000 and 1,000,000 wild models. In some modalities of methods for detecting bladder cancer or urothelial carcinoma of the upper tract in an individual where the individual has previously undergone treatment for bladder cancer or urothelial carcinoma of the upper tract, the step of detecting the presence of one or more more mutations in TP53, PIK3CA, FGFR3, KRAS, ERBB2, CDKN2A, MLL, HRAS, MET, VHL, the step of detecting the presence of at least one mutation in the TERT promoter or both is performed by increasing the sensitivity of sequencing instruments massively parallel with an error reduction technique that includes: a) assigning a unique identifier (UID) to each model molecule, b) amplifying each model molecule uniquely marked to create UID families and c) redundant sequencing of the amplification products .

[955] In some modalities of methods to detect bladder cancer or urothelial carcinoma of the upper tract in an individual where the individual has previously undergone treatment for bladder cancer or urothelial carcinoma of the upper tract, the method also includes conducting cytology in the sample , in which the presence of one or more mutations in TP53, PIK3CA, FGFR3, KRAS, ERBB2, CDKN2A, MLL, HRAS, MET, VHL, the presence of at least one mutation in the TERT promoter, the presence of aneuploidy or a positive cytology indicates that the individual has bladder cancer.

[956] In some modalities of methods for detecting bladder cancer in an individual where the individual has previously undergone treatment for bladder cancer or upper urethral carcinoma, the method also includes administering transurethral resection of the bladder (TURB) , Intravesical BCG (Bacillus Calmette-Guerin), intravesical chemotherapy, adjuvant chemotherapy, neoadjuvant chemotherapy, cystectomy or cystoprostatectomy, radiation therapy, immunotherapy, immune checkpoint inhibitors or any combination of the above.

[957] In some modalities of methods to detect urothelial carcinoma of the upper tract in an individual in whom the individual has previously undergone treatment for bladder cancer or urothelial carcinoma of the upper tract, the method further includes administering transurethral section, intravesical BCG (Bacillus Calmette-Guerin), chemi-

intravesical therapy, adjuvant chemotherapy, neoadjuvant chemotherapy, ureterectomy or nephroureterectomy, radiotherapy, immunotherapy, immune checkpoint inhibitors, or any combination thereof.

[958] This document provides methods and materials for identifying one or more chromosomal abnormalities (for example, aneuploidy). In some cases, this document provides methods and materials for using amplicon-based sequencing data to identify a mammal as having a disease or disorder associated with one or more chromosomal abnormalities. For example, the methods and materials described herein can be applied to a sample obtained from a mammal to identify the mammal as having one or more chromosomal abnormalities. For example, the methods and materials described herein can be applied to a sample obtained from a mammal to identify the mammal as having a disease or disorder associated with one or more chromosomal abnormalities. For example, a prenatal mammal can be identified as having a disease or disorder based, at least in part, in the presence of one or more aneuploidies. This document also provides methods and materials for identifying and / or treating a disease associated with one or more chromosomal abnormalities. In some cases, one or more chromosomal abnormalities can be identified in the DNA obtained from a sample obtained from a mammal. For example, a mammal identified as having cancer based, at least in part, on the presence of one or more chromosomal abnormalities can be treated with one or more cancer treatments.

[959] As demonstrated in this document, a new approach (called WALDO for Within-Sample-AneupLoidy-DetectiOn, can be used to evaluate the sequencing data obtained from amplicons to identify the presence of one or more chromosomal abnormalities ( example, aneuploidy.) For example, WALDO can employ supervised machine learning to detect small changes in various chromosomal arms that are often present in cancers. As described here, WALDO was used to look for gains and losses in chromosomal arm in 1,677 tumors, as well as in 1,522 liquid blood biopsies from cancer patients or normal individuals, aneuploidy was detected in 95% of cancer biopsies and in 22% of liquid biopsies. Using single nucleotide polymorphisms (SNPs ) within amplified interspersed nucleotide elements (LINEs), WALDO concomitantly assessed allelic imbalances, microsatellite instability and tification of samples. WALDO can be used in samples that contain only a few nanograms (ng) of DNA and that have only 1% neoplastic content.

[960] The ability to use amplicon-based sequencing readings to detect one or more chromosomal anomalies provides a unique and unrealized opportunity to obtain high depth of coverage with improved sensitivity at relatively low cost. In addition, the ability to use amplicon-based sequencing readings allows one to detect one or more chromosomal anomalies (eg, aneuploidies) of samples containing limited amounts of DNA. This approach can be used in a variety of applications, including, but not limited to, diagnostics (eg, prenatal diagnoses and / or cancer diagnostics) and Faroese sciences.

[961] In general, one aspect of this document presents a method for detecting aneuploidy in the genome of a mammal. The method includes, or consists essentially of, sequencing a plurality of amplicons obtained from a sample obtained from the mammal to obtain sequencing readings; group sequencing readings into clusters of genomic intervals; calculate sums of distributions of sequencing readings in each genomic interval using the equation∑ ~ (∑, ∑), where Ri is the number of sequencing readings, I is the number of clusters in a chromosomal arm, N is a Gaussian distribution with parameters ie i2, where i is the average number of sequencing readings in each genomic interval and where i2 is the variation in the sequencing readings in each genomic interval; calculate a Z score of a chromosomal arm using the quantile function 1-CDF (∑, ∑); and to identify the presence of an aneuploidy in the mammal's genome when the Z score is outside a significance threshold. The plurality of amplicons can include from about 10,000 amplicons to about 1,000,000 amplifiers (for example, the plurality of amplicons can include about

38,000 amplicons). Genomic ranges can include from about 100 nucleotides to about 125,000,000 nucleotides (for example, genomic ranges can include about 500,000 nucleotides). The mammal may be a human. The sample can be a liquid biopsy. The liquid biopsy can be blood, urine, saliva, cyst fluid, sputum, tissue, feces, Pap smears or cerebrospinal fluid. The liquid biopsy can be a blood sample (for example, a plasma sample). The blood sample can include fetal DNA without cells. The sample can include a fraction of neoplastic cells. The fraction of neoplastic cells in the sample can include less than about 1% (for example, less than about 0.5%) of the entire sample. Amplicons can include single long interleaved nucleotide elements (LINEs). The sequencing step can also include amplifying DNA from the sample obtained from the mammal to obtain the amplicons. Amplification can be performed using a single pair of primers. Amplicons can include about 100 to about 140 base pairs. Each amplicon can be sequenced between 1 and 20 times. The method can include about 100,000 to about 25 million sequencing readings. Each cluster can include about two hundred genomic intervals. The method can also include supervised machine learning. Supervised machine learning can employ a support vector machine model.

[962] In another aspect, this document presents a method for detecting one or more polymorphisms in the genome of a mammal. The method includes, or essentially consists of, sequencing a plurality of amplicons obtained from a mammal sample to obtain variant sequencing readings; sequencing a plurality of amplicons obtained from a mammalian reference sample to obtain reference sequencing readings; group variant sequencing readings and reference sequencing readings into clusters of genomic intervals; selecting a chromosomal arm with a sum of the variant sequencing readings and the reference sequencing readings in both alleles that is greater than about 3; determine a variant allele frequency (VAF) of the selected chromosomal arm, where said VAF is the number of variant sequencing readings / total number of sequencing readings; and identifying the presence of one or more polymorphisms in said selected chromosomal arm of the mammal if the AVF is between about 0.2 and about 0.8. The sequencing step can include assigning a unique identifier (UID) to each amplicon, amplifying each uniquely tagged amplificon to create UID families, and redundant sequencing of the amplification products. The sequencing step can also include calculating a Z score of a variant on that selected chromosome arm using the equation ~, where wi is the depth of ∑

UID in a variant i, Zi is the Z score of variant i, and k is the number of variants observed in the chromosome arm.

One or more polymorphisms may include substitutions, insertions, deletions, indels and / or combinations of single bases.

The pluralities of amplicons can range from about 10,000 amplicons to about 1,000,000 amplicons (for example, the pluralities of amplicons can include about 38,000 amplicons). Genomic ranges can include from about 100 nucleotides to about 125,000,000 nucleotides (for example, genomic ranges can include around 500,000 nucleotides). The mammal can be a human.

The sample can be a liquid biopsy.

The liquid biopsy can be blood, urine, saliva, cyst fluid, sputum, tissue, feces, Pap smears or brain spinal fluid.

The liquid biopsy can be a blood sample (for example, a plasma sample). The blood sample can include fetal DNA without cells.

The sample can include a fraction of neoplastic cells.

The fraction of neoplastic cells in the sample can include less than about 1% (for example, less than about 0.5%) of the entire sample.

Amplicons can include single long interleaved nucleotide elements (LINEs). The sequencing step can also include amplifying DNA from the sample obtained from the mammal to obtain the amplicons.

Amplification can be performed using a single pair of primers.

Amplicons can include about 100 to about 140 base pairs.

Each amplicon can be sequenced between 1 and 20 times.

The method can include about 100,000 to about 25 million sequencing readings.

Each cluster can include about two hundred geographic ranges.

The method can also include machine learning

pervisioned. Supervised machine learning can employ a support vector machine model.

OTHER MODALITIES

[963] It should be understood that, although the invention has been described in conjunction with its detailed description, the above description is intended to illustrate and not to limit the scope of the invention, which is defined by the scope of the appended claims. Other aspects, advantages and modifications are within the scope of the claims.

EXAMPLES Example 1: Detection and localization of surgically resectable cancers with a liquid biopsy with multiple analytes

[964] Many of the tests currently approved for early detection of cancer are of a procedural nature and include colonoscopy, mammography and cervical cytological analysis. To date, the vast majority of cancer patients evaluated with liquid biopsies based on mutations have advanced disease. Yet another problem with liquid biopsies is the identification of the underlying organ. As the same genetic mutations lead to various types of tumors, liquid biopsies based on these changes generally fail to identify the location of the primary tumor, leading to a positive blood test.

[965] This example describes a new blood test, called CancerSEEK, which addresses the problematic issues described above. The test uses combined assays for genetic alterations and protein biomarkers and has the ability not only to identify the presence of relatively early cancers, but also to identify the organ of origin of these cancers (Fig. 1).

[966] CancerSEEK is a non-invasive test widely applicable to most cancers. The eight types of cancer studied here account for 360,000 (60%) of the estimated deaths from cancer in the

USA in 2017 and its earlier detection could reduce deaths from these diseases. At the time of this release, the cost of CancerSEEK is less than $ 500, which is comparable to or less than other screening tests for single cancers, such as colonoscopy, while this test can detect at least eight different types of cancer. Materials and Methods Samples of plasma, white blood cells and tumor DNA

[967] The study was approved by the Research Ethics Committee at each institution and samples were obtained after informed consent. Patients with cancer from stage I to III who underwent surgical resection at participating institutions were included in the study. Blood was collected from patients before any therapy was performed (that is, before neoadjuvant therapy in patients who received neoadjuvant therapy) and before surgery in all patients. If the sample was collected on the day of surgery, care was taken to ensure that blood was collected before administration of anesthesia, as anesthesia can increase the levels of circulating biomarkers (Cohen et al., 2017 Proc Natl Acad Sci USA 114: 10202-10207). General demographic data, surgical pathology and AJCC stage (7th edition) were documented. The 'healthy' cohort consisted of peripheral blood samples obtained from 812 individuals with a median age of 55 years (interquartile range IQR 28 to 65) with no history of cancer. The samples for cancer and healthy control were processed identically. Plasma samples from 46 of the 1,005 cancer patients and 181 of the 812 normal samples were previously evaluated using a different approach (Cohen et al., 2017 Proc Natl Acad Sci U S A 114: 10202-10207) (Table 2).

[968] DNA was purified from an average of 7.5 mL of plasma using a circulating QIASymphony DNA kit (cat #

1091063), as specified by the manufacturer. The DNA of peripheral WBCs has also been purified with the QIAsymphony DP DNA Midi Kit (Cat # 937255), as specified by the manufacturer. The tissues were fixed in formalin and embedded in paraffin (FFPE) according to standard histopathological procedures and also purified with a QIAsymphony DP DNA Midi Kit (Cat # 937255). Mutation detection and analysis

[969] For plasma DNA amplification, 61 pairs of primers (see below) were designed to amplify segments from 66 to 80 bp containing regions of interest from 16 genes.

[970] The 61 pairs of primers were divided into two non-overlapping sets, each containing 28 or 33 pairs of primers. Each of these two sets of primers was used to amplify DNA in six independent 25 μl reactions, as described elsewhere (see, for example, Wang et al., 2016 Elife 5), except that 15 cycles were used for the initial amplification. The PCR products were purified with AMPure XP beads (Beckman Coulter, PA, USA) and 1% of the purified PCR products were amplified in a second round of PCR, as described elsewhere (see, for example, Wang et al. , 2016 Elife 5), but using 21 cycles. The PCR products from the second round of amplification were then purified with AM-Pure and sequenced on an Illumina MiSeq or HiSeq 4000 instrument.

[971] The model-specific portion of the readings was matched to the reference strings using custom scripts written in SQL and C #. The readings of a common model molecule were then grouped based on the unique identifier sequences (UIDs) that were incorporated as molecular bar codes, as described elsewhere (see, for example, Kinde et al., 2011 Proc Natl Acad Sci USA 108: 9530-9535). Artifactual mutations introduced during the sample preparation or sequencing steps were reduced by requiring that a mutation be present in> 90% of the readings in each UID family. Redundant readings resulting from optical duplication have been eliminated by requiring that readings with the same UID and sample index be at least 5,000 pixels apart when located on the same block. The mutations that met one of the following two criteria were considered (i) present in the COSMIC database (Forbes et al., 2017 Nucleic Acids Res 45: D777-D783) or (ii) predicted as inactivating genes tumor pressors (nonsense mutations, insertions or deletions outside the frame, mutations in the canonical splice site). Synonymous mutations, except those at the exon ends, and intronic mutations, except those at the splice sites, were excluded. Plasma protein evaluation

[972] The Bioplex 200 platform (Biorad, Hercules CA) was used to determine the concentration of multiple target proteins in plasma samples. Luminex ball-based immunoassays (Millipore, Bilerica NY) were performed following the manufacturer's protocols and concentrations were determined using 5 parameter log curve adjustments (using Bioplex Manager 6.0) with standards and quality controls provided by the supplier . The HCCBP1MAG-58K panel was used to detect FGF2, Osteopontin, sFas, IL-8 / CXCL8, prolactin, HE4, HGF, AFP, CA125, IL6, CA15-3, TGFa, CYFRA21-1, CEA, CA19-9 and Leptin. The HANG2MAG-12K panel was used to detect PAR, sPECAM-1, TSP-2, sEGFR, AXL and sHER2 / sEGFR2 / sErbB2. The HCMBMAG-22K panel was used to detect DKK1, GDF15, Osteoprotegerin (OPG) and neuron-specific enolase (NSE). The HCCBP4MAG-58K panels were used to detect Kallikrein-6, CD44, Midkine and Mesothelin. The HAGP1MAG-12K panel was used to detect follistatin, G-CSF, angiopoietin-2 and endoglobin. The panel

HCCBP3MAG-58K was used to detect SHBG, Galectin and Myeloperoxidase. The HTMP1MAG-54K panel was used to detect TIMP-1 and TIMP-2. LRG-1 and Vitronectin were not included in this study, as they could not be reproducibly evaluated with a single immunoassay platform. Algorithm for classification of ctDNA status

[973] The classification of a sample's ctDNA status was obtained from a statistical test comparing the normalized mutation frequencies of the sample of interest with the normalized mutation frequencies distributions of, respectively, normal and cancer samples in the training set. Specifically, the frequency of the mutant allele (MAF), defined as the ratio between the number of substitutes and the number of UIDs, was first normalized based on the MAFs observed for each mutation in a set of normal controls. After this specific normalization of the mutation, the MAF of each mutation in each well was compared to two MAF reference distributions: 1) a distribution constructed from the normal control plasmas in the training set plus a set of 188 WBCs of unrelated healthy individuals 2) a distribution constructed from cancer samples in a training set that included only mutations found in plasma that were also present with MAF> 5% in the corresponding primary tumors. Corresponding p-values, pN and pC, were thus obtained. For each mutation, the log ratio of these two p values was calculated, and the minimum and maximum of these logarithmic ratios in the six wells were eliminated so that the results were less sensitive to points outside the curve. An "omega" score was then determined according to the following formula:

$! "Ω = ∗,! #% Where wi is the proportion of UIDs in well i of the total number of UIDs for this mutation present in the four wells. When a mutation identified in a plasma sample had Ω> 1 and not was identified in the patient's primary tumor, white blood cell DNA (WBCs) from the same patient whenever WBCs were available (23% of cancer patients were evaluated. WBC DNA was tested with the same panel of 61 amplicons for ensure that the plasma mutation was not the result of clonal hematopoiesis of undetermined potential (Jaiswal et al., 2014 N Engl J Med 371: 2488-2498). The WBCs of normal individuals were assessed in an identical way whenever a mutation with Ω> 1 was found in the plasma. Any mutation identified in the WBCs and in the plasma was excluded from the analysis. The exclusion requirement was that the ratio between the maximum MAF in plasma and the maximum MAF in WBC was less than 100 .

[974] The mutation with the highest pontuação score in each patient or normal control was then considered the “top mutation” and is listed in Table 3. This score was used in logistic regression, as well as in the concentrations of the following 10 proteins , selected through an optimization step, prolactin, OPN, IL6, CE6, CEA, CA125, HGF, myeloperoxidase, CA19-9, Midkine and TIMP-1. To be conservative, a non-linear transformation was applied to the resources used by Logistic Regression. Specifically, if the concentration of a protein in the sample of interest is less than the 95th percentile of the concentration found for that same protein among the normal samples in the training set, the protein concentration will be set to zero; otherwise, the log of that concentration was used. For the Ω score, the same log threshold transformation was used, but with a constant threshold equal to 1. The R glmnet package

(version 2.10-13) was then used to perform logistic regression, with the lambda parameter set to zero, as described elsewhere (see, for example, Friedman et al., 2010 Journal of Statistical Software 33: 74862). The importance of each resource was assessed by multiplying its coefficient (see below) by the difference in the average resource between normal and cancer samples. Ten 10-fold cross-validation rounds were performed. The classification calls obtained in an average round of 10 times the cross-validation (CV) are listed for each of the 812 normal individuals and 1,005 cancer patients in Table 2. Coefficients of the logistic regression model and importance scores . Logistic regression coefficient Score of importance Score Ω 1.77E + 00 7.55E + 00 CA-125 4.15E-02 1.37E + 00 CEA 2.33E-04 1.17E + 00 CA19-9 1.20E -02 5.18E-01 Prolactin 3.51E-05 4.76E-01 HGF 2.45E-03 3.03E-01 OPN 1.45E-05 1.72E-01 Myeloperoxidase 5.40E-03 9.31E- 02 TIMP-1 7.34E-06 7.05E-02

[975] To predict the type of cancer, the same 11 characteristics (mutation score and levels of ten proteins) were used, plus the patient's gender and the other 29 proteins evaluated in this study. The prediction of the type of cancer was performed only in the cancer samples that were correctly classified as cancer by Logistic Regression. Random Forest, as implemented in the randomForest package (version 4.6-12) (see, for example, Liaw et al., 2001 R news 2: 18-22) was used for this prediction. Ten rounds of CV were performed 10 times and, for consistency, in each round and in each fold the same partition used by Logistic Regression was used by Random Forest. The classification calls obtained in an average round of CV 10 times (the same round for which the cancer status is reported in Table 3) are listed in Table 6.

[976] To determine the agreement between the mutations identified in the plasma and those identified in the primary tumors (Table 5), only 155 cases were considered in which a mutation could be identified with high confidence in the plasma (score Ω> 3, Table 3) and in which the primary tumor contained any mutation present in a fraction of mutant allele> 5% (Table 1). This approach allowed us to avoid the classification of tumors with low neoplastic content. Sample identification

[977] To confirm this, samples of plasma, WBC and primary tumor DNA from the same patient primers were used to amplify ~ 38,000 single long interleaved nucleotide elements (LINEs) from across the genome, as described elsewhere ( see, for example, Kinde et al., 2012 PloS one 7: e41162). These ~ 38,000 LINES contain 26,220 common polymorphisms that can establish or refute the sample's identity among plasma, white blood cells and tumor samples. The genotype at each polymorphic location was identified and the percentage of agreement between the samples of interest was calculated. Agreement was defined as the number of corresponding polymorphic sites that were identical in both samples divided by the total number of genotypes that had adequate coverage in both samples. Two samples were considered compatible if the agreement was> 0.99 and at least

5,000 amplicons had adequate coverage. Statistical analysis

[978] Continuous variables have been reported as means and standard deviations or medians and vary as needed, while categorical variables have been reported as integers and percentages

tages. Confidence intervals (CI) for sensitivities were calculated using a binomial distribution. The p values were calculated with a unilateral binomial test using the R stats package (version 3.3.1). Results

[979] To identify a panel of protein and gene markers that can be used to detect many solid tumors at a stage prior to the appearance of distant metastases, a PCR-based assay was designed to simultaneously evaluate multiple regions of driver genes that are commonly mutated in a variety of cancers. Four challenges faced this design. First, the test must consult a sufficient number of bases to allow a large number of cancers to be detected. Second, each database consulted must be sequenced thousands of times to detect mutations present in low prevalence. Third, the more bases are consulted, the greater the likelihood that artifact mutations will be identified, reducing the signal-to-noise ratio. And fourth, a test that can be implemented in a sorting configuration must be cost effective and high throughput, limiting the amount of sequencing that must be performed. To address these contrasting challenges, a minimum number of short amplicons was identified that would allow the detection of at least one driver gene in each of the eight tumor types evaluated. Using publicly available sequencing data, it was used to determine that there was a fractional relationship of the power law between the number of amplicons needed and the sensitivity of detection, with a plateau of ~ 60 amplicons (Fig. 2). Increasing the number of amplicons above a threshold level would not detect substantially more cancers, but would increase the likelihood of false positive results. This diminishing marginal utility defined the ideal number of amplicons.

[980] Based on these data, a panel of 61 amplicons was designed with each amplicon querying an average of 33 bp in one of the 16 genes (see Materials and Methods). As shown in Fig. 2, this panel would theoretically detect 41% (liver) to 95% (pancreas) of cancers in the Catalog of Somatic Mutations in Cancer (COSMIC) data set (Forbes et al., 2017 Nucleic Acids Res 45: D777-D783). In practice, the panel performed considerably better, detecting at least one mutation in 82%, two mutations in 47% and more than two mutations in 8% of the 805 cancers evaluated in our study (points in Fig. 2, Fig. 3 and Table 1). A larger fraction of tumors was detected than predicted by the COSMIC data set because the PCR-based sequencing assay was more sensitive to detecting mutations than conventional sequencing across the genome. Based on this analysis of DNA from primary tumor tissues, the maximum predicted detection capacity of circulating tumor DNA (ctDNA) varied by tumor type, ranging from 60% for liver cancer to 100% for ovarian cancer (Fig. 2).

[981] Armed with this small but robust amplifier panel, two approaches have been developed that have enabled the detection of the rare mutations that are expected to be present in the plasma. First, a multiplex PCR was used to directly and exclusively mark each original model molecule with a DNA barcode. This project minimized the errors inherent in massively parallel sequencing and made efficient use of the small amount of cellless DNA present in the plasma. In addition, the total amount of DNA recovered from the plasma was divided into several aliquots and independent assays were performed on each replicate. This decreased the number of DNA molecules per well; however, it increased the fraction of each mutant molecule per well, facilitating the analysis. Since the sensitivity of detection is often limited by the fraction of mutant alleles in each replicate, this partitioning strategy allowed for an increase in the signal-to-noise ratio and identification of mutations present at a lower prevalence than possible if all plasma DNA be evaluated at once.

[982] The second component of CancerSEEK is based on protein biomarkers. The literature was searched to find potentially useful proteins for early detection and diagnosis of cancer in at least one of the eight types of cancer described above with sensitivities> 10% and specificities> 99%. 41 potential protein biomarkers were identified and evaluated in preliminary studies on plasma samples from individuals without cancer and from patients with cancer. 39 of these proteins could be evaluated reproducibly through a single immunoassay platform and these were used to analyze all plasma samples. Ten of these 39 proteins have proven to be useful for discriminating cancer patients from healthy controls and are described below. Protein biomarkers analyzed and included in the exemplary Cancer-SEEK test. Proteins Assessed Included in the Can- test Used to identify in this study cerSEEK exemplifying cancer type AFP Yes No Yes Angiopoietin-2 Yes No Yes AXL Yes No Yes CA125 Yes Yes Yes CA15-3 Yes No Yes CA19-9 Yes Yes Yes CD44 Yes No Yes CEA Yes Yes Yes CYFRA 21-1 Yes No Yes DKK1 Yes No Yes Endogline Yes No Yes FGF2 Yes No Yes Folistatin Yes No Yes Galectin-3 Yes No Yes G-CSF Yes No Yes

GDF15 Yes No Yes HE4 Yes No Yes HGF Yes Yes Yes IL-6 Yes Yes Yes IL-8 Yes No Yes Kallikrein-6 Yes No Yes Leptin Yes No Yes LRG-1 No No No Mesothelin Yes No Yes Midkine Yes Yes Yes Myeloperoxidase Yes Yes Yes NSE Yes No Yes OPG Yes No Yes OPN Yes Yes Yes PAR Yes No Yes Prolactin Yes Yes Yes sEGFR Yes No Yes sFas Yes No Yes SHBG Yes No Yes sHER2 / sEGFR2 / sErbB2 Yes No Yes sPECAM-1 Yes No Yes TGFa Yes No Yes Thrombospondin-2 Yes No Yes TIMP-1 Yes Yes Yes TIMP-2 Yes No Yes Vitronectin No No No

[983] This study included 1,005 patients with stage I to III cancers of the ovary, liver, esophagus, pancreas, stomach, cervix, lung or breast. No patient received neoadjuvant chemotherapy before blood samples were collected. None had metastasis that was evident at the time of entry into the study, and all underwent surgical resection with the intention of curing. The average age at diagnosis was 64 years (range, 22 to 93). The eight types of cancer were chosen because they are common and because there are no blood-based tests for previous detection of them in common clinical use. The histopathological and clinical characteristics of the patients are summarized in Table 2.

[984] The most common stage in the presentation was stage II of the

American Joint Commission on Cancer (AJCC), responsible for 49% of patients, with the remaining patients with stage I (20%) or stage III (31%) disease. The number of samples per stage for each of the eight tumor types is summarized below.

A total of 812 individuals with a median age of 55 years (range 17 to 88) with no known history of cancer, high-grade dysplasia, autoimmune disease or chronic kidney disease acted as a healthy control cohort.

Cancer patients evaluated in this study by type and stage of tumor.

Tumor type AJCC stage Patients (n) Proportion of cases (%)

I 32 15 II 114 55 Breast III 63 30 I-III 209 - I 77 20 II 191 49 Colorectum III 120 31 I-III 388 - I 5 11 II 29 64 Esophagus III 11 24 I-III 45 - I 5 11 II 19 43 Liver III 20 45 I-III 44 - I 46 44 II 27 26 Lung III 31 30 I-III 104 - I 9 17 II 4 7 Ovary III 41 76 I-III 54 - I 4 4 II 83 89 Pancreas III 6 6 I-III 93 -

I 21 31 II 30 44 Stomach III 17 25 I-III 68 -

[985] CancerSEEK assesses levels of 10 proteins and mutations in

2,001 genomic positions; each genomic position can be mutated in several ways (single-base substitutions, insertions or deletions). The presence of a mutation in a tested gene or an increase in the level of any of these proteins would classify a patient as positive. Strict statistical methods were employed to ensure the accuracy of the test. Logarithmic ratios were used to assess the mutations and incorporated them into a logistic regression algorithm that took into account the mutation data and the levels of protein biomarkers to score the results of the CankerSEEK test. The average sensitivities and specificities were determined by ten iterations of 10 cross-validations. The receiver operating characteristic (ROC) curves for the entire cohort of cancer patients and controls in a representative iteration are shown in Fig. 4A.

[986] The median sensitivity of CancerSEEK among the eight types of cancer evaluated was 70% (p <10-96 unilateral binomial test) and ranged from 98% in liver cancer to 33% in breast cancer (Fig. 4C) . In this sensitivity, specificity was> 99%, that is, only 6 of the individuals without known cancer had a positive result.

[987] The test characteristics that were most important to the algorithm were the presence of a ctDNA mutation followed by elevations in prolactin, OPN, IL-6, CEA, CA125, HGF, Myeloperoxidase, CA19-9, Midkine and TIMP-1 protein levels. The cascaded graphs for each of the ctDNA and protein resources used in CancerSEEK illustrate their distribution between individuals with and without cancer (Fig. 4). The classification of the importance of ctDNA resources and

used in CancerSEEK is provided below and an analysis of the main components showing the grouping of individuals with and without cancer is shown in Fig. 5. The complete data set, including the levels of all proteins studied and the mutations identified in the plasma samples are provided in Table 3 and Table

4. The probabilistic, rather than deterministic, nature of the approach used here to call a positive sample is evident in Fig. 6; each panel represents the sensitivity of CancerSEEK when a specific resource was excluded from the analysis.

[988] A screening test is advantageously capable of detecting cancers at an early stage. The median sensitivity of CancerSEEK was 73% for the most common stage assessed (stage II), similar (78%) for stage III cancers and lower (43%) for stage I cancers (Fig. 4B). Sensitivities for early stage cancers (stage I) were higher in the liver (100%) and lower in esophageal cancer (20%).

[989] The basis of liquid biopsy is that the mutant DNA models in plasma are derived from dying cancer cells and therefore serve as exquisitely specific markers for cancer. To test this expectation, tumor tissues from 155 patients were evaluated in which ctDNA could be detected at statistically significant levels and in which primary tumors were available. The plasma mutation was identical to that found in the same individual's primary tumor in 138 (90%) of these 155 cases (Table 5). This concordance between plasma and primary tumor was evident in all 8 types of cancer and ranged from 100% in ovarian and pancreatic cancers to 82% in stomach cancers.

[990] A major limitation of conventional liquid biopsies is their inability to determine the type of cancer in patients with a positive result, thus posing challenges for clinical follow-up. In addition to increasing the sensitivity of detection, the combination of protein biomarkers and ctDNA helped to identify the type of cancer that may exist with a positive CancerSEEK test. Supervised machine learning was used to predict the type of underlying cancer in patients with positive CancerSEEK tests. The entry algorithm took into account the patient's gender and the data of the protein and ctDNA biomarkers. One of the main objectives of these predictions is to determine the most appropriate follow-up test for the diagnosis or monitoring of cancer after a positive CancerSEEK test. Patients with esophageal and gastric cancer were grouped, as the ideal follow-up for individuals potentially affected with these two cancers would be endoscopy.

[991] An algorithm was used to study the 617 patients with a positive score on the CancerSEEK test. With no clinical information about the patients, the source of the cancer was located in two anatomical sites at a median of 83% of these patients (Fig. 8, Table 6; p <10-77 unilateral binomial test). In addition, the source of the positive test was located in a single organ in a median of 63% of these patients (Fig. 8, Table 6; p <10-47 unilateral binomial test). The accuracy of the prediction varied with the type of tumor and was better for colorectal cancers and lower for lung cancers, as shown below (see also Fig. 8). Confusing matrix of the main predictions of the results of the cancer type location. Breast Colorectum Liver Lung Ovary Pancreas Upper GI Breast 63% 3% 2% 8% 4% 3% 3% Colorectum 26% 84% 30% 48% 15% 15% 44% Predicted cancer Liver 0% 1% 44% 2 % 0% 0% 4% Lung 4% 2% 0% 39% 2% 1% 4% Ovary 3% 0% 0% 2% 79% 0% 0% Pancreas 4% 2% 9% 2% 0% 81% 0% higher GI 0% 9% 14% 0% 0% 0% 46%

[992] The results described here demonstrate that a blood test based on genes and proteins can be used to detect a larger fraction (median of 70%) of the eight main types of cancer. In most samples that tested positive, the underlying cancer type can be predicted simply from the test results without any prior knowledge of the patient's medical history or disease status. The specificity of CancerSEEK was high, with less than 1% of the 812 individuals without known cancer with a positive score. Example 2: Combined approach for liquid biopsy cancer screening tests with increased sensitivity and high specificity

[993] There is a strong correlation between tumor stage and prognosis in many cancers (see, for example, Ansari et al., 2017 Br J Surg 104 (5): 600-607). Very few patients with cancer of the lung, colon, esophagus or stomach who have distant metastases at the time of diagnosis survive for more than five years (see, for example, Howlader et al., 2016 SEER Cancer Statistics Review, 1975-2013 , National Cancer Institute, Bethesda, MD). Therefore, it is clear that early detection of cancers is a key to reducing deaths from these diseases.

[994] Circulating biomarkers provide one of the best ways, in principle, to detect cancers at an earlier stage. Historically, the type of biomarkers used to monitor cancer were proteins (see, for example, Liotta et al., 2003 Clin Adv Hematol Oncol 1 (8): 460-462). More recently, mutant DNA has been explored as a biomarker, as the DNA released by dying cells can escape into body fluids such as urine, faeces and plasma (see, for example, Haber et al., 2014 Cancer Discov 4 (6): 650-661; Dawson et al., 2013 N Engl J Med 368 (13): 1199-1209; Bettegowda et al., 2014 Science translational medicine 6 (224): 224ra224; Kinde et al., 2013 Science translational medicine 5 ( 167): 167ra164; Wang et al., 2015 Science translational medicine 7 (293): 293ra104; Wang et al., 2015 Proc Natl Acad Sci USA 112 (31): 9704-9709; Wang et al., 2016 Elife 5; Springer et al., 2015 Gastroenterology 149 (6): 1501-1510; Forshew et al., 2012 Science translational medicine 4 (136): 136ra168; Vogelstein et al., 1999 Proc Natl Acad Sci USA 96 (16): 9236- 9241; and Dressman et al., 2003 Proc Natl Acad Sci USA 100 (15): 8817-8822). The concept behind this approach, often called "liquid biopsies", is that cancer cells, like normal self-renewing cells, are often reversed. However, studies of circulating viral DNA (ctDNA) indicate that, although ctDNA is elevated in> 85% of patients with advanced forms of many types of cancer, a considerably smaller fraction of patients with early stages of cancer have detectable levels of ctDNA in plasma (see, for example, Bettegowda et al., 2014 Science translational medicine 6 (224): 224ra224; and Wang et al., 2015 Science translational medicine 7 (293): 293ra104).

[995] This example describes the use of a combined approach to cancer screening tests that increases the sensitivity of detecting resectable or otherwise treatable cancers under conditions that preserve high specificity. For example, the assays described in this example combine to detect mutations in ctDNA with the detection of threshold protein markers in plasma. Materials and Methods Samples of plasma, white blood cells and tumor DNA

[996] DNA was purified from plasma using a circulating QIASymphony DNA kit (Qiagen, cat # 1091063). Customized primers containing a unique identifier (UID) and specific amplicon sequences (Table 38) were used to amplify plasma DNA, and the resulting products were sequenced on an Illumina MiSeq or HiSeq instrument. Plasma concentrations of protein biomarkers were determined using Luminex ball-based immunoassays on the Bioplex 200 platform (Biorad, Hercules CA). Plasma samples were classified as positive if the sample contained a KRAS mutation or if the concentration of CA19-9, CEA, HGF or OPN was greater than 100 U / mL, 7.5 ng / mL, 0.92 ng / mL or 158 ng / ml, respectively. All samples were obtained after approval by the Research Ethics Committee at each institution and informed consent.

[997] Samples were obtained after approval by the Institutional Review Committees for Human Research at each institution and informed consent. The study included patients with Stage IA, IB, IIA or IIB (considered resectable) who had peripheral blood collected before surgery, did not receive neoadjuvant therapy and underwent surgical resection at participating institutions between April 2011 and May 2016. General demographic data, surgical pathology and AJCC internship (7th edition) were documented. The 'healthy' cohort consisted of peripheral blood samples obtained from 185 individuals with an average age of 64 years and with no history of cancer. Pancreatic cancer and healthy control samples were collected and processed in an identical manner.

[998] DNA was purified from 3.75 mL of plasma using a circulating QIASymphony DNA kit (cat # 1091063), as specified by the manufacturer. Tumor tissues were fixed in formalin and embedded in paraffin (FFPE) according to standard histopathological procedures and macro-dissected under a microscope to ensure a neoplastic cellularity> 30%. The DNA was purified with a QIAsymphony DP DNA Midi Kit (Cat # 937255), as specified

by the manufacturer. DNA concentrations were assessed by fluorescence using SYBR Green I (Thermo Cat # S7585). Mutation detection and analysis

[999] For amplification of plasma DNA, primer pairs were designed to amplify segments from 66 to 80 bp containing regions of interest from the KRAS and TP53 genes (Table 11 and Table 12). These primers were used to amplify DNA in six independent 25 μl reactions, as described elsewhere (see, for example, Wang et al., 2015 Proc Natl Acad Sci USA 112 (31): 9704-9709). The reactions were purified with AMPure XP beds (Beckman Coulter, PA, USA) and eluted in 50 μl of EB Buffer (Qiagen). A fraction (5 μl) of purified PCR products was then amplified in a second round of PCR, as described elsewhere (see, for example, Wang et al., 2015 Proc Natl Acad Sci USA 112 (31): 9704- 9709). The PCR products were purified with AMPure and sequenced on an Illumina MiSeq or HiSeq 4000 instrument.

[1000] The model-specific portion of the readings was matched to the reference strings using custom scripts written in SQL and C #. The readings of a common model molecule were then grouped based on the unique identifier sequences (UIDs) that were incorporated as molecular bar codes (see, for example, Allen et al., 2017 Ann Surg 265 (1): 185-191 ). Artifactual mutations introduced during the sample preparation or sequencing steps were reduced by requiring that a mutation be present in> 90% of the readings in each UID family. Plasma protein evaluation

[1001] The Bioplex 200 platform (Biorad, Hercules CA) was used to determine the concentration of multiple target proteins in plasma samples. Luminex ball-based immunoassays were performed following the manufacturer's protocols and concentrations were determined using 5 parameter log curve settings (using Bioplex Manager 6.0) with standards and quality controls provided by the supplier. Plasma samples were diluted 6 times for the CA19-9, CEA, HGF, OPN and prolactin assay and 5 times for the midkine assay. Plasma samples were classified as positive if the concentration of CA19-9, CEA, HGF or OPN was greater than 100 U / mL, 7.5 ng / mL, 0.92 ng / mL or 158 ng / mL, respectively. The dynamic ranges of these immunoassays for CA19-9, CEA, HGF, OPN, prolactin and midkine were 2.74 - 2,000 U / mL, 78.19 -

57,000 pg / mL, 27.43 - 20,000 pg / mL, 548.7 - 400,000 pg / mL, 137.17 - 100,000 pg / mL and 13.72 - 10,000 pg / mL, respectively. Algorithm for classification of ctDNA status

[1002] The classification of a sample's ctDNA status was obtained from a statistical test comparing the normalized mutation frequencies of the sample of interest with a distribution of normal controls. Specifically, MAF, defined as the ratio of the number of supermutants to the number of UIDs, was first normalized based on the MAFs observed in a set of normal controls for each mutation. After this specific normalization of the mutation, the MAF of each mutation in each well was compared to a reference distribution of the MAFs constructed from normal controls with all mutations included, and a p-value was calculated from that distribution. The lowest p-value among all mutations detected in a given sample was considered the "superior mutation". The classification of a sample's ctDNA status was based on the p-value of that higher mutation being below or above a certain threshold. The threshold was selected based on the desired specificity observed among an independent set of normal controls. Therefore, no training was carried out in any other sample, except for these controls; in particular, neither the 182 healthy controls nor the 221 patients with pancreatic cancer described in the main text were included in the controls used for training the algorithm. Statistical analysis

[1003] Continuous variables were reported as averages and standard deviations or medians and vary as needed, while categorical variables were reported as integers and percentages. Confidence intervals (CI) for sensitivities were calculated using a binomial distribution. The survival curves were estimated by the Kaplan-Meier method and the differences between the curves were investigated with the log-rank test. Statistically significant variables in the univariate analyzes were submitted to the Cox proportional regression model of multivariable risk.

[1004] The risk ratio (HR) and the 95% confidence interval (CI) for variables included in the multivariable model have been reported. A p-value <0.05 was considered to be statistically significant. Results Characteristics of patients with PDAC and presumed healthy controls

[1005] Two hundred and twenty-one patients with surgically resectable pancreatic cancer were evaluated in this study. The histopathological and clinical characteristics of these patients are summarized in Table 7. A total of 182 individuals of similar age, with no known history of cancer, autoimmune disease or chronic kidney disease, acted as a healthy control cohort.

[1006] Twenty percent of patients did not experience symptoms typically associated with pancreatic cancer. The size of primary tumors in the presentation ranged from 0.6 cm to 13 cm, with an average size of 3.0 cm. The most common stage in the presentation was the American Commission's Joint Commission on cancer stage (AJCC), Stage IIB,

responsible for 77% of patients, with the remaining patients harboring Stage IA (5%), Stage IB (8%) or Stage IIA (10%) (Table 7) Patient survival correlated with stage, as represented graphically in Figure 12 and as expected in previous clinical studies (see, for example, Allen et al., 2017 Ann Surg 265 (1): 185-191). PCR-based assay to identify tumor-specific KRAS mutations in plasma samples

[1007] A PCR-based assay was designed to simultaneously evaluate the two codons (codons 12 and 61) of the KRAS gene that are most frequently mutated in the PDAC, as well as in the surrounding codons. The trial employed a sensitive technology called the Safe-Sequencing System (Safe-SeqS) (see, for example, Kinde et al., 2011 Proc Natl Acad Sci USA 108 (23): 9530-9535, 2011). Safe-SeqS incorporates molecular barcodes that uniquely mark each model molecule, dramatically minimizing errors that routinely occur in massively parallel sequencing. This approach can identify a mutant model among up to 10,000 normal models. Using this technology, KRAS mutations in plasma were identified in 66 of the 221 (30%: 95% CI 24-36%) cases of pancreatic cancer (see below and Table 8). Sixty-two (94%) and four (6%) of the mutations were in codons 12 and 61, respectively, with the most common G> T trans- versions observed (Table 8). Mutations were found more frequently in Stage II patients than in Stage I patients (see below, Table 8 and Fig. 9A). In addition, while the frequency of the mutant allele did not correlate with tumor size (Table 8 and Fig. 11A), mutations were found more frequently in larger tumors than in smaller tumors (see below, Table 8 and Fig. 9B ).

Proportion of samples stratified by the AJCC stage detected in each individual test and in all combinations thereof.

Proportion of detected samples (95% confidence interval) Type of test Stage IA Stage IB Stage IIA Stage IIB Stage I&II (12 cases) (17 cases) (22 cases) (170 cases) (221 cases) KRAS ctDNA 25% (5-57%) 0% (0-20%) 18% (5-40%) 35% (28-42%) 30% (24-36%)

1219/2130 CA19-9 17% (2-48%) 41% (18-67%) 36% (17-59%) 54% (46-62%) 49% (43-56%) CEA + HGF + OPN 25% (5-57%) 6% (0-29%) 14% (3-35%) 19% (14-26%) 18% (13-24%) KRAS + CA19-9 ctDNA 33% (10-65%) 41% (18-67%) 50% (28-72%) 65% (58-72%) 60% (53-67%) Mutations in the KRAS + 33% ctDNA (10-65 %) 6% (0-29%) 32% (14-55%) 47% (39-55%) 42% (35-48%) CEA + HGF + OPN CA19-9 + CEA + HGF + OPN 25% (5-57%) 47% (23-72%) 36% (17-59%) 59% (52-67%) 54% (47-61%) Combination trial 33% (10-65%) 47 % (23-72%) 50% (28-72%) 69% (62-76%) 64% (57-70%)

Proportion of stratified samples by tumor size detected in each individual assay and in all combinations thereof. Proportion of detected samples (95% confidence interval) Assay type ≤ 1.5 cm 1.5-2.0 cm 2.0-2.5 cm 2.5-3.0 cm 3.0-3, 5 cm 3.5-4.0 cm> 4.0 cm (24 cases) (12 cases) (47 cases) (38 cases) (36 cases) (36 cases) (42 cases) KRAS ctDNA 21% (7 -42%) 17% (2-48%) 9% (2-20%) 32% (18-49%) 42% (26-59%) 45% (24-68%) 43% (28-59 %) CA19-9 25% (10-47%) 33% (10-65%) 43% (28-58%) 45% (29-62%) 58% (41-74%) 59% (36- 79%) 67% (50-80%) CEA + HGF + OPN 25% (10-47%) 8% (0-38%) 17% (8-31%) 21% (10-37%) 8% (2-22%) 18% (5-40%) 24% (12-39%) KRAS + CA19-9 ctDNA 38% (19-59%) 50% (21-79%) 47% (32- 62%) 55% (38-71%) 78% (61-90%) 73% (50-89%) 74% (58-86%) 1220/2130 Mutations in the KRAS + CEA + HGF + 38% ctDNA (19-59%) 25% (5-57%) 26% (14-40%) 47% (31-64%) 47% (30-65%) 55% (32-76%) 50% (34 -66%)

OPN CA19-9 + CEA + HGF + OPN 38% (19-59%) 33% (10-65%) 47% (32-62%) 53% (36-69%) 64% (46-79%) 64% (41-83%) 67% (50-80%) Combination test 46% (26-67%) 50% (21-79%) 51% (36-66%) 61% (43-76% ) 81% (64-92%) 77% (55-92%) 74% (58-86%) Protein biomarkers in various types of cancer.

Type of Cancer # Cases% CA19-9% CEA% CA125% AFP% Prolactin% HGF% OPN% TIMP-1% Folistatin% G-CSF% CA15-3 Breast 150 3% 4% 1% 1% 8% 3% 3 % 0% 1% 3% 1% CRC 322 5% 17% 0% 1% 10% 11% 8% 8% 10% 9% 0% Esophagus 43 7% 5% 0% 0% 2% 33% 19% 26 % 2% 14% 5% Gastric 65 11% 15% 0% 5% 3% 34% 20% 11% 8% 8% 3% Liver 53 21% 9% 6% 40% 11% 25% 28% 17% 8 % 6% 6% Lung 109 4% 13% 0% 1% 11% 1% 2% 0% 0% 0% 3% Ovarian 86 13% 1% 12% 3% 20% 3% 3% 10% 1% 0 % 19% Pancreas 412 52% 8% 0% 0% 0% 7% 6% 7% 8% 1% 0%

1221/2130

[1008] The number of mutant models in the plasma can be calculated from the fraction of the mutant allele and the DNA concentration in each plasma sample (Table 8). This number is usually very low, with 15 (23%) of patients with detectable mutations in the KRAS with <2 models of mutants per ml of plasma. The average number of mutant models per mL of plasma was 9.4 (Table 8). These results emphasize that extremely sensitive techniques can be used to detect mutations in patients with early-stage pancreatic cancer. Mutations in KRAS were observed in only one of the 221 individuals in the supposed healthy cohort, a 69-year-old man with no known cancer.

[1009] The basis for the concept of liquid biopsy is that the mutant DNA models identified in the circulation are derived from cancers. Therefore, it was important to determine whether the KRAS mutations identified in these patients' plasma samples were also present in their primary carcinomas. Primary carcinomas of 50 of the 66 patients with detectable KRAS mutations in plasma were obtained. In the 50 cases, the mutation found in plasma was identical to that found in primary carcinoma, providing another orthogonal measure of specificity. Simultaneous evaluation of CA19-9 and KRAS mutations in plasma

[1010] We sought to determine whether a combination of the KRAS ctDNA test with CA19-9, the PDAC biomarker, would result in greater sensitivity compared to the KRAS ctDNA test alone. Recent studies have shown that CA19-9 may be elevated in patients with pancreatic cancer two years before diagnosis (see, for example, O'Brien et al., 2015 Clin Cancer Res 21 (3): 622-631). However, elevations of CA19-9 have also been observed in non-malignant conditions, and 5% of the population cannot produce the CA19-9 antigen due to the genetic variation of the germline, limiting its use for screening purposes (see , for example, Lennon et al., 2010 Diagnostic and Therapeutic Response Markers, Pancreatic Cancer, (Springer New York, New York, NY), pp 675-701). However, it was reasoned that CA19-9 could be useful as a screening biomarker if the threshold for scoring a positive result was high enough. A threshold of 100 U / mL was chosen based on previous data that this level is not found among healthy individuals who do not have a clinical history of pancreatic-mobile disease (see Kim et al., 2004 J Gastroenterol Hepatol 19 (2 ): 182-186).

[1011] Using this predefined high threshold, CA19-9 was detected in 109 of the 221 (49%: CI 95% 43-56%) patients with pancreatic cancer and in none of the 182 healthy controls, confirming its specificity when used in this way (Table 8 and Table 9). As expected, the number of patients with detectable levels of CA19-9 increased with the stage and size of the tumor (Fig. 9 and Table 8). One issue addressed in the present study was whether these two biomarkers - changes in KRAS and a positive CA19-9 score - were independent indicators of the presence of the disease. The overlap was found to be only partial, as shown in the Venn diagram in Fig. 10. Although 42 patients (19%) had elevated levels of CA19-9, as well as detectable mutations in the plasma KRAS, 91 additional patients had mutations in the elevated KRAS or CA19-9, but not both (Fig. 10). Thus, the combined sensitivity of these analyzes was 60% (95% CI 53-67%), higher than the sensitivity of any of them (Figure 9). As such, this example demonstrates that the two assays could be combined without substantially increasing the rate of false positives, because each was extremely specific at the thresholds used. Increase sensitivity by including other biomarkers of proteins

[1012] Encouraged by the results described above, an attempt was made to further increase sensitivity by combining ctDNA KRAS and CA19-9 mutations with other protein biomarkers (Table 10). In a pilot study on a small number of pancreatic cancer samples independent of those studied here, the potential utility of other proteins that were found to be elevated in cancer, including alpha-fetoprotein (AFP), CA15-3, leptin, IL - 6, carcinoid embryonic antigen (CEA), CA-125, interleukin 8 (IL-8), sFas, prolactin, osteopontin (OPN), basic fibroblast growth factor (FGF2), hepatocyte growth factor ( HGF), fragment of cytokeratin-19 (CYFRA 21-1), human epididymis protein 4 (HE4), transforming growth factor alpha (TGF-α), growth / differentiation factor 15 (GDF15), related protein 1 to dickkopf (DKK1), neuron specific enolase (NSE), osteoprotegerin (OPG), TIMP metallopeptidase 1 (TIMP-1) inhibitor, TIMP metallopeptidase 2 (TIMP-2) inhibitor, mesothelin, midkine, kallikrein -6, CD44, AXL receptor tyrosine kinase, soluble human epidermal growth factor 2 (sHER2) receptor, d receptor the soluble epidermal growth factor (sEGFR), urokinase-type soluble plasminogen activating receptor (suPAR) and the platelet endothelial cell adhesion molecule (sPE-CAM-1) was evaluated. Of these 29 protein biomarkers, five — CEA (see, for example, Nazli et al., 2000 Hepatogastroenterology 47 (36): 1750-1752), HGF (see, for example, Di Renzo et al., 1995 Cancer Res 55 ( 5): 1129-1138), midkine (see, for example, Ikematsu et al., 2000 Br J Cancer 83 (6): 701-706), OPN (see, for example, Koopmann et al., 2004 Cancer Epidemiol Biomarkers Prev 13 (3): 487-491), and prolactin (see, for example, Levina et al., 2009 Cancer Res 69 (12): 5226-5233) —were chosen for further analysis.

[1013] When the levels of these five markers were assessed in plasmas of the cohort of 221 patients with pancreatic cancer, an association was observed between plasma concentrations of pro-lactin and midkine and the surgical site (P <0.01, test χ2, degrees of freedom = 5), suggesting that blood collection conditions may have raised the levels of these two markers. There was no significant correlation between the levels of CA19-9, CEA, HGF or OPN and collection sites, nor was there any correlation between the presence of mutations in the KRAS ctDNA and the collection site (P> 0.01, χ2, degrees of freedom = 5). After further investigation, it was found that the levels of prolactin and midkine were significantly elevated in samples that were collected after administration of anesthesia, but before surgical excision (Fig. 14). The results of prolactin were consistent with previous studies, showing that anesthetics raise levels of this protein (see, for example, Thorpe et al., 2007 PLoS One 2 (12): e1281). To ensure that anesthesia did not affect the levels of the other protein biomarkers described above, paired plasma samples were collected before and immediately after administration of anesthesia in 29 new patients. The only proteins found to be elevated by anesthesia were prolactin and midkine (Fig. 15), in perfect agreement with the correlation between the collection site and the protein levels observed above. Prolactin and midkine were therefore excluded from further analysis.

[1014] Unlike CA19-9, there is no predefined threshold for the use of CEA, HGF or OPN as markers for pancreatic cancer. As a result, the appropriate thresholds were determined in an independent set of 273 plasma samples from healthy controls. To be conservative, thresholds were chosen for each protein 10% higher than the maximum values observed in any of the 273 normal plasma samples. Notably, when these thresholds were applied to the independent test set of 182 plasma samples, all three protein markers maintained

100% specificity (Table 9). The sensitivity of each of these three markers was less than that obtained with the KRAS or CA19-9 mutations when each marker was used alone, but their levels were less dependent on stage and size than the KRAS or CA19-9 mutations (Fig 13 and Table 8). In combination with KRAS mutations and CA19-9 assays, this panel of five-member biomarkers ("combination assay") detected 141 (64%: 95% CI 57-70%) of the 221 resectable cancers (Table 7, Fig. 9A , Fig. 10 and Table 8).

[1015] Some of the patients detectable by the combination test were particularly important. Forty-five (20%) patients did not present symptoms classically associated with pancreatic cancer (Table 8). The combination trial identified 27 (60%) of these individuals, of which 19 (70%) showed no evidence of recurrence with an average follow-up of 12 (range 3 to 16) months (Table 8). Of the 29 patients with the initial stages of the disease (Stages IA and IB) recognized by the AJCC, 12 (41%) were detectable by the combination test (Fig. 9A), of which 7 (58%) did not present evidence of recurrence at the end of the study, with an average follow-up of 19 months (range 2 to 25).

[1016] Another notable but worrying result from this study was that patients with poorer survival were more likely to have a positive test. Of the 221 pancreatic cancer patients studied, 122 (56%) were alive at the end of the study, with an average follow-up of 13 (7 - 21) months. The combination assay was found to provide a prognostic value independent of conventional clinical and histopathological features. In particular, multivariate analyzes showed that the independent predictors of overall survival were the combined trial status (HR = 1.76, 95% CI, 1.10-2.84, p = 0.018), increased age (HR = 1, 04, CI 95% 1.02-1.06, p = 0.001), degree of differentiation (poorly differentiated, HR = 1.72, CI 95% 1.11–2.66, p =

0.015), lymphovascular invasion (present, HR = 1.81, 95% CI 1.06 - 3.09, p = 0.028), nodal disease (present, HR = 2.35, 95% CI 1.20 - 4, 61, p = 0.013) and margin status (HR = 1.59, 95% CI 1.01 - 2.55, p = 0.050) (Table 7, Fig. 16). TP53 in a multiplex assay

[1017] While almost all pancreatic cancers harbor mutations in KRAS, a large fraction (~ 75%) also contains mutations in TP53 (see, for example, Jones et al., 2008 Science 321 (5897): 1801- 1806; Biankin et al., 2012 Nature 491 (7424): 399-405; and Waddell et al., 2015 Nature 518 (7540): 495-501). In addition, TP53 is the most commonly mutated gene in cancer (see, for example, Vogelstein et al., 2013 Science 339 (6127): 1546-1558), making it an attractive target for the detection of ctDNA in future studies involving other types of tumors. Determine whether the frequencies of mutant TP53 alleles in plasma correlate with those of KRAS, and also determine whether a mutant TP53 assay in plasma can increase the sensitivity of the mutant KRAS assay, the 152 carcinomas for which tumor samples and plasma corresponded were available were evaluated. Mutations at one of the "critical points" identified in generic PDAC studies (see, for example, Jones et al., 2008 Science 321 (5897): 1801-1806) were researched for the first time. A total of 64 (42%) carcinomas contained a TP53 mutation in one of these positions. It was then determined whether these same mutations could be identified in the plasma of these 64 patients, using assays based on Safe-SeqS similar to those described above for KRAS, but using specific primers for certain TP53 mutations.

[1018] Mutations in TP53 have been identified in 13 (20%) of the 64 plasma samples (see below). Two observations were interesting. First, 12 of the 13 plasma samples containing a mutation

Detectable TP53 also contained a detectable KRAS mutation. Thus, the TP53 mutation assays did not substantially increase the sensitivity for the detection of pancreatic cancer, as expected from the high prevalence of KRAS mutations noted above. Second, there was a strong correlation between the frequencies of mutant alleles of TP53 and KRAS mutations in the plasma of the 12 patients whose plasma contained detectable amounts of both mutations (Fig. 3, Pearson's r = 0.885). This provides further validation of the reliability of the ctDNA assay and its quantitative nature. List of exemplary TP53 mutations detected in patients with

PDAC Plasma DNA Mean frequency Fragments Sample identification Concentration Mutation identified in the plasma of the mutant allele mutants / mL tra # (ng / mL) (%) PANC 335 plasma 13.28 TP53 p.R196 *, c.586C> T 0.795 32.5 PANC 336 13.67 TP53 p.G266E, c.797G> A 0.929 39.1 PANC 552 19.44 TP53 p.R175H, c.524G> A 0.673 40.3 PANC 387 11.11 TP53 p.R175H , c.524G> A 0.195 6.7 PANC 467 6.76 TP53 p.R248Q, c.743G> A 0.344 7.2 PANC 468 10.28 TP53 p.S241F, c.722C> T 0.139 4.4 PANC 469 5.98 TP53 p.C238F, c.713G> T 0.051 0.9 PANC 545 12.23 TP53 p.Y236C, c.707A> G 0.137 5.2 PANC 547 10.73 TP53 p.Y234C, c.701A> G 0.069 2.3 PANC 552 10.00 TP53 p.C238Y, c.713G> A 0.317 9.8 PANC 692 9.83 TP53 p.V272M, c.814G> A 0.101 3.1 PANCA 1105 11.49 TP53 p .H193Y, c.577C> T 0.098 3.5 PANCA 1109 27.57 TP53 p.Y234C, c.701A> G 0.351 29.8 List of exemplary CDKN2A mutations detected in patients with PDAC Frequency Mean identification of the mutation identified in plasma Mutation identified in tumor tissue sample # mut allele before (%) PANC 335 PLS1 CDKN2A p.R58 *, c.172C> T CDKN2A p.R58 *, c.172C> T 0.432 PANC 552 PLS 1 CDKN2A p.D84G, c.251A> G CDKN2A p.D84G, c .251A> G 0.166 PANC 641 PLS 1 CDKN2A g.21971208G> T (splice site) CDKN2A g.21971208G> T (splice site) 0.143 PANC 447 PLS CDKN2A p.R80 *, c.238C> T CDKN2A p.R80 *, c.238C> T 0.102 PANC 398 PLS 1 CDKN2A p.V51D, c.152T> A CDKN2A p.V51D, c.152T> A 0.091 PANC 609 PLS 1 CDKN2A p.R80 *, c.238C> T CDKN2A p .R80 *, c.238C> T 0.071

PANC 455 PLS CDKN2A p.H83Y, c.247C> T CDKN2A p.H83Y, c.247C> T 0.061 PANC 517 PLS 1A CDKN2A p.R58 *, c.172C> T CDKN2A p.R58 *, c.172C> T 0.026 PANC 547 PLS 1 CDKN2A p.R58 *, c.172C> T CDKN2A p.R58 *, c.172C> T 0.026 PANC 648 PLS 1 CDKN2A p.A76T, c.226G> A CDKN2A p.A76T, c.226G > A 0.016 PANC 715 PLS 1 CDKN2A p.M54fs, c.162> G CDKN2A p.M54fs, c.162> G 0.014 PANC 763 PLS 1 CDKN2A p.H83Y, c.247C> T CDKN2A p.H83Y, c.247C > T 0.013 PANC 509 PLS 1 CDKN2A p.H83Y, c.247C> T CDKN2A p.H83Y, c.247C> T 0.012 PANC 545 PLS 1 CDKN2A p.R80fs, c.239ACCCG> CDKN2A p.R80fs, c.239ACCCG> 0.011 PANC 634 PLS 1 CDKN2A p.A76T, c.226G> A CDKN2A p.A76T, c.226G> A 0.006 Example 3: Sensitivity and specificity of ctDNA and protein biomarkers Materials and Methods Phase A

[1019] Healthy Cohort: Ten thousand non-symptomatic women are recruited. The age range of participants is 65 to 75 years, as it captures patients at maximum risk of cancer of the eight types that are the target of detection. Women with oophorectomies, known cancer of any type other than non-melanoma skin cancer, are excluded from the study. The plasma of each participant is obtained at the entrance of the study. In participants with positive results, as well as in a random sample of participants with negative tests, an additional plasma sample is collected 3 months after the first test. When both tests are positive for the same mutation, PET / CT of the entire body is performed. If the PET / CT exam is positive, patients will be treated as appropriate by their doctors. This management includes annual follow-up ctDNA tests. The annual follow-up of patients through an electronic questionnaire, combined with telephone interviews, when necessary, is obtained in all individuals, whether positive or negative.

[1020] Management cohorts: Two hundred patients with Stage III colon cancer and 50 patients with resectable pancreatic cancer are recruited from at least 10 locations in Australia, New Zealand and Singapore. Tumor samples are tested to identify mutations that can be used for subsequent testing of ctDNA. Plasma samples are collected from all patients prior to surgery (including those randomized to receive routine care). Additional plasma samples are collected at 3, 6, and 12 months in patients whose ctDNA tests are positive before surgery. No additional ctDNA analysis is performed on ctDNA negative patients or those randomized to receive routine care. Patients are randomized 1: 1 for the treatment reported by ctDNA (with an increase or decrease in treatment compared to the standard of care or routine care [blind to the results of the ctDNA test]). Phase B

[1021] Healthy Cohort: Forty thousand more non-symptomatic women are recruited. The inclusion and exclusion criteria are the same as in Phase A. In addition to the longer follow-up and the larger number of patients, there are at least two other differences between Phase A and Phase B. First, the protein biomarkers samples that exceeded the specificity threshold (> 99.5%) in Phase A are included. Patients who test positive for one or more of these protein biomarkers are managed identically to those with positive ctDNA results. Second, a "control" Healthy Cohort is added. They represent individuals recruited from the same population, but in which blood samples are not collected to assess the disease or guide management. These controls are used to assess the ability of screening tests to detect disease before it is normally detected based on the symptoms or standard medical care of healthy individuals. Comparisons between stage and survival are also made in the screened and control cohorts.

[1022] Management cohorts: More than 1,000 Stage III colon cancer patients (a total of 1,200 patients) and an additional 210 patients with resectable pancreatic cancer (out of 250 patients) are recruited, but now in 30 locations instead of 10 locations. Likewise, in Phase A, tumor samples are collected during surgery on all new patients and mutations identified so that they can be used in subsequent ctDNA tests. In addition, protein biomarkers for colorectal or pancreatic cancer that exceeded the specificity threshold (> 99.5%) in Phase A are included. Patients who test positive for these protein biomarkers are managed identically to those with positive ctDNA results. Plasma samples are collected from all patients before surgery and at 3, 6 and 12 months in patients whose ctDNA tests are positive before surgery. Patients are randomized 1: 1 for the treatment informed by ctDNA (with an increase or decrease in treatment compared to the standard of care or routine care [blind to ctDNA result]). Assessments

[1023] The Safe-SeqS-based ctDNA test used in the Healthy Cohort incorporates 61 amplicons that represent the most commonly mutated regions of cancer control genes. It is estimated that ~ 70% of the eight types of cancer to be targeted have mutations in at least one of the regions covered by these amplicons. The fraction of plasmas that mutate in at least one of these amplicons is less than 70% because not all cancers give rise to ctDNA (see Bettegowda et al., Sci Transl Med. 2014 Feb 19; 6 (224): 224ra24 . It hurts:

10.1126 / scitranslmed.3007094.).

[1024] For the Management Cohort, a targeted sequencing assay is first used to identify at least one FFPE cancer mutation obtained from surgery or biopsy. This Safe-SeqS-based test employs 128 amplicons and we use it to detect at least one pathogenic mutation in the conductive gene in 395 of the 401 colorectal cancers and 200 of the 200 pancreatic cancers tested. A specific amplicon that detects a mutation in each patient's tumor is then used to evaluate that patient's plasma at the specified time points - in other words, a fully personalized assay.

[1025] In addition to tests on circulating tumor DNA (ctDNA), a panel of ~ 25 protein biomarkers is evaluated using a Luminex platform on a subset of plasma samples from the Healthy Cohort. The assays for these proteins have been well established. If the positivity thresholds are set to a high level, they will provide additional information that can be combined with the results of the ctDNA test to improve sensitivity without compromising specificity. Markers with> 99.5% specificity in Phase A are incorporated in Phase B of the study. Example 4: Detecting gynecological malignancies using a combined approach to cancer screening tests

[1026] The diagnosis of gynecological malignancy (eg, cervical cancer, ovarian cancer and endometrial cancer) usually includes Pap smear (Pap), transvaginal ultrasound (TVUS) and / or detection of the CA-25 biomarker. However, screening with current diagnostic approaches is not recommended for the general population, as it leads to "major damage, including major surgical interventions in women who do not have cancer" (see, for example, Moyer, 2012 Annals of internal medicine 157: 900-904). Thus, new diagnostic approaches are urgently needed.

[1027] This example describes a new blood test, called PapSEEK, which addresses the problematic issues described above. In this test, the DNA of the sampled fluid (for example, cells containing fluid sampled in the endocervical canal) can be used in an assay (for example, a PCR-based multiplex test) to simultaneously evaluate genetic changes that usually occur in endometrial or ovarian cancers (Fig. 19). Overall, 1,915 samples of 1,658 individuals were included in the studies described here, including 656 patients with endometrial or ovarian cancer and 1002 healthy controls. Age, race, histopathological diagnosis, stage and other clinical information for cancer patients are provided in Table 13. The tested samples from these patients are listed in Table 14. Materials and Methods Patient samples

[1028] All samples in this study were obtained according to protocols approved by the Institutional Review Boards of Johns Hopkins Medical Institutions (Baltimore, MD), McGill University (Montreal, QC, Canada), Gothenburg University (Gothenburg, Sweden) , Bio-reclamationIVT (Chestertown, MD), Memorial Sloan Kettering Cancer Center (New York, NY) and Danish Scientific Ethics Committee (Copenhagen, Denmark). Demographic, clinical and pathological staging data were collected for each cancer patient are listed in Table 13. The average age of 714 women without cancer used for Pap brush analysis was 34 years (range: 17 to 67 years) . The average age of 125 women without cancer used for the analysis with the Tao brush was 29 years (range: 18 to 74 years). The entire histopathology was reviewed by certified pathologists. DNA was extracted from tumors, Pap smear fluid and plasma, as described elsewhere (see, for example, Kinde et al., 2011 Proc Natl Acad Sci USA 108: 9530-9535; and Bettegowda et al. , 2014 Sci Transl Med 6: 224ra224) For intrauterine sampling, the Tao Brush IUMC Endometrial Sampler (Cook Medical Inc., Bloomington, IN) was gently inserted at the level of the uterine fundus. The outer sheath was then pulled back and the brush rotated 360 degrees clockwise and then counterclockwise. Then, the outer sheath was pushed again and the device was removed. The sample was placed in Thin-Prep buffer, from which the DNA was purified using an AllPrep DNA kit (Qiagen, Germany) according to the manufacturer's instructions. The purified DNA from all samples was quantified as described elsewhere (see, for example, Rago et al., 2007 Cancer research 67: 9364-9370).

[1029] Healthy controls included patients with normal cytological findings in Pap smears and without a history of gynecological tumors. Ovarian cancer patients with a history of tubal ligation were excluded from the study. Somatic mutation detection and analysis

[1030] Pap smear fluid DNA, Tao brush samples or primary tumors were amplified in three multiplex PCR reactions consisting of 139 pairs of primers that were designed to amplify segments from 110 to 142 bp, as described in elsewhere (see, for example, Wang et al., 2016 Elife 5). These segments contain regions of interest for the following 18 genes: AKT1, APC, BRAF, CDKN2A, CTNNB1, EGFR, FBXW7, FGFR2, KRAS, MAPK1, NRAS, PIK3CA, PIK3R1, POLE, PPP2R1A, PTEN, RNF43, and TP53. For each sample, three multiplex reactions were performed, each containing non-overlapping amplicons, as described elsewhere (see, for example, Wang et al., 2016 Elife 5). Each sample was evaluated in two duplicate wells. Plasma DNA was amplified in two multiplex PCR reactions consisting of 61 pairs of primers that were designed to amplify segments from 67 to 81 bp. Each sample was evaluated in six duplicate wells. These segments contained regions of interest for the following genes: AKT1, APC, BRAF, CDKN2A, CTNNB1, EGFR, FBXW7, FGFR2, GNAS, HRAS, KRAS, NRAS, PIK3CA, PPP2R1A, PTEN, and TP53.

[1031] Safe-SeqS, an error-reducing technology for detecting low-frequency mutations (see, for example, Kinde et al., 2011 Proc Natl Acad Sci USA 108: 9530-9535), was used to all sequencing analyzes. One initiator in each pair included a sequence of unique identifiers (UIDs), composed of 14 degenerate bases with the same chance of being A, C, T or G. High-quality sequence readings were selected with based on the quality scores generated by the sequencing instrument to indicate the probability that a base was called in error. The readings of a common model molecule were then grouped based on the UIDs that were incorporated as molecular bar codes. Artifact mutations introduced during sample preparation or sequencing steps were reduced by requiring that a mutation be present in> 90% of the readings in each UID family (that is, be classified as a "supermutant") (see, for example, example, Kinde et al., 2011 Proc Natl Acad Sci USA 108: 9530-9535). Statistical analysis of sequencing data

[1032] All Pap brush and Tao brush samples were analyzed using a mutant allele fraction (MAF) approach. Mutations that met one of the following two criteria were considered (i) present in the COSMIC database (see, for example, Forbes et al., 2017 Nucleic Acids Res 45: D777-D783) or (ii) predicted as inactivator of tumor suppressor genes (senseless mutations, insertions or deletions outside the frame, mutations in the canonical splicing site). Synonymous mutations, except those at the end of the exon (see, for example, Jung et al., 2015 Nat Genet 47: 1242-1248) and intronic mutations, except those at the splice sites, have been excluded. The MAF in the sample of interest was first normalized based on the distribution of MAFs for the same mutation in the control group. After this specific normalization of the mutation, a p value was obtained comparing the MAF of each mutation in each well with a reference distribution of the MAFs constructed from normal controls, where all mutations were included. Stouffer's Z score was then calculated from the p values of two wells, weighted by the number of UIDs.

[1033] A sample was classified as positive when any of its mutations had a value above the corresponding thresholds for any of the following three criteria: 1) the difference between its MAF and the corresponding maximum MAF observed for that mutation. controls; 2) the ratio of the Stouffer Z score to the average of the six non-zero Stouffer Z scores for the same mutation in the controls, or 3) your Stouffer Z score alone when the mutation was not observed in the controls.

[1034] Sensitivity and specificity were obtained from a 10-fold cross-validation. In each round, samples of Pap brushes from 90% of the 714 women without cancer served as controls. In each of the ten rounds, the remaining 10% of the Pap brush samples from women without cancer were scored for specificity. All other samples were scored once in each of the 10 rounds for a total of ten times, and were considered positive in general if they obtained a positive result in half the time (that is, 5 or more rounds). The mutations in the samples with a positive result are listed in Table 15.

[1035] Analysis of plasma samples was performed using an empirical Bayes approach. A Beta distribution was first adjusted based on MAFs in a set of controls using the maximum probability estimate. The MAFs of all mutations were then adjusted accordingly. A p value was calculated for each mutation in each well by comparison with the distribution of adjusted MAFs between controls. A general p-value for each mutation was obtained as the product of the p-values for all 6 wells. Sensitivity and specificity were obtained from a 10-fold cross-validation. In each round, normal plasma samples from 192 healthy subjects served as controls, as for the Pap brush samples described above. A sample was considered positive if it was positive in 5 or more rounds. The mutations in the samples with a positive result are listed in Table 17.

[1036] Confidence intervals for sensitivities and specificities were calculated assuming binomial distributions with the real sensitivities and specificities defined as the corresponding success probabilities. Detection and analysis of aneuploidy

[1037] For each sample, a single pair of primers was used to amplify ~ 38,000 loci of long intercalated nucleotide elements (LINEs) across the genome (see, for example, Kinde et al., 2012 PLoS One 7: e41162). Massively parallel sequencing was performed on Illumina instruments. One of the primers includes a UID as a molecular bar code as described above to reduce the error rates associated with PCR and sequencing. The sequencing data was then processed to identify significant gains or losses from a single chromosome arm, as well as allelic imbalance in 39 chromosome arms, using Within- Sample AneupLoidy DetectiOn (WALDO) software (see, for example, Example 6). WALDO incorporates a support vector machine (SVM) to discriminate between aneuploid and euploid samples. The SVM was trained using 3150 synthetic aneuploid samples with low neoplastic content and 677 euploid peripheral white blood cell (WBC) samples. A sample was scored as positive (aneuploid), if the SVM discriminating score exceeds a certain threshold or if

Significant gains in chromosome arms 7q and 8q are maintained. These chromosomal arms are often acquired in the endometrial and ovarian cancers (see, for example, Cancer Genome Atlas Research, 2013 Nature 497: 67-73; and Cancer Genome Atlas Research, 2011 Nature 474: 609-615) . Results Evaluation of somatic mutations in Pap brush samples from patients with endometrial or ovarian cancer

[1038] It was expected that the amount of DNA spilled from the neoplastic cells would be a smaller fraction of the total DNA in the Pap brush samples, with most of the DNA emanating from normal cells. Therefore, a sensitive PCR-based error reduction technology, called the Safe-Sequencing System (Safe-SeqS), was used to identify mutations in these samples (see Methods and Materials). In summary, the initiators were designed to amplify 139 regions, covering

9,392 distinct nucleotide positions in the 18 genes of interest (Table 39). Three multiplex PCR reactions, each containing non-overlapping amplicons, were then performed on each sample.

[1039] This trial was applied to Pap brush samples from 382 women with endometrial cancer, 245 women with ovarian cancer and 714 women without cancer. It was found that 81% of patients with endometrial cancer had detectable mutations, including 78% of patients with early stage disease (stages I and II) and 89% of patients with advanced stage disease (stages III and IV; beautiful S2). The most commonly mutated genes were PTEN (64%), TP53 (41%), PIK3CA (31%), PIK3R1 (29%), CTNNB1 (21%), KRAS (18%), FGFR2 (11%), POLE ( 9%), APC (9%), FBXW7 (8%), RNF43 (7%), and PPP2R1A (5%). The median fraction of the mutant allele (MAF) was 4.0% (95% confidence interval (CI): 3.5% to 4.5%) (Table 15).

[1040] Twenty-nine percent of the 245 ovarian cancer patients had detectable mutations in their Pap brush samples. These included 28% of patients with early-stage disease and 30% of patients with advanced-stage disease (Table 14). The most commonly mutated genes were TP53 (74%). The median MAF was 0.54% (CI 95%: 0.4 %% to 0.87%) (Table 15). This trial was also applied to 714 women without cancer and found that 1.3% had a detectable mutation, producing a specificity of 98.7% (95% CI: 97.6% to 99.4%) (Fig. 20 ).

[1041] Tumor tissue was available in 83% and 84% of patients with endometrial and ovarian cancer who donated Pap brush samples, respectively. Using the same multiplex assay applied to the Pap brush samples, a mutation in the conductor gene was identified in 98% and 82% of endometrial and ovarian cancer tissues, respectively (Table 16). Of the patients with endometrial and ovarian cancer with a conductor mutation identified in their primary tumor, 85% and 29%, respectively, had mutations in their Pap brush samples. On the other hand, of the positive Pap brush samples from patients with endometrial or ovarian cancer, 93% contained at least one genetic driver mutation that was identical to that seen in their primary tumor. The fraction of Pap brush samples with mutations that were also found in primary tumors was higher in patients with endometrial cancer (97%) than in patients with ovarian cancer (73%). Assessment of aneuploidy in Pap brush samples

[1042] In addition to somatic mutations, aneuploidy is found in the vast majority of endometrial and ovarian cancers (see, for example, Cancer Genome Atlas Research, 2013 Nature 497: 67-73; Cancer Genome Atlas Research, 2011 Nature 474: 609- 615; and Vogelstein et al., 2013 Science 339: 1546-1558). To assess aneuploidy, a PCR-based method was used to amplify ~ 38,000 loci of long intercalated nucleotide elements (LINEs) with a single pair of primers. LINEs have spread throughout the genome via retro-transposition and are found in all 39 autosomal non-acrocentric arms. After sequencing, the data were processed to identify gains or losses in the unique chromosomal arms.

[1043] Aneuploidy was detected in Pap brush samples from 38% (n = 382) of patients with endometrial cancer, including 34% and 51% of those with early and late stage disease, respectively (Table 14). Aneuploidy was also detected in the Pap brush samples of 11% (n = 245) of patients with ovarian cancer, including 15% and 9.3% of those with early and late stage disease, respectively ( Table 14). In endometrial and ovarian cancers, the most commonly altered arms were 4p, 7q, 8q and 9q. On the other hand, when the aneuploidy test was applied to the Pap brush samples from 714 women without cancer, only one woman was positive (Fig. 20).

[1044] Even if a sample does not contain a genetic change in one of the 18 genes evaluated, it can still be aneuploid and detectable by the methods provided here. This conjecture was supported by the identification of six patients (three with endometrial cancer and three with ovarian cancer) who had no mutations in their Pap brush samples or primary tumors (where available), but whose Pap smear samples showed aneuploidy. The combined test that incorporated the tests described above for mutations plus aneuploidy was dubbed "PapSEEK". PapSEEK classifies a sample as positive if it has a mutation or an abnormal number of chromosomal arm. Eighty-one percent of Pap brush samples from women with endometrial cancer were positive for PapSEEK, including 78% of patients with early-stage disease and 92% of patients with late-stage disease (Fig. 21 and Fig. 22) . Thirty-three percent of the Pap brush samples from women with ovarian cancer were positive for PapSEEK, including 34% of patients with early-stage disease and 33% of patients with late-stage disease (Fig. 21 and Fig. 22). Only 1.4% of the Pap brush samples from 714 women without cancer were positive for PapSEEK, producing a specificity of 98.6% (95% CI: 97.4% to 99.3%) (Fig. 20) . Evaluation of Tao brush samples from patients with ovarian or endometrial cancer

[1045] A more direct and minimally invasive sampling of the intrauterine cavity (instead of the endocervical canal) may increase the sensitivity of this approach in detecting gynecological cancers. To explore this possibility, intrauterine samples were collected using a Tao brush, which is a flexible and narrow brush, covered by a retractable sheath that allows direct sampling of the entire endometrial cavity, without prejudice to the myometrium or contamination. cervical canal. It has been approved by the Food and Drug Administration for endometrial sampling and can be used on an outpatient basis without the need for anesthesia. Advantageous for a potential screening test, it is well tolerated by patients.

[1046] PapSEEK was applied to Tao brush samples collected from 123 patients with endometrial cancer, 51 patients with ovarian cancer and 125 women without cancer. Ninety-three percent of the Tao brush samples from patients with endometrial cancer contained genetic changes detected by PapSEEK, including 90% and 98% of patients with early and late stage disease, respectively (Fig. 22). The most commonly mutated genes in the Tao brush samples were PTEN (63%), TP53 (42%), PIK3CA (36%), PIK3R1 (20%), KRAS (17%), CTNNB1 (15%), FGFR2 (15%), RNF43

(11%), PPP2R1A (7%), POLE (7%), and FBXW7 (6%), similar to that observed in the Pap brush samples. The median MAF was 24.7% (95% CI: 21.3% to 26.9%), considerably higher than that observed in Pap brush samples, in which the median MAF was 4.0% (CI 95%: 3.5% to 4.5%; Table S4).

[1047] Genetic changes detectable by PapSEEK were found in 45% (95% CI: 31% to 60%) of Tao brush samples from 51 women with ovarian cancer, including 47% and 44% of patients in inCIial and late stages, respectively (Fig. 22). The most commonly mutated genes were TP53 (86%), consistent with the data from the Pap brush samples. The median MAF was 0.88% (CI 95%: 0.61% to 2.8%), higher than in the Pap brush samples (median 0.54%; CI95%: 0.4% to 0, 87%; Table 15).

[1048] PapSEEK was applied to Tao brush samples from 125 women without cancer. None (0%) of these women presented a positive result for mutations, producing a specificity of 100% (CI 95%: 97% to 100%; Fig. 20).

[1049] The Tao and Pap brush samples were available from the same women in 145 patients (103 with endometrial cancer and 42 with ovarian cancer). In endometrial cancers, PapSEEK was positive in 91% of Tao brush samples and in 81% of Pap brush samples (p = 0.02, McNemar test in medium P). Likewise, the fraction of patients with ovarian cancer with a positive PapSEEK test was higher in the Tao brush (45%) than in the Pap brush (17%; p = 0.002, McNemar test in the P medium; Table 13).

[1050] Tumor tissue was available in 90% and 88% of patients with endometrial and ovarian cancer who donated Tao brush samples, respectively. PapSEEK identified mutations in the conductive gene in 97% and 80% of endometrial and ovarian cancer tissues, respectively (Table 16). Of the patients with endometrial and ovarian cancer with a conductor mutation identified in their primary tumor, 93% and 42%, respectively, had detectable mutations in their Tao brush samples. On the other hand, of the positive Tao brush samples from patients with endometrial or ovarian cancer, 91% contained at least one genetic conductor mutation that was identical to that seen in their primary tumor. The fraction of samples from Tao brushes with mutations that were also found in primary tumors was higher in patients with endometrial cancer (97%) than in patients with ovarian cancer (53%). Evaluation of ctDNA in patients with ovarian cancer

[1051] Ovarian cancers that were inaccessible by Pap or Tao brush sampling due to anatomical or other factors can be detectable by circulating tumor DNA (ctDNA) in the plasma. This was tested on 83 ovarian cancer patients who donated Pap brush and plasma samples. Due to the smaller size of the degraded ctDNA, the primers were designed to amplify short fragments from 67 to 81 bp, covering 1,931 distinct nucleotide positions in 16 genes of interest. To demonstrate the specificity of this test, it was applied to plasma samples from 192 healthy individuals; none (0%) presented a positive result, with 100% specificity (95% CI: 98% to 100%).

[1052] 43% (95% CI: 33% to 55%) of the plasma of the 83 ovarian cancer patients were found to have detectable ctDNA. The detected mutations are listed in Table 17. As expected, sensitivity to plasma ctDNA was greater in patients with advanced than in early stage tumors (56% vs. 35%; Fig. 23). For early-stage disease, the median MAF in plasma was 0.85%, lower than the median MAF (5.7%) in Pap smears. At least one of the mutations identified in the plasma can be identified in 88% of the corresponding primary tumor.

[1053] In the Pap brush samples from this same cohort of 83 patients, 40% were positive by the PapSEEK test. Individuals who obtained positive results in the Pap brush and plasma samples only partially overlapped (Fig. 21). As a result, 63% (CI 95%: 51% to 73%) of the patients were positive in at least one of the two tests. Those with positive results included 54% of patients with early-stage disease and 75% with advanced-stage disease, respectively (Table 13, Fig. 23). Discussion

[1054] As described here, a multiplex PCR-based test (PapSEEK) was designed and applied to detect genetic changes in Pap brush or Tao brush samples. These samples are obtained in a minimally invasive and convenient way during routine visits to the office. Most endometrial cancers can be detected with PapSEEK: 93% with Tao brush and 81% with Pap brush. A substantial fraction of ovarian cancer can also be detected with PapSEEK: 45% with Tao Brush and 33% with Pap brush. The specificity of PapSEEK was high, with only 0% and 1.4% of women without a positive cancer test with Tao and Pap brush samples, respectively (Fig. 24). It has also been shown that assays for plasma ctDNA can be used in conjunction with Pap-SEEK in Pap smear samples, increasing the sensitivity of ovarian cancer detection to 63%.

[1055] It was notable that the sensitivity for detecting ovarian cancer at an early stage was as high as for the disease at an advanced stage (47% vs. 44% for Tao; 34% vs. 33% for Pap). Without wishing to be limited by theory, there are at least two possible explanations for this unexpected but attractive discovery. First, it has been shown that some types of ovarian cancer originate in

fallopian tubes, which could facilitate its early detection with Pap-SEEK when tumor cells are launched into the uterine cavity. Second, in advanced stage tumors, the fallopian tubes are often entangled and obliterated by the disease and are therefore less likely to serve as a conduit for tumor cells to pass into the uterus or endocervical canal. In this scenario, the addition of plasma ctDNA analysis to Pap or Tao brush sampling can be particularly beneficial.

[1056] A subset of samples tested here was composed of high-grade and early-stage cancers. Currently available diagnostic modalities have low sensitivity for these lesions (see, for example, Fishman et al., 2005 Am J Obstet Gynecol 192: 1214-1221; Sharma et al., 2012 Ultrasound Obstet Gynecol 40: 338-344; and Hamilton et al., 2006 British journal of cancer 94: 642-646). Although high-grade subtypes comprise only about 10% of incident brain cancers, they account for more than 40% of deaths from the disease (see, for example, Moore et al., 2011 Clin Obstet Gynecol 54: 278 -291). As these high-grade cancers usually arise from an atrophic endometrial fundus and can metastasize before abnormalities in the image, transvaginal ultrasound has a limited role in screening and early diagnosis. Thus, it was encouraging that PapSEEK detected 85% (n = 34) and 89% (n = 9) of high-grade endometrial cancers confined to the endometrium in the Pap and Tao brush samples, respectively. In the case of ovarian cancer, the tested cohort included only a small number of early and high-grade cases, consistent with the unfortunate fact that these types of cancer are usually diagnosed only in advanced stages. However, it is notable that 36% (n = 11) were positive in the combined tests of Pap and plasma samples, and that 80% (n = 5) were positive in Tao brush samples.

[1057] The study described here was retrospective. The samples examined were derived from patients with known cancer, although a substantial fraction were from patients with early-stage lesions. In a screening scenario, cancers would be advantageously at an early stage and the sensitivities for detection would be closer to the sensitivity for early-stage cancers observed in the present study. In addition, the age groups of controls and cases are typically more compatible in a prospective study than in the present retrospective study. Some of the ovarian cancer patients who had detectable mutations in their Pap brush or Tao brush samples did not show identical mutations in their primary tumors. This was not a problem with endometrial cancers, in which at least one mutation in the brush samples was almost always (97%) found in the corresponding primary tumors. But this phenomenon has been observed in patients with ovarian cancer, especially with the Tao brush. At least one identifiable mutation in the Pap brush can be identified in 73% of the corresponding primary ovarian tumors, while the same occurred in only 53% of Tao brush samples.

[1058] Without wishing to be limited by theory, a possible explanation for the disagreement between the mutations in brush samples and ovarian cancer from the same patients is that the assay detects mutations that do not exist in vivo, representing technical artifacts. This is not believed to be likely, however, given that the specificity of the trials was 100% and 99% on samples of Tao brush and Pap brush, respectively, from women without cancer. Another possible explanation is the heterogeneity of the tumor. Only a small part of the tumors analyzed was sampled and sequenced, and the additional mutations found in the Pap smear or in the intra-oral samples

uterine tumors may represent mutations in other parts of the tumor. It is also possible that some mutations are from small synchronous endometrial cancers or early premalignant endometrial lesions that were not noticed by the pathologist. A significant proportion of women with ovarian cancer have synchronous endometrial cancer, with risk factors, including Lynch syndrome, polycystic ovary syndrome, perimenopause, obesity, nulliparity and unopposed estrogen replacement therapy (see, for example, Al Hilli et al., 2012 Gyne-cologic oncology 125: 109-113; Walsh et al., 2005 Obstetrics and gyne-cology 106: 693-699; Zaino et al., 2001 Gynecologic oncology 83: 355- 362; and Song et al., 2014 Int J Gynecol Cancer 24: 520-527).

[1059] Although tumor heterogeneity or multiple synchronous tumors are viable explanations that are often used to explain disagreements in liquid biopsy studies, without wishing to be bound by theory, it is possible that clonal expansions of non-malignant cells may play a role. a role in these observations. Clonal proliferations that are not considered neoplastic have been described in uterine lavage, bone marrow, skin and other tissues (see, for example, Steensma et al., 2015 Blood 126: 9-16; Coombs et al., 2017 Cell Stem Cell 21 (3): 374-382; Young et al., 2016 Nat Commun 7: 12484; Krimmel et al., 2016 Proc Natl Acad Sci USA 113: 6005-6010; and Nair et al., 2016 PLoS Med 13: e1002206 ). Of particular interest are the clonal proliferations of endometrial cells that cause endometriosis, a sometimes debilitating condition that affects millions of women. Recently, it has been shown that these lesions, which can occur throughout the abdomen and are derived from the endometrium, are clonal proliferations that can be caused by the same mutations detected in the endometrial cancers (see, for example, Anglesio et al. , 2017 N Engl J Med 376: 1835-1848). Without wishing to be bound by theory, it is possible that hormonal and physiological changes that contribute to or result from ovarian cancer stimulate or select such clonal proliferations in the endometrial lining. On the one hand, this possibility argues against the exquisite specificity that is the conceptual basis for all liquid biopsies. On the other hand, it could actually increase the sensitivity of the detection of ovarian cancer, without decreasing the specificity, if large clonal proliferations are almost exclusively found in women with gynecological neoplasms. Clonal proliferations that represent> 0.03% of the total cells in the endometrial lining are detectable by the methods provided here. Example 5: Detecting urological malignancies using a combined approach to cancer screening tests

[1060] According to the American Cancer Society, it is estimated that

79,030 new cases of bladder cancer (BC) and 18,540 deaths occur in the United States alone in 2017 (see, for example, Siegel et al., 2017 CA Cancer J Clin 67: 7-30), with many patients with BC multiple recurrences before progression, providing ample waiting time for early detection and treatment before metastasis (see, for example, Netto, 2013, Anv Path 20: 175-203). Cytology and cystoscopy in urine with transurethral biopsy (TURB) are currently the gold standard for the diagnosis and monitoring of bladder cancer. Although urine cytology is valuable for detecting high-grade neoplasms, it is unable to detect the vast majority of low-grade tumors (see, for example, Netto et al., 2016 Urol Clin North Am 43:63 -76; Lotan et al., 2003 Urology 61: 109-18; and Zhang et al., 2016 Cancer Cytopathol 124: 552-564). This fact, together with the high cost and invasive nature of repeated cystoscopy and TURB procedures, has led to many attempts to develop new strategies.

non-invasive technologies, including urine or serum-based genetic and protein assays for screening and surveillance (see, for example, Kawauchi et al., 2009 Hum Pathol 40: 1783-1789; Kruger et al., 2003 Int J Oncol 23: 41-48; Skacel et al., 2003 J Urol 169: 2101-2105; Sarosdy et al., 2006 J Urol 176: 44-47; Moonen et al., 2007 Eur Urol 51: 1275-80; Fradet et al., 1997 Can J Urol 4: 400-405; Yafi et al., 2015 Urol Oncol 33: 66.e25-66.e31; Serizawa et al., 2010 Int J Cancer 129 (1): 78-87 ; Kinde et al., 2013 Cancer Res 73: 7162-7167; Hurst et al., 2014 Eur Urol 65: 367-369; Wang et al., 2014 Oncotarget 5: 12428-12439; Ralla et al., 2014 Crit Rev Clin Lab Sci 51: 200-231; Ellinger et al., 2015 Expert Rev Mol Diagn 15: 505-516; Bansal et al., 2014 Clin Chim Acta 436: 97-103; Goodison et al., 2012 PLoS One 7: e47469; and Allory et al., 2014 Eur Urol 65: 360-366). The trials currently approved by the US Food and Drug Administration (FDA) include the ImmunoCyt test (Scimedx Corp), nuclear matrix protein immunoassay 22 (NMP22) test (Matri-tech) and multi-target FISH (UroVysion) (see, for example, Kawauchi et al., 2009 Hum Pathol 40: 1783-1789; Kruger et al., 2003 Int J Oncol 23: 41-48; Skacel et al., 2003 J Urol 169: 2101-2105; Sarosdy et al., 2006 J Urol 176: 44-47; Moonen et al., 2007 Eur Urol 51: 1275-80; Fradet et al., 1997 Can J Urol 4: 400-405; and Yafi et al., 2015 Urol Oncol 33: 66.e25-66.e31). Sensitivities between 62% and 69% and specificities between 79% and 89% have been reported for some of these tests; however, due to inconsistencies in the performance of the test, cost or necessary technical knowledge, the integration of these tests in routine clinical practice has not yet occurred.

In addition, as urine cytology is relatively insensitive to detecting recurrence, cystoscopies are performed every three months in these patients in the USA, and the cost of managing these patients is higher than the cost of handling any other type of cancer and equals $ 3 billion annually (see, for example, Netto et al., 2010 Pathology 42: 384-394).

[1061] Furthermore, the annual incidence of these upper tract urothelial carcinomas (UTUCs) in western countries is 1-2 cases per

100,000, but occurs at a much higher rate in populations exposed to aristolochic acid (AA) (Chen et al., 2012 Proc Natl Acad Sci USA, 109 (21): 8241-8246; and Grollman et al., 2013 Environ Mol Mutagen, 54 (1): 1-7; and Lai et al., 2010 J Natl Cancer Inst, 102 (3): 179-186). Nephroureterectomy can be curative for patients with UTUC when detected at an early stage (Li et al., 2008 Eur Urol. 54 (5): 1127- 34). However, these cancers are largely silent until the appearance of overt clinical symptoms, usually hematuria, and as a result, most patients are diagnosed only at an advanced stage (Roupret et al., 2015 Eur Urol. 68 (5 ): 868-79). Diagnostic tests for the detection of UTUC at an early stage are not currently available.

[1062] A widely applicable approach to non-invasive cancer detection (for example, early-stage cancer, such as BCs or UTUCs) may be medically and economically important.

[1063] This example describes a new blood test, called UroSEEK, which addresses the problematic issues described above. In this test, DNA from urine samples can be used in an assay (for example, a PCR-based multiplex test) to simultaneously evaluate genetic changes that usually occur in BCs or UTUCs. An outline of the approach used in the study of bladder cancer is provided in Fig. 25, and an outline of the approach used in the UTUC study is provided in Fig. 31. Materials and Methods Patients and samples

[1064] Urine samples were prospectively collected from patients at four participating institutions, including Johns Hopkins Hospital, Baltimore, MD, USA; AC Camargo Cancer Center, São

Paulo, Brazil; Osaka University Hospital, Osaka, Japan; and Hacettepe University Hospital, Ankara, Turkey. The study was approved by the Institutional Review Boards of Johns Hopkins Hospital and all other participating institutions. Adequate material transfer agreements have been reached. Patients with a known history of malignancy other than bladder cancer were excluded from the study. The study included two cohorts of patients.

[1065] The early detection cohort comprised 570 patients who were referred to a urology clinic at one of the hospitals above because of hematuria or lower urinary tract symptoms (Table 18). The second cohort (322 patients) represented patients with a previous established diagnosis of Bladder Cancer (BC) who are under surveillance for disease recurrence (Surveillance Cohort). The primary tumors of these patients harbored mutations in at least one of the 11 genes assessed through multiplex or singleplex assays. A minimum follow-up of 12 months from the date of urine collection was required for cases without evidence of incident or recurrent tumors in the early detection or surveillance cohorts, respectively. Urine samples were collected before any procedure, such as cystoscopy, performed during patient visits. A total of 892 urine samples were analyzed in the study, composed of two types of samples. The first were residual urinary cells after processing with standard BD SurePath ™ liquid-based cytological protocols (Becton Dickinson and Company; Franklin Lakes, NJ, USA). To allow for standard care, SurePath® waste fluids were kept refrigerated for 6-8 weeks prior to submission for DNA purification to allow for any potential cytology repeated processing needs of the same sample. The second type of sample was composed of fresh urine samples with bio-bank, in which 15 - 25 mL of urine samples were stored at 4 ° C for up to 60 minutes before centrifugation (10 minutes at 500 g) and pellets stored at less than 80 ° C before DNA purification. Urine from 188 healthy individuals, with an average age of 26 years, was also obtained and processed in the same way in bio-deposited fresh urine samples.

[1066] Samples of tumor tissue fixed in formalin embedded in paraffin (FFPE) from trans-urethral resections (TURB) or cystectomies were collected in 413 of 892 cases. When several different tumors from the same patient were available (due to recurrences), the oldest tumor tissue obtained after donating the urine sample was used in the Early Detection Cohort. In the surveillance cohort, tumors that preceded the donation of the urine sample were used in 146 of the 322 patients. In the other 176 surveillance cases, the oldest tissue obtained after the donation of the urine sample was used. A genitourinary pathologist reviewed all the histological slides to confirm the diagnosis and select a representative area of the tumor with the highest possible tumor cellularity for this case. The corresponding FFPE blocks were stained with a 16 gauge sterile needle. One to three nuclei were obtained by tumor and placed in sterile 1.5 ml tubes for DNA purification, as described elsewhere (see, for example, Kinde et al., 2013 Cancer Res 73: 7162-7167). Electronic medical records were reviewed to obtain medical history and follow-up data in all patients. UTUC cohort studied

[1067] Sequential UTUC patients scheduled to perform a unilateral radical nephroureterectomy at the National Hospital of the University of Taiwan in 2012-2016 were invited to participate in the study. All patients provided informed consent using the consent form and study design reviewed and approved by the Institutional Review Committees at National Taiwan University and Stony Brook University. A total of 56 UTUC patients were included in the study after the exclusion of four patients with macroscopic hematuria and one patient with a tumor-urine DNA incompatibility by the identity test. DNA from urinary cells from 188 urine samples donated by healthy individuals in the USA, with an average age of 40 years, aged between 19 and 60 years, was used to assess the specificity of the UroSEEK test. White blood cell (WBC) DNA from 94 normal US individuals was used to assess the technical specificity of PCR analysis. Biological samples - UTUC cohort

[1068] Urine samples were obtained from patients the day before surgery. Urinary cells were isolated by centrifugation at 581g for 10 minutes at room temperature, washed three times in saline using the same centrifuge conditions and stored frozen until DNA was isolated using a Qiagen kit # 937255 (Ger- mantown, MD). DNA was purified from freshly resected frozen samples of tumors of the upper tract and renal cortex by standard phenol-chloroform extraction procedures, as described elsewhere (see, for example, Chen et al., 2012 Proc Natl Acad Sci USA, 109 (21): 8241-8246; and Jelakovic et al., 2012 Kidney Int. 81 (6): 559-67). One upper urinary tract tumor per patient was analyzed; in cases with tumors in multiple sites, pelvic renal tumors were preferably selected whenever available. Samples of tumors fixed in formalin and embedded in paraffin were prepared and classified by a urological pathologist, and the presence of one or more urothelial carcinomas of the upper tract was confirmed by histopathology for each enrolled individual. Relevant clinical and demographic data were obtained by reviewing the records of each individual. EGFR was calculated using the MDRD equation

(see, for example, Levey et al., 2006 Ann Intern Med. 145 (4): 247-54) and used to determine the stage of CKD (see, for example, Levey et al., 2005 Kidney Int. 67 ( 6): 2089-100). DNA adduct analysis

[1069] The levels of AL-DNA adduct (7- (deoxyadenosine-N6-yl) aristolactam I; dA-AL-I) in 2 µg of DNA from the normal renal cortex of UTUC patients were quantified by liquid chromatography high efficiency - electrospray ionization / multistage mass spectrometry (UPLC-ESI / MSn) with a linear quadrupole ion trap mass spectrometer (LTQ Velos Pro, Thermo Fisher Scientific, San Jose, CA) as described elsewhere places (see, for example, Yun et al., 2012 Chem Res Toxicol. 2012 25 (5): 1119-31). Mutation analysis

[1070] Three separate assays were used to look for abnormalities in urinary cell DNA. First, a multiplex PCR was used to detect mutations in regions of ten genes commonly mutated in urological cancers CDKN2A, ERBB2, FGFR3, HRAS, KRAS, MET, MLL, PIK3CA, TP53 and VHL (see, for example, Netto, 2011 Nat Rev Urol 9: 41-51; Mo et al., 2007 J Clin Invest 117: 314-325; Sarkis et al., 1993 J Natl Cancer Inst 85: 53-59; Lin et al., 2010 Urol Oncol 28: 597- 602; Sarkis et al., 1994 J Urol 152: 388-392; Sarkis et al., 1995 J Clin Oncol 13: 1384-1390; Wu, 2005 Nat Rev Cancer 5: 713-725; and Cancer Genome Atlas Research Network, 2014 Nature 507: 315-322). The pairs of primers used for this multiplex PCR were divided into a total of three multiplex reactions, each containing non-overlapping amplifiers (see below). These primers were used to amplify DNA in 25 µL reactions, as described elsewhere (see, for example, Kinde et al., 2011 Proc Natl Acad Sci USA 108: 9530-9535), except that 15 cycles were used to the initial amplification. Second, the DETERT promoting region was evaluated. A single amplification primer was used to amplify a 73 bp segment containing the TERT promoter region known to harbor mutations in the BC (see, for example, Kinde et al., 2013 Cancer Res 73: 7162-7167). The conditions used to amplify it were the same as those used in the multiplex reactions described above, except for the use of Phusion GC buffer (Thermo-Fisher) instead of HF buffer and 20 cycles for the initial amplification. The TERT promoter region could not be included in the multiplex PCR due to the high GC content of the former. The PCR products were purified with AMPure XP beads (Beckman Coulter, PA, USA) and 0.25% of the purified PCR products (multiplex) or 0.0125% of the PCR products (TERT singleplex) were amplified in a second round of PCR, as described elsewhere (see, for example, Wang et al., 2016 Elife 5: 10.7554 / eLife.15175). The PCR products from the second round of amplification were then purified with AMPure and sequenced in an Illumina instrument. For each identified mutation, the frequency of the mutant allele (MAF) was determined by dividing the number of readings identified exclusively with mutations (see, for example, Kinde et al., 2011 Proc Natl Acad Sci USA 108: 9530-9535) by number of readings identified uniquely total. Each DNA sample was evaluated on two independent PCRs, both for the TERT promoter and for multiplex assays, and the samples were scored as positive only if both PCRs showed the same mutation. The frequencies of mutant alleles and the number of UIDs listed in Table 19, Table 20, Table 22 and Table 23 refer to the average of the two independent assays.

[1071] To assess the statistical significance of putative mutations, the white blood cell DNA of 188 normal, unrelated individuals was assessed. A variant seen in samples from cancer patients was only classified as a mutation if seen in one

MAF much higher than that observed in normal WBCs. Specifically, the classification of a sample's ctDNA status was based on two complementary criteria applied to each mutation: 1) the difference between the mean MAF in the sample of interest and the corresponding maximum MAF observed for the same mutation in a set of controls and 2) the Stouffer Z score obtained by comparing the MAF in the sample of interest with a distribution of normal controls. To calculate the Z score, the MAF in the sample of interest was first normalized based on the specific distributions of MAF mutations observed among all controls. After this specific normalization of the mutation, a P value was obtained by comparing the MAF of each mutation in each well with a reference distribution of the MAFs built from normal controls, where all mutations were included. Stouffer's Z score was then calculated from the p values of two wells, weighted by the number of UIDs. The sample was classified as positive if the difference or the Stouffer Z score of its mutations was above the thresholds determined from normal WBCs. The threshold for the difference parameter was defined by the highest MAF observed in all normal WBCs. The threshold for Stouffer's Z score was chosen to allow a false positive among the 188 normal urine samples studied. Analysis of aneuploidy.

[1072] Aneuploidy was evaluated with Fast-SeqS, which uses a single pair of primers to amplify ~ 38,000 loci spread across the genome (see, for example, Kinde et al., 2012 PLoS One 7: e41162). After massively parallel sequencing, the gains or losses of each of the 39 chromosomal arms covered by the assay were determined using a bespoke statistical learning method. A support vector machine (SVM) was used to discriminate between aneuploid and euploid samples. SVM was trained using 3150 synthetic aneuploid samples of low neoplastic cell fraction and 677 euploid peripheral white blood cell (WBC) samples. The samples were scored as positive when the aneuploidy score in the entire genome was> 0.7 and there was at least one gain or loss in one chromosomal arm. Identity checks.

[1073] A multiplex reaction containing 26 primers that detect 31 common SNPs on chromosomes 10 and 20 was performed using the amplification conditions described above for multiplex PCR. The primers used for this identity assessment are listed in Fig. 34 (Table 42) for bladder cancer cohorts and Fig. 33 (Table 41) for UTUC cohorts. Statistical analysis

[1074] The performance characteristics of urinary cytology, Uro-SEEK and its three components were calculated using the statistical software MedCalc (medcalc.org/calc/diagnostic_test.php). Results Characteristics of the early detection cohort.

[1075] A flow diagram indicating the number of patients evaluated in this study and the main results is provided in Fig. 26.

[1076] A total of 570 patients were included in the early detection cohort, each with a analyzed urine sample. 90% of the patients had hematuria, 3% had symptoms of the lower urinary tract (LUTS) and 9% had other indications suggesting that they were at risk for BC. The median age of the participants was 58 years (range 5 to 89) (Table 18). 70% of the patients were male. 175 (31%) of the patients developed BC after an average follow-up period of 18 months (range 0 to

40 months). For each patient who developed BC, two other patients who had similar symptoms but did not develop BC during the follow-up period were selected. By design, then, the fraction of cases in this developing cohort of BC was higher than the fraction (5%) of patients with similar presentations who would have developed BC in standard clinical practice. The characteristics of the developing tumors in the 570 patients are summarized below. Demographic, clinical and genetic characteristics of the early detection cohort. UroSEEK or Multiplex positive TERT Aneuploidy UroSEEK Cytology Gender n% cytology has ten genes positive positive positive positive * tive * Men without recurrence 172 59% 3 (2%) 10 (6%) 2 (1%) 13 (8%) 0 (0%) 13 (8%) Men with recurrence 32 11% 26 (81%) 21 (66%) 19 (59%) 29 (91%) 16 (50%) 30 (94%) Women without recurrence 81 28% 2 (2%) 2 (2%) 1 (1%) 5 (6%) 0 (0%) 5 (6%) Women with recurrence 9 3% 4 (44%) 4 (44%) 3 ( 33%) 6 (67%) 1 (11%) 6 (67%) Indication Hematuria without recurrence 346 61% 6 (2%) 15 (4%) 5 (1%) 22 (6%) 0 (0%) 17 (5%) Hematuria with recurrence 163 29% 108 (66%) 90 (55%) 76 (47%) 134 (82%) 18 (11%) 32 (2%) LUTS without recurrence 11 2% 0 (0 %) 2 (18%) 0 (0%) 2 (18%) 0 (0%) 2 (18%) LUTS with recurrence 3 1% 2 (67%) 1 (33%) 0 (0%) 2 ( 67%) 1 (33%) 2 (67%) Others without recurrence 38 7% 1 (3%) 0 (0%) 1 (3%) 2 (5%) 0 (0%) 2 (5%) Others with recurrence 9 2% 9 (100%) 8 (89%) 5 (56%) 9 (100%) 2 (22%) 9 (100%) Diagnosis of tumor detected PUNLMP 2 1% 0 (0%) 1 (50%) 0 (0%) 1 (50%) 0 (0%) 0 (0%) CIS 7 5% 4 (57%) 4 (57%) 1 (14%) 6 (86%) 3 (43%) 6 (86%) LGTCC 31 21% 15 (48%) 18 (58%) 9 (29%) 22 (71%) 0 (0%) 4 (13%) HGTCC 49 33% 34 (69%) 28 (57%) 26 (53 %) 40 (82%) 4 (8%) 11 (22%) INTCC 61 41% 48 (79%) 36 (59%) 35 (57%) 57 (93%) 9 (15%) 16 (26% ) Cytological diagnosis * Positive 21 6% 16 (76%) 12 (57%) 16 (76%) 20 (95%) N / AN / A Atypical 105 30% 21 (20%) 21 (30%) 12 (11 %) 30 (29%) N / AN / A Negative 221 64% 4 (2%) 9 (4%) 1 (0.4%) 12 (5%) N / AN / A * Cytology was available in only a subset of cases. N / A: Not available Genetic analysis in bladder cancer cohorts.

[1077] Three separate tests were carried out for genetic abnormalities that can be found in BC-derived urinary cells (Fig. 26). First, the mutations were evaluated in selected regions of ten genes that were shown to be frequently altered in urothelial tumors (Table 19). To that end, a specific set of primers was designed to allow the detection of mutations in only 0.03% of urinary cells. The ability to detect these low mutant fractions was a result of the incorporation of molecular barcodes in each of the primers, substantially reducing the artifacts associated with massively parallel sequencing. Second, TERT promoter mutations were evaluated. A singleplex PCR was used for this analysis because the unusually high GC content of the TERT promoter prevented its inclusion in the multiplex PCR project. Third, the extent of aneuploidy was assessed using a technique in which a single PCR is used to co-amplify ~

38,000 members of a subfamily of the long-1 interlaced nucleotide element (L1 retrotransposons, also called LINEs). L1 retro-transposons, like other human replications, have spread throughout the genome via retrotransposition and are found in all 39 autosomal non-acrocentric arms.

[1078] The multiplex assay detected mutations in 68% of the 175 urinary cell samples from individuals who developed BC during the course of this study (95% CI, 61% to 75%) (Table 19). A total of 246 mutations were detected in 8 of the 10 target genes (Fig. 27A and Table 19). The average frequency of the mutant allele in urinary cells with detectable mutations was 18% and ranged from 0.17% to 99%. The most commonly altered genes were TP53 (45% of the total mutations) and FGFR3 (20% of the total mutations; Fig. 27A). At the thresholds used, 1.7% of the 395 patients in the Early Detection Cohort who did not develop BC during the course of the study had a detectable mutation in any of the ten genes. Within the same limits

ares, none of the 188 urinary cell samples from healthy individuals showed any mutation in any of the ten genes analyzed (100% specificity, 95% CI 98% to 100%).

[1079] TERT promoter mutations were detected in 57% of the 175 urinary cell samples from patients who developed cancer during the study interval (95% CI 49% to 64%; Table 20). The average frequency of the TERT mutant allele in urinary cells was 14% and ranged from 0.18% to 78%. The mutations were detected in three positions: 98% of the mutations were in hg1295228 (79%) and hg 1295250 (19%), which are 66 and 88 bp upstream of the initial transcription site, respectively. These positions have previously been shown to be involved in the appropriate transcriptional regulation of TERT. In particular, the mutant alleles recruit the GABPA / B1 transcription factor, resulting in the H3K4me2 / 3 tag of the active chromatin and reversing the epigenetic silencing present in normal cells. 4% of the 395 patients in this cohort who did not develop BC during the course of the study had a detectable mutation in the TERT promoter. Only one of the 188 urine samples from healthy individuals had a mutation in the TERT promoter.

[1080] Aneuploidy was detected in 46% (95% CI 39% to 54%) of the 175 urinary cell samples from patients who developed BC during the course of the study (Table 20 and Table 21). The most commonly altered arms were 5q, 8q and 9p. All three of these arms harbor known genes for oncogenes and tumor suppressors, which have been shown to change copy number in many cancers, including BC. 1.5% of urinary cell samples from 395 patients who did not develop BC during the course of the study exhibited aneuploidy. None of the 188 urinary samples from healthy individuals had aneuploidy when evaluated using the same technology.

Comparison with primary tumors

[1081] Tumor samples from 102 of the patients included in this cohort were available for comparison and were studied with the same three assays used to study urinary cell samples (Table 20). In 91 (89%) of these 102 cancers, at least one mutation in the eleven genes studied was mutated (in the panel of 10 genes or in the TERT promoter). In addition, at least one of the mutations identified in the urine samples of these 102 patients was also identified in 83% of the corresponding BC (Table 19 and Table 20). The BC analysis also shed light on the basis of "false negatives", that is, the reason why 21% of urine samples from patients who developed BC did not show detectable mutations in the 11 tested genes. The reason could have been that the corresponding BC did not harbor a mutation in these 11 genes or that it did, but the fraction of neoplastic cells in the urine sample was not high enough to allow its detection with the assays used. At least one mutation in at least one of the 11 genes in 62% of primary tumors was identified in patients with false negative urine tests for mutations (Table 22 and Table 23). The results indicate that 38% of the 29 false negative tests for mutations were due to the fact that none of the consulted mutations were present in the tumor and that the other 62% of the false negatives were due to insufficient amounts of cancer cells in the urine. UroSEEK: biomarkers in combination.

[1082] As noted above, the ten gene multiplex assay, the TERT singleplex assay and the aneuploidy assays produced sensitivities of 68%, 57% and 46%, respectively, when used separately (Table 19, Table 20 and Table 21). 45 samples without mutations in the TERT promoter can be detected by mutations in one of the other ten genes (Fig. 28A and Table 19). On the other hand, 35 samples without detectable mutations in the multiplex assay can be detected due to mutations in the TERT promoter (Fig. 28A and Table 20). Ten of the urine cell samples with no detectable mutations in the 11 genes could be detected by the aneuploidy assay (Fig. 28A and Table 21). Thus, when the three tests were used together (test called "UroSEEK"), and a positive result in any of the tests was sufficient to classify a sample as positive, the sensitivity increased to 83% (95% CI 76% at 88%). Only one of the 188 samples from healthy individuals was scored as positive by the UroSEEK (99.5% specificity, CI 97% to 100%). Twenty-six (6.5%) of the 395 patients in this cohort who did not develop BC during the course of the study had a positive result by the UroSEEK test (specificity 93%, CI 91% to 96%). On average, UroSEEK positivity preceded the diagnosis of BC by 2.3 months and in eight cases by more than one year (Fig. 29A and Table 18). UroSEEK plus cytology

[1083] Since cytological and UroSEEK tests are non-invasive and can be performed on the same urine sample, their performance in combination has been assessed. There were 347 patients in the early detection cohort in which cytology was available (Table 18). Among the 40 patients who developed biopsy-proven cancer in this cohort, 17 were positive by cytology (43% sensitivity). None of the 299 patients who did not develop cancer were positive by cytology (100% specificity). UroSEEK was positive in 100% of the 17 cytology positive urine cancer patients and in 95% of the 23 cytology negative urine cancer patients. Thus, in combination, the UroSEEK plus cytology provided 95% sensitivity (95% CI 83% to 99%), an increase of 12% over the UroSEEK and an increase of 52% over the cytology. Among the 299 patients in the early detection cohort who did not develop BC during the course of the study, 20 (6.6%) were positive by UroSEEK or cytology, giving the combination of UroSEEK and cytology a specificity of 93% ( 95% CI 90% to 96%). Characteristics of the surveillance cohort

[1084] The surveillance strategy was different from that used for early detection. Patients in whom a BC has been surgically excised for treatment and diagnosis usually have tumor tissue available, and in most of these tumors, a mutation can be identified. For example, it was discovered during the course of this study that a mutation in at least one of the 11 genes surveyed was present in 95.2% of the assessed BCs. All patients selected for the surveillance study had BC confirmed by biopsy and had a urine sample collected from 0 to 5 years after surgery. 322 patients who donated urine and whose BC contained a mutation in at least one of the 11 analyzed genes were evaluated. It was determined whether a single urine sample taken a relatively short time after surgical excision of the BC could reveal residual disease in these 322 patients, as evidenced by subsequent recurrence. 187 (58%) of the 322 patients developed clinically evident BC after an average follow-up period of 10.7 months (range 0 to 51 months). The histopathological types and tumor stages of these patients are summarized below and in detailed Table 24. The average age of the participants was 62 years (range, 20 to 93). As expected by the BC demographics, 75% of the patients were male. Demographic, clinical and genetic characteristics of the surveillance cohort. Multiplex posi- TERT UroSEEK Cytology or Gender n% of ten Positive aneuploidy UroSEEK positive positive positive * positive cytology * genes Men without recurrence 59 30% 3 (5%) 8 (14%) 3 (5%) 10 (17%) 0 (0%) 8 (14%) Men with recurrence 90 45% 45 (50%) 53 (59%) 20 (22%) 59 (66%) 20 (22%) 53 (59%) Women without recurrence 17 9% 5 (29%) 3 (18%) 0 (0%) 6 (35%) 0 (0%) 6 (35%) Women with recurrence 33 17% 15 (45%) 19 (58%) 11 ( 33%) 33 (100%) 6 (18%) 19 (58%)

Original PUNLMP Tumor Diagnosis 12 4% 5 (42%) 2 (17%) 1 (8%) 6 (50%) 0 (0%) 2 (17%) CIS 25 8% 11 (44%) 13 (52%) 6 (24%) 14 (56%) 5 (20%) 10 (40%) LGTCC 107 35% 27 (25%) 34 (32%) 8 (7%) 41 (38%) 0 ( 0%) 59 (55%) HGTCC 62 20% 22 (36%) 24 (39%) 10 (16%) 30 (49%) 4 (7%) 16 (26%) INTCC 104 34% 39 (38% ) 47 (45%) 29 (28%) 54 (52%) 20 (19%) 34 (33%) Original Stage of Tumor pTis 25 8% 11 (44%) 13 (52%) 6 (24%) 14 (56%) 5 (20%) 10 (40%) pTa 181 58% 54 (30%) 60 (33%) 19 (19%) 77 (43%) 4 (2%) 77 (43%) pT1 71 23% 28 (39%) 35 (49%) 22 (31%) 39 (55%) 14 (20%) 23 (32%) pT2 23 7% 9 (9%) 9 (39%) 7 (30% ) 12 (52%) 5 (22%) 10 (43%) pT3 9 3% 1 (11%) 2 (22%) 0 2 (22%) 1 (11%) 1 (11%) pT4 1 0, 3% 1 (100%) 1 (100%) 0 1 (100%) N / AN / A Routine cytological diagnosis * Positive 30 15% 21 (21%) 25 (83%) 20 (67%) 27 (90%) N / AN / A Atypical 95 48% 38 (40%) 43 (45%) 18 (19%) 50 (53%) N / AN / A Negative 71 36% 12 (17%) 13 (18 %) 3 (4%) 19 (27%) N / AN / A * Cytology was available in only a subset of ca SOS. N / A: Not available Surveillance cohort genetic analysis

[1085] The multiplex urinary cell assay detected mutations in 49% of urinary cell samples from patients who developed recurrent BC during the study interval (95% CI 45% to 60%; Table 24 and Table 25). The average frequency of the mutant allele in the urinary cells with detectable mutations was 16% and ranged from 0.08% to 93%. The most commonly altered genes were FGFR3 (43% of 134 mutations) and TP53 (30% of 134 mutations; Fig. 27B). Seven percent of the 135 patients who did not develop recurrent BC during the course of the study had a detectable mutation in their urinary cell sample (these are considered to be false positives; see Discussion). The average interval between a positive multiplex test and the diagnosis of recurrent BC was 7 months (range 0 to 51 months).

[1086] Mutations in the TERT promoter were detected in 51% of urinary cell samples from patients who developed recurrent BC during the study interval (95% CI 44% to 58%; Table 26). The average frequency of the TERT mutant allele in urinary cells with detectable mutations was 6% and ranged from 0.23% to 43%. The changes were detected in the same three positions as observed in the urine cells of the early detection cohort. 10% (95% CI 83% to 94%) of the 135 patients who did not develop recurrent BC during the course of the study had a detectable mutation of the TERT promoter in their urine sample (false positives). The average interval between a positive TERT test and the diagnosis of recurrent BC was 7 months (range 0 to 40 months).

[1087] Aneuploidy was detected in 30% (95% CI, 24% to 37%) of urinary cell samples from patients who developed recurrent BC during the course of the study (Table 27). The most commonly altered arms were 8p, 8q and 9p, as in the Early Detection cohort. Two percent of the 135 patients who did not develop recurrent BC during the course of the study exhibited aneuploidy in at least one of their urinary cell samples. Combined markers - Surveillance cohort

[1088] As noted above, the ten gene multiplex assay, the TERT singleplex assay and the aneuploidy assays produced sensitivities of 49%, 51% and 30%, respectively, when used separately (Table 25, Table 26 and Table 27). Thirty-two samples without mutations in the TERT promoter can be detected by mutations in one of the other ten genes (Fig. 28B and Table 25). On the other hand, 41 samples with no detectable mutations in the multiplex assay can be detected due to mutations in the TERT promoter. Three of the urine cell samples without detectable mutations can be detected by the aneuploidy assay. Thus, the sensitivity of Uro-SEEK was 66% (95% CI 59% to 73%). Fourteen percent of the 135 patients in this cohort who did not develop BC during the course of the study obtained positive results by the UroSEEK test, producing a specificity of 86% (95% CI 77% to 91%). On average, UroSEEK positivity preceded the diagnosis of BC by 7 months and in 47 cases by more than one year (Fig. 29B and Table 24).

[1089] There were 196 patients in the Surveillance cohort for whom cytology was available (Table 24). Among the 120 patients who developed recurrent BC in this cohort, 30 (25%) were positive by cytology. On the other hand, no positive cytological results were observed in patients whose tumors did not recur. UroSEEK was positive in 90% of patients with recurrent BC whose urine was positive by cytology and in 61% of 90 patients with recurrent BC whose urine was negative by cytology. Thus, in combination, the UroSEEK plus cytology provided 71% sensitivity (95% CI, 61.84% to 78.77) (Fig. 28D and Table 22). Among the 76 patients who did not develop recurrent BC during the course of the study and in which cytology was available, 18% had a positive score for both cytology and UroSEEK, providing 82% specificity (95% CI 71% a 90%;) Low vs. urothelial neoplasms high degree in the early detection and surveillance cohorts

[1090] The advantage of UroSEEK over cytology was particularly evident in low-grade tumors (low-malignant potential urethral papillary neoplasms and low-grade noninvasive papillary carcinomas). There were a total of 49 low-grade tumors evaluated in this study, in which cytology was available (six from the early detection cohort and 43 from the surveillance cohort). None of these low-grade tumors were detected by cytology (sensitivity 0%; 95% CI 0.0% to 6.7%). On the other hand, UroSEEK detected 67% (95% CI 51% to 81%) of low grade tumors (identical rate of 67% in the two cohorts; Fig. 30). Similarly, there were a total of 102 high-grade tumors (in situ urothelial carcinoma, high-grade noninvasive papillary carcinoma or infiltrated high-grade urothelial carcinoma) evaluated in this study, in which cytology was available (34 from the Detection) and 68 in the Surveillance cohort). Cytology was positive in 45% of these patients (50% and 41% in the Early Detection and Surveillance cohorts, respectively), while the UroSEEK was positive in 80% of them (100% and 71% in the Early Detection and Surveillance cohorts. , respectively; see below. Cytology vs. UroSEEK performance summary. UroSEEK Cytology Diagnosis Negative test Result sensitivity n Positive test Negative test Sensitivity 95% CI Positive test 95% CI biopsy test PUN- 0.00 % at 49 0 49 0% 33 16 67% 54.36% at 79.38% LMP / LGTCC 6.06% CIS / HGTCC / I 33.63% at 102 46 56 45% 82 20 80% 71.03% a 86.39% NTCC 52.21% Total 151 Characteristics of the UTUC cohort

[1091] Thirty-two women and twenty-four men aged 39 to 85 years participated in the study (see below; individual data are in Table 28). This gender distribution, atypical in UTUC patients in western countries where men predominate (Shariat et al., 2011 World J Urol. 29 (4): 481-6), is consistent with previous epidemiological studies of individuals from Taiwan with known exposures to AA (see, for example, Chen et al., 2012 Proc Natl Acad Sci USA, 109 (21): 8241-8246). Tobacco use was reported by 18% of this cohort, all male. Based on the estimated values of the glomerular filtration rate (eGFR), renal function was not affected (chronic kidney disease (CKD) stage 0-2) in 45% of individuals, while mild to moderate kidney disease ( CKD stage 3) or severe disease (CKD stages 4-5) was observed for 43% and 12% of the cohort, respectively. Demographic, clinical and genetic characteristics of the UTUC cohort stratified by the UroSEEK results.

Positive multiplex n% TERT positive Positive aneuploidy Positive UroSEEK of ten genes All individuals 56 100% 64% 29% 39% 75% Gender Men 24 43% 71% 33% 54% 83% Women 32 57% 59% 25% 28% 69% CKD stage 0–2 25 45% 68% 36% 44% 76% 3A 14 25% 50% 21% 43% 71% 3B 10 18% 80% 20% 40% 80% 4 4 7% 25% 50% 0% 50% 5 3 5% 100% 0% 33% 100% Tumor Grade Low 6 11% 67% 50% 17% 67% High 50 89% 64% 26% 42% 76% Tumor stage Ta 11 20% 73% 55% 45% 82% T1 8 14% 50% 0% 38% 75% T2 10 18% 80% 20% 10% 80% T3 24 43% 67% 33% 54% 79% T4 3 5% 0% 0% 0% 0% Upper urinary tract tumor site Lower ureter 17 30% 76% 18% 35% 76% Upper ureter 1 2% 100% 0% 0% 100% Ureterovesical junction 2 4% 0% 0% 0% 0% Lower ureter and ureter 2 4% 100% 50% 50% 100% upper Renal pelvis 21 38% 57% 38% 38% 76% Renal pelvis and lower ureter 4 7% 75% 25% 50% 100% Renal pelvis and upper ureter 5 9% 40% 40% 60% 60% Renal pelvis, lower ureter, ure- 4 7% 75% 25% 50% 75% having upper Synchronous bladder cancer Present 21 38% 52% 29% 33% 62% Absent 35 63% 71% 29% 43% 83% Risk factors for UTUC Aristolactam-DNA adducts 54 96% 65% 30% 39% 74% present Smoking history 10 18 % 70% 30% 60% 70% CKD, chronic kidney disease

[1092] Tumors were confined to a single location along the upper urinary tract in most cases (38% of the renal pelvis; 39% of the ureter), while multifocal tumors affecting the renal pelvis and ureter occurred in 23% of patients. Synchronous bladder cancer (diagnosed within 3 months before nephroureterectomy) was present in 38%. Histologically, 89% of tumors were classified as high grade, with the majority categorized as muscle invasive (T2-T4, 66%). Mutational analysis - UTUC cohort

[1093] Three separate tests were performed for genetic abnormalities that can be found in UTUC-derived urinary cells (Fig. 32, Table 29, Table 30, Table 31 and Figure 33). First, mutations were evaluated in selected exomic regions of ten genes (CDKN2A, ERBB2, FGFR3, HRAS, KRAS, MET, MLL, PIK3CA, TP53 and VHL) that are frequently altered in urological tumors (Sfakianos et al., 2015 ). To that end, a specific set of multiplex primers has been designed to allow mutations to be detected in only 0.03% of urinary cells (Table 40). The ability to detect these low mutant fractions was a result of the incorporation of molecular barcodes into each of the initiators, substantially reducing the artifacts associated with massively parallel sequencing. Second, TERT promoter mutations have been evaluated, based on previous evidence that TERT promoter mutations are often found in UTUCs. A singleplex PCR was used for this analysis because the unusually high GC content of the TERT promoter prevented its inclusion in the multiplex PCR design. Third, the extent of aneuploidy was assessed using a technique in which a single PCR is used to co-amplify ~ 38,000 members of a subfamily of the long, interspersed nucleotide-1 element (retrotransposons L1). L1 retrotransposons, like other human replicates, have spread throughout the genome via retrotransposition and are found in all 39 autosomal non-acrocentric arms.

[1094] The multiplex assay detected mutations in 36 of the 56 urinary cell samples from UTUC patients (64%, 95% CI 51% at

76% (Table 29). A total of 57 mutations were detected in nine of the ten target genes (Fig. 34). The mean frequency of the mutant allele (MAF) in urinary cells was 5.6% and ranged from 0.3% to 80%. The most commonly altered genes were TP53 (58% of 57 mutations) and FGFR3 (16% of 57 mutations) (Table 18). None of the 188 urine cell samples from healthy individuals showed a detectable mutation in any of the ten analyzed genes (100% specificity, 97.5% 100% CI).

[1095] TERT promoter mutations were detected in 16 of the 56 urinary cell samples from patients with UTUC (29%, 95% CI 18% to 42%) (Table 30). The median MAF of TERT in urinary cells was 2.22% and ranged from 0.59% to 46.3%. One of the 188 urine samples from healthy individuals had a mutation (TERT g.1295250C> T with an MAF of 0.39%). In UTUC urinary cell samples, mutations were detected in three positions: 94% of the mutations were in hg1295228 (67%) and hg1295250 (28%), which are 69 and 91 bp upstream of the transcription start site, respectively. It has been shown earlier that these positions are involved in the appropriate transcriptional regulation of TERT. In particular, the mutant alleles recruit the transcription factor GABPA / B1, resulting in the H3K4me2 / 3 mark of the active chromatin and reversing the epigenetic silencing present in normal cells. Exposure to aristolochic acid in the UTUC Cohort

[1096] Aristolochic acid activated metabolites covalently bind to exocyclic amino groups in purine bases, with preference for dA, leading to characteristic A> T> transversions. To determine whether individuals in the cohort were exposed to AA, renal cortical DNA adducts were qualified using mass spectrometry. All but two of the 56 patients had detectable aristolactam (AL) -DNA adducts with levels ranging from 0.4 to 68 dA-AL adducts per 108 nucleotides. In addition, the AA> T signature mutation associated with AA was highly represented in the mutational spectra of TP53 (18/32 A> T) and HRAS (2/2 A> T) found in urinary cells (Table 30). Analysis of aneuploidy in the UTUC Cohort

[1097] Aneuploidy was detected in 22 of the 56 urinary cell samples from UTUC patients (39%, 95% CI 28% to 52%, Table 31 and Fig. 33), but in none of the 188 cell samples of healthy individuals. The most altered arms were 1q, 7q, 8q, 17p and 18q. Some of these arms harbor oncogenes of known tumors or suppressor genes that have been shown to suffer changes in copy number in many cancers (Vogelstein et al., 2013). Comparison with primary tumors - the UTUC Cohort

[1098] Tumor samples from all 56 patients included in this study were available for comparison and were studied using the same three assays used to analyze urinary cell samples. This comparison served two purposes. First, it allowed to determine whether the mutations identified in the urinary cells were derived from the tumor sample available from the same patient. There were a total of 39 cases of UTUC in which a mutation can be identified in the urinary cells. In 35 (90%) of these 39 cases, at least one of the mutations identified in the urine sample (Table 29 and Table 30) was also identified in the corresponding tumor DNA sample (Table 32 and Table 33). When all 80 mutations identified in the urinary cells were considered, 63 (79%) were identified in the corresponding tumor sample (Table 32 and Table 33). In any of the three trials, the discrepancies between urine and tumor samples can be explained by the fact that only one tumor per patient was accessible, even though more than one anatomically distinct tumor was often clinically evident. In addition, DNA was extracted from only one piece of tissue from each tumor, and intratumoral heterogeneity may have been responsible for some of the discrepancies.

[1099] Tumor data helped to determine why 17 of the 56 urinary cell samples from UTUC patients did not contain detectable mutations. The reason could have been that the primary tumors did not have a mutation present in the genetic panel or that the primary tumor contained that mutation, but the fraction of neoplastic cells in the urine sample was not high enough to allow for its detection. From the evaluation of primary tumor samples, it was found that four (24%) of the 17 urine samples without detectable mutations were from patients whose tumors did not contain any of the mutations investigated (Table 32). The conclusion was that the main reason for the failure of the mutation test was an insufficient number of cancer cells in the urine, which represented 13 (76%) of the 17 failures.

[1100] There were 22 cases in which aneuploidy was seen in urinary cell samples. Overall, 96% of the chromosomal gains or losses observed in urinary cells were also seen in primary tumors (examples in Fig. 35). On the other hand, there were 34 cases in which aneuploidy was not observed in the urine cell samples. The evaluation of 56 tumors with the same assay showed that all but three were aneuploid, as well as in mutations, the main reason for the failure of the aneuploidy assay was insufficient amounts of neoplastic DNA in urinary cells. Combined biomarkers - the UTUC Cohort

[1101] There are two factors that can limit the sensitivity of genetically based biomarkers. First, a sample can only be classified as positive for the biomarker if it contains DNA from a sufficient number of neoplastic cells to be detected by the assay.

Second, the tumor from which the neoplastic cells were derived must harbor the genetic alteration that is being researched.

Combination tests can increase sensitivity by assessing more genetic changes and, therefore, are more likely to detect at least one genetic change present in the tumor.

However, mutations in clinical samples are usually present at low allele frequencies (Table 29 and Table 30), requiring high coverage of all bases surveyed.

It would be prohibitively expensive to perform entire exome sequencing with 10,000x coverage.

In this study, the selected regions of 11 genes (including TERT) were carefully evaluated together with the analysis of the copy number of 39 chromosomal arms.

Even if a tumor does not contain a genetic change in one of the 11 genes evaluated, it can still be aneuploid and detectable by urinary cell testing for aneuploidy.

The sensitivity of aneuploidy detection is less than that of mutation assays.

Simulations showed that DNA containing a minimum of 1% of neoplastic cells is necessary for the reliable detection of aneuploidy, while mutations present in only 0.03% of the DNA models can be detected by the mutation assays used in this study.

However, samples of urinary cells that had relatively high fractions of neoplastic cells, but did not contain a detectable mutation in the 11 genes surveyed, should still be detected due to their aneuploidy, because, as noted above, 53/56 UTUCs studied here were aneuploid.

In addition, some of the mutations in the 11 genes consulted, such as large insertions or deletions or complex changes, may be undetectable by assays based on mutations, but a sample with this undetectable mutation may still be positive in an aneuploidy test.

[1102] To determine whether these theoretical arguments made a difference in practice, the performance of the biomarker was assessed using combined approaches, collectively called UroSEEK. As noted above, the ten gene multiplex assay, the TERT synergle assay and the aneuploidy assays produced sensitivities of 64, 29% and 39%, respectively, when used separately. Twenty-three samples without mutations in the TERT promoter showed a positive result for mutations in one of the other ten genes (Venn diagram in Fig. 32). On the other hand, three samples without mutations detectable with the multiplex assay obtained positive results for mutations in the TERT promoter (Fig. 32). And, three of the urinary cell samples without detectable mutations were positive for aneuploidy (Fig. 32). Thus, when the three tests were used together, and a positive result in any of the tests was sufficient to classify a sample as positive, the sensitivity increased to 75% (95% CI 62.2% to 84.6 %). Only one of the 188 samples from healthy individuals scored as positive by UroSEEK (specificity of 99.5%, CI 97.5 to 100%).

[1103] To determine the basis for the increased sensitivity provided by the combined assays, data on primary tumors from the three patients whose urinary cell samples exhibited aneuploidy, but did not show detectable mutations, were evaluated. It was found that these three tumors did not contain mutations in the 11 genes surveyed, explaining why these same tests were negative when applied to urinary cell DNA. As noted above, these three tumors were aneuploid, offering the opportunity to detect these variations in the number of copies in the urinary cell samples. Correlation with clinical characteristics

[1104] A cancer biomarker should advantageously be able to detect tumors at an early stage, allowing surgical removal of the lesions before generalized metastasis. The UroSEEK was sensitive in the detection of early and late tumors. The result was positive in 15 (79%) of the 19 patients with tumors in stage Ta or T1 and in 27 (73%) in 37 patients with tumors in stage T2-T4. Ten-year cancer-specific survival rates show that 91% of UTUC patients with stage T1 cancers must be cured by surgery, compared with only 78%, 34% and 0% of patients with stage 2 tumors, 3 or 4, respectively.

[1105] UroSEEK sensitivity was independent of a variety of clinical parameters beyond the tumor stage, including sex, CKD stage, tumor grade, tumor location, and risk factors for the development of UTUC, indicating that the assay is suitable for evaluating diverse patient populations. In addition, Uro-SEEK was considerably more sensitive than urine cytology in this cohort. Cytology was available in 42 cases, with only four (9.5%) diagnosed as cytologically carcinoma. Even if the samples classified as "suspected of malignancy" by cytology were considered positive, the sensitivity was only 26% (including the four classified as positive and the seven classified as suspicious). UroSEEK detected all four cases classified as positive by cytology, five out of seven cases with suspected malignancy and 22 out of 31 samples classified by cytology as inconclusive or negative. Example 6: Detection of aneuploidy in cancer patients by amplifying long intercalated nucleotide elements (LINEs)

[1106] This example describes a new approach to the detection of aneuploidy based on amplicons. This approach, called WALDO for Within-Sample-AneupLoidy-DetectiOn (WALDO), employs supervised machine learning to detect small changes in the multiple chromosomal arms that are often present in cancers. It is shown here that WALDO can be applied to identify gains or losses in the chromosomal arm with improved sensitivity and equivalent specificity compared to previous approaches. In addition, machine learning can be incorporated to make aneuploidy calls across the genome, in which samples are classified according to their aneuploidy status. This example reports the results of WALDO in thousands of samples, including tissues from ten different types of tumors, as well as liquid plasma biopsies from patients with cancer. When two samples are available for comparison, WALDO can be used to assess the genetic relationship or find somatic mutations in LINEs. Thus, this approach can be used to provide an estimate of the somatic mutation load, evaluate signatures of carcinogens and detect instability of microsatellites (Fig. 1). Materials and Methods Samples

[1107] A total of 1,678 tumors were evaluated in this study (see below). Number of samples, Correspon- Mu data- Sample origin Sample type Number of samples including tooth replicates Normal tation Peripheral white blood cells (WBC) Normal 176 677 N / A No Tumor Invasive breast carcinoma (BRCA) 45 45 No No Colon adenocarcinoma and rectal tumor adenocarcinoma (COAD; COADREAD) 536 536 No No Tumor Colorectal adenoma 32 32 N / A No Tumor Esophageal carcinoma (ESCA) 42 42 No No Squamous cell carcinoma of the head and neck tumor (HNSC) 96 96 No No Tumor Heptaocellular Carcinoma of the Liver (LIHC) 56 56 No No Tumor Ovarian Serous Cystadenocarcinoma (OV) 157 157 No No Tumor Pancreatic Adenocarcinoma (PAAD) 345 345 No No Tumor Stomatal adenocarcinoma (STAD) 28 28 No No Tumor Endometrial carcinoma of the uterine body (UCEC) 296 296 No No Defective colorectal carcinoma of tumor repair (cell line) compatibility 6 6 Yes No

Normal Plasma 402 566 N / A No Plasma Pancreatic adenocarcinoma (PAAD) 547 547 No Yes Plasma Invasive breast carcinoma (BRCA) 28 28 No Yes Colon adenocarcinoma and Plasma adenocarcinoma rectum (COAD; COADREAD) 167 167 No Yes Plasma carcinoma esophagus (ESCA) 17 17 No Yes Plasma Heptaocellular Liver Carcinoma (LIHC) 54 54 No Yes Plasma Stomatal adenocarcinoma (STAD) 16 16 No Yes Plasma Serous Ovarian Cystadenocarcionoma (OV) 14 14 No Yes Lung Plasma 113 113 No Yes

[1108] The number of cancers for each histopathological subtype is listed in the Appendices. The tumors were fixed in formalin and embedded in paraffin (FFPE). In all cases, the DNA was purified using QIAsymphony (cat # 937255). Peripheral white blood cells (WBCs) were purified from the blood of 176 healthy individuals. The plasma was purified from 566 healthy individuals and 982 cancer patients. DNA was purified from WBCs and plasma using kit numbers Qiagen (cat # 1091063) and (cat # 937255), respectively. Most of the plasma samples used in this study were independently assessed for mutations in one of the twelve commonly mutated genes. The fraction of mutant alleles in these plasma samples was used to estimate the content of neoplastic cells. All individuals participating in the study signed a free and informed consent form after approval by the institutional review boards of the hospitals in which they were collected. Fast-SeqS

[1109] For each DNA sample evaluated, FAST-SeqS was used to amplify approximately 38,000 amplicons with a single pair of primers (Kinde et al., 2012 PloS ONE 7: e41162). Massively parallel sequencing was performed on Illumina instruments (HiSeq 2500, HiSeq 4000 or MiSeq). During amplification, de-generated bases at the 5 'end of the primer were used as molecular barcodes to uniquely label each model DNA molecule, as described elsewhere (see, for example,

Kinde et al., 2011 Proceedings of National Academy of Sciences 108: 9530-9535). This ensured that each model DNA molecule was counted only once. In all cases in this document, the term "readings" refers to readings that are uniquely identified. Depending on the experiment, each reading was sequenced between 1 and 20 times. For each sample of WBC and tumor DNA, 100,000 to 25 million readings were used for analysis. For each plasma DNA sample,

100,000 to 15 million readings were used. Normal DNA replicates were included in all sequencing experiments and used to assess stochastic and experimental variability. Sample alignment and genomic interval grouping

[1110] Bowtie2 was used to align readings with the human reference genome set GRC37. 37,669 exact matches (33,844 excluding sex chromosomes) were identified for the reference genome. These exact matches allow for the inclusion of common polymorphisms. Polymorphisms included 24,720 single nucleotide polymorphisms (SNPs) and 1,500 insertion and exclusion polymorphisms (indel), with allelic frequencies lower than> 1% in the 1000 genome database (Consortium 2012 Nature 491: 56- 65).

[1111] In light of the experimental and stochastic variation, it was expected that the number of readings mapped for each genomic region of any euploid sample would be variable. To minimize this variability, clusters of 500-kb genomic intervals with similar reading depth are identified on all chromosomes in multiple euploid samples. This step allowed us to estimate the expected variability in the reading depth in a sample when there was no aneuploidy. Genomic intervals smaller and larger than 500 kb were tested and it was found that 500 kb produced reasonable performance.

the tests described below at a reasonable computational expense.

[1112] The grouping of the genomic intervals of 500 kb was carried out as follows. Each test sample was compared to euploid samples that had similar amplicon sizes. This was done because smaller amplicons will be over-represented in amplicons generated from DNA that is small in size before amplification. The size of the amplicons generated by FastSeqS varies from 100 to 140 bp (Kinde et al., 2012 PloS ONE 7: e41162). Plasma DNA size is 140 to 180 bp (Diehl et al., 2005) Proceedings of the National Academy of Sciences of the United States of America 102: 16368-16373; Chan et al., 2004 Clinical chemistry 50: 88-92; Jahr et al., 2001 Cancer research 61: 1659-1665; and Giacona et al., 1998 Panthers 17: 89-97), so that the largest amplicons in LINE will be substantially underrepresented in plasma DNA compared to WBC DNA, for example. It was found that seven euploid samples were sufficient for comparison with any test sample; the use of more than seven euploid samples did not substantially increase performance. The seven euploid samples were derived from a plasma DNA collection of 677 WBC or 566 from normal individuals, collectively referred to as the "euploid reference set". For each test sample p, the seven normal samples with the shortest Euclidean distance to p were selected, defined as D (p, q) = & ∑ (('(-! () Where, pn and qn are the fraction of amplicons of size n in samples p and the sum is above all amplicon sizes in the two samples. Before calculating the euclidean distances between the test samples from the samples in the euploid reference set, the following amplicons were excluded : (i) Using maximum likelihood estimates, amplicons were classified by variation between the seven euploid samples and the first 1% excluded.

(ii), any amplicons with <10 readings in one sample, but> 50 readings in any of the other six samples were removed. In each sample, the 500-kb genomic intervals were scaled by subtracting the mean and dividing by the standard deviation of the readings in each sample.

[1113] The genomic intervals of 500 kb in scale were then grouped into the seven normal samples selected as follows. First, each 500-kb i genomic interval was assigned to a primary cluster Ci. Then, the readings in the genomic interval i in all samples were compared with the average number of readings in the seven samples in all other genomic intervals. i 'that occurred in the remaining 21 autosomal chromosomes. Significant results (paired t test p> 0.05, f test p> 0.05) were tested during the search for similarity. If the average number of readings in the genomic interval i 'is not significantly different from the number of readings in the genomic interval i, it was added to the Ci cluster. This process was repeated for each of the 4361 genomic intervals, producing 4361 clusters. Each interval i belonged to its primary cluster, but the same interval also belonged to an average of 176 other clusters (range from 100 to 252 clusters among 190 representative samples). The number of exclusive clusters was less than 4361, because some were composed of the same genomic ranges of 500 kb. The number of unique clusters was typically 4310 to 4330 out of 190 representative samples. The clusters contained an average of approximately two hundred genomic intervals of 500 kb (see Fig. 45). The scale readings were not randomly distributed (see Fig. 46A). However, the distribution of readings at scale within ~ 200 genomic intervals in each cluster followed an approximately normal distribution (example in Fig. 46B-C).

Identifying gains or losses of the chromosome arm in a test sample

[1114] WALDO used the seven euploid samples described above only to define groups of genomic intervals with similar amplification properties. The statistical tests for aneuploidy in WALDO were based on the reading distributions within the test sample and independently of the reading distributions in any euploid sample. For a test sample, the maximum probability was used to estimate the means  and the variations 2 of the genomic intervals in each of the 4,361 clusters defined by the seven euploid samples that were chosen to match based on the length of the amplicon . The robustness of these estimates was improved by iteratively removing the peripheral genomic intervals within the test sample from the clusters. Clusters containing less than 10 genomic intervals were not included in the analysis. For each cluster, any genomic interval of 500 kb that meets the criteria min (2 * CDF (µ, σi2), 2 * (1-CDF (µ, σi2)) <0.01 was removed from all clusters. Then, parameters  and 2 and each cluster were re-estimated by maximum likelihood.The two steps were repeated until no external genomic interval remains.The statistical significance of the total readings was then estimated from all genomic intervals of 500 kb in the arm, since the sums of normally distributed random variables are also normally distributed random variables, the calculation was straightforward (see Fig 47). For each chromosomal arm, ∑ ~ (∑, ∑) was calculated, where Ri is the scale reading and I is the number of clusters in the arm. Z scores were produced using the quantile function 1-CDF (∑, ∑). Positive Z scores> α represented gains and negative Z scores <-α represented losses, with α being the threshold of significance selected. allelic balance at arm level

[1115] Common polymorphisms of 1000 genomes (24,720 single nucleotides and 1,500 indels, MAF> 1%) were used as candidate heterozygous sites. For each of the 677 normal samples, polymorphic sites were identified that could be safely called heterozygous and diploid. Polymorphisms were defined as those with varying allelic frequencies (VAF) (0.4 <VAF0.6), where VAF = # readings without reference / total readings. VAFs were modeled at these sites as random variables taken from a normal distribution with  = 0.5; the variance 2 was estimated by maximum likelihood as a function of the reading depth (Fig. 48). To determine whether the alleles on a chromosomal arm in a test sample were unbalanced, the subset of polymorphic sites was identified in which the two alleles were present and in which the sum of the readings in both alleles was> 25. AVF obtained served was then compared with the normal distribution, using the expected variance for the observed reading depth, producing a two-sided P-value. All p values in a chromosomal arm were transformed into Z and combined using the weighted Stouffer method, with the reading depth observed at each site used as its weight. The formula used for this calculation was ∑ ~, where wi is the depth of the UID in variant i, Zi is the Z score of variant i, and k is the number of variants observed in the chromosome arm. A chromosomal arm was classified as having an allelic imbalance if the resulting Z score was greater than the selected statistical significance threshold α (unilateral test). Generation of synthetic aneuploid samples

[1116] Data from 63 presumably euploid samples were selected, each containing at least 9 million readings and each derived from the DNA of normal WBCs. Synthetic aneuploid samples were created by adding (or subtracting) readings from various chromosomal arms to the readings from these normal DNA samples. Readings of 1, 5, 10, 15, 20 or 25 chromosomal arms selected at random were added or subtracted from each sample. The additions and subtractions were designed to represent fractions of neoplastic cells that vary from 0.5% to 10% and resulted in synthetic samples containing exactly nine million readings. The readings for each chromosome arm were added or subtracted uniformly. For example, when five chromosomal arms that were lost were modeled, each was lost to the same degree and we did not incorporate the heterogeneity of the tumor into the model. In addition, synthetic samples containing two or more of the same extra arms of the chromosome were not created, such as synthetic cells containing 4 copies of chromosome 3p. This simplified approach did not comprehensively cover all biologically plausible aneuploidy events. However, limiting the possible combinations of altered arms made the generation of samples computable and the resulting support vector machine worked well in practice.

[1117] The samples generated synthetically in which readings from just a single chromosomal arm were added or subtracted allowed us to estimate the performance of WALDO when only a single chromosomal arm of interest was won or lost. The synthetic set in which the arms of chromosome 5-25 were altered allowed the evaluation of the performance of WALDO in typical samples derived from cancers. As shown in Fig. 37, most cancers show gains or losses from multiple chromosomes. The algorithms used to generate the synthetic samples are shown as pseudocode in Fig.49 and Fig. 50. Detection of aneuploidy throughout the genome

[1118] A two-class support vector machine (SVM; Cortes 1995 Machine learning 20: 273-297) was trained to discriminate between euploid samples and synthetic samples in which readings from 5 to 25 chromosomal arms were added or subtracted. The training set contained 677 negative WBC samples (presumably euploid WBCs containing 3 to 15 million readings) and 3150 positive samples, all synthetic, as described above. SVM training was performed with the e1071 package in R, using the radial base kernel and standard parameters (Meyer et al., 2015 R package version: 1.6-3). Each sample had 39 Z-score characteristics, representing gains and losses in the chromosome arm. 677 synthetic samples were randomly sampled so that the sizes of the negative and positive classes were equivalent, and this was repeated ten times. Each sample to be classified was scored by all ten SVMs and the ten scores were averaged to produce a final score.

[1119] The number of data readings in experimental samples can vary widely, especially when the samples are derived from sources with limited amounts of DNA, such as plasma. Samples with low readings can generate artificially high SVM scores if the depth of the reading is not taken into account. The reading depth was therefore controlled by modeling the change in SVM scores as a function of the reading depth in normal samples. In particular, each of the 63 WBC euploid samples was subjected to random sampling to produce ten repetitions of less readable euploid samples with a reading depth ranging from 100,000 to 9 million. All euploid samples of reduced sample

were scored using the 10 SVMs and the scores were calculated as mean. This procedure produced 630 SVM scores for euploid samples of reduced sampling in each reading depth. All scores were converted into proportions, locating the sample at each reading depth with the minimum SVM score and dividing all scores in the same depth by that value. The average ratio r in each depth decreased monotonically due to the increase in the reading depth (Fig. 51). The relationship between reading depth and SVM score was modeled using the following equation (A = -7.076 * 10 ^ -7 and B = - 1.946 * 10 ^ -1). The raw SVM scores were corrected by dividing by the ratio r, using the formula log -1 - 0 = 12 + 4. /

[1120] To classify a sample as aneuploid, it was first determined whether a single arm on a chromosome was lost or gained in a statistically significant way. A statistically significant gain from a single chromosomal arm was defined as one whose Z score was> 4 σ above the maximum Z score observed in the 677 normal WBC samples. Likewise, a statistically significant loss of a single chromosomal arm was defined as that whose Z score was <-4 σ below the minimum Z score observed in the 677 normal WBC samples. The allelic imbalance based on SNPs was defined for a chromosomal arm whose Z score was> 4 σ above the maximum Z score observed in the 677 normal WBC samples. Only samples in which no chromosomal arm was gained or lost when defined in this way were subjected to SVM analysis. The justification for this process was that SVM was designed to identify samples with a large number of gains or losses in the chromosomal arm, but with relatively low neoplastic cell fractions. SVM was not designed to detect aneuploidy in samples with neoplastic cell fractions> 10%, which were easily identified by assessing their Z scores and comparing them with the 677 normal samples, as described in the first part of this paragraph. Somatic sequence mutations and microsatellite instability (MSI)

[1121] When corresponding normal samples were available, we attempted to detect somatic mutations of single base substitution (SBS), insertion and exclusion (indel) based on LINE amplicon sequences and alignments. In these cases, the molecular barcode approach was used to reduce errors. For SBS, only amplicons with at least 200 readings and 50 unique molecular bar codes were considered. For indels, readings were considered that were observed in at least two groups in the sequencing instrument. Mutations in the SBS were identified by directly comparing amplicons from the test sample with amplicons from the corresponding normal and did not require any alignment with the reference genome. Amplicons with less than 50 readings in the corresponding normal sample were excluded. A somatic SBS was defined as one in which at least five readings from the test sample differed from any normal reading by exactly one nucleotide substitution.

[1122] Indels were similarly called. The amplifiers of the test sample and the corresponding normal sample were first aligned with the reference genome (GRc37) with Bowtie2 (Langmead 2012 Nature Methods 9: 357-359). A somatic indel was defined as one in which at least ten readings from the test sample differed from any normal reading due to the same insertion or exclusion.

[1123] The instability of microsatellites in a test sample was determined by counting the number of somatic indels in mononucleotide tracts of> 3 nucleotides. There were 17,488 of these mononucleotide tracts in the LINE amplicons that were studied. Somatic indels in monotracts were expected to be rare in a normal sample. Therefore, the null distribution of counts can be modeled as Poisson (λ = 1), where λ is the average number of somatic indels in a monotract in a normal sample. One sample was called harboring MSI if the number of somatic indels was statistically significant. To assess how often normal samples would be scored as MSI using this process, the total readings on normal samples were randomly divided into two equal partitions. The first partition was used as a reference sample and the second partition was used as a test sample. Sample match

[1124] To compare one sample with another, the amplicons were first aligned to the reference genome GRC37 with Bowtie2. Common polymorphisms from 1000 genomes were used to identify the genotypes at 26,220 sites in each sample. Each polymorphic site was designated as "0" (homozygous reference,> 0.95 readings of corresponding reference allele, minimum of ten readings), "1" (heterozygous, 0.05-0.95 UIDS corresponding to reference or alternative allele, minimum ten readings for each allele) or "2" (homozygous alternative,> 0.95 UIDs corresponding to the alternative allele, minimum ten readings). Agreement was defined as the number of corresponding polymorphic sites that were identical in both samples (that is, they were "0", both "1" or both "2") divided by the total number of genotypes that had adequate coverage in both samples. Two samples were considered compatible if the agreement was> 0.98 and at least 15,000 amplicons had adequate coverage.

Changes in the number of somatic copies of the TCGA

[1125] The latest Firehose Cancer Genome Atlas level 4 somatic copy change files have been downloaded (Aggregate_AnalysisFeatures.Level_4.2016 012800.0.0) from the GISTIC analysis (Beroukhim et al., 2007 Proceedings of the National Academy of Sciences 104: 20007-20012) of the Affymetrix SNP6 arrangements. 9 types of TCGA tumor were selected that corresponded to our cohorts of primary tumor samples (invasive breast carcinoma (BRCA), colon adenocarcinoma (COAD), colon or rectal adenocarcinoma (COADREAD), esophageal carcinoma (ESCA), carcinoma) head and neck squamous cell name (HNSC), hepatic hepatocellular carcinoma (LIHC), ovarian serous cystadenocarcinoma (OV), pancreatic adenocarcinoma (PAAD), stomach adenocarcinoma (STAD) and uterine body endometrial carcinoma (UCEC) . A total of 6276 samples were available (invasive breast carcinoma (BRCA): 1081, colon adenocarcinoma, colon or rectal adenocarcinoma (COAD; COADREAD): 2755, esophageal carcinoma (ESCA): 185, head and neck squamous cell carcinoma ( HNSC): 523, hepatic hepatocellular carcinoma (LIHC): 371, serous ovarian cystadenocarcinoma (OV): 580, pancreatic adenocarcinoma (PAAD): 185, stomach adenocarcinoma (STAD): 442, endometrial body carcinoma uterine (UCEC): 540). The data consisted of GISTIC log2 ratios representing gains or losses for each chromosomal arm (Mermel et al., 2011 Genome biology 12: R41). Reasons> 0.2 or <0.2 were considered gains or losses (Laddha et al., 2014 Molecular cancer reserve 12: 485-490). Comparison with a prior art to detect changes in the single chromosome arm

[1126] The fraction of readings mapped to each chromosomal arm in each of the 677 WBC samples and their means and deviations.

standards were calculated (Kinde et al., 2011 Proceedings of the National Academy of Sciences 108: 9530-9535). The score for each arm was calculated as: zi, chrN = (chrNi - µchrN) / σchrN, where chrNi represents the normalized reading counts for that chromosomal arm and µchrN and σchrN represent the mean and standard deviation of the normalized reading counts. Results Statistical principles underlying WALDO

[1127] Unlike conventional approaches to assess changes in the number of copies, WALDO does not compare the normalized reading counts for each chromosomal arm in a test sample with the fraction of readings in each chromosomal arm in other samples. Such conventional comparisons are subject to batch effects and other artifacts associated with variables that are difficult to control. To assess sequencing data for the entire genome, aneuploidy was detected by comparing LINE reading counts within 4361 genomic intervals, each containing 500 kb of sequence. The reading counts within the 500 kb genomic intervals in a sample were compared only with the reading counts within other genomic intervals within the same sample - hence the designation “Within the Sample” in WALDO.

[1128] In euploid samples, the number of LINE readings within each 500 kb genomic range must accompany the number of readings in certain other genomic regions. Genomic intervals that go together do so because the amplicons within them amplify to similar lengths. Here, such accompanying genomic regions are called "clusters". It is possible to identify clusters from sequencing data in euploid samples. In a test sample, it is determined whether the number of readings in each genomic interval in each predefined cluster is within the expected limit of the other clusters in the same sample. If the readings within a genomic range are outside the statistically expected limit and there are many outsiders on the same chromosomal arm, that chromosomal arm will be classified as aneuploidy. The statistical basis for this test is described in Materials and Methods. In summary, although the number of readings on each LINE is not randomly distributed across the genome, the distribution of readings to scale within each cluster is approximately Normal. A convenient property of Normal distributions is that the sum of several Normal distributions is also a Normal distribution. It is, therefore, possible to calculate the theoretical average and the variation of the summed readings in each chromosome arm simply by adding the means and the variations of all the clusters represented in that chromosome arm.

[1129] WALDO also employs several other innovations that make it applicable to the analysis of amplicons generated by PCR from clinical samples. One of these innovations is to control the amplification bias resulting from the strong dependence of the data on the size of the initial model. Another is the use of a Support Vector Machine (SVM) to allow the detection of aneuploidy in samples containing low neoplastic fractions. The conceptual and statistical bases of WALDO are detailed in the Materials and methods section of this document. Evaluation of gains and losses of the chromosomal arm in primary tumor samples

[1130] WALDO was used for the first time to study gains and losses of the chromosome arm in 1,677 samples of primary tumors of ten types of cancer. One of the outputs of the WALDO is a z-score for each of the 39 non-acrocentric arms on the autosomal chromosomes. The z scores of each of these chromosomal arms in each of the primary tumor samples assessed in this study are provided in Table 35. These results were compared with those obtained by The Cancer Genome Atlas (TCGA) in independent samples of the same types of tumor (Zack et al., 2013 Nature genetics 45: 1134-1140; and Beroukhim et al., 2007 Proceedings of the National Academy of Sciences 104: 20007-20012). The fraction of tumor samples with gain or loss in each chromosomal arm was identified in our data and in the TCGA, considering all types of tumors together and each type of tumor individually. As shown in the upper half of Figure 37, the fraction of samples in all types of cancer scored as gain by WALDO or as gain by the TCGA algorithm (GISTIC) is shown for each chromosomal arm and the fraction of samples with loss is shown in the lower half. The correlations between the gains at the arm level scored in this study and those at the TCGA are shown in Figure 39A (R2 = 0 = 45) and the losses at the arm level are shown in Fig. 39B (R2 = 0.39). Considering that the samples were from completely different patients, the specific chromosomal arms gained and lost in both data sets were remarkably similar. The chromosomal arms with the highest gain were 1q, 3q, 7p, 7q, 8q and 20q and relatively few losses were observed in these arms. Those with the most losses were 4p, 4q, 8p and 18q and relatively few gains were observed in these arms. The arms with the least gains or losses were 10p, 16p, 19p and 19q.

[1131] Likewise, high correlations have been observed for many specific types of tumors in cases where a sufficient number of cancer was available for comparison (see below and Fig. 40).

Gain correlation Loss correlation Samples WALDO Samples GISTIC BRCA 0.629 0.436 89 181 COAD; COADREAD 0.582 0.428 536 2755 ESCA 0.043 0.07 42 185 HNSC 0.537 0.344 96 523 LIHC 0.64 0.287 56 371 OV 0.067 0.127 157 580 PAAD 0.384 0.702 345 185 STAD 0.552 0.555 28 442 UCEC 0.325 0.165 296 540

[1132] The highest correlations were for pancreatic adenocarcinomas and liver cancer (R2 = 0.70 and R2 = 0.64, respectively). An interesting result of this analysis was the large number of chromosomal arms that were aneuploid in the vast majority of cancer cases. The average number of chromosomal arms lost or gained by cancer was 14, with an interquartile range of 5 to 22. This large number was used for the development of the Support Vector Machine described below.

[1133] 32 benign colon tumors (colorectal adenomas) were also evaluated. It was found that 25 of them presented gains or losses of chromosomes. The average number of chromosomal arms that were lost or gained by a benign tumor was 4, with an interquartile range from 1 to 9.75. Benign tumors have not yet been studied by TCGA, so comparison was not possible. However, the comparison with colorectal cancers showed that the benign tumors had much less changes in the chromosomal arm than those seen in cancers. In addition, the chromosomal arms altered in adenomas overlapped those of cancers, and the direction of changes (gains versus losses) was preserved.

[1134] WALDO also allows the determination of allelic imbalances based on the SNPs with the LINEs that are sequenced concurrently. This provides a totally independent measure of changes in the chromosome arm than the number of readings in the 500 kb genomic intervals. Note that the measurements of the allelic imbalance represent the ratios between the number of readings of the reference allele and those of the variant allele. This ratio will be the same if the chromosomal arm that contains the reference allele is won or if the arm that contains the variant allele is lost. However, without wishing to be linked to the theory, it would be expected that there would be a strong relationship between the chromosomes that exhibit allelic imbalances and those that exhibit gains or losses in the same tumor. It was found that 63% of chromosomal arms with allelic imbalance also had a significant gain or loss in the same chromosomal arm. Other uses of SNPs within LINEs are described here.

[1135] Next, the sensitivity and specificity of WALDO to draw gains or losses from a single chromosome arm were compared (see Materials and Methods). Both methods were applied to LINE amplicon sequencing data of 677 samples of normal peripheral white blood cells (WBC), with each WBC sample amplified and sequenced independently at an average depth of 9.5 million readings . These experimental data have been augmented by 24,570 synthetic samples with unique chromosomal changes (see Materials and Methods). Sensitivity was calculated as the total number of altered arms correctly identified, divided by the total number of altered arms in the synthetic samples. Specificity was calculated as 1 minus the total number of incorrectly called altered arms, divided by the total number of normal arms in the experimental data from normal WBC samples. For WALDO and the previous method, three thresholds of significance (± 1.96, ± 3.0, ± 5.0) and three neoplastic cell fractions (1%, 5%, 10%) were considered. For all neoplastic cell thresholds and fractions, WALDO showed greater specificity and sensitivity (see below and Table 34).

Sensitivity Specificity Sensitivity of Point Specificity Dilution Threshold WALDO WALDO score Z tation Z 0.010 1.96> or <-1.96 0.221 0.969 0.144 0.952 0.010 3> or <-3 0.031 0.999 0.020 0.995 0.010 5> or <-5 0.000 1,000 0.000 1,000 0.050 1.96> or <-1.96 0.969 0.969 0.899 0.952 0.050 3> or <-3 0.917 0.999 0.748 0.995 0.050 5> or <-5 0.671 1,000 0.443 1,000 0.100 1.96> or <-1, 96 0.999 0.969 0.988 0.952 0.100 3> or <-3 0.995 0.999 0.997 0.995 0.100 5> or <-5 0.997 1,000 0.839 1,000

[1136] To further assess WALDO's ability to detect single chromosomal abnormalities, DNA from patients with trisomy 21 was also evaluated. The DNA of individuals with trisomy was physically mixed at a ratio of 2 ng normal DNA and 0.2 ng of Trisomy 21 DNA. The mixtures were created to replicate typical fetal fractions in non-invasive prenatal tests (approximately 10%). Using polymorphisms in LINE amplicons, the sample's trisomy mixture rate (range from 7.7% to 10.4%) was estimated. Using a threshold of z of 2.5, it was found that only 2M of readings detected trisomy 21 in fetal fractions normally observed (sensitivity 95%). 16 normal WBCs were then sampled at various reading depths. In 2M readings using the same threshold, specificity was 100%. Sensitivities and specificities at other reading depths and other mixtures of trisomy 21 samples are summarized in Fig. 41. Detection of aneuploidy in samples with low DNA fractions from neoplastic cells

[1137] Many potential applications for the detection of aneuploidy in cancer involve identifying a relatively small fraction of neoplastic cell DNA within a large set of DNA derived from normal cells. A notable application is liquid biopsy, that is, the assessment of body fluids, such as urine, saliva, cyst fluid or sputum, for evidence of cancer. Since aneuploidy is a general cancer characteristic of practically all types (see Fig. 37), the detection of aneuploidy can be used for this purpose.

[1138] To employ WALDO in liquid biopsies, a two-stage approach was used. The first employed a search for individual gains or losses from the chromosomal arm or allelic imbalance, as described above. Simulations with synthetic DNA showed that this approach could detect an individual gain or loss of a chromosomal arm with sensitivities> 90% in specificities> 99% when the fraction of DNA contributed by neoplastic cells was> 5% of the total DNA. To detect aneuploidy in samples with lower DNA fractions from neoplastic cells, the fact that the average number of chromosomal arm gains or losses per tumor was high (Kinde et al., 2012 PloS ONE 7: e41162) was explored. A variety of approaches to distinguish samples containing low fractions of neoplastic DNA with multiple chromosomal abnormalities from euploid samples was therefore considered. These approaches included counting the number of significant arms, combining the scores of the most significant arms, and adding the square Z scores of the windows. Based on synthetic samples, it was found that the ideal approach was obtained with a Support Vector Machine (among many tested machine learning algorithms). Support Vector Machine training is designed to be generally applicable to any type of cancer, and not based on patterns of gains and losses typical of specific types of cancer. With synthetic samples, the Support Vector Machine can detect aneuploidy in 78% of the samples with a fraction of neoplastic cells of 1% with a specificity of 99%, as determined by cross-validation. Therefore, this algorithm based on Support Vector Machine was incorporated into WALDO for the evaluation of clinical samples with low neoplastic composition (see Fig. 38).

[1139] WALDO was then used to attempt to assess aneuploidy in plasma samples from 961 cancer patients and 566 healthy individuals (see Materials and methods). Cancers of 8 different types were evaluated (see Table 36). The fraction of neoplastic cells in each cancer sample was considered as the fractions of mutant alleles determined from deep sequencing data. The samples were divided into those with neoplastic cell fractions> 1% (122 samples), between 0.5% and 1% (96 samples) and <0.5% (738 samples). Sensitivity was defined as the proportion of samples from cancer patients scored as aneuploid, while specificity was defined as 1 minus the fraction of samples from healthy patients scored as aneuploid. The receiver operating curves (ROC) are shown in Fig. 38 for these three ranges of neoplastic fractions. With strict specificity (99%), aneuploidy was identified in 42% of samples with neoplastic cell fractions> 1% (see Fig. 38A). As expected, sensitivity decreased with decreasing fractions of neoplastic cells (see Fig. 38B, 38C). With 99% specificity, WALDO detected aneuploidy in 24% of the samples with fractions of neoplastic cells from 0.5 to 1% and in 19% of the samples with fractions of neoplastic cells from 0 to 0.5%. The patient's specific type of cancer was not highly correlated with positive calls for aneuploidy. However, the number of model molecules that were evaluated correlated with sensitivity.

[1140] In plasma samples with a higher content of neoplastic cells, it was possible to determine which chromosomal arms were won or lost. Among 558 of the plasma samples that had a paired primary tumor, 188 samples had a significant gain or loss

in the chromosomal arm and 54% had a concomitant gain or loss in the primary tumor. In samples with low neoplastic content, none of the individual arms was gained or lost at statistically significant levels, but the WALDO Support Vector Machine component was able to detect small deviations in several chromosomal arms that distinguished them of euploid samples. Sample match

[1141] DNA profiling with short tandem repetitions is a well-established forensic technique that is now used routinely. SNP panels have also been carefully selected to ensure sample identity, such as between tumors and normal samples from the same patients (Kidd et al., 2006 Forensic science international 164: 20-32; and Pengelly et al., 2013 Genome medi - movie 5:89). LINEAS amplified in FAST-SeqS contain 26,220 common polymorphisms, including variants detected in> 1% in 1,000 genomes (Consortium 2012 Nature 491: 56-65). Theoretically, these polymorphisms provide a powerful way to analyze DNA samples evaluated for aneuploidy without any additional work or cost. To determine whether such identification was possible in practice, a measure of agreement between two samples was designed (see Materials and methods). This to measure concordance was then used in replicates of 176 normal WBC samples to each other, using ~ 5 replicates per sample, for a total of 676 WBC samples. The entry for WALDO was 676 samples, without specifying the sample name, so there was a total of 456,976 (676 x 676) possible matches. WALDO correctly matched all replicated samples with high agreement (> 99.9%), with no false correspondence. This protocol was then performed on 970 plasma samples and 1,684 tumor samples. The 558 plasma samples must correspond to the corresponding primary tumor samples and no other correspondence should be observed. This produced

7,038,409 ((970 +1,683) x (970 +1,683)) possible matches. Almost all 2653 samples coincided as expected, that is, only with each other or with the corresponding primary tumors, with an agreement> 99.8%. However, two plasma samples were found that did not correspond to the expected primary tumors and 12 plasma samples that corresponded to other plasma samples derived from different donors. In all these "incompatible cases", FastSeqS data indicated high concordances (> 99.8%).> Incompatibilities were, therefore, probably the result of incorrect sample identification and illustrated the usefulness of identity verification sample with WALDO. Mutation load, cancerous signatures, instability of microsatellites

[1142] When two samples, one normal and one cancer, are available in the same patient, it is possible to discern LINE mutations that are in one sample, but not in the other. For this application, the molecular barcode to reduce sequencing errors (see, for example, Kinde et al., 2011 Proceedings of National Academy of Sciences 108: 9530-9535) can be used, as obtained from experimental and bioinformatics components of WALDO (see Materials and Methods).

[1143] To determine whether somatic mutation detection was viable, ten upper urinary tract urothelial carcinomas (UTUCs) and normal tissues from the same patients were evaluated. These samples were previously analyzed by exotic sequencing (Hoang et al., 2013 Science translational medicine 5: 197ra102-197ra102). For each tumor sample, the number of somatic mutations and the single base substitution spectrum (SBS) (A-> T, A-> C, etc.) were counted. It was found that the number of SBS in the LINEs was highly correlated with the number of SBS in the exomes of these tumors (R2 = 0.98, p <2.6 * 10-8). The spectrum of mutations in the LINEs was similarly correlated with the spectrum of mutations in exotic sequences (R2 = 0.95, p <1.8 * 10-6). Notably, six of these tumors were from patients exposed to aristolochic acid and the pathognomonic signature (A-> T, T-> A) of this mutagen was prominent in these six tumors (see Fig. 42, Fig. 43 and Fig. 44).

[1144] The LINEs evaluated by WALDO house 17,488 mononucleotide tracts of> 3 nucleotides. As mononucleotide tracts are particularly sensitive to defects in the incompatibility repair, it was determined whether WALDO could be used to assess the incompatibility repair deficiency. For this purpose, the number of indels in the 17,488 LINE mononucleotide tracts was evaluated. The number of indels in six colorectal cancers deficient in repair was found, on average, 35 and ranged from 10 to 67. The normal tissues of these patients harbored only zero or one indel, and the difference between cancers and normal tissues was highly significant. (p <7.2 * 10-4). Example 7: Quantification across the genome of rare mutations by bottleneck sequencing

[1145] The accumulation of random somatic mutations in the nuclear and mitochondrial genomes over time underlies fundamental theories of carcinogenesis, neurodegeneration and aging (see, for example, Stratton et al., 2009 Nature 458: 719- 724; Kennedy et al., 2012 Mech Ageing Dev 133: 118-126; and Vijg et al., 2014 Current opinion in genetics & development 26: 141-149). Direct observation of these rare mutations in the human body with age, therefore, has the potential to improve our understanding of human diseases. Currently, there is no simple high-throughput method to directly and systematically quantify the somatic mutational load in normal and non-diseased human tissues at the genome level. State-of-the-art DNA sequencing technologies (NGS) address this problem, but their sequencing error rate limits the detection of rare mutations (see, for example, Albertini et al., 1990 Annu Rev Genet 24: 305-326; and Cole et al., 1994 Mutat Res 304: 33-105).

[1146] This example describes a Bottleneck Sequencing System (BotSeqS) technology designed to accurately detect rare point mutations in any molecular barcode library in a completely impartial way. BotSeqS, a next-generation sequencing method that simultaneously quantifies rare somatic point mutations in the mitochondrial and nuclear genomes. BotSeqS combines molecular barcode with a simple dilution step just before the amplification of the library. In this Example, BotSeqS is used to show accumulations of rare mutations, dependent on age and tissue, and to demonstrate that the somatic mutational charge in normal tissues can vary by several orders of magnitude, depending on biological and environmental factors. This example also shows great differences between the mutational patterns of the mitochondrial and nuclear genomes in normal tissues. Finally, the spectra of mutation of normal tissues were different from each other, but similar to the cancers that appeared in them. This technology can provide information about the number and nature of genetic changes in normal tissues and can be used to address a variety of fundamental questions about the genome of diseased tissues. Materials and Methods Samples of human tissue

[1147] Normal and non-diseased tissues for this study were purchased from five different sources (Table 43). For COL229 to COL237 and SIN230, the colon or duodenum was obtained from patients consented to Johns Hopkins Hospital with the approval of the Institutional Review Board.

For COL373 to COL375 and BRA01 to BRA09, the NIH NeuroBioBank (neurobiobank.nih.gov) was asked for a frozen colon and postmortem brain, with the request being approved and granted by the University of Maryland Brain and Tissue Bank (Baltimore, Maryland) and University of Miami Brain Endowment Bank (Miami, Florida). For KID034 to KID038, blocks of post-mortem renal cortex (200 mg), frozen by flash, were purchased from the Windber Research Institute (Windber, Pennsylvania). COL238 and COL239 were reported elsewhere (see, for example, Parsons et al., 1995 Science 268: 738-740; Hamilton et al., 1995 The New England journal of medicine 332: 839-847; and De Vos et al., 2004 American journal of human genetics 74: 954-964). SA_117, SA_118, SA_119, AA_105, AA_124 and AA_126 were as reported elsewhere (see, for example, Hoang et al., 2013 Science Translational Medicine 5: 197ra102). The initial logic for the colon and brain sample size was to acquire at least three individuals in each age group, in order to understand the average trend of somatic mutational patterns for each age group.

The age groups of the colon and brain were selected based on the growth and maintenance of the human body: early development of the body under 10 years old, young adult and adult body ~ 20-40 years old, adult body maintained with> 90 years.

For the colon, tissue from the young child's age group (SIN230) was later determined as a duodenum, leaving only two individuals representing the young child's age range for the colon epithelium.

For normal kidney, the criteria for kidney acquisition were a control group matched for age and a non-smoker for the kidneys of smokers and samples exposed to aristolochic acid.

All normal renal controls were Caucasian and therefore less likely to originate from a high-risk population exposed to AA (for example, Asia). From the same source of kidney tissue, three aliquots of normal post-mortem kidney, flash-frozen, from a five-month-old individual were available as technical replicates and to further test an age trend for exposed normal kidneys. non-carcinogenic. Preparation of molecules linked to the Illumina Y adapter

[1148] Genomic DNA (34 ng to 1 µg) in 55 µL TE buffer was fragmented using BioRuptor (Diagenode) at high intensity for 15 s on and 90 s off, using 7 cycles at 3 ° C. After random fragmentation, Illumina Y adapters were ligated to DNA fragments using the TruSeq DNA PCR-Free kit (Illumina) according to a standard Illumina standard low DNA entry protocol with selection for 350 bp pellet sizes. This resulted in molecules attached to the adapter in a total volume of 20 µL. Dilution of molecules attached to the Y adapter

[1149] Five ten-fold serial dilutions were performed on 96-well PCR plates starting with 2 µL of molecules attached to the adapter (before PCR) in 18 µL of dilution buffer (TE containing 1 ng / µL of pBlueScript). The samples were mixed by gently pipetting with a multichannel pipette. Two µL of each sample was then transferred to 18 µL of fresh dilution buffer using a multichannel pipette. The mixing and transfer were repeated for a total of five serial dilutions. Only 2 µL of each dilution (1/10 of the total volume) was used as a model for each PCR. A 103-fold dilution was performed as follows: (i) use of 2 µL of the total of 20 µL of molecules attached to the adapter (10-fold dilution); (ii) mixing of 2 µL of molecules connected to the adapter with dilution buffer in a total volume of 20 µL (dilution 10 times); and (iii) use of 2 µL of molecules attached to adapters diluted from the total volume of 20 µL in the PCR reaction (10-fold dilution, see below). The five serial dilutions resulted in dilution factors of 103, 104, 105, 106, and 107. PCR amplification of diluted Y-linked molecules

[1150] Custom HPLC purified PCR primers (IDT), TS-PCR Oligo1 (5'-AATGATACGGCGACCACCGAG * A; SEQ ID NO: 808) and TS-PCR Oligo2 (5'-CAAGCAGAAGACGGCATACGA * G; SEQ ID NO: 809 ), were designed with a phosphorothioated bond (*) at the 3 'end. The PCR was performed in a total volume of 50 µL with 0.5 µM TS-PCR Oligo1, 0.5 µM TS-PCR Oligo2, Q5 2X High Fidelity HotStart Master Mix (NEB) in the final concentration of 1X and 2 µL of molecules attached to the adapter diluted as a model. PCR was performed on the Thermo HyBaid PCR Express HBPX thermal cycler. The following PCR program was used: 1) 98 ° C for 30 s 2) 98 ° C for 10 s, 69 ° C for 30 s, 72 ° C for 30 s for 18 cycles and 3) 72 ° for 2 min. The PCR reactions were purified with AMPure XP (Agilent) in a 1.0X sphere / sample ratio according to the manufacturer's protocol. MiSeq execution and analysis

[1151] A subset of amplified BotSeqS sequencing libraries was evaluated on an Illumina MiSeq instrument (~ 5M clusters passed filter per library) to empirically deduce the ideal dilution. The "ideal dilution" was determined to result in 5 to 10 PCR duplicates per molecule when scaled to 1/2 lane of a HiSeq instrument (clusters of ~ 70 M passed through the filter by library in the Quick Run mode) . For example, for a 500 ng gDNA entry in the library preparation without TruSeq PCR (selecting for the 350 bp insertion size), the amplified libraries of the 104-, 105-, 106-fold dilutions were sequenced to a depth of 2x 50 bp in MiSeq. Three well-coded samples with different bar codes (which were also coded with molecular bar codes) were multiplexed on a Mi-

Seq to test three dilutions of each sample. The output.bam files were loaded into Galaxy and Picard's Estimate Library Complexity Tool (Galaxy Tool Version 1.56.0) was executed using the standard parameters. The ideal dilutions showed distributions ranging from one to four members per family, with singletons comprising ~ 60-80% of the total count. In general, with an input of 500 ng of gDNA in the library preparation without TruSeq PCR, the 105-fold dilution yielded ~ 10 members per family in a subsequent run of HiSeq used for BotSeqS. From our sequencing data, we estimated that the average number of high-quality groups needed to identify a rare mutation in colon tissues was (1) 30 M in a normal child, (2) 12 M in a normal young adult and (3) 5.8 M in a normal elderly adult. Whole genome sequencing

[1152] Thirty-two entire genome sequencing libraries (WGS) were generated from the 34 individuals in this study. In the two remaining individuals without WGS, COL238 and COL239, the Sanger sequence was performed to exclude clonal variants in the BotSeqS data. Of the final 20 µL of the molecules attached to the adapter used to prepare the BotSeqS libraries (before dilution), 10 µL were used to amplify a library for entire genome sequencing using the TruSeq PCR Primer Cocktail (Illumina) and Tru- Seq PCR Master Mix (Illumina) according to the TruSeq PCR protocol. PCR reactions were purified with AMPure XP (Agilent) at 1.0X sphere / sample according to the manufacturer's instructions. The libraries were sequenced in PE 2x100 bp in Illumina HiSeq with coverage> 30x. Peak sensitivity experience

[1153] Two DNA mixtures were prepared from the DNA of normal spleen samples PEN93 and PEN95. Complete genome sequence data was available in these two samples (see, for example, Jiao et al., 2011 Science 331: 1199-1203) and SNPs in PEN93 that were not present in PEN95 could be identified. Both mixtures contained the same amount of PEN95 DNA, but the low peak mixture contained only 10% of the PEN93 DNA contained in the high peak mixture. The BotSeqS libraries of these samples were first analyzed using the normal BotSeqS pipeline to minimize clonal and germline mutations. In fact, only a total of two mutations were detected between the two libraries; these two mutations probably represented rare mutations in the PEN95 sample and suggest a mutation frequency of ~ 8 x 10-7 mutation / bp. Then, the data were processed through the BotSeqS pipeline without filtering the mutations present in the dbSNP (build 130 and 142). Seven PEN93 specific SNPs were identified in low peak mixtures and 89 PEN93 specific SNPs in high peak mixtures. After normalization for the number of sequenced bases, the "mutation frequency" (number of SNPs / bp specific to PEN93) was 2.71 x 10-6 for samples with low peak and 2.01 x 10- 5 for high peak samples. The difference between the low entry peak and the high entry peak was 7.4 times, within the expected 10-fold dilution range, given the relatively low number of mutations identified in the low entry peak sample. Characterization of BotSeqS specificity

[1154] As a measure of specificity, we identified rare mutations as usual, except that we used mutations that were present on only one tape and not both. Specifically, mutations were present in ≥ 90% of the Watson family members and the reference sequence was present in ≥ 90% of the Crick family members, or vice versa, but they met our other criteria as they were "rare". False Watson and Crick pairs were then created, where the Watson tape had overlapping, but different, coordinates from the Crick tape and vice versa, to determine whether they contained the same mutation by chance. BotSeqS works with low coverage throughout the genome, generated through the bottleneck dilution step, and prevented this analysis in the nuclear DNA. Instead, mtDNA was used due to multiple copies of mtDNA per cell. The coverage of mtDNA with BotSeqS is much greater than that of nuclear DNA and facilitated the identification of overlapping molecules. 30 BotSeqS control libraries were processed in this way and a total of 146 mutations in mtDNA were identified present on only one strand. Using this data set, each sample was searched for overlapping molecules and identified 27 examples. None of Watson and Crick's 27 false pairs shared the same artifact mutation.

[1155] Non-random shear could produce another type of artifact, falsely suggesting that a family's Watson and Crick tapes were actually derived from two different molecules that coincidentally had the same genomic coordinate. To test these artifacts, pairs of the Watson and Crick family were identified that contained the variant on the Watson ribbon and the reference sequence on the Crick ribbon, or vice versa, but this time included heterozygous variants of the germline, rather than just the variants rare, and in nuclear DNA and not in mtDNA. There are many more heterozygous variants in nuclear DNA than in mtDNA because mtDNA is derived only from the oocyte. Disagreements of interest may arise as a result of the diversion of a Watson strip with a Crick strip derived from a different model molecule - that is, non-random shear. Alternatively, disagreements can result from an amplification error in one of the two tapes during an initial PCR cycle. Using our WGS data, we first identified 8,535,891 nuclear heterozygote variants observed among the 30 DNA samples used for the BotSeqS control libraries (median of 268,180 variants per library with a range of 121,851 to 529,922, with the same common variables present in many libraries). Of the 8,535,891 nuclear heterozygous variants, we identified a total of 3,960,818 families (median of 123,134 families per library ranging from 65,832 to 222,135) for which both tapes could be evaluated. Of these,

3,960,807 families had the sequence concordant in the variant position on both tapes; only 11 heterozygous variants were discordant (that is, the variant was present in ≥ 90% of the Watson family members and the reference sequence was present in ≥ 90% of the Crick family members, or vice versa). The rate of heterozygous variants that did not agree with the germ line was 2.78 x 10-6 (11 out of 3,960,818) per bp. This rate is compatible with the known error rate of high-fidelity DNA polymerases and can easily represent an amplification error that occurred on one of the two strands during the first PCR cycle, so it represents an overestimation of shear artifacts. In addition, it is important to note that BotSeqS eliminates these amplification errors, requiring mutations to be observed on both tapes. Since BotSeqS requires mutations to be observed in both strands, the real false positive rate can be estimated at ~ (1/3) (2.78 x 10-6) (2.78 x 10-6) = 2 , 58 x 10-12. Generation of change tables and BotSeqS molecule

[1156] The sequence alignments and the call for variants were performed with the Illumina secondary analysis package (CASAVA

1.8) using ELANDv2 corresponding to the human reference genome GRCh37 / hg19. High-quality readings were selected for further analysis only if they meet all of the following criteria: (i) passed the chastity filter, (ii) readings mapped to a suitable pair, (iii) ≤ 5 mismatches in the reference sequence and (iv) perfect identity to reference the sequence in the first and last five bases of each reading. The sequencing readings were grouped into families based on identical endogenous bar codes with paired ends. Family members were subdivided into two possible sequencing guidelines to determine the number of family members derived from Watson and Crick. The Watson and Crick families had identical genomic coordinates, with each end sequenced in opposite readings. The quality indexes of identical changes within a family were calculated as the average among family members. The output for each BotSeqS library was two annotated tables of changes and model molecules (that is, families). Selection of high quality changes and molecules

[1157] Custom algorithms were written in Microsoft SQL Server Management Studio to query the change tables and molecules for each BotSeqS library. The selection criteria are detailed in Table 44 - Table 48. In general, the selection was based on the quality, clonality and ability to map simple base pair substitutions. For example, it is known that one of the main sources of errors faced by all short readout alignment callers and variants is an artifact that arises when variants are mapped to repetitive regions in the genome, including regions of low complexity and number of variants. copies (see, for example, Li et al., 2014 Bioinformatics 30: 2843-2851). The BotSeqS pipeline eliminates this universal error in a later step, filtering out the genomic noise of repetitive DNA and structural variants (detailed in Table 48). Indels were excluded because they are prone to alignment artifacts and are ~ 10 times less frequent than spontaneous point mutations. High-quality single-base substitutions were defined as those with average quality scores (within the family) of ≥ Q30 and ≥ 2 readings and ≥ 90% mutation fraction on the Watson and Crick tapes. Variants were considered clonal if the position of the variant was present in the WGS data for that sample or observed in> 1 model molecules (ie, both strands of more than one UID). Any positions present in dbSNP130 or dbSNP142 were also excluded. It was realized that dbSNP filtering dramatically minimized recurrent sequencing or mapping artifacts and highly changeable regions. For example, homopolymer sectors (≥8 bp) are active mutation points that flood the list of mutations. It was observed that almost all were filtered with dbSNP142. Finally, families that contained mutations> 1 were excluded as possible mapping artifacts. Calculation of mutation frequency

[1158] Mutation frequencies were determined for each BotSeqS library (see Table 51) by dividing the total number of rare mutations by the total bp sequenced. The total bp sequenced was defined by the number of families x 2 x reading length of each family. The average length of the libraries was ~ 500 bp, so that the 100 bp paired end readings are unlikely to overlap. Only models with perfect identity for the reference sequence in the first and last 5 bp of each reading were considered. Readings were cut, excluding cycles 6 and 7 to ensure quality. Therefore, the actual reading length was 88 bases (100-7-5 = 88). For samples from which the BotSeqS replicated technical libraries were generated, the mean mutation frequency of the technical replicates was considered the sample mutation frequency.

Validation of somatic mutations

[1159] All rare mutations in the nuclear genome and mtDNA have undergone visual inspection of the sequencing readings. For rare nuclear mutations, Sanger sequencing was performed on a representative set (514 of 876 mutations). Of these, 514 of 514 (100%) were confirmed as invisible by Sanger sequencing (excluding COL238 and COL239 samples that did not have a corresponding WGS). This demonstrated that these mutations were not present in the germ line or present in a highly clonal manner. The mutations confirmed to be absent in the Sanger sequence are shown in Table 50. Comparison with cancer genomes

[1160] Nineteen MAF files representing nuclear somatic mutations of 19 types of TCGA tumors were downloaded from synapse.org/#!Synapse:syn1729383 (see, for example, Kandoth et al., 2013 Nature 502: 333-339 ). From the TCGA data, only single-base substitutions were considered and somatic mutations in ultra-mutated tumors were excluded. Somatic mutations in the mitochondrial DNA of colorectal and renal tumors were derived from the supplementary file 2 by Ju et al. (2014 eLife 3). Statistics

[1161] For the study design, no prior analysis of power or randomization was performed because the variance was initially unknown. The aim of the study was to find large biologically significant differences between the cohorts. To find big differences, sample sizes can be small. Even with the small sample size, however, no violations of the assumptions of the tests were detected, including violations about the homogeneity of the variations. The T test and ANOVA analyzes were performed using GraphPad Prism 5.0f. Fisher's exact test was performed using version R 3.2.2. The analysis of the main components was performed in R. All samples analyzed were reported in the manuscript. Results Principles underlying BotSeqS

[1162] The main characteristic of BotSeqS is the dilution of any type of sequencing library before PCR amplification. This dilution creates a bottleneck and allows for efficient random sampling of double-stranded model molecules with a minimal amount of sequencing. Rare mutations, which would normally be masked by an abundance of wild-type sequences in conventional libraries, account for much more of the signal at the corresponding genomic position in a bottleneck library. Dilution also increases the likelihood that the "Watson" and "Crick" strands of a DNA molecule will be sequenced redundantly, a critical feature for BotSeqS 'high accuracy and the relatively small amount of sequencing required to implement it. The presence of the same rare mutation in both strands can substantially decrease artifacts and increase specificity (see, for example, Schmitt et al., 2012 PNAS USA 109: 14508-14513). Finally, the random nature of the dilution allows DNA molecules from the nuclear and mitochondrial genomes to be evaluated in a library. Generation of BotSeqS libraries

[1163] An Illumina TruSeq standard kit without PCR was used to generate 44 BotSeqS libraries from the normal tissues of 34 individuals (Table 43). This included nine individuals with one or two technical replicates. In addition, 10 of our 12 cohorts had more than one biological replica, each containing two to six individuals.

[1164] The preparation of the BotSeqS libraries begins with the random cutting of genomic DNA (Fig. 52). This fragments the genomes into DNA molecules of varying size, each having unique final coordinates called endogenous bar codes (see, for example, Kinde et al., 2011 PNAS USA 108: 9530-9535). After attaching the standard sequencing adapters to the DNA molecules, the library is diluted to reduce the number of molecules in the population. To identify the correct dilution factor, a ten-fold dilution series was evaluated on a MiSeq instrument (Fig. 53). After dilution, PCR amplification of the library generates several copies (duplicates) of each DNA molecule. Endogenous bar codes allow the grouping of sequencing readings into families, also known as UIDs, for unique identifiers (see, for example, Kinde et al., 2011 PNAS USA 108: 9530-9535); each family represents the progeny derived from the PCR of a single ribbon model and each member of a family represents the sequence of a single cluster on the Illumina instrument. Next, we consider that the Watson tape is the sequence derived from the first reading of the sequencing instrument (Illumina P5 adapter) and the Crick tape is the sequence derived from the second reading (Illumina P7 adapter) of each family member (Fig. 52). To be considered a potential mutation, BotSeqS required that the identical sequence change be observed in ≥ 90% of Watson and in ≥ 90% of the Crick family members and that each family be composed of at least two members. BotSeqS libraries were analyzed using an Illumina HiSeq 2500 instrument in fast execution mode, with readings of 100 pairs each. A median of 70 million (M) clusters per library passed the standard Illumina quality filters (range 37 to 188 M clusters per library; Table 43). BotSeqS data processing pipeline

[1165] The purpose of the BotSeqS pipeline was to accurately identify rare somatic point mutations and calculate the frequency of these mutations in the sample. To process the data for this purpose, the raw sequencing data were inserted into Illumina's secondary analysis package (CASAVA 1.8) with ELANDv2 mapping for the human reference genome GRCh37 / hg19. The BotSeqS pipeline starts by selecting high quality readings for analysis (see Materials and Methods). The data are organized into two tables for each BotSeqS library: (i) a "change" table listing all differences from the reference sequence and (ii) a unique molecule table listing all families. Importantly, each table contains tape information; almost half (median of 45%, range from 8% to 62%) of the unique molecules of each BotSeqS library had the Watson and Crick tapes represented in the data set, ensuring specificity in the subsequent mutation analysis. In addition, most BotSeqS libraries (37 of 44) had an average number of family members between 5 and 20 (Fig. 56), further demonstrating that the libraries have been subject to successful bottlenecks.

[1166] To identify rare somatic mutations, it was necessary to eliminate the germ lines and clonal variants from the BotSeqS data (we defined clonal as those present in both chains of more than one model molecule). We performed the sequencing of the entire genome (WGS) from the same DNA sample or from the same libraries that were diluted for BotSeqS in 32 of the 34 individuals in this study (Table 43). For the two remaining individuals (COL238 and COL239), Sanger sequencing was performed to eliminate clonal variables, demonstrating that WGS was not necessary for BotSeqS. The vast majority (92% median, range 88-94%) of the variants were found as a germ line, easily identifiable from the corresponding WGS data set. In addition to clonality, we eliminate potential artifacts considering only well-mapped positions and using other filters (Table 44 - Table 48 and Materials and Methods). The requirement for the presence of mutations in both tapes was in fact necessary because, in the absence of this filter, there were a large number of G> T transversions (Fig. 57), known to represent artifacts in the preparations of the NGS library (see , for example, Costello et al., 2013 Nucleic acid research 41: e67). A "peak" validation experiment was carried out further, mixing the normal DNA of one individual (PEN93) with the normal DNA of another individual (PEN95) for two different reasons. Using BotSeqS, it was possible to detect specific PEN93 SNPs in both samples with a 7.4-fold difference in frequency between low and high peaks, within the expected error of the intended 10-fold difference (see Materials and Methods) .

[1167] Of the 44 BotSeqS libraries, a total of 666 and 876 rare somatic point mutations were identified in mtDNA and nuclear DNA, respectively (Table 49 and Table 50). All rare mutations passed visual inspection and a subset was sequenced by Sanger to confirm that the mutations were not of the germ line or highly prevalent in the evaluated samples (see Materials and Methods). As expected in previous studies, the mtDNA point mutation frequencies (1.40 ± 1.29 x10-5 mutation / bp, mean ± sd) were significantly higher than those of nuclear DNA (5.23 ± 3.47 x 10-7) in 25 control subjects (two drag t tests, P <0.0001; Table 51). The specificity of BotSeqS was further determined using heterozygous calls that differ from the germ line to estimate a false positive rate of 2.58 x 10-12 (see Materials and Methods). Mutation frequencies vary with DNA repair ability and exposure to carcinogens

[1168] It was first asked whether BotSeqS can detect the elevated levels of mutations in normal tissues of individuals deficient in incompatibility repair. Individuals with biallelic inactivating germline mutations in incompatibility repair machines show higher levels of mutation in both normal tissues and tumors (see, for example, Parsons et al., 1995 Scene 268: 738-740, 1995; Science 268: 738-740; and Shlien et al., 2015 Nature genetics 47: 257-262). Therefore, DNA was tested from individuals' normal colon epithelium (COL238 and COL239) with inactivating mutations of the biallelic germline in the Post-Meiotic Segregation 2 (PMS2) gene. Using BotSeqS, it was found that the average frequency of nuclear DNA mutation in these two brothers (6.63 ± 3.47 x10-5 mutations / bp; ages 16 and 18) was significantly higher than in individuals with similar ages (5.13 ± 1.73 x 10-7 for COL235, COL236, COL237, COL374; mean age of 24 years) with efficient incompatibility repair (two-tailed t test, P <0.05, Fig. 53a ). This 129-fold increase in the frequency of nuclear mutation was associated with a significant difference in the nuclear mutation spectrum between the PMS2 + / + and PMS2 - / - cohorts (Fisher's exact test, P = 0.04, Fig. 53b).

[1169] It was also tested whether BotSeqS could identify a high number of mutations in the normal tissues of individuals exposed to environmental carcinogens. The entire genome of urothelial carcinomas of the upper tract was performed previously, representing a type of cancer associated with exposure to aristolochic acid (AA) or smoking (see, for example, Hoang et al., 2013 Science translational medicine 5: 197ra102). Tobacco smoke mutagens and AA are metabolized to form DNA adducts in the normal renal cortex (see, for example, Hoang et al., 2013 Science translational medicine 5: 197ra102; and Randerath et al., 1989 Journal of the

National Cancer Institute 81: 341-347). Four normal renal cortexes matched by age of individuals (KID034, KID035, KID036, KID037; mean age 64 years) with no known exposure to tobacco smoke or AA were compared with the normal renal cortex of three heavy smokers (SA_117, SA_118, SA_119; mean age 65 years), as well as with three individuals exposed to AA (AA_105, AA_124, AA_126; mean age 79 years). The frequencies of nuclear point mutation in smokers and kidneys exposed to AA were significantly higher, at 27 and 36 times, respectively, than in the unexposed controls (one-way ANOVA with Bonferroni's multiple comparison post-test, P <0.0001 for AA and P <0.001 for smoke) (Fig. 53a). This increase in the number of mutations in the nuclear genome was associated with a significantly altered nuclear mutational spectrum (Fisher's exact test with Bonferroni's multiple comparison correction, P = 2.58 x 10-8 for AA and P = 1.51 x 10 -15 for smoke) (Fig. 53b). Interestingly, the mtDNA point mutation frequencies and spectra between the unexposed and exposed groups were not significantly different, despite the dramatic difference in their nuclear genomes (Fig. 53a, b). Rare mutations accumulate with age

[1170] Many strains of evidence indicate that the human body accumulates random mutations with age. BotSeqS was designed to directly measure differences like these and we tested whether the rare frequencies of point mutations in the DNA of three normal human tissues depended on age. The normal colonic epithelium of 11 individuals showed mutation frequencies that increased significantly with age, on average 30 times in mtDNA and 6.1 times in nuclear DNA, for 91 years (see below and Fig. 54; unidirectional ANOVA with multiple Bonferroni) post-comparison test, P <0.001 for both). Likewise, mutation frequencies increased an average of 19 times in mtDNA and 6.5 times in nuclear DNA over 64 years in normal renal cortexes. The frequencies of mutations in the cerebral frontal cortex also increased significantly with age, although more slowly, by 7.3 times in mtDNA and 5.7 times in nuclear DNA for more than 90 years (unidirectional ANOVA with post-test of multiple comparison of Bonferroni, P <0.001 for mtDNA and P <0.05 for nuclear). Summary of the rare frequencies of mutation in normal human tissues Tissue Frequency of mutation (x10-7 mutations / bp) Lifetime Difference Normal genome individuals Small child Young Adult Mid adult age (years) mtDNA lifetime Brain 9 18 ± 7 43 ± 6 131 ± 18 89.5 7.3 Kidney 5 15 nd 277 ± 64 63.8 18.5 Colon 11 12 ± 17 112 ± 43 365 ± 103 90.8 30.4 Nuclear Brain 9 1.1 ± 0.3 2.2 ± 1.1 6.3 ± 2.3 89.5 5.7 Kidney 5 1.2 nd 7.8 ± 1.5 63.8 6.5 Colon 11 1.8 ± 0, 5 5.5 ± 1.6 11 ± 1.5 90.8 6.1 na: not determined

[1171] Within the data set, the frequencies of point mutations in the brain versus colon tissues in three different age groups (children <10 years; adults between 20 and 40 years; and elderly people ≥ 90 years) can be directly compared. Interestingly, the frequency of nuclear mutation in the colon was not significantly different from that of the brain in children (1.81 ± 0.45 x 10-7 in the colon vs. 1.06 ± 0.27 x 10-7 in the brain, Bidirectional ANOVA with Bonferroni multiple comparison post-test, P> 0.05). However, the frequency of mutation in the colon was significantly higher than that of the brain in young adults (5.51 ± 1.62 x10-7 in the colon vs2.16 ± 1.11 x 10-7 in the brain, bidirectional ANOVA with Bonferroni multiple comparison post-test, P <0.05) as well as in elderly adults (1.10 ± 0.15 x 10-6 in the colon vs. 6.29 ± 2.31 x 10-7 in the brain, Bidirectional ANOVA with Bonferroni's multiple comparison post-test, P <0.01) (Fig. 58). No significant differences were found between the frequency of mutation of mtDNA in the colon versus that of the brain in relatively young individuals (children or young adults). However, the frequency of mtDNA mutation in the colon was significantly higher than that of the brain in elderly subjects (3.65 ± 1.03 x 10-5 in the colon vs. 1.31 ± 0.18 x 10-5 in the brain, two-way ANOVA with Bonferroni post-test multiple comparison, P <0.0001) (Fig. 58). Mutational patterns in mtDNA are very different from nuclear DNA

[1172] The spectra of the rare point mutations in each normal tissue studied were examined. Mutations in mtDNA were dominated by transitions (97% in the colon, 89% in the kidney and 91% in the brain) with a heavy ribbon bias, as expected in previous studies12 (Fig. 54 and Table 49). The ratio of transitions to transversions was notably different in mtDNA (mean 15.3) compared to nuclear DNA (mean 1.1) in the three tissues.

[1173] To further assess the differences in mutation frequencies between the two genomes, we calculated the ratio between mtDNA and nuclear mutation frequencies for each individual (Table 51). The frequencies of point mutation in mtDNA were on average 24.5 times higher than the nuclear genome in normal tissues (control cohort, Fig. 59). In patients with a history of exposure or defects in DNA repair, the proportions were significantly lower due to the concomitantly higher number of nuclear (but not mythochondrial) DNA mutations in these individuals compared to those in the control cohort (unidirectional ANOVA with Bonferroni multiple comparison post-test, P <0.05) (Fig. 59). Mutational spectra are tissue specific

[1174] Although rare mutations in mtDNA are dominated by transitions, there are still tissue-specific mtDNA differences that can be seen in the pie charts in Fig. 3. For example, the mitochondrial transitions C: G to T: A were more prominent and the less prominent A: T to G: C transitions in the normal colon (54% and

42%, respectively) and in the brain (51% and 40%, respectively) compared to normal kidney tissues (36% and 53%, respectively). The spectra of mutation in the nuclear DNA of the three tissues were much more diverse. For example, C: G to T: A transitions predominated in the normal colon (44% in the colon compared to 22% in the kidney and 29% in the brain), while the normal kidney and brain harbored a proportionally larger fraction of A: T to G: C transitions (25% in the kidney and 19% in the brain compared to 15% in the colon), as well as A: T to C: G transitions (12% in the kidney and 16% in the brain compared to 5% in the colon). In addition, the transversions from A: T to T: A were more frequent in the kidneys (16%) compared to the colon (6%) and brain (6%). Paired comparisons of mutational spectra within each genome revealed significant differences between the pattern of kidney and colon replacement (Fisher's exact test with Bonferroni's multiple comparison correction, P = 0.0029 in mtDNA and P = 0.0312 in Nuclear DNA).

[1175] The spectra of the rare mutations found in normal kidney and colon tissues have been compared with clonal DNA mutations in cancers derived from the cells of these organs, using publicly available data for the latter (see, for example, Ju et al., 2014 eLife 3 and Kandoth et al., 2013 Nature 502: 333-339). The frontal cortex of the brain was excluded in this analysis because it was not clear what type of tumor should be used for comparison. To look for similarities and differences between the normal and tumor mutation spectra, an analysis of the main components in the nuclear and mtDNA spectra derived from data from the normal renal cortex, normal colon epithelium, clear cell renal carcinoma and colorectal carcinoma. The spectra of the rare mutations in normal colon and kidney tissues were found to be very similar to those of the corresponding type of cancer (Fig. 60).

Example 8: Secure sequencing system

[1176] Genetic mutations underlie many aspects of life and death, including, for example, evolution and disease, respectively (see, for example, Luria et al., 1943 Genetics 28: 491-511; Ro- ach et al., 2010 Science 328: 636-639; Durbin et al., 2010 Nature 467: 1061-1073; Shibata, 2011 Carcinogenesis 32: 123-128; McMahon et al., 2007 N Engl J Med 356: 2614- 2621; Eastman et al., 1998 J Infect Dis 177: 557-564; Chiu et al., 2008 Proc Natl Acad Sci USA 105: 20458-20463; and Fan et al., 2008 Proc Natl Acad Sci USA 105: 16266- 16271). The detection of such mutations, particularly at a stage prior to their dominance in the population, is likely to be essential to optimize diagnosis and / or therapy. For example, in neoplastic diseases, all motivated by somatic mutations, the applications for detecting rare mutants are multiple; they can be used to help identify residual diseases in the surgical margins or lymph nodes, to follow the course of therapy when evaluated in plasma and perhaps to identify patients with surgically curable initial disease when evaluated in feces, sputum, plasma and others body fluids (see, for example, Hoque et al., 2003 Cancer Res 63: 5723-5726; Thunnissen et al., 2003 J Clin Pathol 56: 805-810; and Diehl et al., 2008 Gastroenterology 135: 489 -498).

[1177] This example describes a “Safe-SeqS” (Safe-Sequencing System) to obtain a very high level of precision and sensitivity of the sequence data. Safe-SeqS can be used to assess the fidelity of a polymerase, the precision of synthesis of synthesized nucleic acids in vitro, and the prevalence of mutations in the nucleic acids of nucleic or mitochondrial cells. Safe-SeqS can also be used to detect and / or quantify somatic mosaics and mutations. See also document WO 2012/142213, incorporated herein by reference in its entirety.

Endogenous UID Materials and Methods

[1178] Genomic DNA from human pancreas or cultured lymphoblast cells was prepared using Qiagen kits. The pancreatic DNA was used for the capture experiment and the lymphoblastoid cells were used for the reverse PCR experiment. The DNA was quantified by optical absorbance and with qPCR. The DNA was fragmented to an average size of ~ 200 bp by acoustic shear (Covaris), then repaired at the end, with tail A and connected to Y-shaped adapters, according to standard Illumina protocols. The ends of each model molecule provide endogenous UIDs corresponding to their chromosomal positions. After PCR-mediated libraries amplification with primers inside the adapters, the DNA was captured with a filter containing 2,594 nt corresponding to six cancer genes. After capture, 18 cycles of PCR were performed to ensure sufficient quantities of model for sequencing on an Illumina GA IIx instrument.

[1179] For reverse PCR experiments (Fig. 65), we linked custom adapters (IDT, Table 57) instead of standard Y-shaped Illumina adapters to the sheared cellular DNA. These adapters maintained the complementary region to the universal sequencing primer, but did not have the graft sequences necessary for hybridization with the Illumina GA IIx flow cell. Bound DNA was diluted in 96 wells and the DNA in each 8-well column was amplified with a single forward primer containing one of the 12 index sequences at its 5 'end plus a standard reverse primer (Table 57). Amplifications were performed using Phusion HotStart I (NEB) in 50 µL reactions containing 1X Phusion HF buffer, 0.5 mM dNTPs, 0.5 µM from each forward and reverse primer (both with

5'-phosphorylated) and 1U of Phusion polymerase. The following cycling conditions were used: a cycle of 98◦C for 30s; and 16 cycles of 98◦C for 10s, 65◦C for 30s, and 72◦C for 30s. All 96 reactions were pooled and then purified using a Qiagen Minute Fight PCR Purification Kit (ref. 28004) and a QIAquick Gel Extraction kit (ref. 28704). To prepare the circular models required for reverse PCR, the DNA was diluted to ~ 1 ng / uL and ligated with T4 DNA Ligase (Enzymatics) for 30 minutes at room temperature in a 600uL reaction containing 1X T4 DNA Ligation Buffer and 18,000U of T4 DNA Ligase. The ligation reaction was purified using a Qiagen MinElute kit. Inverse PCR was performed using Phusion Hot Start I in 90 ng circular model distributed in twelve reactions of 50 uL, each containing 1X Phusion HF Buffer, 0.25mM dNTPs, 0.5uM of each of the KRAS primers forward and backward. (Table 57) and 1U of Phusion polymerase. The KRAS-specific initiators contained graft sequences for hybridization with the Illumina GA IIx flow cell (Table 57). The following cycling conditions were used: a cycle of 98 ° C for 2 minutes; and 37 cycles of 98◦C for 10 seconds, 61◦C for 15 seconds, and 72◦C for 10 seconds. The final purification was performed with a NucleoSpin Extract II kit (Macherey-Nagel) and eluted in 20 µL of NE buffer. The resulting DNA fragments contained UIDs composed of three sequences: two endogenous, represented by the two ends of the original sheared fragments plus the exogenous sequence introduced during the indexing amplification. As 12 exogenous sequences were used, this increased the number of distinct UIDs by 12 times compared to that obtained without exogenous UIDs. This number can be easily increased by using a larger number of distinct initiators. Exogenous UIDs

[1180] Genomic DNA from normal human colonic mucous membranes or blood lymphocytes was prepared using Qiagen kits.

Colonic mucosa DNA was used for experiments with CTNNB1 and mitochondrial DNA, while lymphocyte DNA was used for experiments in CTNNB1 and in polymerase fidelity.

The DNA was quantified with Digital PCR using primers that amplified single copy genes from human cells (Analysis of Polymerase Fidelity and CTNNB1), qPCR (mitochondrial DNA) or by optical absorbance (oligo-nucleotides). Each strand of each model molecule was encoded with a 12 or 14 base UID using two cycles of specific amplification PCR, as described in the text and in Fig. 63. The specific amplicon primers contained sequences of universal markers at their ends 5 'for a later amplification step.

The UIDs constituted 12 or 14 random nucleotide sequences attached to the 5 'end of the advanced amplicon specific primers (Table 57). These initiators can generate 16.8 and 268 million distinct UIDs, respectively.

It is important that the number of distinct UIDs far exceeds the number of molecules in the original model to minimize the likelihood that two different original models will acquire the same UID.

UID assignment PCR cycles included Phusion Hot Start II (NEB) in a 45 μl reaction containing 1X Phusion HF buffer, 0.25 mM dNTPs, 0.5 uM each forward (containing 12-14 Ns ) and reverse primers and 2U of Phusion polymerase.

To maintain the final concentrations of the model <1.5 ng / uL, multiple wells were used to create some libraries.

The following cycling conditions were used: an incubation of 98◦C for 30 seconds (to activate Phusion Hot Start II); and two cycles of 98◦C for 10 seconds, 61◦C for 120 seconds, and 72◦C for 10 seconds.

To ensure complete removal of the first cycle primers, each well was digested with 60 U of a specific single-stranded DNA nuclease (Exonuclease-I; Enzymatics) at 37 ° C for 1 hour.

After 5 minutes of heat inactivation at 98◦C, complementary primers were added to the introduced universal markers (Table 57) at a final concentration of 0.5 µM each. These primers contained two terminal phosphorothioates to make them resistant to any residual Exo- nuclease-I activity. They also contained 5 'graft sequences necessary for hybridization with the Illumina GA IIx flow cell. Finally, they contained an index sequence between the graft sequence and the universal tag sequence. This index sequence allows PCR products from multiple different individuals to be analyzed simultaneously in the same flow cell compartment of the sequencer. The following cycling conditions were used for the subsequent 25 PCR cycles: 98◦C for 10 seconds and 72◦C for 15 seconds. No intermediate purification steps were performed in an effort to reduce losses of model molecules.

[1181] After the second round of amplification, the wells were consolidated and purified using a Qiagen QIAquick PCR Purification Kit (cat. 28104) and eluted in 50 μl of EB Buffer (Qiagen). Fragments of the expected size were purified after agarose gel electrophoresis (mtDNA libraries) or polyacrylamide (all other libraries). For purification of the agarose gel, the eight 6 µL aliquots were loaded into the wells of a 2% size selection gel (Invitogen) and the bands of the expected size were collected in the Buffer EB, as specified by the manufacturer. For purification of the polyacrylamide gel, ten aliquots of 5 µL were loaded into the wells of a 10% TBE polyacrylamide gel (Invitrogen). The gel slices containing the fragments of interest were excised, crushed and eluted as described elsewhere (see, for example, Durbin et al., 2010 Nature 467: 1061-1073). Analysis of fusion polymerase fidelity

[1182] The amplification of a human genomic DNA fragment within the BMX gene (RefSeq Accession NM_203281.2) was performed for the first time using the PCR conditions described above. The model was diluted so that an average of one model molecule was present in every 10 wells of a 96-well PCR plate. Fifty PCR reactions were then performed in 1X Phusion HF buffer, 0.25 mM dNTPs, 0.5 µM of each forward and reverse primer (Table 57) and 2U of Phusion polymerase. The cycling conditions were 98 ° C for 30 seconds; and 19 cycles of 98◦C for 10 seconds, 61◦C for 120 seconds and 72◦C for 10 seconds. The primers were removed by digestion with 60 U of Exonuclease-I at 37◦C for 1 hour, followed by 5 minutes of heat inactivation at 98◦C. No purification of the PCR product was performed, either before or after digestion with Exonuclease-I. The entire content of each well was then used as a model for the exogenous UIDs strategy described above. Sequencing

[1183] The sequencing of all libraries described above was performed using an Illumina GA IIx instrument, as specified by the manufacturer. The total length of the readings used for each experiment ranged from 36 to 73 bases. The base call and sequence alignment were carried out with the Eland (Illumina) pipeline. Only high quality readings that meet the following criteria were used for subsequent analysis: (i) the first 25 bases passed the standard Illumina chastity filter; (ii) all bases in the reading had a quality index ≥20; and (iii) ≤ 3 incompatibilities with the expected sequences. For exogenous UID libraries, we additionally require that UIDs have a quality index ≥30. A relatively high frequency of errors was observed at the end of readings in the endogenous UID libraries prepared with the standard Illumina protocol, presumably introduced during the shear or final repair, so that the first and last three bases of these labels were excluded from the analysis. Safe-SeqS Analysis

[1184] High-quality readings have been grouped into UID families based on their endogenous or exogenous UIDs. Only UID families with two or more members were considered. Such UID families included the vast majority (≥99%) of sequencing readings. To ensure that the same data was used for both conventional and Safe-SeqS analyzes, UID families containing only one member were also excluded from the conventional analysis. In addition, a base was only identified as a "mutant" in the conventional sequencing analysis if the same variant was identified in at least two members of at least one UID family (ie, two mutations) when comparing the conventional analysis to that of the Safe-SeqS with exogenous UIDs. For comparison with Safe-SeqS with indigenous UIDs, we require at least two members from each of the two UID families (that is, four mutations) to identify a position as "mutant" in conventional analysis. With endogenous or exogenous UIDs, a super-mutant was defined as a family of UIDs in which ≥95% of members shared the same mutation. Thus, UID families with <20 members had to be 100% identical in the changing position, while a combined rate of 5% replication and sequencing error was allowed in UID families with more members. To determine polymerase fidelity using Safe-SeqS and compare the results with previous analyzes of Polymerase fusion by Phusion, it was necessary to realize that the previous analyzes would only detect mutations present in both chains of PCR products (see , for example, Shibata, 2011 Carcinogenesis 32: 123-128). This would be equivalent to the analysis of PCR products generated with one cycle less with Safe-SeqS, and the appropriate correction was made in Table 53A. Unless otherwise stated, all values listed in the text and in the

Tables represent means and standard deviations. Results endogenous UIDs

[1185] UIDs, sometimes called barcodes or indexes, can be assigned to fragments of nucleic acids in various ways. These include the introduction of exogenous sequences via PCR or ligation. Even more simply, the randomly cut genomic DNA contains UIDs consisting of the sequences at the two ends of each sheared fragment (Fig. 62 and Fig. 65). The paired end sequencing of these fragments produces UID families that can be analyzed as described above. To employ such endogenous UIDs in Safe-SeqS, two separate approaches were used: one designed to evaluate many genes simultaneously and the other designed to evaluate a single genetic fragment in depth (Fig. 62 and Fig. 65, respectively) .

[1186] For the evaluation of multiple genes, the standard Illumina sequencing adapters were attached to the ends of the DNA fragments cut to produce a standard sequencing library and then captured genes of interest in a solid phase. In this experiment, a library made from the DNA of ~ 15,000 normal cells was used and 2,594 bp of six genes were targeted for capture. After excluding the known single nucleotide polymorphisms, 25,563 apparent mutations were also identified, corresponding to 2.4 x10-4 ± mutations / bp (Table 52). Based on previous analyzes of mutation rates in human cells, at least 90% of these apparent mutations are likely to represent mutations introduced during model and library preparation or base call errors. Note that the error rate determined here (2.4 x 10-4 mutations / bp) is considerably less than that reported in experiments using the Illuminina instrument, because we use very strict criteria for base calls.

[1187] With Safe-SeqS analysis of the same data, it was determined that 69,505 original model molecules were evaluated in this experiment (that is, 69,505 UID families were identified, with an average of 40 members per family, Table 52 ). All polymorphic variants identified by conventional analysis were also identified by Safe-SeqS. However, only 8 super-mutants were observed among these families, corresponding to 3.5 x 10-6 mutations / bp. Thus, Safe-SeqS has reduced presumptive sequencing errors by at least 70 times.

[1188] The Safe-SeqS analysis can also determine which range of a model is mutated; therefore, an additional criterion for calling mutations may require the mutation to appear in only one or both bands of the double-stranded model originally. Massively parallel sequencers are able to obtain sequence information from both ends of a model in two sequential readings. (This type of sequencing experiment is called a "paired end" on the Illumina platform, but similar experiments can be performed on other sequencing platforms, where they can be called by another name.) double-stranded models can be differentiated by the observed orientation of the strings and the order in which they appear when the sequence information is obtained from both ends. For example, a pair of UID tapes can consist of the following two sets of sequences when each end of a model is sequenced in sequential readings: 1) A sequence in the sensory orientation that starts at position 100 of chromosome 2 in first reading followed by a sequence in the antisense orientation that starts at position 400 of chromosome 2 in the second reading; and 2) A sequence in the antisense orientation that starts at position 400 of chromosome 2 in the first reading followed by a sequence in the sensory orientation that starts at position 100 of chromosome 2 in the second reading. In the capture experiment described above, 42,222 of 69,505 UIDs (representing 21,111 original double stranded molecules) in the region of interest represented UID strand pairs. These 42,222 UIDs comprised

1,417,838 bases in the region of interest. By allowing a mutation to occur only within the UID strand pairs (on one or both strands), two super mutants were observed, producing a mutation rate of 1.4 x 10-6 super mutants / bp. When requiring a mutation to occur on only one strand of a pair of UID strands, only one super mutant was observed, producing a mutation rate of 7.1 x 10-7 super mutants / bp. When requiring a mutation to occur on both strands of a pair of UID tapes, only one super mutant was observed, producing a mutation rate of 7.1 x 10-7 super mutants / bp. Therefore, requiring mutations to occur in only one or both types of models can further increase the specificity of Safe-SeqS.

[1189] A strategy employing endogenous UIDs was also used to reduce false-positive mutations after profound sequencing of a single region of interest. In this case, a library prepared as described above from ~ 1,750 normal cells was used as a model for inverse PCR employing primers complementary to a gene of interest, so that the PCR products could be used directly for sequencing (Fig . 65). With conventional analysis, an average of 2.3 x 10-4 mutations / bp was observed, similar to that observed in the capture experiment (Table 52). Given that only 1,057 independent molecules of normal cells were evaluated in this experiment, as determined by Safe-

SeqS, all the mutations observed in the conventional analysis probably represented false positives (Table 52). With Safe-SeqS analysis of the same data, no super mutants were identified in any position. Exogenous UIDs

[1190] Although the results described above show that Safe-SeqS can increase the reliability of massively parallel sequencing, the number of different molecules that can be examined using endogenous UIDs is limited. For fragments cut to an average size of 150 bp (range 125-175), sequencing 36 base pairs in pairs can evaluate a maximum of ~ 7,200 different molecules containing a specific mutation (2 readings x 2 orientations x 36 bases / readings x 50 bases of variation at each end of the fragment). In practice, the actual number of UIDs is less because the shear process is not entirely random.

[1191] To make more efficient use of the original models, a Safe-SeqS strategy was developed that employed a minimum number of enzymatic steps. This strategy also allowed the use of degraded or damaged DNA, such as that found in clinical samples or after treatment with bisulfite for the examination of cytosine methylation. As shown in Fig. 63, this strategy employs two sets of PCR primers. The first set is synthesized with standard phosphoramidite precursors and contains sequences complementary to the gene of interest at the 3 'end and different tails at the 5' ends of the forward and reverse primers. The different tails allowed for universal amplification in the next stage. Finally, there was a patch of 12 to 14 random nucleotides between the tail and the sequence-specific nucleotides in the direct primer. Random nucleotides form UIDs. An equivalent way of assigning UIDs to fragments, not used in this study, would employ 10,000 forward primers and 10,000 reverse primers synthesized in a microarray. Each of those 20,000 primers would have specific primers for the gene at the 3 'ends and one of 10,000 specific, predetermined, non-overlapping UID sequences at the 5' ends, allowing 108 (ie, [104] 2) possible combinations of UID. In either case, two cycles of PCR are performed with the primers and a high-fidelity polymerase, producing a single-stranded double-stranded DNA fragment from each of the two strands of each original model molecule (Fig. 63). Residual UID assignment primers, unused, are removed by digestion with a specific single-chain exonuclease without further purification, and two new primers are added. As an alternative or in addition to this management, you can use a silica column that selectively retains larger size fragments or you can use solid phase reversible immobilization spheres (SPRI) under conditions that selectively retain larger fragments to eliminate minor and nonspecific amplification artifacts. This purification can potentially help to reduce the accumulation of primer-dimer in later stages. The new initiators, complementary to the tails introduced in the UID assignment cycles, contain graft sequences at the 5 'ends, allowing solid phase amplification in the Illumina instrument and phosphorothioate residues at the 3' ends to make them resistant to any remaining exonuclease. After 25 additional PCR cycles, the products are loaded onto the Illumina instrument. As shown below, this strategy allowed us to evaluate most of the input fragments and was used for several illustrative experiments. Analysis of DNA polymerase fidelity

[1192] The measurement of error rates for DNA polymerases is essential for their characterization and determines the situations in which they are

These enzymes can be used. The error rate of the Polymerase polymerase was measured, since this polymerase has one of the lowest reported error frequencies of any commercially available enzyme and therefore presents a particular challenge for a vitro-based approach. A single model molecule of human DNA, comprising a segment of a human gene chosen arbitrarily, was first amplified through 19 rounds of PCR. The PCR products of these amplifications, in their entirety, were used as models for the Safe-SeqS, as described in Fig. 63. In seven independent experiments of this type, the number of UID families identified by sequencing was 624,678 ± 421,274, which is consistent with an amplification efficiency of 92 ± 9.6% per PCR round.

[1193] The error rate of the Phusion polymerase, estimated by cloning PCR products encoding β-galactosidase in plasmid vectors and transforming into bacteria, is reported by the manufacturer to be 4.4 x 10 -7 errors / bp / PCR cycle. Even with base calls with very high accuracy, conventional analysis of Illumina sequencing data revealed an apparent error rate of 9.1 x10-6 errors / bp / PCR cycle, more than an order of magnitude higher than greater than the reported Phusion polymerase error rate (Table 53A). On the other hand, the Safe-SeqS of the same data revealed an error rate of 4.5 x 10-7 errors / bp / PCR cycle, almost identical to the measurement for the polymerase Phusion in biological assays (Table 53A). The vast majority (> 99%) of these errors were single-base substitutions (Table 54A), consistent with previous data on mutation spectra created by other prokaryotic DNA polymerases (Tindall et al. 1988 Biochemistry 27: 6008-6013 ; de Boer et al., 1988 Genetics 118: 181-191; Eckert et al., 1990 Nucleic Acids Res 18: 3739-3744).

[1194] Safe-SeqS also allowed a determination of the total number of distinct mutational events and an estimate of the PCR cycle in which the mutation occurred. Nineteen PCR cycles were performed in wells containing a single model molecule in these experiments. If a polymerase error occurred in cycle 19, there would be only one super mutant produced (from the chain containing the mutation). If the error occurred in cycle 18, there must be two super mutants (derived from the mutant chains produced in cycle 19), etc. Therefore, the cycle in which the error occurred is related to the number of super mutants that contain that error. Data from seven independent experiments show a relatively consistent number of total polymerase errors observed ((2.2 ± 1.1 x 10-6 distinct mutations / bp), in good agreement with the expected number of simulation observations (1, 5 ± 0.21 x 10-6 distinct mutations / bp). The data also shows a highly variable time of occurrence of polymerase errors between experiments (Table 55). It is difficult to obtain this type of information using their conventional analysis. next generation sequencing data, partly due to the prohibitively high apparent mutation rate noted above. Analysis of oligonucleotide composition

[1195] A small number of errors during the synthesis of oligonucleotides from phosphoramidite precursors is tolerable for most applications, such as PCR or routine cloning. However, for synthetic biology, where many oligonucleotides must be joined, these errors represent a major obstacle to success. Intelligent strategies to make the gene-building process more efficient have been developed (see, for example, Kosuri et al., 2010 Nat Biotechnol 28: 1295-129; and Matzas et al., 2010 Nat Biotechnol 28: 1291-1294 ), but all such strategies would benefit from a more accurate synthesis of the oligonucleotides themselves. Determining the number of errors in the synthesized oligonucleotides is difficult because the fraction of oligonucleotides containing errors may be less than the sensitivity of conventional next-generation sequencing analyzes.

[1196] To determine whether Safe-SeqS could be used for this determination, standard phosphoramidite chemistry was used to synthesize an oligonucleotide containing 31 bases that were designed to be identical to those analyzed in the polymerase fidelity experiment described above. In the synthetic oligonucleotide, the 31 bases were surrounded by sequences complementary to the primers that could be used for the Safe-SeqS UID assignment steps (Fig. 63). Performing Safe-SeqS on ~ 300,000 oligonucleotides, it was found that there were 8.9 ± 0.28 x 10-4 and that these errors occurred along the oligonucleotides (Fig. 66A). Oligonucleotides contained a large number of insertion and exclusion errors, representing 8.2 ± 0.63% and 25 ± 1.5% of the total super-mutants, respectively. It is important to note that both the position and the nature of the errors were highly reproducible among seven independent replicates of this experiment performed on the same oligonucleotide batch (Fig. 66A). This nature and distribution of errors had little in common with the errors produced by the Phusion polymerase (Fig. 66B and Table 56), which were distributed in the expected stochastic pattern among replicated experiments. The number of errors in the oligonucleotides synthesized with phosphoramidites was approximately 60 times greater than in the equivalent products synthesized by the Phusion polymerase. These data, in toto, indicate that the vast majority of errors in the first were generated during their synthesis and not during the Safe-SeqS procedure.

[1197] Does Safe-SeqS preserve the ratio of mutants: normal sequences in the original models? To resolve this issue, two 31-base oligonucleotides of identical sequence, with the exception of nt 15

(50:50 C / G instead of T) were synthesized and mixed in nominal fractions / normal fractions of 3.3% and 0.33%. Through the Safe-SeqS analysis of the oligonucleotide mixtures, it was found that the ratios were 2.8% and 0.27%, respectively. Thus, the UID assignment and amplification procedures used in Safe-SeqS do not significantly alter the proportion of strings of variants and therefore provide a reliable estimate of this proportion when unknown. This is also supported by the reproducibility of variant fractions when analyzed in independent Safe-SeqS experiments (Fig. 66A). DNA sequence analysis of normal human cells

[1198] The exogenous UID strategy (Fig. 63) was then used to determine the prevalence of rare mutations in a small region of the CTNNB1 gene of ~ 100,000 normal human cells from three unrelated individuals. By comparing the number of UID families obtained in the Safe-SeqS experiments (Table 53B), it was calculated that the majority (78 ± 9.8%) of the input fragments were converted into UID families. There was an average of 68 UID members / families, easily fulfilling the necessary redundancy for Safe-SeqS (Fig. 67). Conventional analysis of Illumina sequencing data revealed an average of 118,488 ± 11,357 mutations among the ~ 560 Mb of sequence analyzed per sample, corresponding to an apparent mutation prevalence of 2.1 ± 0.16 x 10-4 mutations / bp (Table 53B). Only an average of 99 ± 78 super-mutants was observed in the Safe-SeqS analysis. The vast majority (> 99%) of the super-mutants were single-base substitutions and the calculated mutation rate was 9.0 ± 3.1 x 10-6 mutations / bp (Table 54B). Thus, Safe-SeqS reduced the apparent frequency of mutations in genomic DNA by at least 24 times (Fig. 64).

[1199] A possible strategy to increase the specificity of Safe-SeqS is to perform the amplification of the library (and possibly the UID assignment cycles) in several wells. This can be done in just 2 or 384 wells using standard PCR plates or scaled to many more wells when using a microfluidic device (thousands to millions). When executed in this way, the indexing sequences can be introduced in models that are exclusive to the wells in which the model is amplified. Rare mutations, therefore, should give rise to two super mutants (that is, one from each chain), both with the same well index sequence. When performing Safe-SeqS with exogenous UIDs in the CTNNB1 models described above and diluted in 10 wells (each well producing amplified models with a different index sequence), the mutation rate was reduced even more than 9.0 ± 3.1 x 10-6 to 3.7 ± 1.2 x 10-6 super-mutants / bp. Therefore, analyzing models in several compartments - in order to generate differentially encoded models based on the compartment in which the models were amplified - can be an additional strategy to increase the specificity of Safe-SeqS. Analysis of DNA sequences from mitochondrial DNA

[1200] The identical strategy was applied to a short segment of mitochondrial DNA in ~ 1,000 cells from each of the seven unrelated individuals. Conventional analysis of Illumina sequencing libraries produced with the Safe-SeqS procedure (Fig. 63) revealed an average of 30,599 ± 12,970 mutations between ~ 150 Mb of analyzed sequence per sample, corresponding to an apparent prevalence of mutation of 2 , 1 ± 0.94 x 10-4 mutations / bp (Table 53C). Only 135 ± 61 super-mutants were observed in the Safe-SeqS analysis. As in the CTNNB1 gene, the vast majority of mutations were single-base substitutions, although occasional single-base deletions were also observed (Table 54C). The mutation rate calculated in the analyzed mtDNA segment was 1.4 ± 0.68 x 10-5 mutations.

tions / bp (Table 53C). Thus, Safe-SeqS reduced the apparent frequency of mutations in genomic DNA by at least 15 times. Example 9: Detection of genetic and protein biomarkers in combination with the detection of aneuploidy

[1201] Samples from several patients were tested for the presence of genetic biomarkers (NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A and / or GNAS) and protein biomarkers (CA19-9, CEA, HGF, OPN, CA125, prolactin, TIMP-1 and / or MPO). The same samples were also tested for the presence of aneuploidy using the WALDO methods described here. The results are shown in Table 59. As can be seen, the test for genetic and protein biomarkers can complement the aneuploidy test (for example, some patients are negative for the test for genetic and protein biomarkers, although they are positive for aneuploidy and vice versa), so that the presence of cancer can be more accurately detected and completely using both tests.

[1202] Once an individual is identified as having cancer by testing for genetic biomarkers and proteins, the aneuploidy test, or both, the individual may undergo further diagnostic tests and / or increased monitoring (for example, any the various additional diagnostic tests and / or enhanced monitoring methods described here) and / or administering a therapeutic intervention (for example, any of the various therapeutic interventions described here).

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[1232] Table 2. Histopathological and clinical characteristics of cancer patients and healthy controls, Tumor CancerSEEK CancerSEEK Primary sample ID Type AJCC Histopathology Logistics Result Patient ID # plasma # Sample ID # Age Sex Race tumor St Age Histopathology Score Regression CRC test 455 CRC 455 PLS 1 Not Available 60 Male Caucasian Colorectal I Adenocarcinoma 0.681 Negative CRC 456 CRC 456 PLS 1 CRC 456 PT1 59 Female Caucasian Colorectal I Adenocarcinoma 0.986 Positive CRC 457 CRC 457 PLS 1 CRC 457 PT1 69 Female Caucasian Colorectal II Adenocarcinoma 458 CRC 458 PLS 1 CRC 458 PT1 70 Colorectal Caucasian Female II Adenocarcinoma 0.693 Negative CRC 459 CRC 459 PLS 1 CRC 459 PT1 43 Colorectal Caucasian II Adenocarcinoma 0.515 Negative CRC 460 CRC 460 PLS 1 CRC 460 PT1 72 Colorectal Caucasian Male Adenocarcinoma II 7 Collarectal Caucasian II CRC 461 CRC 461 PLS 1 CRC 461 PT1 70 Male Caucasian Colorectal I Adenocarcinoma 0.117 Negative CRC 462 CRC 462 PLS 1 CRC 462 PT1 67 Colorectal Caucasian Female I Adenocarcinoma 0.381 Negative CRC 463 CRC 463 PLS 1 CRC 463 PT1 47 Colorectal Caucasian Female I Adenocarcinoma 0.892 Positive CRC 464 CRC 464 PLS 1 CRC 464 PT1 78 Male Adenocarcinoma Colectal Negative CRC 465 CRC 465 PLS 1 CRC 465 PT1 78 Male Caucasian Colorectal II Adenocarcinoma 0.698 Negative CRC 466 CRC 466 PLS 1 CRC 466 PT1 56 Female Caucasian Colorectal III Adenocarcinoma 0.356 Negative CRC 467 CRC 467 PLS 1 CRC 467 PT1 51 Male Caucasian 0.878 Positive CRC 468 CRC 468 PLS 1 CRC 468 PT1 66 Male Caucasian Colorectal III Adenocarcinoma 0.998 Positive CRC 469 CRC 469 PLS 1 CRC 469 PT1 80 Female Caucasian Colorectal II Adenocarcinoma 0.117 Negative CRC 470 CRC 470 PLS 1 CRC 470 PT1 53 Female II Caucasian Female Adenocarcinoma 0.967 Positive CRC 471 CRC 471 PLS 1 CRC 471 PT1 72 Female Caucasian Colorectal III Adenocarcinoma 0.915 Positive CRC 472 CRC 472 PLS 1 CRC 472 PT1 66 Male Caucasian Colorectal II Adenocarcinoma 0.574 Negative CRC 473 CRC 473 PLS 1 CRC 473 PT1 81 Male Caucasian Colorectal II Adenocarcinoma 0.934 Positive CRC 474 CRC 474 PLS 1 CRC 474 PT1 63 Female Caucasian Colorectal III CR7 47 Adenocarcinoma 0 CR 475 PLS 1 CRC 475 PT1 72 Colorectal Caucasian Female I Adenocarcinoma 0.750 Negative CRC 476 CRC 476 PLS 1 Colorectal Caucasian II Adenocarcinoma 0.999 Positive CRC 477 CRC 477 PLS 1 CRC 477 PT1 78 Caucasian Colorectal II CRC 477 Adenocarcinoma CRC 478 PLS 1 CRC 478 PT1 63 Female Caucasian Colorectal III Adenocarcinoma 0.439 Negative CRC 479 CRC 479 PLS 1 CRC 479 PT1 47 Male Caucasian Colorectal II Adenocarcinoma 0.969 Positive CRC 480 CRC 480 PLS 1 CRC 480 PT1 59 Male Caucasian Colorectal III Adenocarcinoma 481 CRC 481 PLS 1 CRC 481 PT1 83 Colorectal Caucasian Female II Adenocarcinoma 0.951 Positive CRC 482 CRC 482 PLS 1 CRC 482 PT1 59 Male Caucasian Colorectal III Adenocarcinoma 0.606 Negative CRC 483 CRC 483 PLS 1 CRC 483 PT1 79 Female Caucasian Colorectal I Adenocarcinoma 0.116 Negative CRC 484 CRC 484 PLS 1 CRC 484 PT1 73 Male Black Colorectal I Adenocarcinoma 0.904 Positive CRC 486 CR6 486 PT1 71 Colorectal Black Female I Adenocarcinoma 0.973 Positive CRC 487 CRC 487 PLS 1 CRC 487 PT1 57 Colorectal Black Female I Adenocarcinoma 0.117 Negative CRC 488 CRC 488 PLS 1 Not Available 22 Colorectal Black Female II Adenocarcinoma 0.988 Positive CRC 489 CRC 489 PLS PT1 67 Colorectal Caucasian Female II Adenocarcinoma 0.991 Positive CRC 490 CRC 490 PLS 1 CRC 490 PT1 71 Colorectal Caucasian Male III Adenocarcinoma 0.397 Negative CRC 491 CRC 491 PLS 1 CRC 491 PT1 67 Colorectal Black Male III Adenocarcinoma 0.840 CRC 492 CRC 492 492 PT1 74 Male Caucasian Colorectal III Adenocarcinoma 0.906 Positive CRC 493 CRC 493 PLS 1 CRC 493 PT1 74 Male Caucasian Colorectal II Ade nocarcinoma 0.739 Negative CRC 494 CRC 494 PLS 1 CRC 494 PT1 80 Male Caucasian Colorectal II Adenocarcinoma 0.448 CRC 495 CRC 495 PLS 1 Not Available 81 Male Caucasian Colorectal I Adenocarcinoma 0.116 Negative CRC 496 CRC 496 PLS 1 CRC 496 PT1 72 Female Caucasian Adenocarcinoma 0.330 Negative CRC 497 CRC 497 PLS 1 CRC 497 PT1 62 Male Caucasian Colorectal III Adenocarcinoma 0.990 Positive CRC 498 CRC 498 PLS 1 CRC 498 PT1 63 Male Caucasian Colorectal III Adenocarcinoma 0.944 Positive CRC 499 CRC 499 Female Colet 1 Not Available Adenocarcinoma 0.543 Negative CRC 500 CRC 500 PLS 1 Not Available 64 Male Caucasian Colorectal I Adenocarcinoma 0.308 Negative CRC 501 CRC 501 PLS 1 CRC 501 PT1 65 Caucasian Female Colorectal I Adenocarcinoma 0.490 Negative CRC 502 CRC 502 PLS 1 CRC 502 PT1 68 Male Caucasian Female Adenocarcinoma 0.121 Negative CRC 503 CRC 503 PLS 1 CRC 503 PT1 57 Male Caucasian Colorectal I Adenocarcinoma 0.99 3 Positive CRC 504 CRC 504 PLS 1 CRC 504 PT1 75 Male Caucasian Colorectal III Adenocarcinoma 0.999 Positive

CRC 505 CRC 505 PLS 1 CRC 505 PT1 47 Colorectal Caucasian Female III Adenocarcinoma 0.323 Negative CRC 506 CRC 506 PLS 1 CRC 506 PT1 66 Colorectal Caucasian III Adenocarcinoma 0.680 Negative CRC 507 CRC 507 PLS 1 CRC 507 PT1 0 Male Caucasian Male Colectal CRC 508 Negative CRC 508 PLS 1 CRC 508 PT1 80 Colorectal Caucasian Female I Adenocarcinoma 0.343 CRC 509 CRC 509 PLS 1 Male Not Available 61 Colorectal Caucasian I Adenocarcinoma 0.480 Negative CRC 510 CRC 510 PLS 1 Not Available 58 Colorectal Caucasian Male 0.3 Adenocarcinoma Negative CRC 511 CRC 511 PLS 1 CRC 511 PT1 50 Colorectal Caucasian II Female Adenocarcinoma 0.125 Negative CRC 512 CRC 512 PLS 1 CRC 512 PT1 69 Colorectal Caucasian II Female Adenocarcinoma 0.868 Positive CRC 513 CRC 513 PLS 1 CRC 513 PT1 72 Male Adenocarcinoma Positive CRC 514 CRC 514 PLS 1 Not Available 89 Male Black Colorectal II Adenocarcinoma 0.998 Positive CRC 515 CRC 515 PL S 1 CRC 515 PT1 38 Male Caucasian Colorectal II Adenocarcinoma 0.640 Negative CRC 516 CRC 516 PLS 1 CRC 516 PT1 78 Male Caucasian Colorectal II Adenocarcinoma 0.664 Negative CRC 517 CRC 517 PLS 1 CRC 517 PT1 61 Female Caucasian Colorectal CRC 518 Negative CR185 518 PLS 1 CRC 518 PT1 54 Colorectal Caucasian Female III Adenocarcinoma 0.999 Positive CRC 519 CRC 519 PLS 1 Not Available 57 Colorectal Caucasian Male III Adenocarcinoma 0.117 Negative CRC 520 CRC 520 PLS 1 CRC 520 PT1 86 Colorectal Caucasian Female III 0.974 CRC Positive CR21 521 PLS 1 CRC 521 PT1 65 Colorectal Caucasian Female III Adenocarcinoma 0.598 Negative CRC 522 CRC 522 PLS 1 CRC 522 PT1 52 Male Colorectal Caucasian II Adenocarcinoma 0.868 Positive CRC 523 CRC 523 PLS 1 CRC 523 PT1 47 Male Caucasian Colorectal III.4 Adenocarcino CRC 524 PLS 1 CRC 524 PT1 59 Male Caucasian Colorectal II Adenocarcinoma 0.983 Positive CRC 525 CRC 525 PLS 1 CRC 525 PT1 70 Male Caucasian Colorectal III Adenocarcinoma 0.968 Positive CRC 526 CRC 526 PLS 1 CRC 526 PT1 69 Male Caucasian Colorectal III Adenocarcinoma 0.521 Negative CRC 527 CRC 527 PLS 1 CRC 527 PT1 71 Male Caucasian Colorectal III Adenocarcinoma CR28 528 CR28 528 CR28 Positive CR28 528 PT1 47 Female Caucasian Colorectal II Adenocarcinoma 0.338 Negative CRC 529 CRC 529 PLS 1 CRC 529 PT1 81 Male Caucasian Colorectal II Adenocarcinoma 0.116 Negative CRC 530 CRC 530 PLS 1 CRC 530 PT1 91 Male Caucasian Colorectal II Adenocarcinoma 1 CR56 531 CRC 531 PT1 69 Colorectal Caucasian Female III Adenocarcinoma 0.540 Negative INDI 001 INDI 001 PLS 1 INDI 001 PT1 70 Caucasian Lung II Non-small cell lung cancer 0.926 Positive INDI 002 INDI 002 PLS 1 INDI 002 PT1 55 Caucasian Female Lung non-lung cell cancer small 0.621 Negative INDI 003 INDI 003 PLS 1 INDI 003 PT1 78 Male Caucasian Lung III Non-small cell lung cancer 0.999 Pos itive INDI 004 INDI 004 PLS 1 INDI 004 PT1 75 Male Caucasian Lung I Non-small cell lung cancer 0.936 Positive INDI 005 INDI 005 PLS 1 INDI 005 PT1 70 Male Caucasian Lung I Non-small cell lung cancer 0.779 Negative INDI 007 INDI 007 PLS 1 INDI 007 PT1 69 Female Caucasian Lung II Non-small cell lung cancer 0.614 Negative INDI 009 INDI 009 PLS 1 INDI 009 PT1 67 Male Caucasian Lung II Non-small cell lung cancer 0.985 Positive INDI 010 INDI 010 PLS 1 INDI 010 PT1 76 Male Caucasian Lung II Cancer non-small cell lung 0.926 Positive INDI 011 INDI 011 PLS 1 INDI 011 PT1 60 Male Caucasian Lung I Cancer lung non-small cell 0.362 Negative INDI 012 INDI 012 PLS 1 INDI 012 PT1 65 Male Caucasian Lung I Non-small cell lung cancer 0.966 Positive INDI 013 INDI 013 PLS 1 INDI 013 PT1 78 Male Caucasian Lung III Non-small cell lung cancer 0.852 Negative INDI 014 INDI 014 PLS 1 INDI 014 PT1 45 Male Caucasian Lung II Pulmonary cancer hand not small cell 0.965 Positive INDI 015 INDI 015 PLS 1 INDI 015 PT1 80 Caucasian Female Lung I lung cancer not small cell 0.121 Negative INDI 016 INDI 016 PLS 1 INDI 016 PT1 63 Female Caucasian Lung III Lung cancer non-small cell 0.990 Positive INDI 017 INDI 017 PLS 1 INDI 017 PT1 66 Male Caucasian Lung I Lung cancer not small cell 0.397 Negative INDI 018 INDI 018 PLS 1 INDI 018 PT1 62 Female Caucasian Lung III Lung cancer not small cell 0.824 Negative INDI 022 INDI 022 PLS 1 INDI 022 PT1 44 Female Caucasian Lung III Non-small cell lung cancer 0.996 Positive INDI 023 INDI 023 PLS 1 INDI 023 PT1 66 Male Caucasian Lung II Non-small cell lung cancer 0.998 Positive INDI 024 INDI 024 PLS 1 INDI 024 PT1 71 Unknown Female Lung II Non-cell lung cancer small 0.606 Negative INDI 025 INDI 025 PLS 1 INDI 025 PT1 86 Female Caucasian Lung I Non-small cell lung cancer 0.972 Positive INDI 026 INDI 026 PLS 1 INDI 026 PT1 81 Caucasian Female Lung I Non-small cell lung cancer 0.593 Negative INDI 027 INDI 027 PLS 1 INDI 027 PT1 89 Colorectal Caucasian II Adenocarcinoma 0.981 Positive INDI 028 INDI 028 PLS 1 INDI 028 PT1 79 Male Caucasian Colorectal II Adenocarcinoma 0.50 Negative INDI 029 INDI 029 INDI 1 INDI 029 PT1 53 Colorectal Caucasian Female II Adenocarcinoma 0.328 Negative INDI 030 INDI 030 PLS 1 INDI 030 PT1 82 Colorectal Caucasian Female II Adenocarcinoma 0.940 Positive INDI 031 INDI 031 PLS 1 INDI 031 PT1 66 Caucasian Colorectal II Adenocarcinoma 0.321 Negative PLS 1 INDI 032 PT1 41 Male Caucasian Colorectal III Adenocarcinoma 0.117 Negative INDI 034 INDI 034 PLS 1 INDI 034 PT1 88 Female Caucasian Colorectal III Adenocarcinoma 0.997 Positive INDI 035 INDI 035 PLS 1 INDI 035 PT1 71 Male Caucasian Colorectal INDI 03I0 Adenocarcinoma 036 PLS 1 INDI 036 PT1 66 Male Caucasian Colorectal I Adenocarcinoma 0.801 Negative INDI 037 INDI 03 7 PLS 1 INDI 037 PT1 86 Colorectal Caucasian Female I Adenocarcinoma 0.788 Negative INDI 038 INDI 038 PLS 1 Colorectal Caucasian II Adenocarcinoma 0.449 Negative INDI 039 INDI 039 PLS 1 INDI 039 PT1 43 Colorectal Caucasian Female Adenocarcinoma 0.99040 INDI 040 PLS 1 INDI 040 PT1 73 Male Caucasian Colorectal I Adenocarcinoma 0.377 Negative INDI 042 INDI 042 PLS 1 INDI 042 PT1 84 Female Caucasian Colorectal I Adenocarcinoma 0.388 Negative INDI 043 INDI 043 PLS 1 INDI 043 PT1 69 Caucasian Female Colorectal III Adenocarcino 044 INDI 044 PLS 1 INDI 044 PT1 44 Colorectal Caucasian Female II Adenocarcinoma 0.979 Positive INDI 045 INDI 045 PLS 1 INDI 045 PT1 61 Colorectal Caucasian Female III Adenocarcinoma 1,000 Positive INDI 046 INDI 046 PLS 1 INDI 046 PT1 66 Colorectal Caucasian Male 0 Adenocarcinoma II

INDI 047 INDI 047 PLS 1 INDI 047 PT1 36 Male Caucasian Colorectal II Adenocarcinoma 0.977 Positive INDI 048 INDI 048 PLS 1 INDI 048 PT1 79 Caucasian Female Breast II Invasive Lobular Carcinoma 0.639 Negative INDI 049 INDI 049 PLS 1 INDI 049 PT1 58 Caucasian Female Mama III Invasive ductal adenocarcinoma 0.122 Negative INDI 050 INDI 050 PLS 1 INDI 050 PT1 44 Invasive ductal Caucasian adenocarcinoma 0.123 Negative INDI 051 INDI 051 PLS 1 INDI 051 PT1 73 Caucasian female Breast III Invasive ductal adenocarcinoma 0.877 Positive INDI 052 INDI 052 PLS 052 PT1 70 Caucasian Female Breast II Invasive Dutch Adenocarcinoma 0.886 Positive INDI 053 INDI 053 PLS 1 INDI 053 PT1 74 Caucasian Female II Breast Invasive Dutch Adenocarcinoma 0.121 Negative INDI 054 INDI 054 PLS 1 INDI 054 PT1 71 Caucasian Female Breast II Invasive Dutch Adenocarcinoma 0.111 Negative INDI 055 INDI 055 PLS 1 INDI 055 PT1 69 Asian Female Breast II Invasive carcinoma (NOS) 0.404 Negative INDI 056 INDI 056 PLS 1 IND I 056 PT1 50 Asian Female Breast II Invasive Ductal Adenocarcinoma 0.332 Negative INDI 057 INDI 057 PLS 1 INDI 057 PT1 84 Asian Female Breast III Invasive Lobular Carcinoma 0.803 Negative INDI 058 INDI 058 PLS 1 INDI 058 PT1 46 Asian Female Breast I Invasive Ductal Adenocarcinoma 0.620 Negative INDI 059 INDI 059 PLS 1 INDI 059 PT1 59 Caucasian Female Breast II Invasive Ductal Adenocarcinoma 0.975 Positive INDI 060 INDI 060 PLS 1 INDI 060 PT1 51 Caucasian Female Mama II Invasive Ductal Adenocarcinoma 0.993 Positive INDI 061 INDI 061 PLS 1 INDI 061 PT1 56 Female Caucasian Breast II Invasive Ductal Adenocarcinoma 0.961 Positive INDI 062 INDI 062 PLS 1 INDI 062 PT1 59 Female Caucasian II Ductal Adenocarcinoma 0.571 Negative INDI 063 INDI 063 PLS 1 INDI 063 PT1 59 Female Caucasian Mama III Invasive Lobular Carcinoma 0.727 Negative INDI 064 INDI 064 PLS 1 INDI 064 PT1 55 Caucasian Female Breast II Invasive ductal adenocarcinoma 0.938 Positive INDI 065 INDI 065 PLS 1 INDI 065 PT1 63 Female Ca ucasiana Mama II Invasive ductal adenocarcinoma 0.564 Negative INDI 066 INDI 066 PLS 1 INDI 066 PT1 62 Caucasian female III invasive lobular carcinoma 0.681 Negative INDI 067 INDI 067 PLS 1 INDI 067 PT1 58 Caucasian female Mama III Invasive dutal adenocarcinoma 0.997 Positive INDI 068 IND8 PLS 1 INDI 068 PT1 52 Caucasian Female Breast II Invasive Ductal Adenocarcinoma 0.514 Negative INDI 069 INDI 069 PLS 1 INDI 069 PT1 59 Caucasian Female II Breast Ductus Adenocarcinoma 0.997 Positive INDI 070 INDI 070 PLS 1 INDI 070 PT1 61 Caucasian Female Breast III Lobular Carcinoma Invasive 1,000 Positive INDI 071 INDI 071 PLS 1 INDI 071 PT1 63 Caucasian Female Breast II Lobular Carcinoma Invasive 0.972 Positive INDI 072 INDI 072 PLS 1 INDI 072 PT1 63 Caucasian Female Breast II Invasive Lobular Carcinoma 0.986 Positive INDI 073 INDI 073 PLS 1 INDI 073 PT1 55 Caucasian Female Breast III Invasive Ductal Adenocarcinoma 0.861 Negative INDI 074 INDI 074 PLS 1 INDI 074 PT1 78 Male Caucasian Pancreas II Adenocarcinoma 1,000 Positive INDI 075 INDI 075 PLS 1 INDI 075 PT1 58 Asian Female Ovary III Epithelial carcinoma 0.955 Positive INDI 076 INDI 076 PLS 1 INDI 076 PT1 49 Black Male Esophagus II Squamous cell carcinoma 0.990 Positive INDI 077 INDI 077 PLS 1 INDI 077 PLS 1 INDI 77 Male Caucasian Liver II Hepatocellular Carcinoma 1,000 Positive INDI 078 INDI 078 PLS 1 INDI 078 PT1 56 Male Caucasian Liver II Hepatocellular Carcinoma 0.992 Positive INDI 079 INDI 079 PLS 1 INDI 079 PT1 42 Male Asian Liver II Hepatocellular Carcinoma 0.999 Positive 0.999 1 INDI 080 PT1 70 Male Asian Liver I Hepatocellular Carcinoma 0.984 Positive INDI 081 INDI 081 PLS 1 INDI 081 PT1 70 Male Asian Colorectal II Adenocarcinoma 0.997 Positive INDI 082 INDI 082 PLS 1 INDI 082 PT1 80 Male Asian Stomach IND3 Negative Adenocarcinoma 083 PLS 1 INDI 083 PT1 50 Male Asian Stomach II Adenocarcinoma 0.993 Positive INDI 084 INDI 084 PLS 1 INDI 084 PT1 78 Asiá Female Stomach II Adenocarcinoma 0.997 Positive INDI 085 INDI 085 PLS 1 INDI 085 PT1 61 Male Asian Stomach I Adenocarcinoma 0.940 Positive INDI 086 INDI 086 PLS 1 INDI 086 PT1 50 Male Asian Stomach I Adenocarcinoma 0.997 Positive INDI 087 INDI 087 PLS 1 1 Asian Female Stomach I Adenocarcinoma 0.999 Positive INDI 089 INDI 089 PLS 1 INDI 089 PT1 59 Colorectal Caucasian II Adenocarcinoma 0.999 Positive INDI 090 INDI 090 PLS 1 INDI 090 PT1 72 Male Caucasian Colorectal II Adenocarcinoma 0.967 Positive INDI 092 INDI 092 INDI 092 INDI 092 INDI 092 64 Male Caucasian Stomach II Adenocarcinoma 0.848 Negative INDI 093 INDI 093 PLS 1 INDI 093 PT1 75 Male Caucasian Colorectal I Adenocarcinoma 0.997 Positive INDI 094 INDI 094 PLS 1 INDI 094 PT1 72 Male Caucasian Colorectal I Adenocarcinoma 0.993 Positive INDI 095 PT1 73 Male Caucasian Stomach II Adenocarcinoma 0.792 Negative INDI 096 INDI 096 PLS 1 INDI 096 PT1 68 Male Caucasian Colorret al I Adenocarcinoma 0.652 Negative INDI 097 INDI 097 PLS 1 INDI 097 PT1 80 Colorectal Caucasian Female III Adenocarcinoma 0.997 Positive INDI 098 INDI 098 PLS 1 INDI 098 PT1 61 Male Colorectal Caucasian II Adenocarcinoma 0.308 Negative INDI 100 INDI 100 PLS 1 INDI 100 Colorectal Caucasian III Adenocarcinoma 0.992 Positive INDI 101 INDI 101 PLS 1 INDI 101 PT1 55 Male Colorectal Caucasian III Adenocarcinoma 0.793 Negative INDI 102 INDI 102 PLS 1 INDI 102 PT1 38 Male Colorectal Caucasian III Adenocarcinoma 0.977 Positive INDI 103 INDI 103 PLS 1 INDI 103 PT Male Caucasian Stomach II Adenocarcinoma 0.583 Negative INDI 104 INDI 104 PLS 1 INDI 104 PT1 63 Male Caucasian Stomach III Adenocarcinoma 0.983 Positive INDI 107 INDI 107 PLS 1 INDI 107 PT1 66 Male Caucasian Colorectal II Adenocarcinoma 0.975 Positive INDI 108 INDI 108 PLS 1 INDI 108 PT1 61 Colorectal Caucasian Female II Adenocarcinoma 0.984 Positive INDI 109 INDI 109 PLS 1 INDI 109 PT1 68 Asian Female Colorectal III Adenocarcinoma 0.997 Positive INDI 110 INDI 110 PLS 1 INDI 110 PT1 64 Male Asian Stomach I Adenocarcinoma 0.972 Positive INDI 111 INDI 111 PLS 1 INDI 111 PT1 54 Male Asian Stomach II Adenocarcinoma 0.938 Positive INDI 112 INDI 112 PLS 1 INDI 112 PT1 66 Male Asian Esophagus II Squamous cell carcinoma 1,000 Positive INDI 113 INDI 113 PLS 1 INDI 113 PT1 82 Male Colorectal Asian II Adenocarcinoma 0.998 Positive INDI 116 INDI 116 PLS 1 INDI 116 PT1 37 Male Asian Stomach I Adenocarcinoma 1,000 Positive INDI 119 INDI 119 PLS 1 INDI 119 PT1 46 Male Asian Esophagus I Adenocarcinoma 1,000 Positive INDI 120 INDI 120 PLS 1 INDI 120 PT1 74 Asian Female Stomach III Adenocarcinoma 0.983 Positive INDI 121 INDI 121 PLS 1 INDI 121 PT1 41 Male Asian Stomach I Adenocarcinoma 0.919 Positive

INDI 122 INDI 122 PLS 1 INDI 122 PT1 24 Asian Colorectal II Female Adenocarcinoma 0.998 Positive INDI 123 INDI 123 PLS 1 INDI 123 PT1 70 Asian Colorectal II Adenocarcinoma 0.996 Positive INDI 124 INDI 124 PLS 1 INDI 124 PT1 70 Asian Colorectal II Adenocarcinoma 1,000 Positive INDI 125 INDI 125 PLS 1 INDI 125 PT1 55 Male Asian Esophagus II Squamous cell carcinoma 0.999 Positive INDI 126 INDI 126 PLS 1 INDI 126 PT1 60 Asian Male Stomach III Adenocarcinoma 0.999 Positive INDI 127 INDI 127 PLS 1 INDI 127 PT1 62 Asian Male Colorectal II Adenocarcinoma 1,000 Positive INDI 128 INDI 128 PLS 1 INDI 128 PT1 48 Asian Female Colorectal III Adenocarcinoma 1,000 Positive INDI 129 INDI 129 PLS 1 INDI 129 PT1 63 Asian Female Liver II Hepatocellular Carcinoma 0.999 Positive INDI 130 INDI 130 PLS 1 INDI 130 PT1 67 Male Asian Stomach II Adenocarcinoma 0.982 Positive INDI 131 INDI 131 PLS 1 INDI 131 PT1 79 Male Asian Stomach II Adenocarcinoma 0.993 Positive I NDI 132 INDI 132 PLS 1 INDI 132 PT1 64 Male Asian Esophagus III Squamous cell carcinoma 0.918 Positive INDI 133 INDI 133 PLS 1 INDI 133 PT1 48 Male Asian Stomach I Adenocarcinoma 0.990 Positive INDI 134 INDI 134 PLS 1 INDI 134 PT1 49 Asian Male Stomach III Adenocarcinoma 0.993 Positive INDI 135 INDI 135 PLS 1 INDI 135 PT1 70 Male Asian Stomach III Adenocarcinoma 0.999 Positive INDI 136 INDI 136 PLS 1 INDI 136 PT1 60 Colorectal Asian Female II Adenocarcinoma 0.984 Positive INDI 137 INDI 137 PLS 1 INDI 137 PT1 58 Male Asian Stomach II Adenocarcinoma 0.998 Positive INDI 139 INDI 139 PLS 1 INDI 139 PT1 65 Asian Female Stomach III Adenocarcinoma 1,000 Positive INDI 140 INDI 140 PLS 1 INDI 140 PT1 48 Asian Male Stomach III Adenocarcinoma 0.992 Positive INDI 141 INDI 141 PLS 1 INDI 141 PT1 67 Asian Male Stomach III Adenocarcinoma 0.965 Positive INDI 143 INDI 143 PLS 1 INDI 143 PT1 35 Male Asian Colorectal II Adenocarcinoma 0.998 Positive INDI 144 IND I 144 PLS 1 INDI 144 PT1 51 Asian Colorectal II Adenocarcinoma 1,000 Positive INDI 145 INDI 145 PLS 1 INDI 145 PT1 70 Male Asian Colorectal II Adenocarcinoma 0.999 Positive INDI 146 INDI 146 PLS 1 INDI 146 PT1 65 Male Asian Colorectal II Adenocarcinoma 0.934 Positive IND 147 INDI 147 PLS 1 INDI 147 PT1 62 Asian Female Stomach II Adenocarcinoma 0.953 Positive INDI 148 INDI 148 PLS 1 INDI 148 PT1 72 Male Asian Colorectal II Adenocarcinoma 0.998 Positive INDI 150 INDI 150 PLS 1 INDI 150 PT1 47 Asian Female Colorectal II Adenocarcinoma 1,000 Positive INDI 151 INDI 151 PLS 1 INDI 151 PT1 59 Asian Female Stomach II Adenocarcinoma 1,000 Positive INDI 152 INDI 152 PLS 1 INDI 152 PT1 53 Colorectal Asian Female I Adenocarcinoma 0.988 Positive INDI 153 INDI 153 PLS 1 INDI 153 PT1 42 Asian Female Stomach II Adenocarcinoma 0,369 Negative INDI 155 INDI 155 PLS 1 INDI 155 PT1 60 Male Asian Colorectal II Adenocarcinoma 0.836 Negative INDI 156 INDI 156 PLS 1 INDI 156 PT1 56 Male Asian Colorectal III Adenocarcinoma 1,000 Positive INDI 158 INDI 158 PLS 1 INDI 158 PT1 70 Caucasian Colorectal I Adenocarcinoma 0.980 Positive INDI 159 INDI 159 PLS 1 INDI 159 PT1 80 Caucasian Colorectal II Adenocarcinoma 0.852 Negative INDI 160 INDI 160 PLS 1 INDI 160 PT1 76 Male Caucasian Colorectal II Adenocarcinoma 0.890 Positive INDI 161 INDI 161 PLS 1 INDI 161 PT1 57 Male Caucasian Stomach II Adenocarcinoma 0.920 Positive INDI 162 INDI 162 PLS 1 INDI 162 PT1 68 Male Caucasian Stomach III Adenocarcinoma 0.998 Positive INDI 163 INDI 163 PLS 1 INDI 163 PT1 64 Male Caucasian Colorectal III Adenocarcinoma 0.934 Positive INDI 164 INDI 164 PLS 1 INDI 164 PT1 64 Female Caucasian Colorectal I Adenocarcinoma 0.477 Negative INDI 165 INDI 165 PLS 1 INDI 165 PT1 69 Male Caucasian Colorectal I Adenocarcinoma 0.995 Positive INDI 167 PLS 1 INDI 167 PT1 45 Male Asian Stomach II Adenocarcinoma 0.962 Positive INDI 168 INDI 168 PLS 1 INDI 168 PT1 51 Male Asian Stomach II Adenocarcinoma 1,000 Positive INDI 169 INDI 169 PLS 1 INDI 169 PT1 41 Female Asian Colorectal III Adenocarcinoma 0.965 Positive INDI 170 INDI 170 PLS 1 INDI 170 PT1 57 Asian Female Colorectal I Adenocarcinoma 0.992 Positive INDI 171 INDI 171 PLS 1 INDI 171 PT1 64 Male Asian Stomach III Adenocarcinoma 1,000 Positive INDI 172 INDI 172 PLS 1 INDI 172 PT1 44 Asian Female Stomach II Adenocarcinoma 1,000 Positive INDI 173 INDI 173 PLS 1 INDI 173 PT1 76 Male Asian Stomach I Adenocarcinoma 0.999 Positive INDI 175 INDI 175 PLS 1 INDI 175 PT1 68 Colorectal Asian Male III Adenocarcinoma 1,000 Positive INDI 176 INDI 176 PLS 1 INDI 176 PT1 61 Asian Stomach II Adenocarcinoma 0.997 Positive INDI 177 INDI 177 PLS 1 INDI 177 PT1 42 Asian Female Liver III Hepatocellular Carcinoma 1,000 Positive INDI 178 INDI 178 PLS 1 INDI 178 PT1 44 Male Asian Colorectal III Adenocarcinoma 0.967 Positive INDI 179 INDI 179 PLS 1 INDI 179 PT1 57 M think Asian Colorectal III Adenocarcinoma 0.998 Positive INDI 180 INDI 180 PLS 1 INDI 180 PT1 58 Male Asian Colorectal III Adenocarcinoma 0.921 Positive INDI 182 INDI 182 PLS 1 INDI 182 PT1 69 Male Asian Liver III Hepatocellular Carcinoma 1,000 Positive INDI 183 INDI 183 PLS PT1 51 Female Asian Stomach I Adenocarcinoma 0.999 Positive INDI 184 INDI 184 PLS 1 INDI 184 PT1 60 Male Asian Esophagus II Squamous cell carcinoma 0.983 Positive INDI 185 INDI 185 PLS 1 INDI 185 PT1 62 Asian Male Stomach II Adenocarcinoma 1,000 Positive INDI 186 INDI 186 PLS 1 INDI 186 PT1 65 Male Asian Stomach II Adenocarcinoma 1,000 Positive INDI 187 INDI 187 PLS 1 INDI 187 PT1 74 Asian Female Stomach I Adenocarcinoma 0.994 Positive INDI 188 INDI 188 PLS 1 INDI 188 PT1 68 Asian Female Stomach I Adenocarcinoma 0.983 Positive INDI 189 INDI 189 PLS 1 INDI 189 PT1 57 Asian Female Esophagus II Squamous cell carcinoma 1,000 Positive INDI 190 INDI 190 PLS 1 INDI 190 PT1 52 Male Asian Liver III Hepatocellular Carcinoma 0.988 Positive INDI 191 INDI 191 PLS 1 INDI 191 PT1 68 Colorectal Asian Female I Adenocarcinoma 0.947 Positive INDI 192 INDI 192 PLS 1 INDI 192 PT1 58 Asian Colorectal II Adenocarcinoma 0.992 INDI 194 INDI 194 PT1 60 Colorectal Asian Female II Adenocarcinoma 0.968 Positive INDI 195 INDI 195 PLS 1 INDI 195 PT1 51 Colorectal Asian Female III Adenocarcinoma 0.966 Positive INDI 196 INDI 196 PLS 1 INDI 196 PT1 68 Asian Colorectal III Adenocarcinoma 0.955 Positive

INDI 197 INDI 197 PLS 1 INDI 197 PT1 35 Colorectal Asian Female II Adenocarcinoma 0.998 Positive INDI 198 INDI 198 PLS 1 INDI 198 PT1 49 Asian Stomach III Adenocarcinoma 1,000 Positive INDI 199 INDI 199 PLS 1 INDI 199 PT1 82 Asian Male Stomach II Adenocarcinoma 0.992 Positive INDI 200 INDI 200 PLS 1 INDI 200 PT1 43 Female Asian Breast III Invasive ductal adenocarcinoma 0.936 Positive INDI 202 INDI 202 PLS 1 INDI 202 PT1 52 Male Asian Colorectal II Adenocarcinoma 0.984 Positive INDI 203 INDI 203 PLS 1 INDI 203 PT1 73 Male Asian Stomach II Adenocarcinoma 0.995 Positive INDI 205 INDI 205 PLS 1 INDI 205 PT1 59 Male Asian Liver III Hepatocellular Carcinoma 0.997 Positive INDI 206 INDI 206 PLS 1 INDI 206 PT1 72 Male Asian Stomach III Adenocarcinoma 0.957 Positive INDI 207 INDI 207 PLS 1 INDI 207 PT1 52 Female Asian Colorectal II Adenocarcinoma 1,000 Positive INDI 208 INDI 208 PLS 1 INDI 208 PT1 52 Male Asian Liver II Hepatocellular Carcinoma 0.997 Posi INDI 209 INDI 209 PLS 1 INDI 209 PT1 65 Colorectal Asian Female II Adenocarcinoma 0.992 Positive INDI 210 INDI 210 PLS 1 INDI 210 PT1 60 Asian Stomach III Adenocarcinoma 0.991 Positive INDI 211 INDI 211 PLS 1 INDI 211 PT1 77 Male Colorectal Colonectal II 0.993 Positive INDI 212 INDI 212 PLS 1 INDI 212 PT1 36 Colorectal Asian Female III Adenocarcinoma 1,000 Positive INDI 213 INDI 213 PLS 1 INDI 213 PT1 44 Colorectal Asian Female III Adenocarcinoma 0.982 Positive INDI 214 INDI 214 PLS 1 INDI 214 PT1 77 Male Asian Stomach II Adenocarcinoma 0.935 Positive INDI 215 INDI 215 PLS 1 INDI 215 PT1 65 Asian Female Liver II Hepatocellular Carcinoma 0.903 Positive INDI 216 INDI 216 PLS 1 INDI 216 PT1 58 Male Caucasian Colorectal III Adenocarcinoma 0.981 Positive INDI 218 INDI 218 PLS 1 INDI 218 Colorectal III Adenocarcinoma 0.900 Positive INDI 219 INDI 219 PLS 1 INDI 219 PT1 54 Male Caucasian Liver III Hepatocellular Carcinoma 0, 994 Positive INDI 220 INDI 220 PLS 1 Not Available 51 Female Caucasian Lung I Lung cancer not small cell 0.982 Positive INDI 221 INDI 221 PLS 1 INDI 221 PT1 77 Male Caucasian Colorectal III Adenocarcinoma 1,000 Positive INDI 222 INDI 222 PLS 1 INDI 222 PT1 67 Female Colorectal Caucasian II Adenocarcinoma 1,000 Positive INDI 223 INDI 223 PLS 1 INDI 223 PT1 72 Male Colorectal Caucasian I Adenocarcinoma 0.501 Negative INDI 224 INDI 224 PLS 1 INDI 224 PT1 67 Male Caucasian Lung III Small cell lung cancer 1,000 Positive INDI 225 INDI 225 PLS 1 INDI 225 PT1 70 Male Caucasian Colorectal I Adenocarcinoma 0.997 Positive INDI 226 INDI 226 PLS 1 Not Available 47 Caucasian Female Breast III Invasive lobular carcinoma 0.117 Negative INDI 227 INDI 227 PLS 1 INDI 227 PT1 55 Male Caucasian Lung II Non-small cell lung cancer 0.919 Positive INDI 228 INDI 228 PLS 1 INDI 228 PT1 68 Caucasian Female Breast II Invasive ductal adenocarcinoma 0.293 Negative INDI 229 INDI 229 PLS 1 INDI 229 PT1 64 Male Caucasian Colorectal II Adenocarcinoma 0.968 Positive INDI 230 INDI 230 PLS 1 INDI 230 PT1 74 Male Caucasian Colorectal I Adenocarcinoma 0.984 Positive INDI 231 INDI 231 PLS 1 INDI 231 PT1 54 Caucasian Female Lung Non Small Cell 0.999 Positive INDI 233 INDI 233 PLS 1 INDI 233 PT1 73 Colorectal Caucasian Female I Adenocarcinoma 1,000 Positive INDI 234 INDI 234 PLS 1 INDI 234 PT1 66 Colorectal Caucasian Female I Adenocarcinoma 0.125 Negative INDI 236 INDI 236 PLS 1 INDI 236 PT1 72 Male Caucasian Stomach Ad II 0.117 Negative INDI 237 INDI 237 PLS 1 Not Available 51 Caucasian Female Breast I Invasive Ductal Adenocarcinoma 0.434 Negative INDI 238 INDI 238 PLS 1 Not Available 67 Female Caucasian Liver II Hepatocellular Carcinoma 0.999 Positive INDI 239 INDI 239 PLS 1 INDI 239 PT1 70 Male Caucasian Lung III Non-small cell lung cancer 0.987 Positive INDI 241 INDI 241 PLS 1 INDI 241 PT1 79 Male Caucasian Stomach III Adenocarcinoma 0.370 Negative INDI 243 INDI 243 PLS 1 INDI 243 PT1 71 Male Caucasian Colorectal I Adenocarcinoma 0.982 Positive INDI 244 INDI 244 PLS 1 INDI 244 PT1 85 Male Caucasian Colorectal III Adenocarcinoma 0.908 Positive INDI 245 INDI 245 PLI 1 Colorectal III Adenocarcinoma 0.998 Positive INDI 246 INDI 246 PLS 1 INDI 246 PT1 80 Male Caucasian Lung II Non-small cell lung cancer 0.314 Negative INDI 247 INDI 247 PLS 1 Not Available 72 Female Caucasian Lung I Non-small cell lung cancer 0.972 Positive INDI 248 INDI 248 PLS 1 INDI 248 PT1 55 Male Caucasian Colorectal II Adenocarcinoma 0.453 Negative INDI 250 INDI 250 PLS 1 Not Available 81 Male Caucasian Colorectal II Adenocarcinoma 0.985 Positive INDI 251 INDI 251 PLS 1 Not Available 81 Male Caucasian Colorectal II Adenocarcinoma 0.983 Positive INDI 252 1 Not Available 75 Female Caucasian Lung II Non-small cell lung cancer 0.994 Positive INDI 25 3 INDI 253 PLS 1 Not Available 72 Caucasian Colorectal I Adenocarcinoma 0.218 Negative INDI 255 INDI 255 PLS 1 INDI 255 PT1 64 Colorectal Caucasian II Adenocarcinoma 0.950 Positive INDI 256 INDI 256 PLS 1 INDI 256 PT1 73 Caucasian Colorectal III Adenocarcinoma 0.991 Positive INDI 257 INDI 257 PLS 1 INDI 257 PT1 69 Male Caucasian Lung III Non-small cell lung cancer 0.991 Positive INDI 258 INDI 258 PLS 1 INDI 258 PT1 64 Female Caucasian Colorectal I Adenocarcinoma 0.50 Negative INDI 259 INDI 259 PLS 1 INDI 259 PT1 78 Male Caucasian Colorectal II Adenocarcinoma 0.942 Positive INDI 260 INDI 260 PLS 1 Not Available 73 Caucasian Female Stomach I Adenocarcinoma 0.256 Negative INDI 261 INDI 261 PLS 1 INDI 261 PT1 49 Caucasian Female Breast II Invasive Ductal Adenocarcinoma 0.123 Negative INDI 262 INDI 262 PLS 1 INDI 262 PT1 63 Female Caucasian Breast III Invasive lobular carcinoma 0.705 Negative INDI 264 INDI 264 PLS 1 INDI 264 PT1 54 Female Caucasian Lung III Non-small cell lung cancer 0.672 Negative INDI 265 INDI 265 PLS 1 INDI 265 PT1 50 Caucasian female II Breast invasive adenocarcinoma 0.117 Negative INDI 266 INDI 266 PLS 1 Not available 69 Male Caucasian lung I Non-small cell lung cancer 0.930 Positive INDI 267 INDI 267 PLS 1 Not Available 74 Male Caucasian Esophagus II Squamous cell carcinoma 0.767 Negative INDI 268 INDI 268 PLS 1 INDI 268 PT1 60 Colorectal Caucasian Female III Adenocarcinoma 0.995 Positive INDI 269 INDI 269 PLS 1 INDI 269 PT1 66 Male Caucasian Lung non small cell lung cancer 0.618 Negative INDI 270 INDI 270 PLS 1 Not Available 53 Caucasian Female Breast II Invasive Ductal Adenocarcinoma 0.125 Negative INDI 271 INDI 271 PLS 1 INDI 271 PT1 93 Male Caucasian Colorectal III Adenocarcinoma 0.609 Negative INDI 273 INDI 273 PLS 1 Not Available 71 Caucasian Female Lung I Non-small cell lung cancer 0.998 Positive

INDI 274 INDI 274 PLS 1 Not Available 78 Caucasian Female Breast II Invasive Lobular Carcinoma 0.125 Negative INDI 275 INDI 275 PLS 1 Not Available 46 Caucasian Female Mama II Invasive Lobular Carcinoma 0.335 Negative INDI 276 INDI 276 PLS 1 Not Available 63 Male Caucasian Liver II Cholangiocarcinoma 1,000 Positive INDI 277 INDI 277 PLS 1 Not Available 72 Male Caucasian Breast II Invasive Ductal Adenocarcinoma 0.116 Negative INDI 278 INDI 278 PLS 1 Not Available 57 Female Caucasian Ovary III Epithelial Carcinoma 0.984 Positive INDI 279 INDI 279 PLS 1 INDI 279 PT1 87 Female Caucasian Colorectal II Adenocarcinoma 0.967 Positive INDI 280 INDI 280 PLS 1 INDI 280 PT1 72 Unknown Female Breast I Invasive Ductal Adenocarcinoma 0.377 Negative INDI 281 INDI 281 PLS 1 INDI 281 PT1 78 Colorectal Caucasian Male I Adenocarcinoma 0.724 Negative INDI 283 INDI 283 PLS 1 INDI 283 PT Female Caucasian Lung I Lung cancer not small cell 0.593 Negative INDI 284 INDI 284 PLS 1 INDI 284 PT1 75 Female Caucasian Colorectal II Adenocarcinoma 0.918 Positive INDI 285 INDI 285 PLS 1 INDI 285 PT1 57 Female Caucasian Lung Non-small cell lung cancer 0.998 Positive INDI 286 INDI 286 PLS 1 Not Available 49 Male Caucasian Colorectal I Adenocarcinoma 0.620 Negative INDI 287 INDI 287 1 INDI 287 PT1 52 Caucasian Female Breast III Invasive ductal adenocarcinoma 0.996 Positive INDI 288 INDI 288 PLS 1 INDI 288 PT1 75 Caucasian Lung III Non-small cell lung cancer 1,000 Positive INDI 289 INDI 289 PLS 1 INDI 289 PT1 55 Caucasian Female Breast II Carcinoma lobular invasive 0.521 Negative INDI 290 INDI 290 PLS 1 INDI 290 PT1 70 Male Caucasian Colorectal I Adenocarcinoma 0.896 Positive INDI 291 INDI 291 PLS 1 INDI 291 PT1 71 Male Asian Lung Non-small cell lung cancer 0.804 Negative INDI 292 INDI 292 PLS 1 INDI 292 PT1 73 Asian Female Lung III Non-small cell lung cancer 0.772 Negative INDI 293 INDI 293 PLS 1 Not Available 81 Asian Female Co lorretal II Adenocarcinoma 0.996 Positive INDI 295 INDI 295 PLS 1 INDI 295 PT1 59 Female Caucasian Colorectal II Adenocarcinoma 0.939 Positive INDI 296 INDI 296 PLS 1 INDI 296 PT1 43 Female Caucasian Colorectal II Adenocarcinoma 0.940 Positive INDI 297 INDI 297 PLI Caucasian Breast III Invasive Ductal Adenocarcinoma 0.896 Positive INDI 298 INDI 298 PLS 1 INDI 298 PT1 42 Female Colorectal I Adenocarcinoma 0.886 Positive INDI 299 INDI 299 PLS 1 INDI 299 PT1 73 Male Colorectal Caucasian I Adenocarcinoma 0.924 Positive INDI 300 INDI 300 PLS PT1 47 Caucasian Female Breast III Invasive Ductal Adenocarcinoma 0.917 Positive INDI 301 INDI 301 PLS 1 INDI 301 PT1 47 Caucasian Female Mama II Invasive Ductal Adenocarcinoma 0.989 Positive INDI 302 INDI 302 PLS 1 INDI 302 PT1 69 Male Colorectal Caucasian I Adenocarcinoma 0.960 Positive 303 PLS 1 INDI 303 PT1 66 Male Caucasian Colorectal I Adenocarcinoma 0.955 Positive INDI 304 INDI 304 PLS 1 INDI 304 PT1 53 Male Caucasian Colorectal I Adenocarcinoma 0.997 Positive INDI 305 INDI 305 PLS 1 INDI 305 PT1 55 Male Caucasian Colorectal II Adenocarcinoma 1,000 Positive INDI 306 INDI 306 PLS 1 INDI 306 PT1 66 Male Caucasian Colorectal III Adenocarcinoma 7 INDI 30I 1 INDI 307 PT1 64 Male Caucasian Colorectal III Adenocarcinoma 0.944 Positive INDI 308 INDI 308 PLS 1 INDI 308 PT1 39 Male Asian Liver II Hepatocellular Carcinoma 0.974 Positive INDI 309 INDI 309 PLS 1 INDI 309 PT1 39 Male Asian ColorectalII Adenocar 310 PLS 1 INDI 310 PT1 78 Male Asian Colorectal II Adenocarcinoma 0.999 Positive INDI 311 INDI 311 PLS 1 INDI 311 PT1 52 Asian Female Colorectal III Adenocarcinoma 0.977 Positive INDI 312 INDI 312 PLS 1 INDI 312 PT1 56 Asian Female Breast II Adenocarcinoma INDI 313 INDI 313 PLS 1 INDI 313 PT1 43 Male Asian Esophagus II Squamous cell carcinoma 0.939 Positive IN DI 314 INDI 314 PLS 1 INDI 314 PT1 46 Colorectal Caucasian Female I Adenocarcinoma 0.982 Positive INDI 315 INDI 315 PLS 1 INDI 315 PT1 64 Caucasian Female Breast II Invasive Ductal Adenocarcinoma 0.123 Negative INDI 316 INDI 316 PLS 1 INDI 316 PT1 43 Caucasian Female Breast II Invasive ductal adenocarcinoma 0.123 Negative INDI 317 INDI 317 PLS 1 INDI 317 PT1 37 Caucasian female breast II Invasive ductal adenocarcinoma 0.116 Negative INDI 318 INDI 318 PLS 1 INDI 318 PT1 76 Male Caucasian Stomach III Adenocarcinoma 0.997 Positive INDI 319 INDI 319 PLS 1 76 Male Caucasian Stomach I Adenocarcinoma 0.374 Negative INDI 320 INDI 320 PLS 1 INDI 320 PT1 51 Male Caucasian Colorectal II Adenocarcinoma 1,000 Positive INDI 321 INDI 321 PLS 1 INDI 321 PT1 64 Female Caucasian Colorectal III Adenocarcinoma 0.836 Negative INDI 322 INDI 322 PLS 1 INDI 322 PLS 1 INDI 322 PT1 61 Male Caucasian Colorectal III Adenocarcinoma 0.930 Positive INDI 323 INDI 323 PLS 1 INDI 323 PT1 44 Female Caucasian Breast I Adeno invasive dutch carcinoma 0.125 Negative INDI 324 INDI 324 PLS 1 INDI 324 PT1 58 Caucasian Female Lung Non-small cell lung cancer 0.877 Positive INDI 325 INDI 325 PLS 1 INDI 325 PT1 58 Colorectal Caucasian Female III Adenocarcinoma 0.933 Positive INDI 326 INDI 326 PLS 1 INDI 326 PT1 54 Male Caucasian Lung I Lung cancer not small cell 0.992 Positive INDI 327 INDI 327 PLS 1 INDI 327 PT1 71 Colorectal Caucasian Female II Adenocarcinoma 0.954 Positive INDI 328 INDI 328 PLS 1 INDI 328 PT1 71 Colorectal Caucasian Female I Adenocarcinoma 0.993 Positive INDI 329 INDI 329 PLS 1 INDI 329 PT1 87 Colorectal Caucasian Female III Adenocarcinoma 1,000 Positive INDI 331 INDI 331 PLS 1A INDI 331 PT1 55 Caucasian Lung III Non-small cell lung cancer 1,000 Positive INDI 332 INDI 332 PLS 1 INDI 332 PT1 65 Caucasian Female Breast I Invasive ductal adenocarcinoma 0.369 Negative INDI 333 INDI 333 PLS 1A INDI 333 PT1 72 Male Caucasian Lung II Non-small cell lung cancer 0.473 Negative INDI 334 INDI 334 PLS 1 INDI 334 PT1 85 Caucasian Female Breast I Invasive Ductal Adenocarcinoma 0.622 Negative INDI 335 INDI 335 PLS 1A INDI 335 PT1 55 Caucasian Female Lung Non-small cell lung cancer 0.929 Positive INDI 336 INDI 336 PLS 1A INDI 336 PT1 71 Male Caucasian Lung II Non-small cell lung cancer 0.995 Positive INDI 337 INDI 337 PLS 1 INDI 337 PT1 44 Male Caucasian Esophagus III Adenocarcinoma 0.235 Negative INDI 338 INDI 338 PLS 1 INDI 338 PT1 90 Colorectal Caucasian Female II Adenocarcinoma 0.971 Positive INDI 339 INDI 339 PLS 1 INDI 339 PT1 52 Caucasian Female Breast II Invasive ductal adenocarcinoma 0.121 Negative INDI 340 INDI 340 PLS 1 Male Colorectal III III Adenocarcinoma 1,000 Positive INDI 341 INDI 341 PLS 1 INDI 341 PT1 74 Caucasian Female Liver II Cholangiocarcinoma 0.99 Positive INDI 342 INDI 342 PLS 1A INDI 342 PT1 82 Male Caucasian Lung III Non-small cell lung cancer 0.125 Negative

INDI 343 INDI 343 PLS 1 INDI 343 PT1 54 Female Caucasian Colorectal III Adenocarcinoma 0.573 Negative INDI 344 INDI 344 PLS 1 INDI 344 PT1 73 Male Caucasian Colorectal II Adenocarcinoma 1,000 Positive INDI 346 INDI 346 PLS 1A INDI 346 PT1 57 Male Caucasian Lung Lung Cancer not small cell 0.916 Positive INDI 347 INDI 347 PLS 1 INDI 347 PT1 57 Male Caucasian Esophagus I Carcinoma squamous cell 0.650 Negative INDI 348 INDI 348 PLS 1 INDI 348 PT1 44 Female Caucasian Breast II Invasive lobular carcinoma 0.242 Negative INDI 350 INDI 350 PLS 1 INDI 350 PT1 50 Caucasian Female Breast II Invasive Ductal Adenocarcinoma 0.610 Negative INDI 352 INDI 352 PLS 1 INDI 352 PT1 51 Caucasian Female Mama III Invasive Ductal Adenocarcinoma 0.122 Negative INDI 353 INDI 353 PLS 1A INDI 353 PT1 66 Caucasian Female Lung II Non-small cell lung cancer 0.998 Positive INDI 354 INDI 354 PLS 1 INDI 354 PT1 69 Female Caucasian Colorectal III Adenocarcinoma 0.989 Positive INDI 355 INDI 355 PLS 1A IND I 355 PT1 73 Male Caucasian Lung I Lung cancer not small cell 0.863 Positive INDI 356 INDI 356 PLS 1 INDI 356 PT1 84 Colorectal Caucasian female I Adenocarcinoma 0.883 Positive INDI 357 INDI 357 PLS 1A INDI 357 PT1 73 Male Caucasian lung I Non lung cell cancer small 0.580 Negative INDI 358 INDI 358 PLS 1A INDI 358 PT1 64 Caucasian Female Lung II Non-small cell lung cancer 0.686 Negative INDI 359 INDI 359 PLS 1 INDI 359 PT1 67 Caucasian Female Breast I Invasive ductal adenocarcinoma 0.850 Negative INDI 360 INDI 360 PLS 1A INDI 360 PT1 59 Caucasian Female Lung II Non-small cell lung cancer 0.985 Positive INDI 361 INDI 361 PLS 1 INDI 361 PT1 74 Caucasian Female Breast III Invasive lobular carcinoma 0.324 Negative INDI 362 INDI 362 PLS 1 INDI 362 PT1 46 Caucasian Female Breast II Invasive southern adenocarcinoma 0.304 Negative INDI 363 INDI 363 PLS 1 INDI 363 PT1 74 Caucasian Female Breast I Invasive ductal adenocarcinoma 0.121 Negative INDI 364 INDI 364 PLS 1A INDI 364 PT1 72 Female Caucasian Lung I Lung cancer not small cell 0.856 Negative INDI 365 INDI 365 PLS 1 INDI 365 PT1 69 Colorectal Caucasian Female II Adenocarcinoma 0.995 Positive INDI 366 INDI 366 PLS 1 INDI 366 PT1 74 Female Caucasian Pancreas II Adenocarcinoma 0.998 Positive INDI 367 INDI 367 PLS 1 INDI 367 PT1 72 Male Caucasian Liver I Hepatocellular Carcinoma 0.965 Positive INDI 368 INDI 368 PLS 1 INDI 368 PT1 66 Female Caucasian Breast II Invasive Ductal Adenocarcinoma 1,000 Positive INDI 369 INDI 369 PLS 1A INDI 369 PT1 81 Caucasian Male Lung I C non-small cell lung 0.553 Negative INDI 371 INDI 371 PLS 1A INDI 371 PT1 63 Male Caucasian Lung II Cancer lung not small cell 0.998 Positive INDI 372 INDI 372 PLS 1A INDI 372 PT1 67 Female Caucasian Lung II Non-small cell lung cancer 0.995 Positive INDI 373 INDI 373 PLS 1A INDI 373 PT1 72 Male Caucasian Lung I Non-small cell lung cancer 0.842 Negative INDI 374 INDI 374 PLS 1 INDI 374 PT1 5 1 Male Caucasian Colorectal III Adenocarcinoma 0.956 Positive INDI 375 INDI 375 PLS 1 INDI 375 PT1 51 Male Caucasian Liver I Hepatocellular Carcinoma 0.970 Positive INDI 376 INDI 376 PLS 1 Male Unknown Colorectal I Adenocarcinoma INDI 377 0.98 Negative 377 PT1 64 Colorectal Caucasian Female II Adenocarcinoma 1,000 Positive INDI 378 INDI 378 PLS 1 INDI 378 PT1 87 Colorectal Caucasian Female I Adenocarcinoma 0.155 Negative INDI 379 INDI 379 PLS 1 INDI 379 PT1 69 Caucasian Colorectal III III Adenocarcinoma 0.312 Negative INDI 380I INDI 380I INDI 380I 380I INDI 380 PT1 92 Male Caucasian Colorectal II Adenocarcinoma 0.997 Positive INDI 381 INDI 381 PLS 1A INDI 381 PT1 54 Male Caucasian Lung III Non-small cell lung cancer 0.640 Negative INDI 382 INDI 382 PLS 1 INDI 382 PT1 73 Caucasian Female Breast II Invasive dutch adenocarcinoma 0,4 Negative INDI 383 INDI 383 PLS 1A INDI 383 PT1 54 Male Caucasian Lung III Non-small cell lung cancer ena 0.976 Positive INDI 384 INDI 384 PLS 1 INDI 384 PT1 54 Male Caucasian Pancreas II Adenocarcinoma 0.596 Negative INDI 385 INDI 385 PLS 1 INDI 385 PT1 87 Male Caucasian Colorectal I Adenocarcinoma 0.629 Negative INDI 386 INDI 386 Colet 1 INDI 386 III Adenocarcinoma 0.121 Negative INDI 387 INDI 387 PLS 1 INDI 387 PT1 62 Male Caucasian Liver I Hepatocellular Carcinoma 0.955 Positive INDI 388 INDI 388 PLS 1 INDI 388 PT1 77 Male Caucasian Pancreas II Adenocarcinoma 0.992 Positive INDI 389 INDI 389 PLS 1 Caucasian Lung III Non-small cell lung cancer 0.992 Positive INDI 390 INDI 390 PLS 1 INDI 390 PT1 79 Male Caucasian Breast II Invasive ductal adenocarcinoma 0.578 Negative INDI 391 INDI 391 PLS 1 INDI 391 PT1 46 Female Caucasian Liver II Cholangiocarcinoma 1,000 Positive INDI 392 INDI 392 PLS 1A INDI 392 PT1 57 Male Caucasian Lung I Non-small cell lung cancer 0.326 Negative INDI 393 INDI 393 PLS 1 INDI 393 P T1 66 Male Unknown Liver II Hepatocellular Carcinoma 0.911 Positive INDI 394 INDI 394 PLS 1A INDI 394 PT1 77 Male Unknown Lung II Non-small cell lung cancer 0.960 Positive INDI 395 INDI 395 PLS 1A INDI 395 PT1 79 Female lung L lung cancer 0.855 Negative INDI 396 INDI 396 PLS 1 INDI 396 PT1 59 Male Caucasian Colorectal I Adenocarcinoma 0.965 Positive INDI 397 INDI 397 PLS 1 INDI 397 PT1 55 Male Caucasian Colorectal III Adenocarcinoma 0.987 Positive INDI 398 INDI 398 PLS 1 INDI 398 PT1 91 Adenocarcinoma 0.495 Negative INDI 400 INDI 400 PLS 1 INDI 400 PT1 80 Colorectal Caucasian Female II Adenocarcinoma 0.980 Positive INDI 402 INDI 402 PLS 1 Not Available 47 Caucasian Female Breast I Invasive Lobular Carcinoma 0.322 Negative INDI 403 INDI 403 PLS 1A Not Available 63 Male Caucasian Lung III Non-small cell lung cancer 0.569 Negative INDI 404 INDI 404 PLS 1 Not Available 58 Female Caucasian Mam a III Invasive lobular carcinoma 0.121 Negative INDI 405 INDI 405 PLS 1A Not Available 64 Male Caucasian Lung I Non-small cell lung cancer 0.829 Negative INDI 407 INDI 407 PLS 1 Not Available 47 Female Caucasian Breast I Invasive ductal adenocarcinoma 0.300 Negative INDI 408 INDI 408 PLS 1 Not Available 47 Caucasian Female Breast II Invasive Carcinoma (NOS) 0.117 Negative INDI 409 INDI 409 PLS 1 Not Available 65 Caucasian Female II Invasive Lobular Carcinoma 0.238 Negative INDI 411 INDI 411 PLS 1 Not Available 63 Caucasian Female Breast II Invasive Ductal Adenocarcinoma 0.312 Negative INDI 412 INDI 412 PLS 1 Not Available 50 Caucasian Female Breast II Invasive Ductal Adenocarcinoma 0.945 Positive INDI 413 INDI 413 PLS 1 Not Available 65 Caucasian Female Lung Lung Cancer Not Small Cell 0.877 Positive INDI 414 INDI 414 PLS 1 Not Available 71 Asian Male Lung II Non-small cell lung cancer 0.620 Negative INDI 415 INDI 415 PLS 1 Not Available 63 Asiát Male ica Lung II Non-small cell lung cancer 0.979 Positive INDI 417 INDI 417 PLS 1 Not Available 68 Male Asian Lung I Non-small cell lung cancer 0.883 Positive

INDI 418 INDI 418 PLS 1 INDI 418 PT1 63 Colorectal Caucasian Female I Adenocarcinoma 0.953 Positive INDI 419 INDI 419 PT1 56 Colorectal Caucasian Male I Adenocarcinoma 0.813 Negative INDI 421 INDI 421 PLS 1 INDI 421 PT1 67 Negative INDI 422 INDI 422 PLS 1 INDI 422 PT1 79 Female Caucasian Colorectal II Adenocarcinoma 0.994 Positive INDI 423 INDI 423 PLS 1 INDI 423 PT1 65 Male Caucasian Colorectal II Adenocarcinoma 0.991 Positive INDI 424 INDI 424 PLS 1 INDI 424 PT1 68 Female Caucasian Breast invasive dutch 0.413 Negative INDI 425 INDI 425 PLS 1 Not Available 45 Caucasian Female Breast II Adenocarcinoma invasive dutch 0.690 Negative INDI 426 INDI 426 PLS 1 INDI 426 PT1 57 Caucasian Female Breast II Invasive dutch adenocarcinoma 0.666 Negative INDI 427 INDI 427 PLS 1 INDI 427 PT1 73 Colorectal Caucasian Female II Adenocarcinoma 1,000 Positive INDI 428 INDI 428 PLS 1 INDI 428 PT1 65 Male Caucasian Colorre tal III Adenocarcinoma 0.774 Negative INDI 429 INDI 429 PLS 1 Not Available 65 Colorectal Caucasian Female II Adenocarcinoma 1,000 Positive INDI 431 INDI 431 PLS 1 Male Colorectal Caucasian II Adenocarcinoma 0.711 Negative INDI 433 INDI 433 PLS 1 INDI 433 PT1 62 Female Colorectal II Adenocarcinoma 0.985 Positive INDI 434 INDI 434 PLS 1 INDI 434 PT1 73 Caucasian Colorectal II Adenocarcinoma 0.817 Negative INDI 436 INDI 436 PLS 1 INDI 436 PT1 59 Colorectal Caucasian III Adenocarcinoma 0.697 Negative INDI 437 INDI 437 PLI Colorectal Caucasian I Adenocarcinoma 0.950 Positive INDI 438 INDI 438 PLS 1 INDI 438 PT1 59 Male Colorectal Caucasian II Adenocarcinoma 0.777 Negative INDI 439 INDI 439 PLS 1 INDI 439 PT1 72 Male Asian Colorectal III Adenocarcinoma 1,000 Positive INDI 440 INDI 440 PLS 1 Male Asian Esophagus II Adenocarcinoma 0.996 Positive INDI 441 INDI 441 PLS 1 INDI 441 PT1 61 Asian Male Es oophagus II Squamous cell carcinoma 0.980 Positive INDI 442 INDI 442 PLS 1 INDI 442 PT1 41 Asian female II invasive ductal adenocarcinoma 0.956 Positive INDI 443 INDI 443 PLS 1 INDI 443 PT1 62 Asian female Breast II Invasive carcinoma (NOS) 0.878 Positive INDI 444 444 PLS 1 INDI 444 PT1 64 Male Asian Colorectal II Adenocarcinoma 0.993 Positive INDI 445 INDI 445 PLS 1 INDI 445 PT1 47 Asian Female Breast II Adenocarcinoma ductus invasivo 1,000 Positive INDI 446 INDI 446 PLS 1 Unavailable 52 Male Asian Esophagus II Carcinoma squamous cell 0 Positive INDI 447 INDI 447 PLS 1 INDI 447 PT1 33 Asian Female Breast III Invasive dutch adenocarcinoma 1,000 Positive INDI 449 INDI 449 PLS 1 INDI 449 PT1 46 Asian Female Mama III Invasive dutal adenocarcinoma 0.985 Positive INDI 450 INDI 450 PLS 1 INDI 450 PT1 60 Male Asian Esophagus II Squamous cell carcinoma 0.993 Positive INDI 452 INDI 452 PLS 1 Not Available 72 Male Asian Colorectal II Adenocarcinoma 0.999 Positi vo INDI 453 INDI 453 PLS 1 INDI 453 PT1 69 Asian Female Breast I Invasive ductal adenocarcinoma 0.900 Positive INDI 454 INDI 454 PLS 1 INDI 454 PT1 54 Asian Female Mama III Invasive ductal adenocarcinoma 0.901 Positive INDI 455 INDI 455 PLS 1 INDI 455 PT1 52 Female Asian Breast II Invasive Ductal Adenocarcinoma 0.855 Negative INDI 456 INDI 456 PLS 1 INDI 456 PT1 59 Asian Colorectal II Adenocarcinoma 0.994 Positive INDI 457 INDI 457 PLS 1 INDI 457 PT1 59 Male Asian Esophagus II Squamous Cell Carcinoma 1,000 Positive INDI 458I INDI 458 PT1 46 Asian Female Breast III Invasive Carcinoma (NOS) 0.999 Positive INDI 459 INDI 459 PLS 1 INDI 459 PT1 45 Asian Female II Invasive Ductal Adenocarcinoma 1,000 Positive INDI 460 INDI 460 PLS 1 INDI 460 PT1 39 Asian Female Breast III Adenocarcinoma dutal invasive 0.380 Negative INDI 461 INDI 461 PLS 1 INDI 461 PT1 49 Male Asian Colorectal III Adenocarcinoma 0.989 Positive INDI 462 INDI 462 PLS 1 INDI 462 PT1 54 Female Asian Breast III Invasive Ductal Adenocarcinoma 1,000 Positive INDI 463 INDI 463 PLS 1 INDI 463 PT1 33 Asian Colorectal II Adenocarcinoma 1,000 Positive INDI 464 INDI 464 PLS 1 INDI 464 PT1 67 Male Asian Liver III Cholangiocarcinoma 1,000 Positive INDI 465 INDI 465 PLS 1 INDI 465 PLS 1 PT1 45 Male Asian Esophagus II Squamous cell carcinoma 1,000 Positive INDI 466 INDI 466 PLS 1 INDI 466 PT1 60 Colorectal Asian Female II Adenocarcinoma 0.998 Positive INDI 467 INDI 467 PLS 1 INDI 467 PT1 82 Asian Colorectal II Adenocarcinoma 469 1 INDI 468 PT1 62 Colorectal Asian Female II Adenocarcinoma 0.971 Positive INDI 469 INDI 469 PLS 1 INDI 469 PT1 37 Asian Female Breast III Invasive Lobular Carcinoma 0.695 Negative INDI 470 INDI 470 PLS 1 INDI 470 PT1 60 Asian Colorectal III Adenocarcinoma 47.91 Positive INDI 471 PLS 1 INDI 471 PT1 46 Asian Asian Breast III Invasive ductal adenocarcinoma 0.123 Negative INDI 472 INDI 472 PL S 1 INDI 472 PT1 51 Asian Colorectal II Female Adenocarcinoma 0.991 Positive INDI 473 INDI 473 PLS 1 INDI 473 PT1 30 Asian Female Breast III Adenocarcinoma ductal invasive 0.880 Positive INDI 474 INDI 474 PLS 1 INDI 474 PT1 72 Asian Female Breast II Invasive Carcinoma (NOS ) 0.989 Positive INDI 475 INDI 475 PLS 1 INDI 475 PT1 62 Male Asian Liver III Hepatocellular Carcinoma 0.993 Positive INDI 476 INDI 476 PLS 1 INDI 476 PT1 30 Male Asian Esophagus II Squamous Cell Carcinoma 0.996 Positive INDI 477 INDI 477 PLS 1 INDI 477 PLS 1 IND Male Asian Colorectal III Adenocarcinoma 0.928 Positive INDI 478 INDI 478 PLS 1 INDI 478 PT1 54 Asian Female Breast II Invasive Ductal Adenocarcinoma 0.982 Positive INDI 479 INDI 479 PLS 1 INDI 479 PT1 29 Asian Female Breast III Invasive dutch adenocarcinoma 0.824 Negative INDI 480I 1 INDI 480 PT1 60 Caucasian Female Breast III Invasive ductal adenocarcinoma 0.932 Positive INDI 482 INDI 482 PLS 1 INDI 482 PT1 74 Caucasian Female Liver II Ca Hepatocellular rcinoma 0.999 Positive INDI 483 INDI 483 PLS 1 INDI 483 PT1 54 Colorectal Caucasian Female II Adenocarcinoma 0.552 Negative INDI 484 INDI 484 PLS 1 INDI 484 PT1 86 Male Colorectal Caucasian II Adenocarcinoma 0.528 Negative INDI 485 INDI 485 PLS 1 1 Esophagus III Adenocarcinoma 0.448 Negative INDI 487 INDI 487 PLS 1 INDI 487 PT1 72 Caucasian Female Breast II Invasive Ductal Adenocarcinoma 0.117 Negative INDI 488 INDI 488 PLS 1 Not Available 48 Caucasian Female Breast III Invasive Lobular Carcinoma 0.848 Negative INDI 489 INDI 489 PLS 1 INDI PT1 60 Caucasian Female Breast II Invasive Ductal Adenocarcinoma 0.943 Positive INDI 490 INDI 490 PLS 1 INDI 490 PT1 70 Caucasian Female Breast III Ductal Adenocarcinoma Invasive 1,000 Positive INDI 491 INDI 491 PLS 1 INDI 491 PT1 70 Male Caucasian Lung I Small Lung Cell Cancer 0.615 Negative

INDI 492 INDI 492 PLS 1 INDI 492 PT1 65 Male Caucasian Lung I Lung cancer not small cell 0.572 Negative INDI 493 INDI 493 PLS 1 INDI 493 PT1 41 Female Caucasian Breast II Invasive dutch adenocarcinoma 0.116 Negative INDI 494 INDI 494 PLS 1 INDI 494 PT1 83 Male Caucasian Lung III Non-small cell lung cancer 0.997 Positive INDI 495 INDI 495 PLS 1 INDI 495 PT1 60 Female Caucasian lung I Non-small cell lung 0.122 Negative INDI 497 INDI 497 PLS 1 INDI 497 PT1 68 Male Caucasian Lung II Small cell 0.59 Negative INDI 498 INDI 498 PLS 1 INDI 498 PT1 62 Male Caucasian Colorectal II Adenocarcinoma 0.524 Negative INDI 499 INDI 499 PLS 1 Not Available 60 Female Caucasian Lung Non-small cell lung cancer 0.642 Negative INDI 501 INDI 501 PLS 1 INDI 501 PT1 56 Male Caucasian Colorectal III Adenocarcinoma 0.125 Negative INDI 502 INDI 502 PLS 1 INDI 502 PT1 61 Male Caucasian Colorectal III Adenocarcinoma 0.858 Negative INDI 503 INDI 503 PLS 1 Not Available level 67 Male Caucasian Lung I Lung cancer not small cell 0.923 Positive INDI 504 INDI 504 PLS 1 INDI 504 PT1 44 Female Caucasian Breast III Invasive Ductal Adenocarcinoma 0.122 Negative INDI 505 INDI 505 PLS 1 INDI 505 PT1 77 Male Caucasian Lung II Non-cell lung cancer small 1,000 Positive INDI 506 INDI 506 PLS 1 INDI 506 PT1 80 Colorectal Caucasian Female I Adenocarcinoma 0.947 Positive INDI 507 INDI 507 PLS 1 INDI 507 PT1 57 Male Caucasian Lung II Non-small cell lung cancer 0.991 Positive INDI 508 INDI 508 PLS 1 INDI 508 PT1 46 Male Caucasian Colorectal III Adenocarcinoma 0.122 Negative INDI 511 INDI 511 PLS 1 INDI 511 PT1 52 Male Caucasian Colorectal III Adenocarcinoma 0.637 Negative INDI 512 INDI 512 PLS 1 INDI 512 PT1 47 Caucasian Female Breast II Adenocarcinoma ductus indi 513 INDI 513 INDI 513 PT1 55 Caucasian Female Lung I Non-small cell lung cancer 0.502 Negative INDI 514 INDI 514 PLS 1 INDI 514 PT1 73 Male C aucasian Stomach I Adenocarcinoma 0.991 Positive INDI 515 INDI 515 PLS 1 INDI 515 PT1 85 Male Caucasian Colorectal III Adenocarcinoma 0.983 Positive INDI 516 INDI 516 PLS 1 INDI 516 PT1 75 Male Caucasian Pancreas II Adenocarcinoma 0.999 Positive INDI 517 INDI 517 INDI 517 IND Male Caucasian Breast III Invasive Ductal Adenocarcinoma 0.588 Negative INDI 518 INDI 518 PLS 1 INDI 518 PT1 60 Male Caucasian Colorectal III Adenocarcinoma 0.888 Positive INDI 519 INDI 519 PLS 1 INDI 519 PT1 80 Male Caucasian Lung I Lung Cancer Not Small INDIA 521 Negative INDI 521 521 PLS 1 INDI 521 PT1 75 Female Caucasian Colorectal II Adenocarcinoma 0.933 Positive INDI 522 INDI 522 PLS 1 INDI 522 PT1 76 Male Caucasian Lung I lung cancer not small cell 0.208 Negative INDI 523 INDI 523 PLS 1 INDI 523 PT1 84 Male Caucasian Colorectal Ien Aden 0.997 Positive INDI 524 INDI 524 PLS 1 INDI 524 PT1 73 Male Caucasian Lung III Non-small cell lung cancer 0.982 Positive INDI 525 INDI 525 PLS 1 INDI 525 PT1 75 Male Unknown Colorectal I Adenocarcinoma 0.978 Positive INDI 526 INDI 526 PLS 1 INDI 526 PT1 79 Male Caucasian Esophagus III Adenocarcinoma 0.975 Positive INDI 527 INDI 527 PLS 1 INDI 527 PT1 72 Caucasian Female 0.932 Positive INDI 528 INDI 528 PLS 1 Not Available 48 Male Caucasian Colorectal II Adenocarcinoma 0.950 Positive INDI 529 INDI 529 PLS 1 INDI 529 PT1 71 Male Caucasian Colorectal I Adenocarcinoma 0.50 Negative INDI 530 INDI 530 PLS 1 INDI 530 PT1 53 Caucasian Female Breast invasive resistance 0.421 Negative INDI 532 INDI 532 PLS 1 INDI 532 PT1 71 Male Unknown Lung I lung cancer not small 0.813 Negative INDI 533 INDI 533 PLS 1 INDI 533 PT1 77 Male Caucasian Pancreas II Adenocarcinoma 0.909 Positive INDI 534 INDI 534 PLS 1 INDI 534 PLS 1 PT1 47 Female Caucasian Stomach III Adenocarcinoma 0.421 Negative INDI 535 INDI 535 PLS 1 INDI 535 PT1 78 Male Caucasian Pul hand I Non-small cell lung cancer 0.966 Positive INDI 537 INDI 537 PLS 1 INDI 537 PT1 52 Female Caucasian Ovary I Epithelial carcinoma 0.644 Negative INDI 538 INDI 538 PLS 1 INDI 538 PT1 43 Male Caucasian Lung III Non-small cell lung cancer 0.959 Positive INDI 539 INDI 539 PLS 1 INDI 539 PT1 73 Colorectal Caucasian Female II Adenocarcinoma 0.991 Positive INDI 540 INDI 540 PLS 1 INDI 540 PT1 83 Caucasian Female Breast II Invasive Dutch Adenocarcinoma 0.630 Negative INDI 541 INDI 541 PLS 1 Not Available 44 Caucasian Female Breast II Invasive Southern Adenocarcinoma 0.571 Negative INDI 544 INDI 544 PLS 1 INDI 544 PT1 44 Male Caucasian Colorectal III Adenocarcinoma 1,000 Positive INDI 545 INDI 545 PLS 1 INDI 545 PT1 71 Female Caucasian Lung Non-small cell lung cancer 0.950 Positive INDI 546 INDI 546 PLS 1 INDI 546 PT1 76 Female Caucasian Lung II Non-small cell lung cancer 0.837 Negative INDI 547 INDI 547 PLS 1 INDI 547 PT1 55 Male Caucasian Stomach III Adeno carcinoma 0.125 Negative INDI 548 INDI 548 PLS 1 INDI 548 PT1 82 Caucasian Female Breast II Invasive ductal adenocarcinoma 0.951 Positive INDI 549 INDI 549 PLS 1 INDI 549 PT1 74 Caucasian Female Lung III Non-small cell lung cancer 0.898 Positive INDI 550 INDI 550 PLS 1 INDI 550 PT1 52 Male Caucasian Lung III Lung cancer not small cell 0.568 Negative INDI 551 INDI 551 PLS 1 INDI 551 PT1 68 Male Caucasian Colorectal II Adenocarcinoma 0.866 Positive INDI 555 INDI 555 PLS 1 INDI 555 PT1 74 Male Asian Colorectal II Adenocarcinoma 0.858 Positive INDI 558 PLS 1 INDI 558 PT1 57 Male Caucasian Colorectal I Adenocarcinoma 0.966 Positive INDI 559 INDI 559 PLS 1 INDI 559 PT1 54 Caucasian Female Mama III Invasive Lobular Carcinoma 0.847 Negative INDI 560 INDI 560 PLS 1 INDI 560 PT1 72 Male Caucasian Colorectal Aden I79 Adenocarcinoma Ien Negative INDI 561 INDI 561 PLS 1 INDI 561 PT1 56 Female Caucasian Breast III Invasive ductal adenocarcinoma 0.828 Negative INDI 562 INDI 562 PLS 1 INDI 562 PT1 78 Female Caucasian Colorectal I Adenocarcinoma 0.999 Positive INDI 564 INDI 564 PLS 1 INDI 564 PT1 66 Male Caucasian Colorectal I Adenocarcinoma 0.955 Positive INDI 565 INDI 565 PLS 1 INDI 565 PT1 54 Caucasian Female Breast III Adenocarcinoma INDI 566 INDI 566 PLS 1 INDI 566 PT1 36 Caucasian Female Breast II Invasive Ductal Adenocarcinoma 0.412 Negative INDI 567 INDI 567 PLS 1 INDI 567 PT1 64 Male Caucasian Colorectal II Adenocarcinoma 1,000 Positive INDI 568 INDI 568 PLS 1 INDI 568 PT1 55 Caucasian Female Breast II Invasive Carcinoma (NOS) 0.342 Negative INDI 569 INDI 569 PLS 1 INDI 569 PT1 64 Caucasian Female Breast III Invasive Lobular Carcinoma 0.399 Negative INDI 570 INDI 570 PLS 1 INDI 570 PT1 46 Male Caucasian Colorectal II Adenocarcinoma 0.986 Positive INDI 571 INDI 571 PLS 1 INDS 571 PLS 1 571 PT1 61 Caucasian Female Breast III Invasive Ductal Adenocarcinoma 0.121 Negative INDI 572 INDI 572 PLS 1 INDI 572 PT1 59 Male Caucasian Colorectal II Adenocarcinoma 0.834 Negative

INDI 573 INDI 573 PLS 1 INDI 573 PT1 51 Colorectal Caucasian Female I Adenocarcinoma 0.372 Negative INDI 574 INDI 574 PLS 1 INDI 574 PT1 60 Colorectal Caucasian III Adenocarcinoma 0.966 Positive INDI 575 INDI 575 PLS 1 INDI 575 PT1 58 Caucasian Female Breast II Carcinoma invasive 0.974 Positive INDI 577 INDI 577 PLS 1 INDI 577 PT1 65 Asian Female Breast I invasive dutch adenocarcinoma 0.351 Negative INDI 578 INDI 578 PLS 1 Asian Female Breast I Invasive dutch adenocarcinoma 0.811 Negative INDI 579 INDI 579 PL1 1 INDI 579 PT1 33 Asian Female Breast I Invasive Ductal Adenocarcinoma 0.698 Negative INDI 581 INDI 581 PLS 1 INDI 581 PT1 79 Asian Colorectal II Adenocarcinoma 0.998 Positive INDI 582 INDI 582 PLS 1 INDI 582 PT1 72 Male Asian Colorectal II Adenocarcinoma 0.999 Positive INDI 583 INDI 583 PT1 67 Asian Colorectal II Female Adenocarcinoma 0.979 Positive INDI 584 INDI 584 PLS 1 INDI 584 PT1 80 Male Asian Colorectal II Aden ocarcinoma 0.974 Positive INDI 585 INDI 585 PLS 1 INDI 585 PT1 59 Male Asian Esophagus III Squamous cell carcinoma 0.999 Positive INDI 586 INDI 586 PLS 1 INDI 586 PT1 59 Asian Female Liver III Hepatocellular Carcinoma 1,000 Positive INDI 587 INDI 587 PLS 1 INDI 587 PLS 1 1 Male Asian Colorectal II Adenocarcinoma 0.980 Positive INDI 588 INDI 588 PLS 1 INDI 588 PT1 33 Asian Female Breast III Invasive dutal adenocarcinoma 0.125 Negative INDI 589 INDI 589 PLS 1 INDI 589 PT1 43 Asian Female Breast II Invasive dutch adenocarcinoma 0.115 Negative INDI 591 INDI 591 INDI 591 1 INDI 591 PT1 81 Male Asian Colorectal II Adenocarcinoma 0.999 Positive INDI 592 INDI 592 PLS 1 INDI 592 PT1 49 Asian Female Breast II Adenocarcinoma ductal invasive 0.291 Negative INDI 593 INDI 593 PLS 1 INDI 593 PT1 82 Asian Female Breast II Additive ductal invasive 0.9 INDI 594 INDI 594 PLS 1 INDI 594 PT1 56 Colorectal Asian Asian III Adenocarcinoma 0.992 Positive INDI 595 INDI 595 PLS 1 INDI 5 95 PT1 64 Male Asian Esophagus II Squamous cell carcinoma 0.999 Positive INDI 596 INDI 596 PLS 1 INDI 596 PT1 56 Asian Female Breast III Invasive Ductal Adenocarcinoma 0.539 Negative INDI 597 INDI 597 PLS 1 INDI 597 PT1 56 Asian Female Breast II Invasive dutal adenocarcinoma 0.591 Negative INDI 598 INDI 598 PLS 1 INDI 598 PT1 84 Male Asian Colorectal II Adenocarcinoma 0.974 Positive INDI 599 INDI 599 PLS 1 INDI 599 PT1 50 Asian Female Colorectal II Adenocarcinoma 0.897 Positive INDI 600 INDI 600 PLS 1 INDI 600 PT1 85 Asian Male Colorectal II Negative INDI 601 INDI 601 PLS 1 INDI 601 PT1 52 Asian Female Breast III Invasive ductal adenocarcinoma 1,000 Positive INDI 602 INDI 602 PLS 1 INDI 602 PT1 47 Asian Colorectal II Adenocarcinoma 0.998 Positive INDI 603 INDI 603 PLS 1 INDI 603 PT1 58 Asian Female Breast III Invasive ductal adenocarcinoma 0.548 Negative INDI 604 INDI 604 PLS 1 INDI 604 PT1 48 Asian Female Breast II Invasive ductal adenocarcinoma 0.948 Positive INDI 605 INDI 605 PLS 1 INDI 605 PT1 58 Colorectal Asian Female II Adenocarcinoma 0.986 Positive INDI 606 INDI 606 PLS 1 INDI 606 PT1 71 Asian Female Breast II Invasive Ductal Adenocarcinoma 0.681 Negative INDI 607 INDI 607 PLS 1 INDI 607 PT1 46 Female 46 Breast II Invasive ductal adenocarcinoma 0.667 Negative INDI 608 INDI 608 PLS 1 INDI 608 PT1 51 Asian Colorectal II Adenocarcinoma 0.964 Positive INDI 609 INDI 609 PLS 1 INDI 609 PT1 60 Male Asian Colorectal II Adenocarcinoma 0.998 Positive INDI 610 INDI 610 INDI 610 INDI 610 INDI 610 50 Asian Colorectal Female II Adenocarcinoma 0.965 Positive INDI 611 INDI 611 PLS 1 INDI 611 PT1 59 Asian Colorectal II Adenocarcinoma 0.932 Positive INDI 612 INDI 612 PLS 1 INDI 612 PT1 55 Asian Colorectal II Adenocarcinoma 0.774 INDI 613 INDI 613 INDI 613I PT1 52 Asian Female Breast II Invasive lobular carcinoma 0.931 Positive INDI 614 INDI 614 PLS 1 INDI 614 PT1 64 Asian Colorectal II Adenocarcinoma 0.999 Positive INDI 615 INDI 615 PLS 1 INDI 615 PT1 67 Male Asian Colorectal II Adenocarcinoma 0.998 Positive INDI 617 INDI 617 PLS 1 INDI 617 PT1 22 Male Asian Colorectal III Adenocarcinoma 0.923 Positive INDI 618 INDI 618 PLI 1 INDI 618 PLS 1 1 III Adenocarcinoma 0.977 Positive INDI 619 INDI 619 PLS 1 INDI 619 PT1 52 Asian Female Breast II Invasive dutch adenocarcinoma 0.125 Negative INDI 620 INDI 620 PLS 1 INDI 620 PT1 55 Male Asian Colorectal II Adenocarcinoma 0.998 Positive INDI 621 INDI 621 PLS 1 INDI 621 Male Asian Colorectal II Adenocarcinoma 0.910 Positive INDI 622 INDI 622 PLS 1 INDI 622 PT1 65 Male Asian Liver III Hepatocellular Carcinoma 1,000 Positive INDI 623 INDI 623 PLS 1 INDI 623 PT1 48 Asian Female Breast III Invasive dutch adenocarcinoma 0.998 Positive 6 INDI 624 PT1 55 Colorectal Asian Female II Adenocarcinoma 0.986 Positive INDI 625 INDI 625 PLS 1 INDI 625 PT1 60 Asian Female ca Colorectal II Adenocarcinoma 0.961 Positive INDI 626 INDI 626 PLS 1 INDI 626 PT1 79 Male Asian Colorectal II Adenocarcinoma 1,000 Positive INDI 627 INDI 627 PLS 1 INDI 627 PT1 52 Asian Female Breast II Invasive dutch adenocarcinoma 0.927 Positive INDI 628 INDI 628 INDI 628 PT1 51 Asian Colorectal II Female Adenocarcinoma 0.923 Positive INDI 629 INDI 629 PLS 1 INDI 629 PT1 53 Asian Female Breast I Invasive Ductal Adenocarcinoma 0.762 Negative INDI 630 INDI 630 PLS 1 INDI 630 PT1 55 Asian Female Breast I Invasive Dutch Adenocarcinoma INDI 631 0.14 Negative INDI 631 631 PLS 1 INDI 631 PT1 39 Colorectal Asian Female II Adenocarcinoma 0.998 Positive INDI 632 INDI 632 PT1 72 Colorectal Asian Female II Adenocarcinoma 0.998 Positive INDI 633 INDI 633 PLS 1 INDI 633 PT1 52 Asian Colorectal II Adenocarcinoma 6 INDI 635 PLS 1 INDI 635 PT1 50 Male Asian Colorectal II Adenocarcinoma 0.991 Positive INDI 636 INDI 636 PLS 1 INDI 636 PT1 63 Male Asian Liver III Hepatocellular Carcinoma 1,000 Positive INDI 637 INDI 637 PLS 1 INDI 637 PT1 52 Asian Female Breast III Invasive Ductal Adenocarcinoma 0.877 Positive INDI 638 INDI 638 PLS 1 INDI 638 PT1 52 Asian Female Breast I Invasive Adenocarcinoma INDI 6.888 Positive INDI 6 639 PLS 1 INDI 639 PT1 53 Male Asian Liver II Hepatocellular Carcinoma 1,000 Positive INDI 640 INDI 640 PLS 1 INDI 640 PT1 65 Male Asian Colorectal II Adenocarcinoma 0.998 Positive INDI 641 INDI 641 PLS 1 INDI 641 PT1 65 Asian Female Breast III Invasive lobular carcinoma 0,8 Negative INDI 643 INDI 643 PLS 1 INDI 643 PT1 67 Female Asian Colorectal II Adenocarcinoma 0.999 Positive INDI 644 INDI 644 PLS 1 INDI 644 PT1 67 Male Asian Colorectal I Adenocarcinoma 0.925 Positive

INDI 645 INDI 645 PLS 1 INDI 645 PT1 54 Male Asian Liver III Hepatocellular Carcinoma 1,000 Positive INDI 646 INDI 646 PLS 1 INDI 646 PT1 60 Asian Female Mama II Invasive Lobular Carcinoma 0.432 Negative INDI 647 INDI 647 PLS 1 INDI 647 PT1 61 Male Asian Colorectal III Adenocarcinoma 0.999 Positive INDI 648 INDI 648 PLS 1 INDI 648 PT1 63 Male Asian Colorectal III Adenocarcinoma 1,000 Positive INDI 649 INDI 649 PLS 1 INDI 649 PT1 51 Male Asian Colorectal III Adenocarcinoma 0.999 Positive INDI 650 INDI 650 PLS 1 INDI 650 341 Colorectal III Adenocarcinoma 0.857 Negative INDI 651 INDI 651 PLS 1 INDI 651 PT1 74 Male Asian Colorectal III Adenocarcinoma 0.881 Positive INDI 654 INDI 654 PLS 1 INDI 654 PT1 69 Male Asian Colorectal II Adenocarcinoma 0.932 Positive INDI 655 INDI 655 INDI 655 INDI 655 INDI 655 Asian Colorectal II Adenocarcinoma 0.854 Negative INDI 656 INDI 656 PLS 1 INDI 656 PT1 48 Female Asian Breast III Invasive dutal adenocarcinoma o 0.207 Negative INDI 657 INDI 657 PLS 1 INDI 657 PT1 49 Asian Colorectal II Female Adenocarcinoma 1,000 Positive INDI 658 INDI 658 PLS 1 INDI 658 PT1 37 Asian Colorectal I Adenocarcinoma 0.997 Positive INDI 659 INDI 659 PLS 1 INDI 659 PT1 42 Female Asian II Invasive ductal adenocarcinoma 0.310 Negative INDI 660 INDI 660 PLS 1 INDI 660 PT1 79 Colorectal Asian female II Adenocarcinoma 0.955 Positive INDI 661 INDI 661 PLS 1 INDI 661 PT1 58 Asian female Liver III Cholangiocarcinoma 0.989 Positive INDI 662 INDI 662 PLS 1 1 Asian Female Breast I Invasive Ductal Adenocarcinoma 0.614 Negative INDI 663 INDI 663 PLS 1 INDI 663 PT1 52 Male Asian Esophagus II Squamous Cell Carcinoma 1,000 Positive INDI 664 INDI 664 PLS 1 INDI 664 PT1 61 Asian Female Breast I Invasive DEN 0.665 Negative INDI 665 665 PLS 1 INDI 665 PT1 64 Male Asian Esophagus III Adenocarcinoma 1,000 Positive INDI 666 INDI 666 PLS 1 INDI 666 PT1 45 Asian Female Breast II Invasive ductal adenocarcinoma 0.831 Negative INDI 667 INDI 667 PLS 1 INDI 667 PT1 70 Asian female breast II Invasive ductal adenocarcinoma 0.326 Negative INDI 668 INDI 668 PLS 1 INDI 668 PT1 76 Asian female Breast III Invasive lobular carcinoma 0.382 Negative INDI 669 INDI 669 PLS 1 INDI 669 PLS 1 669 PT1 61 Male Caucasian Liver III Hepatocellular Carcinoma 0.996 Positive INDI 670 INDI 670 PLS 1 INDI 670 PT1 74 Male Caucasian Colorectal III Adenocarcinoma 0.999 Positive INDI 671 INDI 671 PLS 1 INDI 671 PT1 61 Caucasian Female Lung Cancer I cell non-small 0.877 672 INDI 672 PLS 1 INDI 672 PT1 63 Caucasian Female Breast II Invasive Lobular Carcinoma 0.117 Negative INDI 673 INDI 673 PLS 1 INDI 673 PT1 58 Female Caucasian Mama II Invasive Lobular Carcinoma 0.943 Positive INDI 674 INDI 674 PLS 1 INDI 674 PT1 70 Male Caucasian Lung I Non-small cell lung cancer 0.626 Negative INDI 675 INDI 675 PLS 1 INDI 675 PT1 92 Male Caucasian Colorectal III Adenocarcinoma 0, 991 Positive INDI 677 INDI 677 PLS 1 INDI 677 PT1 60 Female Caucasian Colorectal II Adenocarcinoma 0.559 Negative INDI 678 INDI 678 PLS 1 INDI 678 PT1 46 Male Caucasian Colorectal III Adenocarcinoma 0.977 Positive INDI 679 INDI 679 PLS 1 INDI 679 PT1 51 Male Caucasian Adenocarcinoma 0.116 Negative INDI 681 INDI 681 PLS 1 INDI 681 PT1 73 Male Caucasian Lung II Non-small cell lung cancer 0.758 Negative INDI 682 INDI 682 PLS 1 Not Available 66 Male Colorectal Caucasian III Adenocarcinoma 0.778 Negative INDI 683 INDI 683 PLS 1 INDI 683 PT1 75 Male Caucasian Colorectal II Adenocarcinoma 0.589 Negative INDI 684 INDI 684 PLS 1 Not Available 66 Male Caucasian Liver III Hepatocellular Carcinoma 0.806 Negative INDI 685 INDI 685 PLS 1 INDI 685 PT1 79 Male Caucasian Colorectal III Adenocarcinoma 0.29 Negative 68 68 INDI 686 INDI 68 66 Male Caucasian Pancreas II Adenocarcinoma 0.984 Positive INDI 687 INDI 687 PLS 1 INDI 687 PT1 56 Caucasian male ana Lung III Non-small cell lung cancer 1,000 Positive INDI 688 INDI 688 PLS 1 INDI 688 PT1 75 Colorectal Caucasian Female II Adenocarcinoma 0.908 Positive INDI 690 INDI 690 PLS 1 Not Available 80 Male Caucasian Lung III Non-small cell lung cancer 0.583 Negative INDI 691 INDI 691 PLS 1 INDI 691 PT1 74 Caucasian Female Lung I Lung cancer not small cell 0.914 Positive INDI 692 INDI 692 PLS 1 INDI 692 PT1 85 Colorectal Caucasian Female III Adenocarcinoma 0.955 Positive INDI 693 PLS 1 INDI 693 PT1 70 Male Caucasian Lung III Cancer non-small cell lung 0.998 Positive INDI 694 INDI 694 PLS 1 INDI 694 PT1 55 Caucasian Female Lung I Cancer non-small cell 0.958 Positive INDI 695 INDI 695 PLS 1 INDI 695 PT1 74 Female Caucasian Pancreas II Adenocarcinoma 0.378 Negative INDI 696 INDI 696 PLS 1 INDI 696 PT1 76 Male Caucasian Lung II Non-small cell lung cancer 0.948 Positive INDI 697 INDI 697 PLS 1 INDI 697 PT1 70 Female Caucasian M ama II Invasive ductal adenocarcinoma 0.974 Positive INDI 698 INDI 698 PLS 1 Not Available 58 Female Caucasian Ovary III Epithelial carcinoma 0.947 Positive INDI 699 INDI 699 PLS 1 Not Available 74 Female Caucasian Pancreas II Adenocarcinoma 0.997 Positive INDI 700 INDI 700 PLS 1 INDI 700 PT1 67 Male Caucasian Lung III Non-small cell lung cancer 0.986 Positive INDI 701 INDI 701 PLS 1 INDI 701 PT1 71 Colorectal Caucasian II Adenocarcinoma 0.874 Positive INDI 702 INDI 702 PLS 1 INDI 702 PT1 74 Male Caucasian Lung II Non-small cell lung cancer 0.997 Positive INDI 703 INDI 703 PLS 1 INDI 703 PT1 79 Male Caucasian Lung I Lung cancer not small cell 0.656 Negative INDI 704 INDI 704 PLS 1 INDI 704 PT1 35 Female Caucasian Breast II Invasive DEN Adenocarcinoma 0.395 Negative INDI 706 INDI 706 PLS 1 INDI 706 PT1 65 Female Caucasian Lung I Non-small cell lung cancer 0.993 Positive INDI 708 INDI 708 PLS 1 INDI 708 PT1 58 Male Caucasian Colorectal III Adenocarcinoma 0.995 Positive INDI 709 INDI 709 PLS 1 INDI 709 PT1 92 Colorectal Caucasian Female II Adenocarcinoma 0.842 Negative INDI 710 INDI 710 PLS 1 INDI 710 PT1 74 Colorectal Caucasian Female III Adenocarcinoma 0.977 Positive INDI 711 INDI 711 PLS 1 INDI Caucasian 711 III Invasive Ductal Adenocarcinoma 0.979 Positive INDI 712 INDI 712 PLS 1 INDI 712 PT1 76 Caucasian Female I Invasive Ductal Adenocarcinoma 0.354 Negative INDI 713 INDI 713 PLS 1 INDI 713 PT1 71 Caucasian Female Mama II Invasive Ductal Adenocarcinoma 0.342 Negative INDI 714 INDI 714 PLS INDI 714 PT1 78 Caucasian Female Breast III Invasive Lobular Carcinoma 0.956 Positive INDI 715 INDI 715 PLS 1 INDI 715 PT1 83 Caucasian Female Breast III Invasive Ductal Adenocarcinoma 0.987 Positive INDI 716 INDI 716 PLS 1 INDI 716 PT1 78 Male Caucasian Colorectal III Adenocarcinoma 0.975 717 INDI 717 PLS 1 INDI 717 PT1 65 Caucasian Female Breast I Invasive Lobular Carcinoma 0.812 Negative

INDI 718 INDI 718 PLS 1 Not Available 61 Female Caucasian Colorectal II Adenocarcinoma 0.829 Negative INDI 720 INDI 720 PLS 1 INDI 720 PT1 52 Male Caucasian Colorectal III Adenocarcinoma 0.654 Negative INDI 721 INDI 721 PLS 1 INDI 721 PT1 56 Caucasian Female Breast II Invasive Adenocarcinoma 0.598 Negative INDI 722 INDI 722 PLS 1 INDI 722 PT1 75 Caucasian Female Breast I Invasive Ductal Adenocarcinoma 0.201 Negative INDI 723 INDI 723 PLS 1 INDI 723 PT1 74 Caucasian Female Ovary III Epithelial Carcinoma 0.993 Positive INDI 724 INDI 724 PLS 1 INDI 724 PT1 67 Male Colorectal Caucasian II Adenocarcinoma 0.230 Negative INDI 725 INDI 725 PLS 1 INDI 725 PT1 61 Male Colorectal Caucasian II Adenocarcinoma 0.636 Negative INDI 726 INDI 726 PLS 1 INDI 726 PT1 60 Male Colorectal Caucasian I Adenocarcinoma 0.315 Negative INDI 729 INDI 729 INDI 729 INDI 729 INDI 729 Male Caucasian Colorectal II Adenocarcinoma 0.455 Negative INDI 731 INDI 731 PLS 1 Not Available 65 Male Caucasian Pancreas II Adenocarcinoma 0.449 Negative INDI 732 INDI 732 PLS 1 Not Available 56 Male Colorectal Caucasian III Adenocarcinoma 0.756 Negative INDI 734 INDI 734 PLS 1 Not Available 71 Female Caucasian Pancreas II Adenocarcinoma 0.938 Positive INDI 735 INDI 735 PLS 1 Not Available 66 Male Caucasian Male7 Pancreatic Positive INDI 736 INDI 736 PLS 1 Not Available 66 Male Caucasian Lung III Non-small cell lung cancer 0.958 Positive INDI 737 INDI 737 PLS 1 Not Available 58 Male Caucasian Stomach I Adenocarcinoma 0.944 Positive INDI 740 INDI 740 PLS 1 Not Available 86 Female Caucasian Pancreas II Adenocarcinoma 0.991 Positive INDI 741 INDI 741 PLS 1 Not Available 67 Pancreatic Caucasian Female III Adenocarcinoma 0.995 Positive INDI 742 INDI 742 PLS 1 Not Available 72 Caucasian Breast II Invasive Ductal Adenocarcinoma 0.826 Negative INDI 743 INDI 743 PLS 1 Not Available 35 Male Caucasian Stomach II Adenocarcinoma 0.117 Negative INDI 745 INDI 745 PLS 1 No D available 65 Male Asian Pancreas II Adenocarcinoma 0.953 Positive INDI 746 INDI 746 PLS 1 INDI 746 PT1 51 Male Asian Colorectal III Adenocarcinoma 0.993 Positive INDI 747 INDI 747 PLS 1 INDI 747 PT1 24 Female Asian Colorectal III Adenocarcinoma 7498 749 PT1 50 Colorectal Asian Female III Adenocarcinoma 0.900 Positive INDI 750 INDI 750 PLS 1 INDI 750 PT1 49 Colorectal Asian III Adenocarcinoma 0.973 Positive INDI 751 INDI 751 PLS 1 INDI 751 PT1 44 Asian Colorectal III Adenocarcinoma 0.982 Positive IND2 752 INDI 752 INDI 752 PT1 58 Male Asian Colorectal III Adenocarcinoma 0.975 Positive INDI 753 INDI 753 PLS 1 INDI 753 PT1 59 Male Asian Esophagus II Squamous cell carcinoma 0.999 Positive INDI 754 PLS 1 INDI 754 PT1 63 Asian Female Breast III Adenocarcinoma negative 755 INDI 755 PLS 1 INDI 755 PT1 70 Male Asian Esophagus III Squamous cell carcinoma 0.996 Positive INDI 7 56 INDI 756 PLS 1 INDI 756 PT1 63 Asian Female Breast II Invasive Ductal Adenocarcinoma 0.543 Negative INDI 757 INDI 757 PLS 1 INDI 757 PT1 58 Asian Female Mama II Invasive Ductal Adenocarcinoma 0.117 Negative INDI 758 INDI 758 PLS 1 INDI 758 PT1 82 Asian Colorectal II Adenocarcinoma 0.987 Positive INDI 759 INDI 759 PLS 1 INDI 759 PT1 62 Asian Female Breast III Invasive Ductal Adenocarcinoma 0.117 Negative INDI 760 INDI 760 PLS 1 INDI 760 PT1 65 Male Asian Liver III Hepatocellular Carcinoma 0.997 Positive INDI 761 INDI 761 PL 63 Colorectal Asian Female II Adenocarcinoma 0.995 Positive INDI 762 INDI 762 PLS 1 INDI 762 PT1 69 Male Asian Esophagus II Adenocarcinoma 0.690 Negative INDI 763 INDI 763 PLS 1 INDI 763 PT1 59 Asian Colorectal I Adenocarcinoma 0.824 INDI 764 INDI 764 INDI 764 PT1 61 Male Asian Colorectal II Adenocarcinoma 0.993 Positive INDI 765 INDI 765 PLS 1 INDI 765 PT1 68 Male Asian Colorectal I Adenocarcinoma 1, 000 Positive INDI 766 INDI 766 PLS 1 INDI 766 PT1 38 Asian Asian Breast II Invasive ductal adenocarcinoma 0.125 Negative INDI 767 INDI 767 PLS 1 Male Asian Colorectal II Adenocarcinoma 0.996 Positive INDI 768 INDI 768 PLS 1 INDI Asian Male Colorectal II Adenocarcinoma 0.735 Negative INDI 769 INDI 769 PLS 1 INDI 769 PT1 46 Male Asian Colorectal II Adenocarcinoma 1,000 Positive INDI 770 INDI 770 PLS 1 INDI 770 PT1 61 Asian Female Colorectal II Adenocarcinoma 0.970 Positive INDI 771 INDI 771 PLS 1 1 Asian Liver II Hepatocellular Carcinoma 0.994 Positive INDI 772 INDI 772 PLS 1 INDI 772 PT1 52 Asian Colorectal II Adenocarcinoma 0.819 Negative INDI 773 INDI 773 PLS 1 INDI 773 PT1 77 Asian Colorectal II Adenocarcinoma PT 7.841 INDI 774 INDI 774 67 Colorectal Asian Female I Adenocarcinoma 0.984 Positive INDI 775 INDI 775 PLS 1 INDI 775 PT1 76 Male Asian Liver II Hepatoc Carcinoma lular 0.999 Positive INDI 776 INDI 776 PLS 1 INDI 776 PT1 61 Male Asian Esophagus II Squamous cell carcinoma 0.998 Positive INDI 777 INDI 777 PLS 1 INDI 777 PT1 67 Asian Colorectal II Adenocarcinoma 0.994 Positive INDI 778 INDI 778 PLS 1 INDI 778 Asian Liver II Hepatocellular Carcinoma 0.999 Positive INDI 779 INDI 779 PLS 1 INDI 779 PT1 61 Asian Colorectal II Adenocarcinoma 1,000 Positive INDI 780 INDI 780 PLS 1 INDI 780 PT1 52 Asian Colorectal II Adenocarcinoma 0.946 Positive INDI 781 INDI 781 INDI 781 INDI 781 INDI 781 57 Male Asian Colorectal II Adenocarcinoma 0.965 Positive INDI 782 INDI 782 PLS 1 INDI 782 PT1 92 Female Caucasian Colorectal III Adenocarcinoma 1,000 Positive INDI 783 INDI 783 PLS 1 INDI 783 PT1 55 Male Caucasian Colorectal III Adenocarcinoma 0.680 Negative INDI 784I 76 Male Caucasian Stomach I Adenocarcinoma 0.985 Positive INDI 785 INDI 785 PLS 1 INDI 785 PT1 77 Male Caucasian Colorectal I Adenocarcinoma 0.302 Negative INDI 786 INDI 786 PLS 1 INDI 786 PT1 76 Male Caucasian Liver I Hepatocellular Carcinoma 0.978 Positive INDI 787 INDI 787 PLS 1 INDI 787 PT1 89 Caucasian Female Breast II Invasive dutch adenocarcinoma 0.114 Negative INDI 788 INDI 788 PLS 77 Colorectal Caucasian Female II Adenocarcinoma 0.688 Negative INDI 789 INDI 789 PLS 1 INDI 789 PT1 70 Male Caucasian Breast II Invasive Ductal Adenocarcinoma 0.117 Negative INDI 790 INDI 790 PLS 1 INDI 790 PT1 53 Caucasian Female Breast II Invasive Lobular Carcinoma 0.771 Negative INDI 791I PLS 1 INDI 791 PT0 52 Caucasian Female Breast III Invasive Ductal Adenocarcinoma 0.320 Negative INDI 792 INDI 792 PLS 1 Not Available 58 Male Caucasian Stomach I Adenocarcinoma 0.377 Negative

INDI 793 INDI 793 PLS 1 Not Available 83 Caucasian Female Breast II Invasive Ductal Adenocarcinoma 0.836 Negative INDI 794 INDI 794 PLS 1 Not Available 43 Female Caucasian Mama II Ductal Adenocarcinoma 0.571 Negative INDI 795 INDI 795 PLS 1 Not Available 68 Caucasian Female Breast II Adenocarcinoma invasive ductal 0.595 Negative INDI 797 INDI 797 PLS 1 INDI 797 PT1 69 Male Caucasian Stomach II Adenocarcinoma 0.955 Positive INDI 798 INDI 798 PLS 1 INDI 798 PT1 75 Female Caucasian Ovary II Epithelial carcinoma 1,000 Positive INDI 799 INDI 799 PLS 1 Not Available 74 Unknown Male Stomach II Adenocarcinoma 0.239 Negative INDI 800 INDI 800 PLS 1 INDI 800 PT1 75 Male Caucasian Liver II Hepatocellular Carcinoma 0.997 Positive INDI 801 INDI 801 PLS 1 INDI 801 PT1 77 Male Caucasian Stomach I Adenocarcinoma 0.293 Negative INDI 802 INDI 802 PLS 1 INDI 802 PT1 60 60 Female Caucasian Pancreas II Adenocarcinoma 0.987 Positive INDI 803 INDI 803 PLS 1 INDI 803 PT1 65 Caucasian Female Esophagus II Adenocarcinoma 0.385 Negative INDI 805 INDI 805 PLS 1 Not Available 61 Colorectal Caucasian Female II Adenocarcinoma 0.632 Negative INDI 806 INDI 806 PLS 1 INDI 806 PT1 65 Colorectal Caucasian Female II Adenocarcinoma 0.661 Negative INDI 808 INDI 808 PLS 1 INDI 808 Colorectal III Adenocarcinoma 0.601 Negative INDI 809 INDI 809 PLS 1 INDI 809 PT1 67 Male Caucasian Colorectal III Adenocarcinoma 0.213 Negative INDI 812 INDI 812 PLS 1 INDI 812 PT1 56 Female Caucasian Ovary II Epithelial carcinoma 1,000 Positive INDI 814 INDI 814 PLS 1 INDI 814 PLS 1 INDI 814 PLS 1 IND Male Caucasian Stomach I Adenocarcinoma 0.386 Negative INDI 815 INDI 815 PLS 1 INDI 815 PT1 77 Female Caucasian Colorectal II Adenocarcinoma 0.997 Positive INDI 816 INDI 816 PLS 1 INDI 816 PT1 58 Female Caucasian Colorectal II Adenocarcinoma 0.336 Negative INDI 817 INDI 817 PLI 79 Male Caucasian Liver III Hepatocellular Carcinoma 0.990 Positive INDI 818 INDI 818 PLS 1 INDI 818 PT1 73 Fê female Caucasian Ovary III Epithelial carcinoma 1,000 Positive INDI 819 INDI 819 PLS 1 INDI 819 PT1 63 Female Caucasian Breast III Invasive lobular carcinoma 0.979 Positive INDI 821 INDI 821 PLS 1 INDI 821 PT1 67 Colorectal Caucasian female III Adenocarcinoma 0.776 Negative INDI 822 INDS 822 PLS 1 INDI 822 PT1 77 Male Caucasian Colorectal II Adenocarcinoma 0.123 Negative INDI 823 INDI 823 PLS 1 INDI 823 PT1 73 Female Caucasian Breast II Adenocarcinoma ductal invasive 0.121 Negative INDI 824 INDI 824 PLS 1 Not Available 75 Male Caucasian Esophagus II Adenocarcinoma 8.998 INDI 825 PLS 1 INDI 825 PT1 68 Female Caucasian Colorectal III Adenocarcinoma 0.511 Negative INDI 826 INDI 826 PLS 1 INDI 826 PT1 60 Male Caucasian Colorectal II Adenocarcinoma 0.925 Positive INDI 827 INDI 827 PLS 1 INDI 827 PT1 74 Caucasian Female Colorectal III Adenocarcinoma 0.9 828 PLS 1 INDI 828 PT1 78 Caucasian Female Breast II Invasive ductal adenocarcinoma 0.117 Negative IND I 829 INDI 829 PLS 1 INDI 829 PT1 47 Male Caucasian Colorectal III Adenocarcinoma 0.122 Negative INDI 830 INDI 830 PLS 0 INDI 830 PT0 61 Female Caucasian Breast II Invasive Ductal Adenocarcinoma 0.954 Positive INDI 831 INDI 831 PLS 1 INDI 831 PT1 79 Caucasian Female Breast I Invasive Ductal Adenocarcinoma 0.175 Negative INDI 832 INDI 832 PLS 1 INDI 832 PT1 84 Caucasian Female Breast II Invasive Lobular Carcinoma 0.831 Negative INDI 833 INDI 833 PLS 1 INDI 833 PT1 78 Caucasian Female Breast I Invasive Ductal Adenocarcinoma 0.876 Positive INDI 835 INDI 835 PLS 1 835 PT1 66 Male Caucasian Colorectal III Adenocarcinoma 0.856 Negative INDI 837 INDI 837 PLS 1 INDI 837 PT1 67 Female Caucasian Colorectal II Adenocarcinoma 0.609 Negative INDI 838 INDI 838 PLS 1 INDI 838 PT1 62 Caucasian Female Colorectal III Adenocarcinoma 0.603 Negative Not Available 74 Caucasian Female Breast II Invasive lobular carcinoma 0.125 Negative INDI 840 INDI 840 PLS 0 Not Available 71 Cauc Female asiana Mama II Invasive dutch adenocarcinoma 0.125 Negative INDI 841 INDI 841 PLS 1 INDI 841 PT1 84 Caucasian Colorectal III Adenocarcinoma 0.994 Positive INDI 842 INDI 842 PLS 1 Not Available 52 Caucasian Female Mama I Invasive dutch adenocarcinoma 0.826 INDI 843 INDI 843 PLS 843 PT1 79 Caucasian Female Esophagus I Squamous Cell Carcinoma 0.122 Negative INDI 844 INDI 844 PLS 1 Not Available 71 Caucasian Female Stomach I Adenocarcinoma 0.876 Positive INDI 846 INDI 846 PLS 1 INDI 846 PT1 44 Male Asian Colorectal II Adenocarcinoma 0.991 Positive INDI 847I 1 INDI 847 PT1 51 Asian Female Liver III Hepatocellular Carcinoma 0.976 Positive INDI 848 INDI 848 PLS 1 INDI 848 PT1 47 Male Asian Colorectal II Adenocarcinoma 0.886 Positive INDI 849 INDI 849 PLS 1 INDI 849 PT1 80 Male Asian Colorectal II Negative II Adenocarcinoma 850 PLS 1 INDI 850 PT1 45 Male Asian Esophagus II Squamous cell carcinoma 1,000 Positive INDI 853 I NDI 853 PLS 1 INDI 853 PT1 82 Male Asian Esophagus II Squamous cell carcinoma 0.998 Positive INDI 854 INDI 854 PLS 1 INDI 854 PT1 59 Male Asian Colorectal II Adenocarcinoma 0.991 Positive INDI 855 INDI 855 PLS 1 INDI 855 PT1 71 Male Asian Colorectal Positive INDI 856 INDI 856 PLS 1 INDI 856 PT1 85 Asian Female Breast III Invasive Ductal Adenocarcinoma 0.116 Negative INDI 857 INDI 857 PLS 1 INDI 857 PT1 42 Asian Female Mama III Invasive Ductal Adenocarcinoma 0.356 Negative INDI 858 INDI 858 PLS 1 INDI 858 PT1 46 Female Asian Breast I Invasive dutch adenocarcinoma 0.480 Negative INDI 859 INDI 859 PLS 1 INDI 859 PT1 55 Male Asian Esophagus II Squamous cell carcinoma 0.992 Positive INDI 860 INDI 860 PLS 1 INDI 860 PT1 56 Asian Female Breast I Invasive dutch adenocarcino 0.111 Negative INDI 861 PLS 1 INDI 861 PT1 69 Male Asian Liver II Hepatocellular Carcinoma 0.997 Positive INDI 862 INDI 862 PLS 1 INDI 862 PT1 67 Male Asian Pancreatic s II Adenocarcinoma 0.910 Positive INDI 864 INDI 864 PLS 1 INDI 864 PT1 28 Male Asian Liver III Hepatocellular Carcinoma 0.999 Positive INDI 865 INDI 865 PLS 1 INDI 865 PT1 65 Asian Female Breast III Invasive dutch adenocarcinoma 0.392 INDI 867 INDI 867 PLS PT1 46 Male Asian Esophagus II Squamous cell carcinoma 0.995 Positive INDI 868 INDI 868 PLS 1 INDI 868 PT1 58 Male Asian Liver III Hepatocellular carcinoma 0.993 Positive INDI 870 INDI 870 PLS 1 INDI 870 PT1 58 Female Caucasian Breast II Adenocarcinoma inductive invasive 0.972 INDI 872 PLS 1 INDI 872 PT1 74 Caucasian Female Breast III Invasive Ductal Adenocarcinoma 0.986 Positive INDI 873 INDI 873 PLS 1 INDI 873 PT1 65 Caucasian Female Breast III Invasive Ductal Adenocarcinoma 0.527 Negative INDI 875 INDI 875 PLS 1 INDI 875 PT1 48 Caucasian Male Breast II Invasive lobular carcinoma 0.252 Negative

INDI 876 INDI 876 PLS 1 INDI 876 PT1 65 Caucasian Female Breast II Invasive Ductal Adenocarcinoma 0.3131 Negative INDI 877 INDI 877 PLS 1 INDI 877 PT1 55 Caucasian Female Breast I Invasive Ductal Adenocarcinoma 0.999 Positive INDI 878 INDI 878 PLS 1 INDI 878 PT1 59 Caucasian Female Breast III Invasive Dutch Adenocarcinoma 0.992 Positive INDI 879 INDI 879 PLS 1 INDI 879 PT1 63 Caucasian Female Mama II Invasive Dutch Adenocarcinoma 0.340 Negative INDI 880 INDI 880 PLS 1 INDI 880 PT1 56 Caucasian Female Breast II Invasive Dutch Adenocarcinoma 0.675 Negative INDI 881 INDI 881 INDI 881 INDI 881 1 INDI 881 PT1 62 Caucasian Female Breast II Invasive Ductal Adenocarcinoma 0.835 Negative INDI 882 INDI 882 PLS 1 INDI 882 PT1 56 Caucasian Degradable Adenocarcinoma 0.779 Negative INDI 883 INDI 883 PLS 1 INDI 883 PT1 67 Caucasian Female Breast II Invasive Lobular Carcinoma 0.602 Negative INDI 884 INDI 884 PLS 1 INDI 884 PT1 38 Asian Female Breast II Invasive ductal adenocarcinoma 0.999 Positive INDI 885 INDI 8 85 PLS 1 INDI 885 PT1 41 Asian Female Breast II Invasive Ductal Adenocarcinoma 0.547 Negative INDI 886 INDI 886 PLS 1 INDI 886 PT1 52 Asian Female Mama II Ductus Adenocarcinoma Invasive 0.233 Negative INDI 887 INDI 887 PLS 1 INDI 887 PT1 41 Asian Female Breast III carcinoma 0.987 Positive INDI 888 INDI 888 PLS 1 Not Available 70 Male Caucasian Pancreas II Adenocarcinoma 0.976 Positive INDI 889 INDI 889 PLS 1 Not Available 63 Colorectal Caucasian II Adenocarcinoma 0.999 Positive INDI 892 INDI 892 PLS 1 Not Available 74 Caucasian Female Stomach I Adenocarcinoma Negative INDI 893 INDI 893 PLS 1 Not Available 75 Male Caucasian Pancreas II Adenocarcinoma 0.972 Positive INDI 896 INDI 896 PLS 1 Not Available 65 Male Caucasian Lung Lung Cancer Not Small Cell 0.864 Positive INDI 897 INDI 897 PLS 1 Not Available 65 Caucasian Female Breast III Invasive lobular carcinoma 0.993 Positive INDI 898 INDI 898 PLS 1 Not Available 79 Female Caucasian Breast IC invasive lobular arcinoma 0.967 Positive INDI 899 INDI 899 PLS 1 Not Available 75 Caucasian Female I Invasive Ductal Adenocarcinoma 0.154 Negative INDI 902 INDI 902 PLS 1 Not Available 78 Caucasian Female Mama III Invasive Ductal Adenocarcinoma 0.997 Positive INDI 903 INDI 903 PLS 1 Not Available 54 Caucasian Female Breast I Invasive lobular carcinoma 0.651 Negative INDI 905 INDI 905 PLS 1 INDI 905 PT1 67 Male Asian Esophagus II Squamous cell carcinoma 1,000 Positive INDI 907 INDI 907 PLS 1 INDI 907 PT1 63 Male Asian Esophagus II Squamous cell carcinoma 0.971 Positive INDI 908 908 PLS 1 INDI 908 PT1 46 Male Asian Esophagus II Squamous cell carcinoma 0.999 Positive INDI 909 INDI 909 PLS 1 INDI 909 PT1 60 Female Asian Esophagus II Squamous cell carcinoma 0.987 Positive INDI 911 INDI 911 PLS 1 INDI 911 PT1 49 Male Asian Esophagus squamous cell 0.887 Positive INDI 913 INDI 913 PLS 1 INDI 913 PT1 77 Female Asian Esophagus III Squamous cell carcinoma 0.835 Negative INDI 915 INDI 915 PLS 1 INDI 915 PT1 71 Caucasian Female Breast II Invasive Ductal Adenocarcinoma 0.328 Negative INDI 917 INDI 917 PLS 1 Not Available 66 Male Caucasian Liver III Hepatocellular Carcinoma 0.997 Positive INDI 919 INDI 919 PLS 1 Not Available 67 Caucasian Female Breast II Invasive Lobular Carcinoma 0.458 Negative INDI 920 INDI 920 PLS 1 Not Available 59 Caucasian Female Ovary III Epithelial Carcinoma 0.944 Positive INDI 921 INDI 921 PLS 1 INDI 921 PT1 67 Caucasian Female Breast II Invasive Ductal 0.458 Negative INDI 922 INDI 922 PLS 1 Not Available 49 Caucasian Female Breast II Invasive Ductal Adenocarcinoma 0.125 Negative INDI 923 INDI 923 PLS 1 Not Available 72 Male Caucasian Esophagus I Adenocarcinoma 0.306 Negative INDI 924 INDI 924 PLS 1 Not Available 73 Caucasian Female Breast III Invasive Lobular Carcinoma 0.395 Negative INDI 926 INDI 926 PLS 1 Not Available 51 Caucasian Female Breast II Invasive ductal adenocarcinoma 0.343 Negative INDI 927 INDI 927 PLS 1 Not Available 59 Caucasian Female Breast II Invasive Ductal Adenocarcinoma 0.304 Negative INDI 928 INDI 928 PLS 1 INDI 928 PT1 75 Male Caucasian Stomach II Adenocarcinoma 0.998 Positive INDI 929 INDI 929 PLS 1 INDI 929 PT1 75 Caucasian Male Stomach II Adenocarcinoma 0.719 Negative INDI 930 INDI 930 PLS 1 INDI 930 PT1 75 Male Caucasian Stomach II Adenocarcinoma 0.672 Negative INDI 931 INDI 931 PLS 1 Not Available 50 Female Caucasian Ovary III Epithelial Carcinoma 0.955 Positive INDI 932 INDI 932 PLS 1 INDI 932PT1 71 Female Caucasian Ovary III Epithelial carcinoma 0 Positive INDI 933 INDI 933 PLS 1 INDI 933 PT1 67 Male Caucasian Esophagus III Adenocarcinoma 0.527 Negative INDI 935 INDI 935 PLS 1 Not Available 75 Male Caucasian Esophagus I Adenocarcinoma 0.810 Negative INDI 936 INDI 936 PLS 1 Not Available 72 Caucasian Female Ovarian 0.363 Epithelial carcinoma Negative INDI 937 INDI 937 PLS 1 INDI 937 PT1 60 Male Caucasian Esophagus III Adenocarcinoma 0.541 N egative LCR 588 LCR 588 PLS1 Not applicable 63 Female Caucasian Normal NA NA 0.260 Negative LCR 589 LCR 589 PLS1 Not applicable 45 Male Caucasian Normal NA NA 0.86 Negative LCR 590 LCR 590 PLS1 Not applicable 54 Male Caucasian Normal NA NA 0.35 Negative LCR 591 LCR 591 PLS1 Not applicable 44 Male Caucasian Normal NA NA 0.125 Negative LCR 592 LCR 592 PLS1 Not applicable 53 Female Caucasian Normal NA NA 0.177 Negative LCR 593 LCR 593 PLS1 Not applicable 65 Male Caucasian Normal NA NA 0.75 Negative LCR 594 LCR 594 PLS1 Not applicable 57 Male Caucasian Normal NA NA 0.121 Negative CSF 595 CSF 595 PLS1 Not applicable 65 Male Caucasian Normal NA NA 0.296 Negative CSF 596 CSF 596 PLS1 Not Available 78 Male Colorectal Caucasian I Adenocarcinoma 0.171 Negative CSF 597 CSF 597 PLS1 Not applicable 51 Male Caucasian Normal NA NA 0.435 Negative LCR 599 LCR 599 PLS1 Not applicable 51 Male Asian Normal NA NA 0.399 Negative LCR 600 LCR 600 PLS1 Not applicable 66 Female Caucasian Normal NA NA 0.121 Negative LCR 601 LCR 601 PLS1 Not applicable 61 Male Caucasian Normal NA NA 0.307 Negative LCR 602 LCR 602 PLS1 Not applicable 69 Male Caucasian Normal NA NA 0.315 Negative LCR 603 LCR 603 PLS1 Not applicable 60 Caucasian Female Normal NA NA 0.125 Negative LCR 604 LCR 604 PLS1 Not applicable 63 Female Caucasian Normal NA NA 0.121 Negative LCR 605 LCR 605 PLS1 Not applicable 67 Male Caucasian Normal NA NA 0.326 Negative LCR 606 LCR 606 PLS1 Not applicable 55 Male Caucasian Normal NA NA 0.799 Negative LCR 607 LCR 607 PLS1 Not applicable 43 Normal Caucasian female NA NA 0.585 Negative

LCR 608 LCR 608 PLS1 Not applicable 66 Caucasian Female Normal NA NA 0.593 Negative LCR 610 LCR 610 PLS1 Not applicable 51 Male Caucasian Normal NA NA 0.125 Negative LCR 611 LCR 611 PLS1 Not applicable 66 Normal Caucasian Female NA NA 0.196 Negative LCR 612 LCR 612 PLS1 Not applicable 62 Normal Caucasian female NA NA 0.116 Negative LCR 613 LCR 613 PLS1 Not applicable 48 Normal Caucasian female NA NA 0.177 Negative LCR 614 LCR 614 PLS1 Not applicable 56 Asian female Normal NA NA 0.396 Negative LCR 616 LCR 616 PLS1 Not applicable 81 Caucasian female Normal NA NA 0.125 Negative LCR 617 LCR 617 PLS1 Not applicable 61 Caucasian Female Normal NA NA 0.122 Negative LCR 618 LCR 618 PLS1 Not applicable 60 Caucasian Female Normal NA NA 0.93 Negative LCR 619 LCR 619 PLS1 Not applicable 55 Caucasian Female Normal NA NA 0.122 Negative LCR 620 LCR 620 PLS1 Not applicable 59 Female Caucasian Normal NA NA 0.494 Negative LCR 621 LCR 621 PLS1 Not applicable 79 Female Caucasian Normal NA NA 0.117 Negative LCR 62 2 CSF 622 PLS1 Not applicable 57 Normal Caucasian female NA NA 0.125 Negative LCR 623 LCR 623 PLS1 Not applicable 45 Normal Caucasian female NA NA 0.835 Negative LCR 624 LCR 624 PLS1 Not applicable 71 Normal Caucasian female NA NA 0.116 Negative LCR 625 LCR 625 PLS1 No applicable 76 Male Caucasian Normal NA NA 0.486 Negative LCR 626 LCR 626 PLS1 Not applicable 60 Female Caucasian Normal NA NA 0.603 Negative LCR 627 LCR 627 PLS1 Not applicable 69 Female Caucasian Normal NA NA 0.861 Negative LCR 628 LCR 628 PLS1 Not applicable 54 Female Caucasian Normal NA NA 0.117 Negative LCR 629 LCR 629 PLS1 Not applicable 54 Male Caucasian Normal NA NA 0.639 Negative LCR 630 LCR 630 PLS1 Not applicable 79 Female Caucasian Normal NA NA 0.298 Negative LCR 631 LCR 631 PLS1 Not applicable 59 Caucasian Female Normal NA NA 0.220 Negative CSF 632 CSF 632 PLS1 Not applicable 66 Female Caucasian Normal NA NA 0.23 Negative CSF 633 LCR 633 PLS1 Not applicable 60 Male Asian Normal NA NA 0.547 Negative CSF 634 CSF 634 PLS1 Not applicable 62 Male Caucasian Normal NA NA 0.121 Negative LCR 635 LCR 635 PLS1 Not applicable 66 Male Caucasian Normal NA NA 0.125 Negative LCR 636 LCR 636 PLS1 Not applicable 72 Female Caucasian Normal NA NA 0.345 Negative LCR 637 LCR 637 PLS1 Not applicable 47 Normal Caucasian Female NA NA 0.125 Negative LCR 638 LCR 638 PLS1 Not Applicable 78 Normal Caucasian Female NA NA 0.23 Negative LCR 639 LCR 639 PLS1 Not Applicable 71 Male Normal Caucasian NA NA 0.222 Negative LCR 640 LCR 640 PLS1 Not Applicable 73 Male Normal Caucasian NA NA NA 0.125 Negative LCR 641 LCR 641 PLS1 Not applicable 56 Male Caucasian Normal NA NA 0.705 Negative LCR 642 LCR 642 PLS1 Not applicable 63 Female Caucasian Normal NA NA 0.29 Negative LCR 643 LCR 643 PLS1 Not applicable 61 Male Caucasian Normal NA NA 0.165 Negative LCR 644 LCR 644 PLS1 Not applicable 61 Caucasian Female Normal NA NA 0.117 Negative CSF 645 CSF 645 PLS1 Not applicable 62 Female Caucasian Normal NA NA 0.510 Negative CSF 646 CSF 646 PLS1 Not applicable 77 Male Caucasian Normal NA NA 0.117 Negative LCR 647 LCR 647 PLS1 Not applicable 76 Female Caucasian Normal NA NA 0.168 Negative LCR 648 LCR 648 PLS1 Not applicable 55 Male Caucasian Normal NA NA 0.22 Negative LCR 649 LCR 649 PLS1 Not applicable 61 Female Asian Normal NA NA 0.125 Negative LCR 650 LCR 650 PLS1 Not applicable 51 Female Asian Normal NA NA 0.747 Negative LCR 652 LCR 652 PLS1 Not applicable 58 Caucasian Female Normal NA NA 0.224 Negative LCR 653 LCR 653 PLS1 Not applicable 75 Caucasian Female Normal NA NA 0.329 Negative LCR 654 LCR 654 PLS1 Not applicable 51 Female Caucasian Normal NA NA 0.117 Negative LCR 655 LCR 655 PLS1 Not applicable 60 Male Asian Normal NA NA 0.22 Negative LCR 658 LCR 658 PLS1 Not applicable 57 Female Caucasian Normal NA NA 0.115 Negative LCR 659 LCR 659 PLS1 Not applicable 61 Female Caucasian Normal NA NA 0.125 Negative LCR 660 LCR 660 PLS1 Not applicable 61 Male Caucasian Normal NA NA 0.125 Negative LCR 661 LCR 661 PLS1 No a applicable 69 Male Caucasian Normal NA NA 0.117 Negative CSF 662 LCR 662 PLS1 Not applicable 59 Male Caucasian Normal NA NA 0.93 Negative LCR 663 LCR 663 PLS1 Not applicable 81 Male Caucasian Normal NA NA 0.317 Negative LCR 665 LCR 665 PLS1 Not applicable 68 Male Caucasian Normal NA NA 0.117 Negative LCR 666 LCR 666 PLS1 Not applicable 72 Male Caucasian Normal NA NA 0.123 Negative LCR 667 LCR 667 PLS1 Not applicable 50 Female Caucasian Normal NA NA 0.122 Negative LCR 668 LCR 668 PLS1 Not applicable 80 Caucasian Female Normal NA NA 0.121 Negative CSF 670 LCR 670 PLS1 Not applicable 63 Asian Female Normal NA NA 0.125 Negative LCR 671 LCR 671 PLS1 Not applicable 52 Female Caucasian Normal NA NA 0.129 Negative LCR 672 LCR 672 PLS1 Not applicable 66 Male Asian Normal NA NA 0.601 Negative LCR 673 LCR 673 PLS1 No applicable 58 Normal Caucasian Female NA NA 0.116 Negative LCR 674 LCR 674 PLS1 Not applicable 63 Normal Caucasian Female NA NA 0.179 Negative LCR 675 LCR 675 PLS1 Not applicable 55 Male Caucasian Normal NA NA 0.187 Negative CSF 676 CSF 676 PLS1 Not applicable 63 Male Caucasian Normal NA NA 0.245 Negative CSF 677 CSF 677 PLS1 Not applicable 73 Male Caucasian Normal NA NA 0.156 Negative CSF 678 CSF 678 PLS1 Not applicable 56 Caucasian Female Normal NA NA 0.186 Negative LCR 679 LCR 679 PLS1 Not applicable 66 Male Caucasian Normal NA NA 0.125 Negative LCR 680 LCR 680 PLS1 Not applicable 64 Female Caucasian Normal NA NA 0.699 Negative

LCR 681 LCR 681 PLS1 Not applicable 61 Female Caucasian Normal NA NA 0.597 Negative LCR 682 LCR 682 PLS1 Not applicable 65 Female Caucasian Normal NA NA 0.631 Negative LCR 683 LCR 683 PLS1 Not applicable 82 Male Caucasian Normal NA NA 0.322 Negative LCR 684 LCR 684 PLS1 Not applicable 56 Asian Female Normal NA NA 0.121 Negative LCR 685 LCR 685 PLS1 Not applicable 63 Caucasian Female Normal NA NA 0.845 Negative LCR 686 LCR 686 PLS1 Not applicable 58 Caucasian female Normal NA NA 0.224 Negative LCR 687 LCR 687 PLS1 Not applicable 51 Caucasian Female Normal NA NA 0.121 Negative LCR 688 LCR 688 PLS1 Not applicable 80 Male Caucasian Normal NA NA 0.419 Negative LCR 689 LCR 689 PLS1 Not applicable 57 Male Caucasian Normal NA NA 0.125 Negative LCR 690 LCR 690 PLS1 Not applicable 81 Male Asian Normal NA NA 0.23 Negative LCR 691 LCR 691 PLS1 Not applicable 53 Male Caucasian Normal NA NA 0.129 Negative LCR 692 LCR 692 PLS1 Not applicable 66 Male Caucasian Normal NA NA 0.770 Negative LCR 693 CSF 693 PLS1 Not applicable 61 Male Caucasian Normal NA NA 0.118 Negative LCR 694 LCR 694 PLS1 Not applicable 59 Female Caucasian Normal NA NA 0.122 Negative LCR 697 LCR 697 PLS1 Not applicable 61 Male Caucasian Normal NA NA 0.308 Negative LCR 698 LCR 698 PLS1 Not applicable 74 Male Caucasian Normal NA NA 0.328 Negative CSF 699 CSF 699 PLS1 Not applicable 71 Female Caucasian Normal NA NA 0.236 Negative LCR 700 CSF 700 PLS1 Not applicable 78 Female Caucasian Normal NA NA 0.755 Negative CSF 701 CSF 701 PLS1 Not applicable 52 Male Caucasian Normal NA NA 0.630 Negative LCR 702 LCR 702 PLS1 Not applicable 85 Female Caucasian Normal NA NA 0.968 Positive LCR 703 LCR 703 PLS1 Not applicable 85 Male Caucasian Normal NA NA 0.117 Negative LCR 704 LCR 704 PLS1 Not applicable 81 Male Caucasian Normal NA NA 1,000 Positive LCR 705 LCR 705 PLS1 Not applicable 54 Female Caucasian Normal NA NA 0.485 Negative LCR 706 LCR 706 PLS1 Not applicable 66 Male Unknown Normal NA NA 0.192 Negative LCR 707 LC R 707 PLS1 Not applicable 82 Male Caucasian Normal NA NA 0.216 Negative LCR 709 LCR 709 PLS1 Not applicable 67 Female Caucasian Normal NA NA 0.400 Negative LCR 710 LCR 710 PLS1 Not applicable 71 Male Caucasian Normal NA NA 0.588 Negative LCR 711 LCR 711 PLS1 Not applicable 66 Male Unknown Normal NA NA 0.128 Negative CSF 712 CSF 712 PLS1 Not applicable 61 Male Unknown Normal NA NA 0.121 Negative CSF 713 CSF 713 PLS1 Not applicable 79 Male Caucasian Normal NA NA 0.861 Negative CSF 714 LCR 714 PLS1 Not applicable 72 Female Caucasian Normal NA NA 0.123 Negative LCR 715 LCR 715 PLS1 Not applicable 52 Male Caucasian Normal NA NA 0.122 Negative LCR 716 LCR 716 PLS1 Not applicable 69 Male Caucasian Normal NA NA 0.538 Negative LCR 717 LCR 717 PLS1 Not applicable 75 Caucasian Female Normal NA NA 0.507 Negative LCR 718 LCR 718 PLS1 Not applicable 52 Male Asian Normal NA NA 0.523 Negative LCR 719 LCR 719 PLS1 Not applicable 60 Male Caucasian Normal NA NA 0.125 Negative LCR 720 LCR 720 PLS1 Not applicable 61 Female Caucasian Normal NA NA 0.116 Negative LCR 721 LCR 721 PLS1 Not applicable 75 Male Caucasian Normal NA NA1717 LCR 722 LCR 722 PLS1 Not applicable 80 Male Caucasian Normal NA NA 0.777 Negative LCR 723 LCR 723 PLS1 Not applicable 61 Normal Asian Female NA NA 0.117 Negative LCR 724 LCR 724 PLS1 Not applicable 52 Asian Normal Female NA NA 0.222 Negative LCR 725 LCR 725 PLS1 Not applicable 65 Caucasian Female Normal NA NA 0.184 Negative LCR 726 LCR 726 PLS1 Not applicable 60 Unknown Female Normal NA NA 0.564 Negative LCR 727 LCR 727 PLS1 Not applicable 65 Male Unknown Normal NA NA 0.125 Negative LCR 728 LCR 728 PLS1 Not applicable 51 Male Unknown Normal NA NA 0.303 Negative LCR 729 LCR 729 PLS1 Not applicable 67 Male Unknown Normal NA NA 0.22 Negative LCR 730 LCR 730 PLS1 Not applicable 71 Male Caucasian Normal NA NA 0.117 Negative CSF 731 CSF 731 PLS1 Not applicable 56 Male Caucasian Normal NA NA 0.294 Negative CSF 732 CSF 732 PLS1 Not applicable 50 Male Caucasian Normal NA NA 0.125 Negative LCR 733 LCR 733 PLS1 Not applicable 80 Male Caucasian Normal NA NA 0.305 Negative LCR 734 LCR 734 PLS1 Not applicable 77 Unknown female Normal NA NA 0.37 Negative LCR 736 LCR 736 PLS1 Not applicable 59 Male Caucasian Normal NA NA 0.404 Negative CSF 737 CSF 737 PLS1 Not applicable 81 Male Caucasian Normal NA NA 0.863 Negative CSF 738 CSF 738 PLS1 Not applicable 84 Female Caucasian Normal NA NA 0.591 Negative CSF 739 CSF 739 PLS1 Not applicable 36 Male Caucasian Normal NA NA 0,390 Negative LCR 740 LCR 740 PLS1 Not applicable 81 Male Caucasian Normal NA NA 0.411 Negative LCR 741 LCR 741 PLS1 Not applicable 65 Male Caucasian Normal NA NA 0.374 Negative LCR 742 LCR 742 PLS1 Not applicable 50 Male Caucasian Normal NA NA 0.860 Negative LCR 743 LCR 743 PLS1 Not applicable 77 Male Caucasian Normal NA NA 0.603 Negative CSF 745 CSF 745 PLS1 Not applicable 60 Female Caucasian Normal NA NA 0.680 Negative CSF 746 CSF 74 6 PLS1 Not applicable 53 Male Caucasian Normal NA NA 0.553 Negative LCR 747 LCR 747 PLS1 Not applicable 80 Male Caucasian Normal NA NA 0.277 Negative LCR 749 LCR 749 PLS1 Not applicable 70 Female Caucasian Normal NA NA 0.21 Negative LCR 750 LCR 750 PLS1 Not applicable 78 Male Caucasian Normal NA NA 0.851 Negative CSF 751 CSF 751 PLS1 Not applicable 76 Male Caucasian Normal NA NA 0.125 Negative CSF 752 CSF 752 PLS1 Not applicable 88 Male Caucasian Normal NA NA 0.855 Negative

LCR 753 LCR 753 PLS1 Not applicable 80 Male Caucasian Normal NA NA 0.199 Negative LCR 754 LCR 754 PLS1 Not applicable 40 Male Caucasian Normal NA NA 0.21 Negative LCR 755 LCR 755 PLS1 Not applicable 55 Normal Caucasian female NA NA 0.125 Negative LCR 757 LCR 757 PLS1 Not applicable 75 Female Caucasian Normal NA NA 0.387 Negative LCR 758 LCR 758 PLS1 Not applicable 73 Male Caucasian Normal NA NA 0.473 Negative LCR 759 LCR 759 PLS1 Not applicable 55 Male Caucasian Normal NA NA 0.736 Negative LCR 760 LCR 760 PLS1 Not applicable 55 Caucasian Female Normal NA NA 0.401 Negative LCR 761 LCR 761 PLS1 Not applicable 62 Male Asian Normal NA NA 0.117 Negative LCR 762 LCR 762 PLS1 Not applicable 73 Male Caucasian Normal NA NA 0.117 Negative LCR 763 LCR 763 PLS1 Not applicable 60 Male Caucasian Normal NA NA 0.21 Negative LCR 764 LCR 764 PLS1 Not applicable 72 Female Caucasian Normal NA NA 0.184 Negative LCR 765 LCR 765 PLS1 Not applicable 51 Male Caucasian Normal NA NA 0.399 Negative LCR 76 6 CSF 766 PLS1 Not applicable 76 Female Caucasian Normal NA NA 0.116 Negative LCR 768 LCR 768 PLS1 Not applicable 56 Male Unknown Normal NA NA 0.886 Positive LCR 769 LCR 769 PLS1 Not applicable 51 Male Caucasian Normal NA NA 0.388 Negative LCR 770 LCR 770 PLS1 No applicable 34 Male Asian Normal NA NA 0.121 Negative LCR 771 LCR 771 PLS1 Not applicable 70 Male Caucasian Normal NA NA 0.388 Negative LCR 772 LCR 772 PLS1 Not applicable 73 Male Caucasian Normal NA NA 0.21 Negative LCR 773 LCR 773 PLS1 Not applicable 44 Male Caucasian Normal NA NA 0.156 Negative LCR 774 LCR 774 PLS1 Not applicable 67 Female Caucasian Normal NA NA 0.600 Negative LCR 775 LCR 775 PLS1 Not applicable 51 Female Caucasian Normal NA NA 0.935 Positive LCR 776 LCR 776 PLS1 Not applicable 50 Male Caucasian Normal NA NA 0.115 LCR negative 777 LCR 777 PLS1 Not applicable 86 Female Caucasian Normal NA NA 0.511 Negative LCR 778 LCR 778 PLS1 Not applicable 57 Male Unknown Normal NA NA 0.209 Negative LCR 779 LCR 779 PLS1 Not applicable 65 Unknown female Normal NA NA 0.721 Negative LCR 780 LCR 780 PLS1 Not applicable 65 Unknown female Normal NA NA 0.122 Negative LCR 781 LCR 781 PLS1 Not applicable 71 Unknown female Normal NA NA 0.125 Negative LCR 782 LCR 782 PLS1 Not applicable 56 Female Caucasian Normal NA NA 0.220 Negative CSF 783 CSF 783 PLS1 Not applicable 68 Male Caucasian Normal NA NA 0.703 Negative CSF 784 CSF 784 PLS1 Not applicable 62 Female Caucasian Normal NA NA 0.638 Negative CSF 785 CSF 785 PLS1 Not applicable 55 Male Caucasian Normal NA NA 0.827 Negative LCR 786 LCR 786 PLS1 Not applicable 75 Normal Caucasian Female NA NA 0.333 Negative LCR 812 LCR 812 PLS1 LCR 812 PT1 77 Pancreas Caucasian Female II Adenocarcinoma 0.999 Positive LCR 814 LCR 814 PLS1 LCR 814 PT1 61 Caucasian Female Pancreas II Adenocarcinoma 0.7 Negative Adenocarcinoma NL 918 NL PLS 918 Not applicable 24 Male Hispanic Normal NA NA 0.117 Negative NL 919 NL PLS 919 Not applicable 18 Male Hispanic Normal NA NA 0.875 Positive NL 920 NL PLS 920 Not applicable 23 Male Caucasian Normal NA NA 0.122 Negative NL 921 NL PLS 921 Not applicable 23 Male Hispanic Normal NA NA 0.125 Negative NL 922 NL PLS 922 Not applicable 30 Hispanic Male Normal NA NA 0.114 Negative NL 923 NL PLS 923 Not applicable 27 Male Hispanic Normal NA NA 0.610 Negative NL 924 NL PLS 924 Not applicable 25 Male Black Normal NA NA 0.335 Negative NL 925 NL PLS 925 Not applicable 29 Male Black Normal NA NA 0.21 Negative NL 930 NL PLS 930 Not applicable 25 Male Black Normal NA NA 0.192 Negative NL 931 NL PLS 931 Not applicable 29 Male Black Normal NA NA 0.300 Negative NL 932 NL PLS 932 Not applicable 25 Male Black Normal NA NA 0.118 Negative NL 938 NL PLS 938 Not applicable 29 Male Black Normal NA NA 0.177 Negative NL 939 NL PLS 939 Not applicable 28 Male Black Normal NA NA 0.599 Negative NL 940 NL PLS 940 Not applicable 29 Male Hispanic Normal NA NA 0.119 Negative NL 941 NL PLS 941 Not applicable 30 Male Black Normal NA NA 0.116 Neg active NL 942 NL PLS 942 Not applicable 30 Male Black Normal NA NA 0.660 Negative NL 943 NL PLS 943 Not applicable 28 Male Hispanic Normal NA NA 0.22 Negative NL 944 NL PLS 944 Not applicable 23 Male Hispanic Normal NA NA 0.341 Negative NL 945 NL PLS 945 Not applicable 29 Male Caucasian Normal NA NA 0.843 Negative NL 946 NL PLS 946 Not applicable 26 Male Hispanic Normal NA NA 0.382 Negative NL 947 NL PLS 947 Not applicable 25 Hispanic Female Normal NA NA 0.558 Negative NL 948 NL PLS 948 Not applicable 24 Male Black Normal NA NA 0.125 Negative NL 949 NL PLS 949 Not applicable 22 Male Caucasian Normal NA NA 0.177 Negative NL 950 NL PLS 950 Not applicable 28 Male Black Normal NA NA 0.320 Negative NL 951 NL PLS 951 Not applicable 18 Male Caucasian Normal NA NA 0.166 Negative NL 952 NL PLS 952 Not applicable 24 Male Caucasian Normal NA NA 0.230 Negative NL 964 NL PLS 964 Not applicable 27 Male Hispanic Normal NA NA 0.25 Negative NL 965 NL PLS 965 Not applicable 28 Male Black Normal NA NA 0.612 Negative NL 1160 NL PLSA 1160 Not applicable 19 Male Normal Black NA NA 0.677 Negative NL 1163 NL PLSA 1163 Not applicable 29 Male Caucasian Normal NA NA 0.125 Negative NL 1164 NL PLSA 1164 Not applicable 28 Hispanic Male Normal NA NA 0.319 Negative NL 1165 NL PLSA 1165 Not applicable 19 Male Hispanic Normal NA NA 0.303 Negative

NL 1166 NL PLSA 1166 Not applicable 30 Normal Black Female NA NA 0.117 Negative NL 1167 NL PLSA 1167 Not applicable 29 Normal Black Male NA NA 0.329 Negative NL 1168 NL PLSA 1168 Not applicable 29 Normal Black Male NA NA 0.21 Negative NL 1169 NL PLSA 1169 Not applicable 24 Black Female Normal NA NA 0.125 Negative NL 1170 NL PLSA 1170 Not applicable 26 Male Hispanic Normal NA NA 0.125 Negative NL 1171 NL PLSA 1171 Not applicable 29 Hispanic male Normal NA NA 0.229 Negative NL 1172 NL PLSA 1172 Not applicable 27 Hispanic Male Normal NA NA 0.334 Negative NL 1173 NL PLSA 1173 Not applicable 26 Male Hispanic Normal NA NA 0.125 Negative NL 1174 NL PLSA 1174 Not applicable 30 Male Black Normal NA NA 0.22 Negative NL 1175 NL PLSA 1175 Not applicable 28 Hispanic male Normal NA NA 0.121 Negative NL 1176 NL PLSA 1176 Not applicable 29 Male Hispanic Normal NA NA 0.125 Negative NL 1177 NL PLSA 1177 Not applicable 23 Hispanic Female Normal NA NA 0.388 Negative NL 1178 NL PLSA 1178 Not applicable 2 6 Male Hispanic Normal NA NA 0.129 Negative NL 1179 NL PLSA 1179 Not applicable 29 Hispanic Female Normal NA NA 0.316 Negative NL 1180 NL PLSA 1180 Not applicable 25 Male Hispanic Normal NA NA 0.21 Negative NL 1181 NL PLSA 1181 Not applicable 26 Male Hispanic Normal NA NA 0.633 Negative NL 1182 NL PLSA 1182 Not applicable 26 Normal Black Male NA NA 0.322 Negative NL 1183 NL PLSA 1183 Not applicable 20 Normal Black Male NA NA 0.97 Negative NL 1184 NL PLSA 1184 Not applicable 24 Normal Black Male NA NA 0.377 Negative NL 1185 NL PLSA 1185 Not applicable 28 Hispanic Female Normal NA NA 0.125 Negative NL 1186 NL PLSA 1186 Not applicable 30 Male Black Normal NA NA 0.177 Negative NL 1187 NL PLSA 1187 Not applicable 30 Male Hispanic Normal NA NA 0.115 Negative NL 1188 NL PLSA 1188 Not applicable 28 Hispanic Female Normal NA NA 0.316 Negative NL 1189 NL PLSA 1189 Not applicable 24 Male Black Normal NA NA 0.317 Negative NL 1190 NL PLSA 1190 Not applicable 25 Male Black Normal NA NA 0.121 Neg active NL 1191 NL PLSA 1191 Not applicable 29 Hispanic Female Normal NA NA 0.320 Negative NL 1192 NL PLSA 1192 Not applicable 27 Male Hispanic Normal NA NA 0.125 Negative NL 1193 NL PLSA 1193 Not applicable 28 Hispanic Female Normal NA NA 0.125 Negative NL 1194 NL PLSA 1194 Not applicable 26 Male Caucasian Normal NA NA 0.334 Negative NL 1195 NL PLSA 1195 Not applicable 27 Male Black Normal NA NA 0.11 Negative NL 1196 NL PLSA 1196 Not applicable 22 Male Hispanic Normal NA NA 0.309 Negative NL 1197 NL PLSA 1197 Not applicable 21 Male Hispanic Normal NA NA 0.489 Negative NL 1198 NL PLSA 1198 Not applicable 26 Black Female Normal NA NA 0.255 Negative NL 1199 NL PLSA 1199 Not applicable 28 Hispanic Female Normal NA NA 0.125 Negative NL 1200 NL PLSA 1200 Not applicable 19 Male Hispanic Normal NA NA 0.316 Negative NL 1201 NL PLSA 1201 Not applicable 30 Male Hispanic Normal NA NA 0.125 Negative NL 1202 NL PLSA 1202 Not applicable 27 Male Hispanic Normal NA NA 0.115 Negative NL 1203 NL PLSA 1 203 Not applicable 24 Male Black Normal NA NA 0.322 Negative NL 1204 NL PLSA 1204 Not applicable 25 Male Black Normal NA NA 0.125 Negative NL 1205 NL PLSA 1205 Not applicable 27 Male Black Normal NA NA 0.125 Negative NL 1206 NL PLSA 1206 Not applicable 28 Male Black Normal NA NA 0.323 Negative NL 1207 NL PLSA 1207 Not applicable 21 Male Hispanic Normal NA NA 0.125 Negative NL 1208 NL PLSA 1208 Not applicable 30 Male Black Normal NA NA 0.125 Negative NL 1209 NL PLSA 1209 Not applicable 29 Male Black Normal NA NA 0.302 Negative NL 1210 NL PLSA 1210 Not applicable 21 Male Black Normal NA NA 0.23 Negative NL 1211 NL PLSA 1211 Not applicable 29 Male Hispanic Normal NA NA 0.310 Negative NL 1212 NL PLSA 1212 Not applicable 27 Normal Hispanic female NA NA 0.121 Negative NL 1213 NL PLSA 1213 Not applicable 26 Male Black Normal NA NA 0.122 Negative NL 1214 NL PLSA 1214 Not applicable 24 Male Hispanic Normal NA NA 0.21 Negative NL 1215 NL PLSA 1215 Not applicable 25 Male Hispanic Normal NA N A 0.122 Negative NL 1216 NL PLSA 1216 Not applicable 29 Normal Black Male NA NA 0.565 Negative NL 1217 NL PLSA 1217 Not applicable 24 Normal Hispanic Male NA NA 0.21 Negative NL 1218 NL PLSA 1218 Not applicable 28 Normal Black Male NA NA 0.320 Negative NL 1219 NL PLSA 1219 Not applicable 25 Male Black Normal NA NA 0.117 Negative NL 1220 NL PLSA 1220 Not applicable 30 Male Hispanic Normal NA NA 0.117 Negative NL 1221 NL PLSA 1221 Not applicable 28 Male Black Normal NA NA 0.125 Negative NL 1222 NL PLSA 1222 Not applicable 24 Male Hispanic Normal NA NA 0.121 Negative NL 1223 NL PLSA 1223 Not applicable 28 Male Hispanic Normal NA NA 0.21 Negative NL 1224 NL PLSA 1224 Not applicable 27 Female Black Normal NA NA 0.114 Negative NL 1225 NL PLSA 1225 Not applicable 28 Male Hispanic Normal NA NA 0.123 Negative NL 1226 NL PLSA 1226 Not applicable 26 Normal Black Female NA NA 0.177 Negative NL 1227 NL PLSA 1227 Not applicable 27 Male Normal Black NA NA 0.125 Negative NL 1228 NL PLSA 1228 Not applicable eligible 30 Male Black Normal NA NA 0,117 Negative NL 1229 NL PLSA 1229 Not applicable 29 Male Hispanic Normal NA NA 0.125 Negative NL 1230 NL PLSA 1230 Not applicable 28 Male Hispanic Normal NA NA 0.125 Negative NL 1231 NL PLSA 1231 Not applicable 28 Hispanic Female Normal NA NA 0.121 Negative

NL 1232 NL PLSA 1232 Not applicable 29 Male Hispanic Normal NA NA 0.117 Negative NL 1233 NL PLSA 1233 Not applicable 26 Male Hispanic Normal NA NA 0.355 Negative NL 1234 NL PLSA 1234 Not applicable 22 Male Hispanic Normal NA NA 0.121 Negative NL 1235 NL PLSA 1235 Not applicable 27 Male Caucasian Normal NA NA 0.575 Negative NL 1236 NL PLSA 1236 Not applicable 28 Male Hispanic Normal NA NA 0.222 Negative NL 1237 NL PLSA 1237 Not applicable 21 Male Black Normal NA NA 0.21 Negative NL 1238 NL PLSA 1238 Not applicable 22 Black Male Normal NA NA 0.320 Negative NL 1239 NL PLSA 1239 Not applicable 25 Male Black Normal NA NA 0.21 Negative NL 1240 NL PLSA 1240 Not applicable 27 Male Black Normal NA NA 0.258 Negative NL 1241 NL PLSA 1241 Not applicable 26 Male Black Normal NA NA 0.532 Negative NL 1242 NL PLSA 1242 Not applicable 29 Male Black Normal NA NA 0.23 Negative NL 1243 NL PLSA 1243 Not applicable 26 Male Black Normal NA NA 0.21 Negative NL 1244 NL PLSA 1244 Not applicable 19 Male Black Normal NA NA 0.321 Negative NL 1245 NL PLSA 1245 Not applicable 29 Male Hispanic Normal NA NA 0.309 Negative NL 1246 NL PLSA 1246 Not applicable 24 Male Black Normal NA NA 0.23 Negative NL 1247 NL PLSA 1247 Not applicable 26 Male Hispanic Normal NA NA 0.727 Negative NL 1248 NL PLSA 1248 Not applicable 24 Male Hispanic Normal NA NA 0.121 Negative NL 1249 NL PLSA 1249 Not applicable 27 Black Female Normal NA NA 0.23 Negative NL 1250 NL PLSA 1250 Not applicable 28 Hispanic Female Normal NA NA 0.488 Negative NL 1251 NL PLSA 1251 Not applicable 23 Normal Black Female NA NA 0.121 Negative NL 1252 NL PLSA 1252 Not applicable 21 Normal Caucasian Female NA NA 0.374 Negative NL 1253 NL PLSA 1253 Not applicable 29 Normal Black Female NA NA 0.121 Negative NL 1254 NL PLSA 1254 Not applicable 25 Female Normal Black NA NA 0.584 Negative NL 1255 NL PLSA 1255 Not applicable 30 Normal Black Female NA NA 0.297 Negative NL 1256 NL PLSA 1256 Not applicable 24 Normal Black Female NA NA 0.117 Negative NL 1257 NL PLSA 1257 Not applicable 30 Black Female Normal NA NA 0.341 Negative NL 1258 NL PLSA 1258 Not applicable 30 Black Female Normal NA NA 0.324 Negative NL 1259 NL PLSA 1259 Not applicable 29 Black Female Normal NA NA 0.604 Negative NL 1260 NL PLSA 1260 Not applicable 30 Normal Hispanic Female NA NA 0.116 Negative NL 1291 NL PLSA 1291 Not applicable 28 Normal Hispanic Female NA NA 0.177 Negative NL 1292 NL PLSA 1292 Not applicable 26 Normal Black Female NA NA 0.376 Negative NL 1293 NL PLSA 1293 Not applicable 27 Normal Hispanic Female NA NA 0.514 Negative NL 1294 NL PLSA 1294 Not applicable 28 Normal Hispanic female NA NA 0.416 Negative NL 1295 NL PLSA 1295 Not applicable 30 Normal Hispanic female NA NA 0.117 Negative NL 1296 NL PLSA 1296 Not applicable 25 Normal Hispanic female NA NA 0.116 Negative NL 1297 NL PLSA 1297 Not applicable 25 Hispanic Female Normal NA NA 0.117 Negative NL 1298 NL PLSA 1298 Not applicable 30 Hispanic Female Normal NA NA 0.122 Negative NL 1299 NL PLSA 1299 Not applicable 21 Fê female Black Normal NA NA 0.125 Negative NL 1300 NL PLSA 1300 Not applicable 30 Black Female Normal NA NA 0.242 Negative NL 1301 NL PLSA 1301 Not applicable 28 Female Caucasian Normal NA NA 0.573 Negative NL 1302 NL PLSA 1302 Not applicable 23 Black Female Normal NA NA 0.833 Negative NL 1303 NL PLSA 1303 Not applicable 27 Normal Black Female NA NA 0.122 Negative NL 1304 NL PLSA 1304 Not applicable 26 Normal Black Female NA NA 0.117 Negative NL 1305 NL PLSA 1305 Not applicable 28 Normal Black Female NA NA 0.192 Negative NL 1306 NL PLSA 1306 Not applicable 26 Hispanic Female Normal NA NA 0.409 Negative NL 1307 NL PLSA 1307 Not applicable 30 Caucasian Female Normal NA NA 0.125 Negative NL 1308 NL PLSA 1308 Not applicable 26 Black Female Normal NA NA 0.21 Negative NL 1309 NL PLSA 1309 Not applicable 21 Normal Black Female NA NA 0.125 Negative NL 1310 NL PLSA 1310 Not applicable 23 Normal Black Female NA NA 0.414 Negative NL 1311 NL PLSA 1311 Not applicable 23 Normal Black Female NA NA 0.553 Negative NL 1312 NL P LSA 1312 Not applicable 23 Normal Black Female NA NA 0.121 Negative NL 1313 NL PLSA 1313 Not applicable 27 Normal Black Female NA NA 0.117 Negative NL 1314 NL PLSA 1314 Not applicable 18 Normal Hispanic Female NA NA 0.125 Negative NL 1315 NL PLSA 1315 Not applicable 27 Normal Black Female NA NA 0.576 Negative NL 1316 NL PLSA 1316 Not applicable 25 Normal Black Female NA NA 0.177 Negative NL 1317 NL PLSA 1317 Not applicable 30 Normal Black Female NA NA 0.382 Negative NL 1318 NL PLSA 1318 Not applicable 21 Normal Black Female NA NA 0.121 Negative NL 1319 NL PLSA 1319 Not applicable 28 Hispanic Female Normal NA NA 0.116 Negative NL 1320 NL PLSA 1320 Not applicable 28 Black Normal Female NA NA 0.25 Negative NL 1321 NL PLSA 1321 Not applicable 26 Normal Hispanic Female NA NA 0.121 Negative NL 1322 NL PLSA 1322 Not applicable 19 Hispanic Female Normal NA NA 0.125 Negative NL 1323 NL PLSA 1323 Not applicable 27 Hispanic Female Normal NA NA 0.121 Negative NL 1324 NL PLSA 1324 Not applicable 21 Hispanic Female No rmal NA NA 0.123 Negative NL 1325 NL PLSA 1325 Not applicable 23 Black Female Normal NA NA 0.31 Negative NL 1326 NL PLSA 1326 Not applicable 29 Hispanic Female Normal NA NA 0.356 Negative NL 1327 NL PLSA 1327 Not applicable 24 Hispanic Female Normal NA NA 0.129 Negative

NL 1328 NL PLSA 1328 Not applicable 21 Black Male / Hispanic Normal NA NA 0.125 Negative NL 1329 NL PLSA 1329 Not applicable 22 Male Black Normal NA NA 0.25 Negative NL 1330 NL PLSA 1330 Not applicable 29 Male Black Normal NA NA 0.21 Negative NL 1331 NL PLSA 1331 Not applicable 18 Caucasian / Hispanic female Normal NA NA 0.122 Negative NL 1332 NL PLSA 1332 Not applicable 30 Male Black Normal NA NA 0.117 Negative NL 1333 NL PLSA 1333 Not applicable 25 Male Black Normal NA NA 0.139 Negative NL PLSA 1334 No applicable 23 Male Black Normal NA NA 0.489 Negative NL 1335 NL PLSA 1335 Not applicable 30 Male Black Normal NA NA 0.849 Negative NL 1336 NL PLSA 1336 Not applicable 21 Male Caucasian / Hispanic Normal NA NA 0.325 Negative NL 1337 NL PLSA 1337 Not applicable 30 Female Black Normal NA NA 0.122 Negative NL 1338 NL PLSA 1338 Not applicable 27 Male Black Normal NA NA 0.88 Negative NL 1339 NL PLSA 1339 Not applicable 21 Male Black Normal NA NA 0.117 Negative NL 1340 NL PLSA 1340 No applicable 21 Male Black Normal NA NA 0.125 Negative NL 1341 NL PLSA 1341 Not applicable 17 Caucasian / Hispanic Female Normal NA NA 0.255 Negative NL 1342 NL PLSA 1342 Not applicable 20 Male Black / Hispanic Normal NA NA 0.205 Negative NL 1343 NL PLSA 1343 Not applicable 19 Male Black Normal NA NA 0.122 Negative NL 1344 NL PLSA 1344 Not applicable 22 Male Black / Hispanic Normal NA NA 0.125 Negative NL 1345 NL PLSA 1345 Not applicable 20 Male Caucasian / Hispanic Normal NA NA 0.125 Negative NL 1346 NL PLSA 1346 Not applicable 30 Male Black Normal NA NA 0.346 Negative NL 1347 NL PLSA 1347 Not applicable 26 Male Caucasian / Hispanic Normal NA NA 0.43 Negative NL 1348 NL PLSA 1348 Not applicable 23 Male Black Normal NA NA 0.125 Negative NL 1349 NL PLSA 1349 Not applicable 28 Male Black Normal NA NA 0.117 Negative NL 1350 NL PLSA 1350 Not applicable 23 Normal Black Female NA NA 0.377 Negative NL 1351 NL PLSA 1351 Not applicable 29 Normal Black / Hispanic Female NA NA 0.317 Negative NL 1352 NL PL SA 1352 Not applicable 21 Male Caucasian / Hispanic Normal NA NA 0.125 Negative NL 1353 NL PLSA 1353 Not applicable 23 Male Black Normal NA NA 0.593 Negative NL 1354 NL PLSA 1354 Not applicable 26 Male Black Normal NA NA 0.29 Negative NL 1355 NL PLSA 1355 No applicable 29 Male Black Normal NA NA 0.116 Negative NL 1356 NL PLSA 1356 Not applicable 25 Caucasian / Hispanic Female Normal NA NA 0.277 Negative NL 1357 NL PLSA 1357 Not applicable 24 Male Caucasian / Hispanic Normal NA NA 0.21 Negative NL 1358 NL PLSA 1358 Not applicable 25 Normal Black Female NA NA 0.553 Negative NL 1359 NL PLSA 1359 Not applicable 29 Caucasian / Hispanic Female Normal NA NA 0.570 Negative NL 1360 NL PLSA 1360 Not applicable 28 Normal Black Male NA NA 0.125 Negative NL 1361 NL PLSA 1361 Not applicable 30 Black Male Normal NA NA 0.123 Negative NL 1362 NL PLSA 1362 Not applicable 30 Male Black Normal NA NA 0.222 Negative NL 1363 NL PLSA 1363 Not applicable 29 Male Caucasian Normal NA NA 0.125 Negative NL 1364 NL P LSA 1364 Not applicable 22 Male Black / Hispanic Normal NA NA 0.441 Negative NL 1365 NL PLSA 1365 Not applicable 23 Male Caucasian / Hispanic Normal NA NA 0.325 Negative NL 1366 NL PLSA 1366 Not applicable 24 Male Black Normal NA NA 0.125 Negative NL 1367 NL PLSA 1367 Not applicable 21 Male Black Normal NA NA 0.383 Negative NL 1368 NL PLSA 1368 Not applicable 25 Male Black Normal NA NA 0.21 Negative NL 1369 NL PLSA 1369 Not applicable 21 Male Caucasian / Hispanic Normal NA NA 0.618 Negative NL 1370 NL PLSA 1370 Not applicable 28 Male Black Normal NA NA 0.304 Negative NL 1371 NL PLSA 1371 Not applicable 28 Male Black Normal NA NA 0.116 Negative NL 1372 NL PLSA 1372 Not applicable 22 Male Black / Hispanic Normal NA NA 0.125 Negative NL 1373 NL PLSA 1373 Not applicable 26 Black Male Normal NA NA 0.311 Negative NL 1374 NL PLSA 1374 Not applicable 26 Male Black Normal NA NA 0.117 Negative NL 1375 NL PLSA 1375 Not applicable 30 Male Caucasian / Hispanic Normal NA NA 0.117 Negative NL 1376 NL PLSA 1376 Not applicable 24 Normal Black Male NA NA 0.121 Negative NL 1377 NL PLSA 1377 Not applicable 24 Caucasian / Hispanic Female Normal NA NA 0.319 Negative NL 1378 NL PLSA 1378 Not applicable 27 Normal Black Male NA NA 0.122 Negative NL 1379 NL PLSA 1379 No applicable 28 Male Caucasian / Hispanic Normal NA NA 0.125 Negative NL 1380 NL PLSA 1380 Not applicable 21 Male Black Normal NA NA 0.255 Negative NL 1381 NL PLSA 1381 Not applicable 24 Male Caucasian / Hispanic Normal NA NA 0.23 Negative NL 1382 NL PLSA 1382 Not applicable 27 Normal Black Female NA NA 0.305 Negative NL 1383 NL PLSA 1383 Not applicable 28 Normal Black Male NA NA 0.377 Negative NL 1384 NL PLSA 1384 Not applicable 25 Normal Black Male NA NA 0.301 Negative NL 1385 NL PLSA 1385 Not applicable 26 Caucasian / Hispanic Female Normal NA NA 0.366 Negative NL 1386 NL PLSA 1386 Not applicable 20 Male Caucasian / Hispanic Normal NA NA 0.122 Negative NL 1387 NL PLSA 1387 Not applicable 23 Caucasian / Hispanic female Normal NA NA 0.489 Negative NL 1388 NL PLSA 1388 Not applicable 19 Black / Hispanic Female Normal NA NA 0.303 Negative NL 1389 NL PLSA 1389 Not applicable 28 Caucasian / Hispanic female Normal NA NA 0.388 Negative NL 1390 NL PLSA 1390 Not applicable 29 Black / Hispanic Normal NA NA 0.403 Negative NL 1391 NL PLSA 1391 Not applicable 29 Normal Black Male NA NA 0.590 Negative NL 1392 NL PLSA 1392 Not applicable 29 Normal Black Male NA NA 0.23 Negative NL 1393 NL PLSA 1393 Not applicable 28 Normal Black Male NA NA 0.481 Negative

NL 1394 NL PLSA 1394 Not applicable 27 Black Male / Hispanic Normal NA NA 0.190 Negative NL 1395 NL PLSA 1395 Not applicable 29 Caucasian / Hispanic Female Normal NA NA 0.177 Negative NL 1396 NL PLSA 1396 Not applicable 27 Black Male / Hispanic Normal NA NA 0.177 Negative NL 1397 NL PLSA 1397 Not applicable 22 Male Caucasian / Hispanic Normal NA NA 0.320 Negative NL 1398 NL PLSA 1398 Not applicable 22 Male Black Normal NA NA 0.23 Negative NL 1399 NL PLSA 1399 Not applicable 25 Black Male / Hispanic Normal NA NA 0.21 Negative NL 1400 NL PLSA 1400 Not applicable 28 Male Caucasian / Hispanic Normal NA NA 0.307 Negative NL 1401 NL PLSA 1401 Not applicable 20 Black Female Normal NA NA 0.118 Negative NL 1402 NL PLSA 1402 Not applicable 23 Black Female Normal NA NA 0.125 Negative NL 1403 NL PLSA 1403 Not applicable 19 Normal Black Female NA NA 0.311 Negative NL 1404 NL PLSA 1404 Not applicable 26 Normal Hispanic Female NA NA 0.125 Negative NL 1405 NL PLSA 1405 Not applicable 29 Normal Black Female NA NA NA 0.123 Negative NL 1406 NL PLSA 1406 Not applicable 26 Normal Black Female NA NA 0.841 Negative NL 1407 NL PLSA 1407 Not applicable 22 Normal Black Female NA NA 0.125 Negative NL 1408 NL PLSA 1408 Not applicable 30 Normal Caucasian Female NA NA 0.125 Negative NL 1409 NL PLSA 1409 Not applicable 22 Normal Black Female NA NA 0.390 Negative NL 1410 NL PLSA 1410 Not applicable 25 Normal Caucasian Female NA NA 0.219 Negative NL 1411 NL PLSA 1411 Not applicable 25 Normal Black Female NA NA 0.116 Negative NL 1412 NL PLSA 1412 Not applicable 27 Normal Black Female NA NA 0.299 Negative NL 1413 NL PLSA 1413 Not applicable 23 Normal Black Female NA NA 0.23 Negative NL 1414 NL PLSA 1414 Not applicable 20 Normal Hispanic Female NA NA 0.345 Negative NL 1425 NL PLSA 1425 Not applicable 25 Normal Black Male NA NA NA 0.121 Negative NL 1426 NL PLSA 1426 Not applicable 24 Normal Black Female NA NA 0.123 Negative NL 1427 NL PLSA 1427 Not applicable 27 Male Caucasian / Hispanic Normal NA NA 0.125 Negative NL 1428 NL PLSA 1428 Not applicable 27 Male Caucasian / Hispanic Normal NA NA 0.553 Negative NL 1429 NL PLSA 1429 Not applicable 27 Male Black / Hispanic Normal NA NA 0.21 Negative NL 1430 NL PLSA 1430 Not applicable 30 Male Black / Hispanic Normal NA NA 0.307 Negative NL 1431 NL PLSA 1431 Not applicable 28 Male Caucasian / Hispanic Normal NA NA 0.219 Negative NL 1432 NL PLSA 1432 Not applicable 25 Male Caucasian / Hispanic Normal NA NA 0.22 Negative NL 1433 NL PLSA 1433 Not applicable 21 Male Black Normal NA NA 0.125 Negative NL PLSA 1434 Not applicable 27 Male Black Normal NA NA 0.125 Negative NL 1435 NL PLSA 1435 Not applicable 29 Male Black Normal NA NA 0.125 Negative NL 1436 NL PLSA 1436 Not applicable 24 Caucasian / Hispanic female Normal NA NA 0.237 Negative NL 1437 NL PLSA 1437 Not applicable 30 Caucasian / Hispanic female Normal NA NA 0.117 Negative NL 1438 NL PLSA 1438 Not applicable 28 Male Black Normal NA NA 0.222 Negative NL 1439 NL PLSA 1439 Not applicable 28 Male Black Normal NA NA 0.11 7 Negative NL 1440 NL PLSA 1440 Not applicable 20 Normal Black Male NA NA 0.177 Negative NL 1441 NL PLSA 1441 Not applicable 27 Normal Black Male NA NA 0.125 Negative NL 1442 NL PLSA 1442 Not applicable 28 Normal Black Male NA NA 0.117 Negative NL 1443 NL PLSA 1443 Not applicable 23 Caucasian / Hispanic female Normal NA NA 0.317 Negative NL 1444 NL PLSA 1444 Not applicable 23 Caucasian / Hispanic male Normal NA NA 0.816 Negative NL 1445 NL PLSA 1445 Not applicable 20 Normal Black / Hispanic female NA NA 0.177 Negative NL 1482 NL PLSA 1482 Not applicable 55 Male Caucasian Normal NA NA 0.125 Negative NL 1483 NL PLSA 1483 Not applicable 79 Male Caucasian Normal NA NA 0.575 Negative NL 1484 NL PLSA 1484 Not applicable 69 Female Caucasian Normal NA NA 0.357 Negative NL 1485 NL PLSA 1485 Not applicable 66 Male Caucasian Normal NA NA 0.117 Negative NL 1486 NL PLSA 1486 Not applicable 66 Male Caucasian Normal NA NA 0.125 Negative NL 1487 NL PLSA 1487 Not applicable 60 Male Caucasian Normal NA N N A 0.338 Negative NL 1488 NL PLSA 1488 Not applicable 66 Male Caucasian Normal NA NA 0.11 Negative NL 1489 NL PLSA 1489 Not applicable 57 Male Caucasian Normal NA NA 0.818 Negative NL 1490 NL PLSA 1490 Not applicable 65 Male Caucasian Normal NA NA 0.125 Negative NL 1491 NL PLSA 1491 Not applicable 62 Male Caucasian Normal NA NA 0.325 Negative NL 1492 NL PLSA 1492 Not applicable 62 Male Caucasian Normal NA NA 0.23 Negative NL 1493 NL PLSA 1493 Not applicable 76 Male Caucasian Normal NA NA 0.297 Negative NL 1494 NL PLSA 1494 Not applicable 58 Female Caucasian Normal NA NA 0.116 Negative NL 1495 NL PLSA 1495 Not applicable 78 Female Caucasian Normal NA NA 0.355 Negative NL 1496 NL PLSA 1496 Not Applicable 77 Female Caucasian Normal NA NA 0.413 Negative NL 1497 NL PLSA 1497 Not Applicable 84 Male Caucasian Normal NA NA 0.117 Negative NL 1498 NL PLSA 1498 Not applicable 76 Male Caucasian Normal NA NA 0.125 Negative NL 1499 NL PLSA 1499 Not applicable 58 Male Black Normal NA NA 0.308 N egative NL 1500 NL PLSA 1500 Not applicable 80 Caucasian Female Normal NA NA 0.199 Negative NL 1501 NL PLSA 1501 Not applicable 59 Caucasian Female Normal NA NA 0.125 Negative NL 1502 NL PLSA 1502 Not applicable 59 Normal Caucasian Female NA NA 0.23 Negative NL 1503 NL PLSA 1503 Not applicable 56 Female Caucasian Normal NA NA 0.117 Negative NL 1504 NL PLSA 1504 Not applicable 57 Male Caucasian Normal NA NA 0.177 Negative NL 1505 NL PLSA 1505 Not applicable 55 Female Caucasian Normal NA NA 0.457 Negative

NL 1506 NL PLSA 1506 Not applicable 66 Male Black Normal NA NA 0.50 Negative NL 1507 NL PLSA 1507 Not applicable 72 Male Caucasian Normal NA NA 0.731 Negative NL 1508 NL PLSA 1508 Not applicable 64 Female Caucasian Normal NA NA 0.125 Negative NL 1509 NL PLSA 1509 Not applicable 67 Male Caucasian Normal NA NA 0.328 Negative NL 1510 NL PLSA 1510 Not applicable 71 Female Caucasian Normal NA NA 0.300 Negative NL 1511 NL PLSA 1511 Not applicable 57 Male Caucasian Normal NA NA 0.514 Negative NL 1512 NL PLSA 1512 Not applicable 56 Caucasian Male Normal NA NA 0.121 Negative NL 1513 NL PLSA 1513 Not applicable 60 Caucasian Female Normal NA NA 0.185 Negative NL 1514 NL PLSA 1514 Not applicable 56 Male Caucasian Normal NA NA 0.29 Negative NL 1515 NL PLSA 1515 Not applicable 55 Male Caucasian Normal NA NA 0.238 Negative NL 1516 NL PLSA 1516 Not applicable 73 Female Caucasian Normal NA NA 0.115 Negative NL 1517 NL PLSA 1517 Not applicable 66 Male Caucasian Normal NA NA 0.305 Negative NL 1518 N L PLSA 1518 Not applicable 70 Male Caucasian Normal NA NA 0.443 Negative NL 1519 NL PLSA 1519 Not applicable 61 Female Caucasian Normal NA NA 0.122 Negative NL 1520 NL PLSA 1520 Not applicable 69 Female Caucasian Normal NA NA 0.125 Negative NL 1521 NL PLSA 1521 Not applicable 69 Female Caucasian Normal NA NA 0.314 Negative NL 1522 NL PLSA 1522 Not applicable 59 Male Caucasian Normal NA NA 0.116 Negative NL 1523 NL PLSA 1523 Not applicable 64 Female Caucasian Normal NA NA 0.14 Negative NL 1524 NL PLSA 1524 Not applicable 62 Caucasian Female Normal NA NA 0.600 Negative NL 1525 NL PLSA 1525 Not applicable 65 Normal Black Female NA NA 0.550 Negative NL 1526 NL PLSA 1526 Not applicable 79 Normal Caucasian female NA NA 0.177 Negative NL 1527 NL PLSA 1527 Not applicable 56 Male Caucasian Normal NA NA 0.296 Negative NL 1528 NL PLSA 1528 Not applicable 61 Female Caucasian Normal NA NA 0.301 Negative NL 1529 NL PLSA 1529 Not applicable 74 Male Caucasian Normal NA NA 0.814 Negative NL 1530 NL PLSA 15 30 Not applicable 66 Male Caucasian Normal NA NA 0.421 Negative NL 1531 NL PLSA 1531 Not applicable 58 Male Caucasian Normal NA NA 0.166 Negative NL 1532 NL PLSA 1532 Not applicable 65 Male Caucasian Normal NA NA 0.409 Negative NL 1533 NL PLSA 1533 Not applicable 56 Male Caucasian Normal NA NA 0.295 Negative NL 1534 NL PLSA 1534 Not applicable 67 Male Caucasian Normal NA NA 0.490 Negative NL 1535 NL PLSA 1535 Not applicable 61 Male Caucasian Normal NA NA 0.29 Negative NL 1536 NL PLSA 1536 Not applicable 68 Male Normal Caucasian NA NA 0.125 Negative NL 1537 NL PLSA 1537 Not applicable 75 Male Caucasian Normal NA NA 0.125 Negative NL 1538 NL PLSA 1538 Not applicable 69 Male Caucasian Normal NA NA 0.97 Negative NL 1539 NL PLSA 1539 Not applicable 70 Male Caucasian Normal NA NA 0.425 Negative NL 1540 NL PLSA 1540 Not applicable 68 Female Other Normal NA NA 0.123 Negative NL 1541 NL PLSA 1541 Not applicable 78 Male Caucasian Normal NA NA 0.617 Negative NL 1542 NL PLSA 1542 No ap eligible 63 Female Black Normal NA NA 0.213 Negative NL 1543 NL PLSA 1543 Not applicable 60 Male Caucasian Normal NA NA 0.388 Negative NL 1544 NL PLSA 1544 Not applicable 63 Male Caucasian Normal NA NA 0.117 Negative NL 1545 NL PLSA 1545 Not applicable 61 Female Black Normal NA NA 0.451 Negative NL 1546 NL PLSA 1546 Not applicable 78 Male Caucasian Normal NA NA 0.388 Negative NL 1547 NL PLSA 1547 Not applicable 70 Black Female Normal NA NA 0.466 Negative NL 1548 NL PLSA 1548 Not applicable 61 Caucasian Female Normal NA NA 0.125 Negative NL 1549 NL PLSA 1549 Not applicable 57 Male Black Normal NA NA 0.731 Negative NL 1550 NL PLSA 1550 Not applicable 76 Male Caucasian Normal NA NA 0.542 Negative NL 1551 NL PLSA 1551 Not applicable 64 Male Asian Normal NA NA 0.23 Negative NL 1552 NL PLSA 1552 No applicable 72 Male Caucasian Normal NA NA 0.415 Negative NL 1553 NL PLSA 1553 Not applicable 76 Female Asian Normal NA NA 0.307 Negative NL 1554 NL PLSA 1554 Not applicable 79 Caucasian Female Normal NA NA 0.117 Negative NL 1555 NL PLSA 1555 Not applicable 58 Male Caucasian Normal NA NA 0.435 Negative NL 1556 NL PLSA 1556 Not applicable 57 Black Female Normal NA NA 0.305 Negative NL 1557 NL PLSA 1557 Not applicable 67 Caucasian Female Normal NA NA 0.673 Negative NL 1558 NL PLSA 1558 Not applicable 67 Female Caucasian Normal NA NA 0.455 Negative NL 1559 NL PLSA 1559 Not applicable 71 Male Caucasian Normal NA NA 0.518 Negative NL 1560 NL PLSA 1560 Not applicable 60 Male Caucasian Normal NA NA 0.847 Negative NL 1561 NL PLSA 1561 Not applicable 62 Male Caucasian Normal NA NA 0.472 Negative NL 1562 NL PLSA 1562 Not applicable 64 Female Caucasian Normal NA NA 0.976 Positive NL 1563 NL PLSA 1563 Not applicable 64 Male Caucasian Normal NA NA 0.399 Negative NL 1564 NL PLSA 1564 Not applicable 60 Male Caucasian Normal NA NA 0.485 Negative NL 1565 NL PLSA 1565 Not applicable 59 Female Caucasian Normal NA NA 0.121 Negative NL 1566 NL PLSA 1566 Not applicable 59 Female Caucasian Normal NA NA 0.305 Negative NL 1567 NL PLSA 1567 Not applicable 60 Normal Caucasian female NA NA 0.116 Negative NL 1568 NL PLSA 1568 Not applicable 57 Normal Caucasian female NA NA 0.207 Negative NL 1569 NL PLSA 1569 Not applicable 70 Male Caucasian Normal NA NA 0.616 Negative NL 1570 NL PLSA 1570 Not applicable 56 Female Caucasian Normal NA NA 0.295 Negative NL 1571 NL PLSA 1571 Not applicable 64 Male Caucasian Normal NA NA 0.303 Negative

NL 1572 NL PLSA 1572 Not applicable 68 Normal Caucasian female NA NA 0.125 Negative NL 1573 NL PLSA 1573 Not applicable 56 Normal Caucasian female NA NA 0.195 Negative NL 1574 NL PLSA 1574 Not applicable 65 Normal Caucasian female NA NA 0.554 Negative NL 1575 NL PLSA 1575 Not applicable 60 Caucasian Female Normal NA NA 0.333 Negative NL 1576 NL PLSA 1576 Not applicable 63 Caucasian Female Normal NA NA 0.121 Negative NL 1577 NL PLSA 1577 Not applicable 55 Caucasian Female Normal NA NA 0.543 Negative NL 1578 NL PLSA 1578 Not applicable 56 Caucasian Male Normal NA NA 0.117 Negative NL 1579 NL PLSA 1579 Not applicable 66 Caucasian Female Normal NA NA 0.256 Negative NL 1580 NL PLSA 1580 Not applicable 59 Male Caucasian Normal NA NA 0.242 Negative NL 1581 NL PLSA 1581 Not applicable 81 Caucasian Female Normal NA NA 0.125 Negative NL 1582 NL PLSA 1582 Not applicable 65 Male Caucasian Normal NA NA 0.97 Negative NL 1583 NL PLSA 1583 Not applicable 66 Male Caucasian Normal NA NA 0.344 Negative NL 1 584 NL PLSA 1584 Not applicable 64 Male Caucasian Normal NA NA 0.117 Negative NL 1585 NL PLSA 1585 Not applicable 66 Male Caucasian Normal NA NA 0.177 Negative NL 1586 NL PLSA 1586 Not applicable 64 Female Caucasian Normal NA NA 0.382 Negative NL 1587 NL PLSA 1587 No applicable 55 Normal Caucasian Female NA NA 0.719 Negative NL 1588 NL PLSA 1588 Not applicable 62 Normal Caucasian Female NA NA 0.508 Negative NL 1589 NL PLSA 1589 Not applicable 63 Normal Caucasian Female NA NA 0.666 Negative NL 1590 NL PLSA 1590 Not applicable 55 Normal Caucasian Female NA NA 0.123 Negative NL 1591 NL PLSA 1591 Not applicable 71 Female Caucasian Normal NA NA 0.188 Negative NL 1592 NL PLSA 1592 Not applicable 68 Female Caucasian Normal NA NA 0.320 Negative NL 1593 NL PLSA 1593 Not applicable 65 Male Caucasian Normal NA NA 0.122 Negative NL 1594 NL PLSA 1594 Not applicable 60 Male Caucasian Normal NA NA 0.117 Negative NL 1595 NL PLSA 1595 Not applicable 75 Male Caucasian Normal NA NA 0.396 Negative NL 1596 NL PLSA 1596 Not applicable 67 Male Caucasian Normal NA NA 0.32 Negative NL 1597 NL PLSA 1597 Not applicable 59 Female Asian Normal NA NA 0.125 Negative NL 1598 NL PLSA 1598 Not applicable 63 Male Caucasian Normal NA NA 0.238 Negative NL 1599 NL PLSA 1599 Not applicable 55 Female Caucasian Normal NA NA 0.116 Negative NL 1600 NL PLSA 1600 Not applicable 64 Male Caucasian Normal NA NA 0.388 Negative NL 1601 NL PLSA 1601 Not applicable 69 Female Caucasian Normal NA NA 0.318 Negative NL 1602 NL PLSA 1602 Not applicable 78 Caucasian Female Normal NA NA 0.242 Negative NL 1603 NL PLSA 1603 Not applicable 60 Male Caucasian Normal NA NA 0.125 Negative NL 1604 NL PLSA 1604 Not applicable 55 Female Caucasian Normal NA NA 0.31 Negative NL 1605 NL PLSA 1605 Not applicable 65 Caucasian Female Normal NA NA 0.125 Negative NL 1606 NL PLSA 1606 Not applicable 61 Male Caucasian Normal NA NA 0.121 Negative NL 1607 NL PLSA 1607 Not applicable 57 Male Caucasian Normal NA NA 0.381 Negative NL 1608 NL PLS A 1608 Not applicable 59 Caucasian female Normal NA NA 0.121 Negative NL 1609 NL PLSA 1609 Not applicable 57 Female Caucasian Normal NA NA 0.301 Negative NL 1610 NL PLSA 1610 Not applicable 63 Black female Normal NA NA 0.21 Negative NL 1611 NL PLSA 1611 Not applicable 63 Male Caucasian Normal NA NA 0.538 Negative NL 1612 NL PLSA 1612 Not applicable 68 Male Caucasian Normal NA NA 0.125 Negative NL 1613 NL PLSA 1613 Not applicable 71 Male Caucasian Normal NA NA 0.177 Negative NL 1614 NL PLSA 1614 Not applicable 63 Caucasian Female Normal NA NA 0.125 Negative NL 1615 NL PLSA 1615 Not applicable 56 Male Caucasian Normal NA NA 0.125 Negative NL 1616 NL PLSA 1616 Not applicable 62 Male Black Normal NA NA 0.815 Negative NL 1617 NL PLSA 1617 Not applicable 73 Caucasian Female Normal NA NA 0.121 Negative NL 1618 NL PLSA 1618 Not applicable 71 Female Caucasian Normal NA NA 0,116 Negative NL 1619 NL PLSA 1619 Not applicable 72 Female Caucasian Normal NA NA 0,200 Negative NL 1620 NL PLSA 1620 Not applicable available 68 Normal Caucasian Female NA NA 0.301 Negative NL 1621 NL PLSA 1621 Not applicable 55 Normal Black Female NA NA 0.177 Negative NL 1622 NL PLSA 1622 Not applicable 56 Normal Black Female NA NA 0.480 Negative NL 1623 NL PLSA 1623 Not applicable 72 Male Caucasian Normal NA NA 0.122 Negative NL 1624 NL PLSA 1624 Not applicable 75 Male Caucasian Normal NA NA 0.121 Negative NL 1625 NL PLSA 1625 Not applicable 56 Female Caucasian Normal NA NA 0.115 Negative NL 1626 NL PLSA 1626 Not applicable 73 Caucasian Female Normal NA NA 0.125 Negative NL 1627 NL PLSA 1627 Not applicable 60 Caucasian Female Normal NA NA 0.192 Negative NL 1628 NL PLSA 1628 Not applicable 76 Female Caucasian Normal NA NA 0.116 Negative NL 1629 NL PLSA 1629 Not applicable 60 Black Female Normal NA NA 0.178 Negative NL 1630 NL PLSA 1630 No applicable 71 Male Asian Normal NA NA 0.121 Negative NL 1631 NL PLSA 1631 Not applicable 63 Female Caucasian Normal NA NA 0.23 Negative NL 1632 NL PLSA 1632 Not applicable 64 Asiát male ica Normal NA NA 0.117 Negative NL 1633 NL PLSA 1633 Not applicable 63 Male Caucasian Normal NA NA 0.222 Negative NL 1634 NL PLSA 1634 Not applicable 67 Female Caucasian Normal NA NA 0.80 Negative NL 1635 NL PLSA 1635 Not applicable 62 Male Caucasian Normal NA NA 0.335 Negative NL 1636 NL PLSA 1636 Not applicable 58 Female Caucasian Normal NA NA 0.125 Negative NL 1637 NL PLSA 1637 Not applicable 60 Female Caucasian Normal NA NA 0.125 Negative

NL 1638 NL PLSA 1638 Not applicable 70 Female Caucasian Normal NA NA 0.310 Negative NL 1639 NL PLSA 1639 Not applicable 56 Female Caucasian Normal NA NA 0.187 Negative NL 1640 NL PLSA 1640 Not applicable 56 Male Caucasian Normal NA NA 0.21 Negative NL 1641 NL PLSA 1641 Not applicable 60 Female Caucasian Normal NA NA 0.123 Negative NL 1642 NL PLSA 1642 Not applicable 64 Male Caucasian Normal NA NA 0.175 Negative NL 1643 NL PLSA 1643 Not applicable 66 Male Caucasian Normal NA NA 0.222 Negative NL 1644 NL PLSA 1644 Not applicable 56 Caucasian Female Normal NA NA 0.222 Negative NL 1645 NL PLSA 1645 Not applicable 66 Male Caucasian Normal NA NA 0.117 Negative NL 1646 NL PLSA 1646 Not applicable 64 Male Caucasian Normal NA NA 0.125 Negative NL 1647 NL PLSA 1647 Not applicable 62 Caucasian Female Normal NA NA 0.125 Negative NL 1648 NL PLSA 1648 Not applicable 59 Male Caucasian Normal NA NA 0.306 Negative NL 1649 NL PLSA 1649 Not applicable 59 Male Asian Normal NA NA 0.747 Negative NL 165 0 NL PLSA 1650 Not applicable 60 Female Caucasian Normal NA NA 0.125 Negative NL 1651 NL PLSA 1651 Not applicable 84 Male Caucasian Normal NA NA 0.311 Negative NL 1652 NL PLSA 1652 Not applicable 67 Female Caucasian Normal NA NA 0.317 Negative NL 1653 NL PLSA 1653 No applicable 70 Male Black Normal NA NA 0.568 Negative NL 1654 NL PLSA 1654 Not applicable 67 Female Caucasian Normal NA NA 0.21 Negative NL 1655 NL PLSA 1655 Not applicable 71 Female Caucasian Normal NA NA 0.720 Negative NL 1656 NL PLSA 1656 Not applicable 68 Male Caucasian Normal NA NA 0.522 Negative NL 1657 NL PLSA 1657 Not applicable 63 Female Caucasian Normal NA NA 0.320 Negative NL 1658 NL PLSA 1658 Not applicable 71 Male Caucasian Normal NA NA 0.324 Negative NL 1659 NL PLSA 1659 Not applicable 59 Male Black Normal NA NA 0.300 Negative NL 1660 NL PLSA 1660 Not applicable 61 Female Caucasian Normal NA NA 0.333 Negative NL 1661 NL PLSA 1661 Not applicable 65 Male Caucasian Normal NA NA 0.334 Negative NL 1662 NL PLSA 1662 Not applicable 58 Male Caucasian Normal NA NA 0.121 Negative NL 1663 NL PLSA 1663 Not applicable 55 Male Asian Normal NA NA 0.168 Negative NL 1664 NL PLSA 1664 Not applicable 59 Male Caucasian Normal NA NA 0.308 Negative NL 1665 NL PLSA 1665 Not applicable 75 Caucasian Female Normal NA NA 0.471 Negative NL 1666 NL PLSA 1666 Not applicable 74 Caucasian Female Normal NA NA 0.304 Negative NL 1667 NL PLSA 1667 Not applicable 72 Black Female Normal NA NA 0.313 Negative NL 1668 NL PLSA 1668 Not applicable 66 Caucasian Female Normal NA NA 0.309 Negative NL 1669 NL PLSA 1669 Not applicable 56 Caucasian female Normal NA NA 0.577 Negative NL 1701 NL PLSA 1701 Not applicable 87 Male Unknown Normal NA NA 0.528 Negative NL 1702 NL PLSA 1702 Not applicable 82 Unknown female Normal NA NA 0.214 Negative NL 1703 NL PLSA 1703 Not applicable 82 Unknown female Normal NA NA 0.713 Negative NL 1704 NL PLSA 1704 Not applicable 66 Unknown female Normal NA NA 0.388 Negative NL 1705 NL PLSA 1705 No the applicable 77 Female Unknown Normal NA NA 0.117 Negative NL 1706 NL PLSA 1706 Not applicable 56 Male Unknown Normal NA NA 0.308 Negative NL 1707 NL PLSA 1707 Not applicable 62 Male Unknown Normal NA NA 0.314 Negative NL 1708 NL PLSA 1708 Not applicable 76 Male Unknown Normal NA NA 0.576 Negative NL 1709 NL PLSA 1709 Not applicable 68 Male Unknown Normal NA NA 0.566 Negative NL 1710 NL PLSA 1710 Not applicable 77 Unknown Female Normal NA NA 0.32 Negative NL 1711 NL PLSA 1711 Not applicable 46 Unknown Female Normal NA NA 0.23 Negative NL 1712 NL PLSA 1712 Not applicable 76 Male Unknown Normal NA NA 0.305 Negative NL 1713 NL PLSA 1713 Not applicable 37 Male Unknown Normal NA NA 0.178 Negative NL 1714 NL PLSA 1714 Not applicable 62 Unknown Female Normal NA NA 0.125 Negative NL 1715 NL PLSA 1715 Not applicable 67 Unknown female Normal NA NA 0.187 Negative NL 1716 NL PLSA 1716 Not applicable 55 Unknown female Normal NA NA 0.125 Negative NL 1717 NL PLSA 1717 Not applicable 58 Male Unknown Normal NA NA 0.333 Negative NL 1718 NL PLSA 1718 Not applicable 44 Unknown Female Normal NA NA 0.349 Negative NL 1719 NL PLSA 1719 Not Applicable 66 Unknown Female Normal NA NA 0.21 Negative NL 1720 NL PLSA 1720 No applicable 72 Unknown Female Normal NA NA 0.444 Negative NL 1721 NL PLSA 1721 Not applicable 64 Unknown Female Normal NA NA 0.123 Negative NL 1722 NL PLSA 1722 Not applicable 55 Unknown Female Normal NA NA 0.125 Negative NL 1723 NL PLSA 1723 Not applicable 74 Male Unknown Normal NA NA 0.116 Negative NL 1724 NL PLSA 1724 Not applicable 71 Unknown Female Normal NA NA 0.599 Negative NL 1725 NL PLSA 1725 Not applicable 28 Unknown Female Normal NA NA 0.624 Negative NL 1726 NL PLSA 1726 Not applicable 67 Male Unknown Normal NA NA 0.114 Negative NL 1727 NL PLSA 1727 Not applicable 53 Unknown female Normal NA NA 0.23 Negative NL 1728 NL PLSA 1728 Not applicable 64 Unknown female Normal NA N A 0.735 Negative NL 1729 NL PLSA 1729 Not applicable 64 Unknown Female Normal NA NA 0.322 Negative NL 1730 NL PLSA 1730 Not applicable 33 Unknown Female Normal NA NA 0.599 Negative NL 1731 NL PLSA 1731 Not applicable 54 Unknown Female Normal NA NA 0.236 Negative NL 1732 NL PLSA 1732 Not applicable 24 Unknown Female Normal NA NA 0.125 Negative NL 1733 NL PLSA 1733 Not applicable 27 Unknown Female Normal NA NA 0.125 Negative NL 1734 NL PLSA 1734 Not applicable 53 Male Unknown Normal NA NA 0.155 Negative

NL 1735 NL PLSA 1735 Not applicable 74 Unknown Female Normal NA NA 0.187 Negative NL 1736 NL PLSA 1736 Not applicable 53 Unknown Female Normal NA NA 0.564 Negative NL 1737 NL PLSA 1737 Not applicable 63 Male Unknown Normal NA NA 0.183 Negative NL 1738 NL PLSA 1738 Not applicable 68 Male Unknown Normal NA NA 0.175 Negative NL 1739 NL PLSA 1739 Not applicable 52 Male Unknown Normal NA NA 0.222 Negative NL 1740 NL PLSA 1740 Not applicable 73 Female Unknown Normal NA NA 0.21 Negative NL 1741 NL PLSA 1741 Not applicable 66 Unknown Female Normal NA NA 0.117 Negative NL 1742 NL PLSA 1742 Not applicable 52 Male Unknown Normal NA NA 0.125 Negative NL 1743 NL PLSA 1743 Not applicable 64 Unknown Female Normal NA NA 0.125 Negative NL 1744 NL PLSA 1744 Not applicable 72 Unknown Female Normal NA NA 0.116 Negative NL 1745 NL PLSA 1745 Not applicable 63 Male Unknown Normal NA NA 0.154 Negative NL 1746 NL PLSA 1746 Not applicable 47 Female Unknown Normal N A NA 0.344 Negative NL 1747 NL PLSA 1747 Not applicable 37 Male Unknown Normal NA NA 0.21 Negative NL 1748 NL PLSA 1748 Not applicable 47 Male Unknown Normal NA NA 0.182 Negative NL 1749 NL PLSA 1749 Not applicable 48 Male Unknown Normal NA NA 0.323 Negative NL 1750 NL PLSA 1750 Not applicable 53 Unknown Female Normal NA NA 0.117 Negative NL 1751 NL PLSA 1751 Not applicable 43 Male Unknown Normal NA NA 0.116 Negative NL 1752 NL PLSA 1752 Not applicable 23 Unknown Female Normal NA NA 0.303 Negative NL 1753 NL PLSA 1753 No applicable 33 Male Unknown Normal NA NA 0.810 Negative NL 1754 NL PLSA 1754 Not applicable 57 Male Unknown Normal NA NA 0.166 Negative NL 1755 NL PLSA 1755 Not applicable 57 Female Unknown Normal NA NA 0.228 Negative NL 1756 NL PLSA 1756 Not applicable 46 Male Unknown Normal NA NA 0.332 Negative NL 1757 NL PLSA 1757 Not applicable 73 Female Unknown Normal NA NA 0.116 Negative NL 1758 NL PLSA 1758 Not applicable 42 Female D hidden Normal NA NA 0.554 Negative NL 1759 NL PLSA 1759 Not applicable 61 Male Unknown Normal NA NA 0.222 Negative NL 1760 NL PLSA 1760 Not applicable 35 Unknown female Normal NA NA 0.377 Negative NL 1761 NL PLSA 1761 Not applicable 47 Unknown female Normal NA NA 0.117 Negative NL 1762 NL PLSA 1762 Not applicable 52 Unknown Female Normal NA NA 0.297 Negative NL 1763 NL PLSA 1763 Not applicable 55 Unknown Female Normal NA NA 0.37 Negative NL 1764 NL PLSA 1764 Not applicable 21 Unknown Female Normal NA NA 0.116 Negative NL 1765 NL PLSA 1765 Not applicable 63 Unknown Female Normal NA NA 0.125 Negative NL 1766 NL PLSA 1766 Not applicable 61 Male Unknown Normal NA NA 0.277 Negative NL 1767 NL PLSA 1767 Not applicable 43 Male Unknown Normal NA NA 0.266 Negative NL 1768 NL PLSA 1768 Not applicable 68 Male Unknown Normal NA NA 0.332 Negative NL 1769 NL PLSA 1769 Not applicable 53 Male Unknown Normal NA NA 0.125 Negative NL 1770 NL PLSA 1770 No applicable 55 Male Unknown Normal NA NA 0.428 Negative NL 1771 NL PLSA 1771 Not applicable 26 Female Unknown Normal NA NA 0.245 Negative NL 1772 NL PLSA 1772 Not applicable 64 Male Unknown Normal NA NA 0.78 Negative NL 1773 NL PLSA 1773 Not applicable 67 Male Unknown Normal NA NA 0.117 Negative NL 1774 NL PLSA 1774 Not applicable 61 Unknown Female Normal NA NA 0.319 Negative NL 1775 NL PLSA 1775 Not applicable 34 Male Unknown Normal NA NA 0.125 Negative NL 1776 NL PLSA 1776 Not applicable 46 Unknown Female Normal NA NA 0.113 Negative NL 1777 NL PLSA 1777 Not applicable 57 Male Unknown Normal NA NA 0.240 Negative NL 1778 NL PLSA 1778 Not applicable 45 Unknown Female Normal NA NA 0.117 Negative NL 1779 NL PLSA 1779 Not applicable 56 Unknown Female Normal NA NA 0.98 Negative NL 1780 NL PLSA 1780 No applicable 63 Male Unknown Normal NA NA 0.125 Negative NL 1781 NL PLSA 1781 Not applicable 66 Female Unknown Normal NA NA 0.121 Negative NL 17 82 NL PLSA 1782 Not applicable 58 Unknown Female Normal NA NA 0.405 Negative NL 1783 NL PLSA 1783 Not applicable 43 Male Unknown Normal NA NA 0.122 Negative NL 1784 NL PLSA 1784 Not applicable 65 Unknown Female Normal NA NA 0.125 Negative NL 1785 NL PLSA 1785 No applicable 35 Unknown Female Normal NA NA 0.117 Negative NL 1786 NL PLSA 1786 Not applicable 65 Unknown Female Normal NA NA 0.125 Negative NL 1787 NL PLSA 1787 Not applicable 32 Unknown Female Normal NA NA 0.236 Negative NL 1788 NL PLSA 1788 Not applicable 73 Unknown Female Normal NA NA 0.436 Negative NL 1789 NL PLSA 1789 Not applicable 62 Unknown Female Normal NA NA 0.670 Negative NL 1790 NL PLSA 1790 Not applicable 54 Unknown Female Normal NA NA 0.121 Negative NL 1791 NL PLSA 1791 Not applicable 55 Unknown Female Normal NA NA 0.313 Negative NL 1792 NL PLSA 1792 Not applicable 52 Male Unknown Normal NA NA 0.338 Negative NL 1793 NL PLSA 1793 Not applicable 68 Female Unknown Normal NA NA 0.355 Negative NL 1794 NL PLSA 1794 Not applicable 62 Male Unknown Normal NA NA 0.354 Negative NL 1795 NL PLSA 1795 Not applicable 24 Unknown Female Normal NA NA 0.240 Negative NL 1796 NL PLSA 1796 Not applicable 23 Unknown Female Normal NA NA 0.488 Negative NL 1797 NL PLSA 1797 Not applicable 52 Unknown Female Normal NA NA 0.221 Negative NL 1798 NL PLSA 1798 Not applicable 52 Male Unknown Normal NA NA 0.116 Negative NL 1799 NL PLSA 1799 Not applicable 63 Male Unknown Normal NA NA 0.234 Negative NL 1800 NL PLSA 1800 Not applicable 52 Unknown Female Normal NA NA 0.123 Negative

NL 1801 NL PLSA 1801 Not applicable 47 Male Unknown Normal NA NA 0.31 Negative NL 1802 NL PLSA 1802 Not applicable 46 Male Unknown Normal NA NA 0.125 Negative NL 1803 NL PLSA 1803 Not Applicable 44 Female Unknown Normal NA NA 0.125 Negative NL 1804 NL PLSA 1804 Not applicable 83 Male Unknown Normal NA NA 0.322 Negative NL 1805 NL PLSA 1805 Not applicable 60 Female Unknown Normal NA NA 0.176 Negative NL 1806 NL PLSA 1806 Not applicable 72 Male Unknown Normal NA NA 0.125 Negative NL 1807 NL PLSA 1807 Not applicable 41 Unknown Female Normal NA NA 0.125 Negative NL 1808 NL PLSA 1808 Not applicable 26 Male Unknown Normal NA NA 0.306 Negative NL 1809 NL PLSA 1809 Not applicable 57 Unknown Female Normal NA NA 0.117 Negative NL 1810 NL PLSA 1810 Not applicable 47 Unknown Female Normal NA NA 0.125 Negative NL 1811 NL PLSA 1811 Not applicable 51 Unknown female Normal NA NA 0.121 Negative NL 1812 NL PLSA 1812 Not applicable 63 Unknown female Normal N A NA 0.596 Negative NL 1813 NL PLSA 1813 Not applicable 57 Unknown Female Normal NA NA 0.21 Negative NL 1814 NL PLSA 1814 Not applicable 42 Unknown Female Normal NA NA 0.322 Negative NL 1815 NL PLSA 1815 Not applicable 41 Unknown Female Normal NA NA 0.11 Negative NL 1816 NL PLSA 1816 Not applicable 36 Unknown Female Normal NA NA 0.121 Negative NL 1817 NL PLSA 1817 Not applicable 57 Male Unknown Normal NA NA 0.125 Negative NL 1818 NL PLSA 1818 Not applicable 67 Male Unknown Normal NA NA 0.125 Negative NL 1819 NL PLSA 1819 No applicable 82 Male Unknown Normal NA NA 0.117 Negative NL 1820 NL PLSA 1820 Not applicable 28 Female Unknown Normal NA NA 0.125 Negative NL 1821 NL PLSA 1821 Not applicable 67 Male Unknown Normal NA NA 0.115 Negative NL 1822 NL PLSA 1822 Not applicable 35 Male Unknown Normal NA NA 0.121 Negative NL 1823 NL PLSA 1823 Not applicable 67 Female Unknown Normal NA NA 0.125 Negative NL 1824 NL PLSA 1824 Not applicable 45 Female D hidden Normal NA NA 0.125 Negative NL 1825 NL PLSA 1825 Not applicable 24 Unknown Female Normal NA NA 0.222 Negative NL 1826 NL PLSA 1826 Not applicable 58 Male Unknown Normal NA NA 0.805 Negative NL 1827 NL PLSA 1827 Not applicable 65 Unknown female Normal NA NA 0.125 Negative NL 1828 NL PLSA 1828 Not applicable 64 Male Unknown Normal NA NA 0.11 Negative NL 1829 NL PLSA 1829 Not applicable 67 Male Unknown Normal NA NA 0.21 Negative NL 1830 NL PLSA 1830 Not applicable 73 Male Unknown Normal NA NA 0.125 Negative NL 1831 NL PLSA 1831 Not applicable 64 Unknown Female Normal NA NA 0.710 Negative NL 1832 NL PLSA 1832 Not applicable 62 Unknown Female Normal NA NA 0.23 Negative NL 1833 NL PLSA 1833 Not applicable 52 Male Unknown Normal NA NA 0.587 Negative NL 1834 NL PLSA 1834 Not applicable 47 Male Unknown Normal NA NA 0.125 Negative NL 1835 NL PLSA 1835 Not applicable 47 Male Unknown Normal NA NA 0.117 Negative NL 1836 NL PLSA 1836 No applicable 57 Unknown Female Normal NA NA 0.125 Negative NL 1837 NL PLSA 1837 Not applicable 51 Unknown Female Normal NA NA 0.253 Negative NL 1838 NL PLSA 1838 Not applicable 52 Unknown Female Normal NA NA 0.175 Negative NL 1839 NL PLSA 1839 Not applicable 51 Unknown Female Normal NA NA 0.309 Negative NL 1840 NL PLSA 1840 Not applicable 32 Unknown female Normal NA NA 0.123 Negative NL 1841 NL PLSA 1841 Not applicable 32 Unknown female Normal NA NA 0.21 Negative NL 1842 NL PLSA 1842 Not applicable 72 Male Unknown Normal NA NA 0.305 Negative NL 1843 NL PLSA 1843 Not applicable 61 Male Unknown Normal NA NA 0.122 Negative NL 1844 NL PLSA 1844 Not applicable 61 Male Unknown Normal NA NA 0.184 Negative NL 1845 NL PLSA 1845 Not applicable 77 Male Unknown Normal NA NA 0.515 Negative NL 1846 NL PLSA 1846 No applicable 71 Male Unknown Normal NA NA 0.125 Negative NL 1847 NL PLSA 1847 Not applicable 28 Female Unknown Normal NA NA 0.122 Negative NL 18 48 NL PLSA 1848 Not applicable 44 Unknown Female Normal NA NA 0.129 Negative NL 1849 NL PLSA 1849 Not applicable 61 Unknown Female Normal NA NA 0.125 Negative NL 1850 NL PLSA 1850 Not applicable 58 Male Unknown Normal NA NA 0.21 Negative NL 1851 NL PLSA 1851 No applicable 51 Male Unknown Normal NA NA 0.125 Negative NL 1852 NL PLSA 1852 Not applicable 28 Male Unknown Normal NA NA 0.166 Negative NL 1853 NL PLSA 1853 Not applicable 62 Male Unknown Normal NA NA 0.115 Negative NL 1854 NL PLSA 1854 Not applicable 64 Male Unknown Normal NA NA 0.125 Negative NL 1855 NL PLSA 1855 Not applicable 61 Unknown Female Normal NA NA 0.125 Negative NL 1856 NL PLSA 1856 Not applicable 61 Unknown Female Normal NA NA 0.91 Negative NL 1857 NL PLSA 1857 Not applicable 64 Male Unknown Normal NA NA 0.177 Negative NL 1858 NL PLSA 1858 Not applicable 51 Unknown Female Normal NA NA 0.234 Negative NL 1859 NL PLSA 1859 Not applicable 62 Unknown Female Normal NA NA 0.325 Negative NL 1860 NL PLSA 1860 Not applicable 58 Unknown Female Normal NA NA 0.803 Negative NL 1861 NL PLSA 1861 Not applicable 51 Unknown Female Normal NA NA 0.116 Negative NL 1862 NL PLSA 1862 Not applicable 51 Unknown Female Normal NA NA 0.125 Negative NL 1863 NL PLSA 1863 Not applicable 63 Unknown Female Normal NA NA 0.116 Negative NL 1864 NL PLSA 1864 Not applicable 25 Unknown Female Normal NA NA 0.177 Negative NL 1865 NL PLSA 1865 Not applicable 62 Unknown Female Normal NA NA 0.116 Negative NL 1866 NL PLSA 1866 Not applicable 55 Unknown Female Normal NA NA 0.324 Negative

NL 1867 NL PLSA 1867 Not applicable 73 Unknown Female Normal NA NA 0.122 Negative NL 1868 NL PLSA 1868 Not applicable 38 Male Unknown Normal NA NA 0.125 Negative NL 1869 NL PLSA 1869 Not applicable 52 Unknown Female Normal NA NA 0.21 Negative NL 1870 NL PLSA 1870 Not applicable 34 Unknown Female Normal NA NA 0.123 Negative PANC 669 PANC 669 PLS 1 Not Available 82 Male Caucasian Pancreas II Adenocarcinoma 0.982 Positive PANC 670 PANC 670 PLS 1 Not Available 44 Female Caucasian Pancreas II Adenocarcinoma 0.560 Negative PANC 671 PANC 671 PLS 1 Not Available 64 Male Caucasian Pancreas II Adenocarcinoma 0.994 Positive PANC 672 PANC 672 PLS 1 Not Available 69 Male Caucasian Pancreas II Adenocarcinoma 0.982 Positive PANC 673 PANC 673 PLS 1 Not Available 61 Female Caucasian Pancreas II Adenocarcinoma 0.423 Negative PANC 674 PANC 674 PLS 1 Available Caucasian Pancreas II Adenocarcinoma 0.993 Positive PANC 675 PANC 675 PLS 1 Not Available 77 Female Caucasian iana Pancreas II Adenocarcinoma 1,000 Positive PANC 676 PANC 676 PLS 1 Not Available 65 Female Caucasian Pancreas II Adenocarcinoma 0.977 Positive PANC 677 PANC 677 PLS 1 Not Available 73 Male Caucasian Pancreas II Adenocarcinoma 1,000 Positive PANC 678 PANC 678 PLS 1 Not Available 48 Black Male Pân II Adenocarcinoma 0.463 Negative PANC 679 PANC 679 PLS 1 Not Available 53 Male Caucasian Pancreas II Adenocarcinoma 0.605 Negative PANC 680 PANC 680 PLS 1 Not Available 57 Female Caucasian Pancreas II Adenocarcinoma 0.822 Negative PANC 757 PANC 757 PLS 1 PANC 757 PT1 70 Caucasian Female Adenocarcinoma 0.231 Negative PANC 758 PANC 758 PLS 1 PANC 758 PT1 56 Male Caucasian Pancreas II Adenocarcinoma 0.689 Negative PANC 759 PANC 759 PLS 1 PANC 759 PT1 56 Female Caucasian Pancreas II Adenocarcinoma 0.832 Negative PANC 760 PANC 760 PL1 1 PANC 760 II Adenocarcinoma 0.872 Positive PANC 761 PANC 761 PLS 1 PANC 761 PT1 53 Male Caucasian Pancreas II Adenocarcinoma 0.904 Positive PANC 762 PANC 762 PLS 1 PANC 762 PT1 70 Caucasian Female Pancreas II Adenocarcinoma 1,000 Positive PANC 764 PANC 764 PLS 1 PANC 764 PT1 58 Male Caucasian Pancreas II Adenocarcinoma 0.989 Positive PANC 765 PANC 765 PLS 765 PANC 765 PLS Caucasian Pancreas II Adenocarcinoma 1,000 Positive PANC 766 PANC 766 PLS 1 PANC 766 PT1 72 Male Caucasian Pancreas I Adenocarcinoma 0.995 Positive PANC 767 PANC 767 PLS 1 PANC 767 PT1 70 Male Caucasian Pancreas II Adenocarcinoma 0.477 Negative PANC 768 PANC 768 PANC 768 PANC 768 PANC 768 PANC 768 PANC 768 PANC 768 Female Caucasian Pancreas II Adenocarcinoma 0.125 Negative PANC 769 PANC 769 PLS 1 PANC 769 PT1 59 Male Caucasian Pancreas II Adenocarcinoma 0.804 Negative PANCA 1001 PANCA 1001 PLS 1 Not Available 74 Male Caucasian Pancreas II Adenocarcinoma 0.980 Positive PANCA 1004 PANCA 1004 PLS Caucasian Pancreas II Adenocarcinoma 0.994 Positive PANCA 1005 PANCA 1005 PLS 1 Not Available 62 Male Cauc asiana Pancreas II Adenocarcinoma 0.993 Positive PANCA 1006 PANCA 1006 PLS 1 Not Available 78 Female Caucasian Pancreas II Adenocarcinoma 0.999 Positive PANCA 1008 PANCA 1008 PLS 1 Not Available 50 Male Caucasian Pancreas II Adenocarcinoma 0.911 Positive PANCA 1009 PANCA 1009 PLS 1 Female 66 Not Available II Adenocarcinoma 1,000 Positive PANCA 1010 PANCA 1010 PLS 1 Not Available 86 Male Caucasian Pancreas I Adenocarcinoma 0.125 Negative PANCA 1011 PANCA 1011 PLS 1 Not Available 72 Male Caucasian Pancreas II Adenocarcinoma 0.999 Positive PANCA 1012 PANCA 1012 PLS 1 Not Available 69 Black Female Pancreatic Adenocarcinoma II 0.794 Negative PANCA 1013 PANCA 1013 PLS 1 Not Available 61 Male Caucasian Pancreas III Adenocarcinoma 0.989 Positive PANCA 1014 PANCA 1014 PLS 1 Not Available 65 Male Caucasian Pancreas II Adenocarcinoma 0.997 Positive PANCA 1015 PANCA 1015 PLS 1 Not Available 72 Male Unknown Pancreas III Adenocarcinoma PANCA 10 16 PANCA 1016 PLS 1 Not Available 54 Male Unknown Pancreas I Adenocarcinoma 0.884 Positive PANCA 1018 PANCA 1018 PLS 1 Not Available 53 Female Caucasian Pancreas II Adenocarcinoma 1,000 Positive PANCA 1020 PANCA 1020 PLS 1 Not Available 56 Female Caucasian Pancreas II Adenocarcinoma PANCA 1022 PANCA 1022 1022 PLS 1 Not Available 70 Male Caucasian Pancreas II Adenocarcinoma 0.994 Positive PANCA 1024 PANCA 1024 PLS 1 Not Available 58 Female Caucasian Pancreas II Adenocarcinoma 0.117 Negative PANCA 1025 PANCA 1025 PLS 1 Not Available 79 Male Caucasian Pancreas III Adenocarcinoma 0.988 Positive PANCA 1028 PANCA 1028 1 Not Available 64 Male Caucasian Pancreas III Adenocarcinoma 0.910 Positive PANCA 1030 PANCA 1030 PLS 1 Not Available 69 Male Caucasian Pancreas II Adenocarcinoma 0.999 Positive PANCA 1031 PANCA 1031 PLS 1 Not Available 82 Female Caucasian Pancreas II Adenocarcinoma 0.978 Positive PANCA 1034 PANCA 1034 Available 63 Female Caucasia na Pancreas II Adenocarcinoma 0.969 Positive PANCA 1036 PANCA 1036 PLS 1 Not Available 69 Caucasian Pancreas II Adenocarcinoma 0.402 Negative PANCA 1037 PANCA 1037 PLS 1 Not Available 63 Female Caucasian Pancreas II Adenocarcinoma 0.857 Negative PANCA 1039 PANCA 1039 PLS 1 Not Available 68 Male Caucasian II Adenocarcinoma 0.693 Negative PANCA 1040 PANCA 1040 PLS 1 Not Available 63 Male Caucasian Pancreas II Adenocarcinoma 0.987 Positive PANCA 1042 PANCA 1042 PLS 1 Not Available 75 Male Caucasian Pancreas II Adenocarcinoma 0.50 Negative PANCA 1043 PANCA 1043 PLS 1 Not Available 63 Male Caucasian Male 0.998 Positive PANCA 1044 PANCA 1044 PLS 1 Not Available 56 Female Caucasian Pancreas II Adenocarcinoma 1,000 Positive PANCA 1047 PANCA 1047 PLS 1 Not Available 48 Male Other Pancreas II Adenocarcinoma 0.824 Negative PANCA 1048 PANCA 1048 PLS 1 Not Available 82 Caucasian Female Pancreas III Adenocarcinoma 0.929 PANCA 1050 PAN CA 1050 PLS 1 Not Available 68 Female Caucasian Pancreas II Adenocarcinoma 0.947 Positive PANCA 1051 PANCA 1051 PLS 1 Not Available 62 Male Caucasian Pancreas II Adenocarcinoma 0.993 Positive PANCA 1052 PANCA 1052 PLS 1 Not Available 78 Male Caucasian Pancreas II Adenocarcinoma PANCA 1053 PANCA 1053 PANCA 1053 PANCA 1053 PANCA 1053 PLS 1 Not Available 78 Female Caucasian Pancreas II Adenocarcinoma 0.987 Positive PANCA 1054 PANCA 1054 PLS 1 Not Available 75 Female Caucasian Pancreas II Adenocarcinoma 0.986 Positive PANCA 1055 PANCA 1055 PLS 1 Not Available 60 Male Caucasian Pancreas II Adenocarcinoma 0.122 Negative PANCA 1056 PANCA 1056 PANCA 1056 Not Available 64 Male Caucasian Pancreas II Adenocarcinoma 0.997 Positive

PANCA 1057 PANCA 1057 PLS 1 Not Available 71 Male Caucasian Pancreas II Adenocarcinoma 0.284 Negative PANCA 1058 PANCA 1058 PLS 1 Not Available 68 Female Caucasian Pancreas II Adenocarcinoma 0.957 Positive PANCA 1059 PANCA 1059 PLS 1 Not Available 55 Male Caucasian Pancreas II Adenocarcinoma 0.877 Positive PANCA 1061 PLS 1 Not Available 67 Male Caucasian Pancreas II Adenocarcinoma 0.999 Positive PANCA 1063 PANCA 1063 PLS 1 Not Available 62 Male Caucasian Pancreas II Adenocarcinoma 0.701 Negative PANCA 1149 PANCA 1149 PLS 1 PANCA 1149 PT1 67 Female Caucasian Pancreatic PANCACA 1152 0.905 Positive 1152 PLS 1 PANCA 1152 PT1 66 Male Caucasian Pancreas I Adenocarcinoma 0.690 Negative PANCA 1153 PANCA 1153 PLS 1 Male PANCAST 1153 PT1 73 Male Caucasian Pancreas II Adenocarcinoma 0.877 Positive PANCA 1155 PANCA 1155 PLS 1 PANCA 1155 PT1 79 Female Caucasian Pancreatic Adenocarcino 11 PANCA 1156 PLS 1 PANCA 1156 PT1 76 Female Cauc asiana Pancreas II Adenocarcinoma 0.224 Negative PAP 938 PAP 938 PLS 1 Not Available 76 Female Caucasian Ovary III Epithelial Carcinoma 1,000 Positive PAP 939 PAP 939 PLS 1 Not Available 75 Female Caucasian Ovary III Epithelial Carcinoma 1,000 Positive PAP 940 PAP 940 PLS 1 Not Available 37 Female Caucasian Ovary III Epithelial Carcinoma 1,000 Positive PAP 941 PAP 941 PLS 1 Not Available 46 Female Caucasian Ovary III Epithelial Carcinoma 1,000 Positive PAP 944 PAP 944 PLS 1 Not Available 59 Female Caucasian Ovary III Epithelial Carcinoma 0.998 Positive PAP 945 PAP 945 PLS 1 Not Available 47 Caucasian Female Ovary III Epithelial Carcinoma 1,000 Positive PAP 946 PAP 946 PLS 1 Not Available 48 Female Caucasian Ovary III Epithelial Carcinoma 1,000 Positive PAP 947 PAP 947 PLS 1 Not Available 71 Caucasian Female Ovary III Epithelial Carcinoma 0.999 Positive PAP 949 PAP 949 PLS 1 Not Available 81 Female Black Ovary III Epithelial carcinoma 1,000 Positive PAP 950 PAP 950 PLS 1 No Dis available 75 Female Other Ovary III Epithelial carcinoma 1,000 Positive PAP 951 PAP 951 PLS 1 Not Available 62 Female Caucasian Ovary III Epithelial carcinoma 0.836 Negative PAP 953 PAP 953 PLS 1 Not Available 48 Female Caucasian Ovary III Epithelial carcinoma 0.998 Positive PAP 954 PAP 954 PLS 1 Not Available 47 Black Female Ovary III Epithelial Carcinoma 1,000 Positive PAP 955 PAP 955 PLS 1 Not Available 53 Female Caucasian Ovary III Epithelial Carcinoma 1,000 Positive PAP 956 PAP 956 PLS 1 Not Available 77 Female Caucasian Ovary III Epithelial Carcinoma 1,000 Positive PAP 957 PAP 957 PLS 1 Not Available 54 Black Female Ovary III Epithelial Carcinoma 0.983 Positive PAP 959 PAP 959 PLS 1 Not Available 53 Female Caucasian Ovary III Epithelial Carcinoma 1,000 Positive PAP 961 PAP 961 PLS 1 Not Available 67 Black Female Ovary III Epithelial Carcinoma 1,000 Positive PAP 962 PAP 962 PLS 1 Not Available 74 Female Caucasian Ovary III Epithelial carcinoma 0.992 Positive PAP 974 PAP 974 PLS 1 Not Available 75 Caucasian Female Ovary I Epithelial Carcinoma 1,000 Positive PAPA 1330 PAPA 1330 PLS 1 Not Available 66 Female Other Ovary III Epithelial Carcinoma 1,000 Positive PAPA 1331 PAPA 1331 PLS 1 Not Available 68 Hispanic Female Ovary III Epithelial Carcinoma 1,000 Positive PAPA 1332 PAPA 1332 PLS 1 Not Available 52 Caucasian Female Ovary I Epithelial Carcinoma 0.971 Positive PAPA 1333 PAPA 1333 PLS 1 Not Available 54 Female Caucasian Ovary III Epithelial Carcinoma 0.994 Positive PAPA 1334 PAPA 1334 PLS 1 Not Available 66 Black Female Ovary III Epithelial Carcinoma 0.999 Positive PAPA 1335 PAPA 1335 PLS 1 Not Available 64 Caucasian Female Ovary III Epithelial Carcinoma 0.999 Positive PAPA 1336 PAPA 1336 PLS 1 Not Available 60 Caucasian Female Ovary I Epithelial Carcinoma 1,000 Positive PAPA 1339 PAPA 1339 PLS 1 Not Available 66 Caucasian Female Ovary I Epithelial Carcinoma 1,000 Positive PAPA 1342 PAPA 1342 PLS 1 Not Available 57 Female Caucasian Ovary III Carcino epithelial ma 1,000 Positive PAPA 1343 PAPA 1343 PLS 1 Not Available 74 Caucasian Female Ovary III Epithelial Carcinoma 1,000 Positive PAPA 1344 PAPA 1344 PLS 1 Not Available 59 Female Caucasian Ovary III Epithelial Carcinoma 0.871 Positive PAPA 1345 PAPA 1345 PLS 1 Not Available 61 Female Caucasian Ovary III Epithelial carcinoma 1,000 Positive PAPA 1346 PAPA 1346 PLS 1 Not Available 59 Female Caucasian Ovary III Epithelial carcinoma 1,000 Positive PAPA 1347 PAPA 1347 PLS 1 Not Available 47 Female Caucasian Ovary I Epithelial carcinoma 1,000 Positive PAPA 1348 PAPA 1348 PLS 1 Not Available 72 Asian Female Ovary III Epithelial carcinoma 0.999 Positive PAPA 1349 PAPA 1349 PLS 1 Not Available 64 Female Caucasian Ovary III Epithelial carcinoma 1,000 Positive PAPA 1350 PAPA 1350 PLS 1 Not Available 58 Female Other Ovary II Epithelial carcinoma 0.941 Positive PAPA 1353 PAPA 1353 PLS 1 Not Available 55 Female Caucasian Ovary I Epithelial carcinoma 0.920 Positive PAPA 1354 PAPA 1354 P LS 1 Not Available 57 Female Caucasian Ovary I Epithelial Carcinoma 0.990 Positive PAPA 1355 PAPA 1355 PLS 1 Not Available 60 Female Caucasian Ovary III Epithelial Carcinoma 0.999 Positive PAPA 1356 PAPA 1356 PLS 1 Not Available 49 Female Caucasian Ovary II Epithelial Carcinoma 1,000 Positive PAPA 1357 PAPA 1357 PLS 1 Not Available 60 Black Female Ovary III Epithelial Carcinoma 1,000 Positive

[1233] NA: Not applicable

[1234] NOS: Not specified otherwise

[1235] Previously analyzed with a different trial in Cohen et al., PNAS (2017).

[1237] Table 3. Mutations identified in plasma samples from cancer patients and healthy controls.

Concentration Mutation Frequency Mutant CancerSEEK Type AJCC Volume DNA plasma identified Pont. mutant allele fragments / mL Pont.

Patient ID # Sample ID # tumor Stage. plasma (mL) (ng / mL) in plasma * Omega frequency (%) plasma Regress.

Logist.

CRC 455 CRC 455 PLS 1 Colorectal I 5 6.08 TP53 p.K120E, c.358A> G 2.81 0.27 5.1 0.681 CRC 456 CRC 456 PLS 1 Colorectal I 4 46.01 TP53 p.S240I, c .719G> T 2.52 0.02 3.2 0.986 CRC 457 CRC 457 PLS 1 Colorectal II 4.5 6.94 TP53 p.R202C, c.604C> T 1.18 0.06 1.4 0.978 CRC 458 CRC 458 PLS 1 Colorectal II 7.5 7.15 TP53 p.R248Q, c.743G> A 1.61 0.16 3.6 0.693 CRC 459 CRC 459 PLS 1 Colorectal II 5 9.81 TP53 p.A276G, c .827C> G 1.31 0.08 2.5 0.515 CRC 460 CRC 460 PLS 1 Colorectal II 5 16.33 CTNNB1 p.S45F, c.134C> T 2.93 0.10 4.9 0.707 CRC 461 CRC 461 PLS 1 Colorectal I 7.5 8.93 TP53 p.Y220H, c.658T> C 0.96 0.03 0.9 0.117 CRC 462 CRC 462 PLS 1 Colorectal I 7.5 5.32 TP53 p.A161T, c .481G> A 0.78 0.03 0.4 0.381 CRC 463 CRC 463 PLS 1 Colorectal I 7.5 5.47 TP53 p.R273H, c.818G> A 0.88 0.09 1.5 0.892 CRC 464 CRC 464 PLS 1 Colorectal III 7.5 3.98 TP53 p.P12S, c.34C> T 0.96 0.02 0.2 0.557 CRC 465 CRC 465 PLS 1 Colorectal II 7.5 2.94 TP53 p.L264fs , c.791delT 1.77 0.06 0.5 0.698 CRC 466 CRC 466 PLS 1 Colorectal III 5 5, 56 CDKN2A p.A76T, c.226G> A 0.70 0.07 1.1 0.356 CRC 467 CRC 467 PLS 1 Colorectal III 5 13.69 TP53 p.G245S, c.733G> A 1.12 0.04 1 , 7 0.878 CRC 468 CRC 468 PLS 1 Colorectal III 5 12.99 TP53 p.E286G, c.857A> G 11.42 0.39 15.6 0.998 CRC 469 CRC 469 PLS 1 Colorectal II 7.5 4.21 TP53 p.A138T, c.412G> A 0.65 0.04 0.6 0.117 CRC 470 CRC 470 PLS 1 Colorectal II 7.5 2.79 APC p.E1306 *, c.3916G> T 5.93 0.36 3.1 0.967 CRC 471 CRC 471 PLS 1 Colorectal III 7.5 6.65 TP53 p.R196 *, c.586C> T 2.80 1.84 37.7 0.915 CRC 472 CRC 472 PLS 1 Colorectal II 5 16, 78 TP53 p.R273H, c.818G> A 0.72 0.02 1.2 0.574 CRC 473 CRC 473 PLS 1 Colorectal II 7.5 6.30 KRAS p.G13D, c.38G> A 3.40 3, 68 71.4 0.934 CRC 474 CRC 474 PLS 1 Colorectal III 5 16.36 TP53 p.R174G, c.520A> G 1.07 0.03 1.3 0.307 CRC 475 CRC 475 PLS 1 Colorectal I 7.5 8, 64 TP53 p.G262D, c.785G> A 0.93 0.02 0.5 0.750 CRC 476 CRC 476 PLS 1 Colorectal II 5 8.45 TP53 p.R249G, c.745A> G 20.05 7.25 188 .8 0.999 CRC 477 CRC 477 PLS 1 Colorectal II 5 9.41 TP53 p.R282W, c.844C> T 1.14 0.10 2.8 0.981 CRC 478 CRC 478 PLS 1 Colorectal III 5 6.79 FBXW7 p.R465C, c.1393C> T 0.63 0.04 0.8 0.439 CRC 479 CRC 479 PLS 1 Colorectal II 5 11, 88 KRAS p.G12C, c.34G> T 2.41 0.06 2.4 0.969 CRC 480 CRC 480 PLS 1 Colorectal III 5 13.30 CTNNB1 p.T41A, c.121A> G 93.61 0.76 31 , 0 0.996 CRC 481 CRC 481 PLS 1 Colorectal II 5 13.85 PIK3CA p.H1047R, c.3140A> G 3.18 0.19 8.0 0.951 CRC 482 CRC 482 PLS 1 Colorectal III 5 4.40 TP53 p. P300S, c.898C> T 1.00 0.05 0.7 0.606 CRC 483 CRC 483 PLS 1 Colorectal I 7.5 5.67 FBXW7 p.R505H, c.1514G> A 0.82 0.02 0.4 0.116 CRC 484 CRC 484 PLS 1 Colorectal I 7.5 8.04 TP53 p.R335H, c.1004G> A 1.13 0.04 1.1 0.904 CRC 486 CRC 486 PLS 1 Colorectal I 7.5 5.08 KRAS p.G12D, c.35G> A 2.07 0.06 1.0 0.973 CRC 487 CRC 487 PLS 1 Colorectal I 7.5 6.05 NRAS p.A59T, c.175G> A 0.92 0.02 0 , 4 0.117 CRC 488 CRC 488 PLS 1 Colorectal II 7.5 5.14 TP53 p.V157F, c.469G> T 4.61 0.59 9.4 0.988 CRC 489 CRC 489 PLS 1 Colorectal II 7.5 5, 77 KRAS p.Q61K, c.181C> A 4.06 0.17 3.0 0.991 CRC 490 CRC 490 PL S 1 Colorectal III 7.5 5.80 TP53 p.L383H, c.1148T> A 1.64 0.02 0.3 0.397 CRC 491 CRC 491 PLS 1 Colorectal III 7.5 13.78 FBXW7 p.R465H, c .1394G> A 1.56 0.17 7.1 0.840 CRC 492 CRC 492 PLS 1 Colorectal III 7.5 7.38 TP53 p.L348S, c.1043T> C 0.95 0.02 0.5 0.906 CRC 493 CRC 493 PLS 1 Colorectal II 7.5 7.91 TP53 p.A347T, c.1039G> A 0.96 0.02 0.4 0.739 CRC 494 CRC 494 PLS 1 Colorectal II 7.5 5.11 APC p.R1450 *, c.4348C> T 2.21 0.49 7.8 0.448 CRC 495 CRC 495 PLS 1 Colorectal I 7 10.65 TP53 p.A119T, c.355G> A 0.80 0.03 1.1 0.116 CRC 496 CRC 496 PLS 1 Colorectal I 7.5 8.00 TP53 p.V274A, c.821T> C 0.97 0.07 1.6 0.330 CRC 497 CRC 497 PLS 1 Colorectal III 7.5 10.72 FBXW7 p. R479Q, c.1436G> A 2.42 0.46 15.0 0.990 CRC 498 CRC 498 PLS 1 Colorectal III 7.5 7.27 TP53 p.R335H, c.1004G> A 1.07 0.05 1.1 0.944 CRC 499 CRC 499 PLS 1 Colorectal I 5 10.24 TP53 p.C238Y, c.713G> A 1.83 0.12 3.6 0.543 CRC 500 CRC 500 PLS 1 Colorectal I 7.5 5.79 TP53 p. P82L, c.245C> T 0.83 0.02 0.4 0.308 CRC 501 CRC 501 PLS 1 Colorectal I 7.5 5 , 04 TP53 p.P300fs, c.898delC 1.04 0.04 0.7 0.490 CRC 502 CRC 502 PLS 1 Colorectal I 7.5 9.95 TP53 p.F328S, c.983T> C 0.87 0.01 0.2 0.121 CRC 503 CRC 503 PLS 1 Colorectal I 7.5 7.15 TP53 p.R174W, c.520A> T 1.14 0.01 0.3 0.993 CRC 504 CRC 504 PLS 1 Colorectal III 7.5 14 , 14 APC p.E1309fs, c.3927delAAAGA 17.49 1.67 72.6 0.999 CRC 505 CRC 505 PLS 1 Colorectal III 7.5 7.98 TP53 p.R306 *, c.916C> T 0.85 0.03 0 , 6 0,323 CRC 506 CRC 506 PLS 1 Colorectal III 7.5 6.87 APC p.I1311fs, c.3931insA 4.40 0.19 4.0 0.680 CRC 507 CRC 507 PLS 1 Colorectal I 7.5 3.79 TP53 p.R273H, c.818G> A 0.86 0.07 0.8 0.818 CRC 508 CRC 508 PLS 1 Colorectal I 7.5 2.69 PTEN p.R130G, c.388C> G 1.32 0.05 0 , 4 0.343 CRC 509 CRC 509 PLS 1 Colorectal I 7.5 5.88 BRAF p.V600M, c.1798G> A 1.05 0.02 0.4 0.480 CRC 510 CRC 510 PLS 1 Colorectal I 7.5 8, 25 TP53 p.R202C, c.604C> T 1.00 0.04 1.1 0.300 CRC 511 CRC 511 PLS 1 Colorectal II 5 16.57 TP53 p.R202C, c.604C> T 0.94 0.05 2 , 3 0.125 CRC 512 CRC 512 PLS 1 Colorectal II 7.5 22.90 KRAS p.G12D, c. 35G> A 2.38 0.08 6.0 0.868 CRC 513 CRC 513 PLS 1 Colorectal II 7.5 24.44 TP53 p.L194H, c.581T> A 1.64 0.02 1.5 0.994 CRC 514 CRC 514 PLS 1 Colorectal II 7 7.21 KRAS p.G12V, c.35G> T 8.10 1.27 28.3 0.998

CRC 515 CRC 515 PLS 1 Colorectal II 7.5 6.14 TP53 p.A353V, c.1058C> T 1.01 0.03 0.6 0.640 CRC 516 CRC 516 PLS 1 Colorectal II 7.5 13.15 CTNNB1 p .T41A, c.121A> G 4.69 0.03 1.4 0.644 CRC 517 CRC 517 PLS 1 Colorectal II 7.5 9.14 TP53 G.7579311C> T (Junction site) 1.08 0.02 0 , 6 0.318 CRC 518 CRC 518 PLS 1 Colorectal III 7.5 10.91 TP53 p.S215fs, c.644insGTG 87.57 8.48 284.9 0.999 CRC 519 CRC 519 PLS 1 Colorectal III 7.5 7.62 TP53 p.V217M, c.649G> A 0.93 0.02 0.6 0.117 CRC 520 CRC 520 PLS 1 Colorectal III 7.5 8.21 KRAS p.G12V, c.35G> T 4.98 0.48 12 , 1 0.974 CRC 521 CRC 521 PLS 1 Colorectal III 7.5 5.23 KRAS p.A146V, c.437C> T 3.64 0.45 7.3 0.598 CRC 522 CRC 522 PLS 1 Colorectal II 7.5 11, 83 TP53 p.C176fs, c.528delC 0.77 0.02 0.7 0.868 CRC 523 CRC 523 PLS 1 Colorectal III 7.5 5.63 TP53 p.R174G, c.520A> G 1.09 0.02 0 , 4 0.451 CRC 524 CRC 524 PLS 1 Colorectal II 7.5 7.83 KRAS p.G12V, c.35G> T 4.09 0.36 8.7 0.983 CRC 525 CRC 525 PLS 1 Colorectal III 7.5 7, 88 KRAS p.G13D, c.38G> A 1.34 0.07 1.6 0.968 CRC 526 CRC 526 PLS 1 Colorectal III 7.5 10.20 AKT1 p.E17K, c.49G> A 2.60 0.10 3.0 0.521 CRC 527 CRC 527 PLS 1 Colorectal III 7.5 4.72 KRAS p.A146T , c.436G> A 3.10 2.30 33.4 0.937 CRC 528 CRC 528 PLS 1 Colorectal II 7.5 6.77 TP53 p.R273C, c.817C> T 1.25 0.04 0.8 0.338 CRC 529 CRC 529 PLS 1 Colorectal II 7.5 4.74 TP53 p.R306 *, c.916C> T 0.76 0.04 0.6 0.116 CRC 530 CRC 530 PLS 1 Colorectal II 7.5 4.99 TP53 p.M384V, c.1150A> G 0.81 0.03 0.5 0.956 CRC 531 CRC 531 PLS 1 Colorectal III 7.5 4.94 KRAS p.G12V, c.35G> T 2.72 0.14 2 , 2 0.540 INDI 001 INDI 001 PLS 1 Lung II 7.5 2.44 TP53 p.K373R, c.1118A> G 0.82 0.07 0.5 0.926 INDI 002 INDI 002 PLS 1 Lung II 7.5 0, 86 TP53 p.R202C, c.604C> T 0.54 0.09 0.2 0.621 INDI 003 INDI 003 PLS 1 Lung III 7.5 2.72 TP53 p.H178P, c.533A> C 1.69 0, 07 0.6 0.999 INDI 004 INDI 004 PLS 1 Lung I 7.5 2.43 GNAS p.R201H, c.602G> A 0.67 0.07 0.5 0.936 INDI 005 INDI 005 PLS 1 Lung I 7.5 3.15 TP53 p.R156fs, c.467delG 0.74 0.05 0.5 0.779 INDI 007 INDI 007 PLS 1 Lung II 7.5 3.24 TP5 3 p.G108S, c.322G> A 0.86 0.05 0.5 0.614 INDI 009 INDI 009 PLS 1 Lung II 7.5 2.59 TP53 p.C176F, c.527G> T 5.51 1.06 8.5 0.985 INDI 010 INDI 010 PLS 1 Lung II 7.5 3.14 PIK3CA p.A1046V, c.3137C> T 0.71 0.05 0.5 0.926 INDI 011 INDI 011 PLS 1 Lung I 7.5 1 , 88 TP53 p.R175H, c.524G> A 0.57 0.06 0.3 0.362 INDI 012 INDI 012 PLS 1 Lung I 7.5 3.06 CDKN2A p.D84V, c.251A> T 2.31 0 , 08 0.8 0.966 INDI 013 INDI 013 PLS 1 Lung III 7.5 3.27 TP53 p.I254F, c.760A> T 0.98 0.03 0.3 0.852 INDI 014 INDI 014 PLS 1 Lung II 7, 5 1.61 TP53 p.E298 *, c.892G> T 5.89 1.36 6.7 0.965 INDI 015 INDI 015 PLS 1 Lung I 7.5 5.45 TP53 p.A88D, c.263C> A 0 , 81 0.05 0.8 0.121 INDI 016 INDI 016 PLS 1 Lung III 7.5 9.56 PIK3CA p.E545D, c.1635G> T 2.38 0.14 4.1 0.990 INDI 017 INDI 017 PLS 1 Lung I 7.5 5.22 TP53 p.A86T, c.256G> A 0.50 0.07 1.2 0.397 INDI 018 INDI 018 PLS 1 Lung III 7.5 4.29 TP53 p.K372fs, c.1114delA 0 , 84 0.13 1.7 0.824 INDI 022 INDI 022 PLS 1 Lung III 7.5 3.72 TP53 p.T211fs, c.633delT 7.31 5.99 68.7 0.996 IN DI 023 INDI 023 PLS 1 Lung II 7.5 7.78 TP53 p.G266V, c.797G> T 7.61 0.90 21.7 0.998 INDI 024 INDI 024 PLS 1 Lung II 7.5 3.11 TP53 p .G244D, c.731G> A 0.44 0.06 0.6 0.606 INDI 025 INDI 025 PLS 1 Lung I 7.5 3.25 TP53 p.P151A, c.451C> G 1.79 0.16 1, 6 0.972 INDI 026 INDI 026 PLS 1 Lung I 7.5 5.75 TP53 p.A353V, c.1058C> T 0.92 0.05 0.9 0.593 INDI 027 INDI 027 PLS 1 Colorectal II 7.5 6.11 TP53 p.R175H, c.524G> A 2.79 2.49 46.8 0.981 INDI 028 INDI 028 PLS 1 Colorectal II 7.5 8.23 TP53 p.G245S, c.733G> A 2.49 1.32 33.4 0.50 INDI 029 INDI 029 PLS 1 Colorectal II 7.5 6.75 CTNNB1 p.A43T, c.127G> A 0.57 0.00 0.1 0.328 INDI 030 INDI 030 PLS 1 Colorectal II 7.5 4 , 52 TP53 p.A189V, c.566C> T 0.60 0.03 0.4 0.940 INDI 031 INDI 031 PLS 1 Colorectal II 7.5 4.39 KRAS p.G12D, c.35G> A 0.90 0 , 03 0.4 0,371 INDI 032 INDI 032 PLS 1 Colorectal III 7.5 4.38 PPP2R1A p.R183W, c.547C> T 0.63 0.05 0.717 INDI 034 INDI 034 PLS 1 Colorectal III 7, 5 14.89 TP53 p.W53 *, c.159G> A 2.65 0.07 3.0 0.997 INDI 035 INDI 035 PLS 1 Colorectal II 7.5 3.07 KRAS p.G12D, c.35G> A 3.13 0.57 5.4 0.581 INDI 036 INDI 036 PLS 1 Colorectal I 7.5 12.97 TP53 p.K372fs, c. 1114delA 1.00 0.04 1.4 0.801 INDI 037 INDI 037 PLS 1 Colorectal I 7.5 3.99 TP53 p.K372fs, c.1114delA 0.92 0.05 0.6 0.788 INDI 038 INDI 038 PLS 1 Colorectal II 7.5 3.25 KRAS p.Q61K, c.181C> A 2.12 0.08 0.8 0.499 INDI 039 INDI 039 PLS 1 Colorectal III 7.5 13.81 BRAF p.D594G, c.1781A> G 1.86 0.03 1.3 0.993 INDI 040 INDI 040 PLS 1 Colorectal I 7.5 6.86 TP53 p.C176fs, c.528delC 0.83 0.03 0.787 INDI 042 INDI 042 PLS 1 Colorectal I 7.5 4.48 KRAS p.G12D, c.35G> A 1.45 0.10 1.4 0.368 INDI 043 INDI 043 PLS 1 Colorectal II 7.5 5.53 TP53 G.7577018C> T (Junction site .) 0.57 0.03 0.5 0.997 INDI 044 INDI 044 PLS 1 Colorectal II 7.5 17.02 TP53 p.Y236fs, c.708delC 0.93 0.07 3.5 0.979 INDI 045 INDI 045 PLS 1 Colorectal III 7.5 44.22 TP53 p.R290C, c.868C> T 0.56 0.03 4.2 1,000 INDI 046 INDI 046 PLS 1 Colorectal II 7.5 37.01 CTNNB1 p.S45A, c.133T> G 0.73 0.01 0.9 0.989 INDI 047 INDI 047 PLS 1 Colorectal II 7.5 2.68 TP53 p.K372fs, c.1114delA 0.52 0.05 0.4 0.977 INDI 048 INDI 048 PLS 1 Breast II 7.5 3.91 TP53 p.T253I, c. 758C> T 0.95 0.06 0.739 INDI 049 INDI 049 PLS 1 Breast III 7.5 2.49 TP53 p.R202C, c.604C> T 0.69 0.05 0.4 0.122 INDI 050 INDI 050 PLS 1 Breast II 7.5 2.71 CDKN2A p.R58 *, c.172C> T 0.42 0.05 0.4 0.123 INDI 051 INDI 051 PLS 1 Breast III 7 4.23 TP53 p.P222S, c .664C> T 0.83 0.05 0.7 0.777 INDI 052 INDI 052 PLS 1 Breast II 7.5 3.32 TP53 p.R337C, c.1009C> T 0.37 0.07 0.8 0.886 INDI 053 INDI 053 PLS 1 Breast II 7.5 4.62 TP53 p.R175H, c.524G> A 0.63 0.05 0.8 0.121 INDI 054 INDI 054 PLS 1 Breast II 7.5 8.96 TP53 p.A159T , c.475G> A 0.86 0.04 1.0 0.121 INDI 055 INDI 055 PLS 1 Breast II 7.5 4.40 TP53 p.T125T, c.375G> A (Exon end) 0.61 0.04 0.6 0.404 INDI 056 INDI 056 PLS 1 Breast II 7.5 1.78 TP53 p.R202C, c.604C> T 0.66 0.06 0.3 0.332

INDI 057 INDI 057 PLS 1 Breast III 7.5 5.88 TP53 p.K372fs, c.1114delA 1.03 0.06 1.0 0.803 INDI 058 INDI 058 PLS 1 Breast I 7.5 2.17 TP53 p.D49N , c.145G> A 0.50 0.06 0.4 0.620 INDI 059 INDI 059 PLS 1 Breast II 7.5 6.58 TP53 p.R202C, c.604C> T 1.01 0.04 0.7 0.975 INDI 060 INDI 060 PLS 1 Breast II 7.5 37.80 BRAF p.K601E, c.1801A> G 1.30 0.01 1.5 0.993 INDI 061 INDI 061 PLS 1 Breast II 7.5 27.99 TP53 p .P47L, c.140C> T 1.04 0.03 2.2 0.961 INDI 062 INDI 062 PLS 1 Breast II 7.5 2.95 TP53 p.R306 *, c.916C> T 0.46 0.08 0 .8 0.571 INDI 063 INDI 063 PLS 1 Breast III 7.5 4.86 GNAS p.R201C, c.601C> T 0.56 0.02 0.2 0.727 INDI 064 INDI 064 PLS 1 Breast II 7.5 12, 86 TP53 p.C242Y, c.725G> A 1.69 0.06 2.5 0.938 INDI 065 INDI 065 PLS 1 Breast II 7.5 4.54 TP53 p.R290C, c.868C> T 1.00 0, 05 0.7 0.564 INDI 066 INDI 066 PLS 1 Breast III 7.5 5.54 KRAS p.V14I, c.40G> A 0.67 0.06 1.0 0.681 INDI 067 INDI 067 PLS 1 Breast III 7.5 19.07 CDKN2A p.T79I, c.236C> T 1.23 0.02 1.1 0.997 INDI 068 INDI 068 PLS 1 Breast II 7.5 9.78 TP53 p.Q104 *, c.310C> T 0, 87 0.02 0.7 0.514 INDI 069 INDI 069 PLS 1 Breast II 7.5 15.08 TP53 p.G245D, c.734G> A 1.01 0.03 1.2 0.997 INDI 070 INDI 070 PLS 1 Breast III 7 , 5 16,58 PTEN p.C136R, c.406T> C 15,54 0,75 38,2 1,000 INDI 071 INDI 071 PLS 1 Mama II 7.5 6.69 TP53 p.R202C, c.604C> T 1 , 04 0.04 0.9 0.972 INDI 072 INDI 072 PLS 1 Breast II 7.5 6.04 TP53 p.Y205C, c.614A> G 0.84 0.03 0.6 0.986 INDI 073 INDI 073 PLS 1 Breast III 7.5 4.19 TP53 p.L369P, c.1106T> C 1.14 0.09 1.2 0.861 INDI 074 INDI 074 PLS 1 Pancreas II 7.5 7.45 KRAS p.G12V, c.35G> T 3.43 0.25 5.7 1,000 INDI 075 INDI 075 PLS 1 Ovary III 7.5 2.60 TP53 p.Y220C, c.659A> G 7.16 8.47 67.8 0.955 INDI 076 INDI 076 PLS 1 Esophagus II 7.5 5.19 No detection.

NA NA NA 0.990 INDI 077 INDI 077 PLS 1 Liver II 7.5 62.35 TP53 p.K132R, c.395A> G 98.06 4.49 862.8 1,000 INDI 078 INDI 078 PLS 1 Liver II 7.5 47 , 19 TP53 p.R249S, c.747G> T 1.86 0.03 4.2 0.992 INDI 079 INDI 079 PLS 1 Liver II 7.5 44.38 TP53 p.C176F, c.527G> T 2.83 0 , 05 6.6 0.999 INDI 080 INDI 080 PLS 1 Liver I 7.5 5.76 TP53 p.H178P, c.533A> C 3.14 0.04 0.784 INDI 081 INDI 081 PLS 1 Colorectal II 7, 5 25.31 TP53 p.W53 *, c.158G> A 30.50 0.23 18.1 0.997 INDI 082 INDI 082 PLS 1 Stomach II 7.5 6.57 TP53 p.A353V, c.1058C> T 0 , 97 0.08 1.6 0.117 INDI 083 INDI 083 PLS 1 Stomach II 7.5 5.25 No detection.

NA NA NA 0.993 INDI 084 INDI 084 PLS 1 Stomach II 7.5 19.44 TP53 p.R156C, c.466C> T 0.71 0.03 2.0 0.997 INDI 085 INDI 085 PLS 1 Stomach I 7.5 2 , 27 No detec.

NA NA NA 0.940 INDI 086 INDI 086 PLS 1 Stomach I 7.5 34.92 KRAS p.A11V, c.32C> T 0.81 0.01 1.1 0.997 INDI 087 INDI 087 PLS 1 Stomach I 7.5 7 , 78 TP53 p.R379H, c.1136G> A 1.18 0.07 1.6 0.999 INDI 089 INDI 089 PLS 1 Colorectal II 7.5 20.39 KRAS p.G12A, c.35G> C 0.35 0 , 03 1.8 0.999 INDI 090 INDI 090 PLS 1 Colorectal II 7.5 28.80 TP53 p.A138T, c.412G> A 0.33 0.04 3.9 0.967 INDI 092 INDI 092 PLS 1 Stomach II 7, 5 3.04 FBXW7 p.R479 *, c.1435C> T 0.48 0.06 0.6 0.848 INDI 093 INDI 093 PLS 1 Colorectal I 7.5 7.60 TP53 p.R379H, c.1136G> A 1 , 04 0.06 1.5 0.997 INDI 094 INDI 094 PLS 1 Colorectal I 7.5 20.90 TP53 p.A353V, c.1058C> T 0.61 0.06 3.8 0.993 INDI 095 INDI 095 PLS 1 Stomach II 7.5 49.78 TP53 p.R202C, c.604C> T 0.99 0.05 7.2 0.792 INDI 096 INDI 096 PLS 1 Colorectal I 7.5 5.5 KRAS p.G13D, c.38G> A 0.60 0.02 0.4 0.652 INDI 097 INDI 097 PLS 1 Colorectal III 7.5 16.40 BRAF p.V600E, c.1799T> A 4.82 0.28 14.2 0.997 INDI 098 INDI 098 PLS 1 Colorectal II 7.5 2.78 TP53 p.R202C, c.604C> T 0.76 0.07 0.6 0.308 INDI 100 INDI 100 PLS 1 Colorectal III 7.5 9.25 TP53 p.V216M, c.646G> A 1.30 0.07 2.0 0.992 INDI 101 INDI 101 PLS 1 Colorectal III 7.5 5.72 TP53 p .H178Q, c.534C> A 1.17 0.02 0.4 0.793 INDI 102 INDI 102 PLS 1 Colorectal III 7.5 11.76 TP53 p.I50fs, c.148insC 1.13 0.04 1.4 0.977 INDI 103 INDI 103 PLS 1 Stomach II 7.5 17.85 KRAS p.A11V, c.32C> T 0.84 0.01 0.5 0.583 INDI 104 INDI 104 PLS 1 Stomach III 7.5 4.21 TP53 p.V272M , c.814G> A 2.38 0.90 11.7 0.983 INDI 107 INDI 107 PLS 1 Colorectal II 7.5 2.43 GNAS p.R201H, c.602G> A 0.54 0.03 0.2 0.975 INDI 108 INDI 108 PLS 1 Colorectal II 7.5 2.95 TP53 p.R267W, c.799C> T 4.91 16.76 152.3 0.984 INDI 109 INDI 109 PLS 1 Colorectal III 7.5 21.96 TP53 p .R248Q, c.743G> A 3.44 4.87 329.4 0.997 INDI 110 INDI 110 PLS 1 Stomach I 7.5 12.51 TP53 p.A159T, c.475G> A 0.98 0.03 1, 3 0.972 INDI 111 INDI 111 PLS 1 Stomach II 7.5 9.17 TP53 p.K372fs, c.1114delA 0.91 0.02 0.738 INDI 112 INDI 112 PLS 1 Esophagus II 7.5 30.29 TP53 p .R249S, c.747G> T 1.09 0.01 1.3 1,000 INDI 113 INDI 113 PLS 1 Colorectal II 7.5 7.73 TP53 p.K372fs, c.1114delA 1.09 0.05 1.1 0.998 INDI 116 INDI 116 PLS 1 Stomach I 7.5 39.39 TP53 p.S260Y, c.779C> A 144.55 40.92 4,964.8 1,000 INDI 119 INDI 119 PLS 1 Esophagus I 7.5 53.59 TP53 p.H179R, c.536A> G 39.32 2.17 359.0 1,000 INDI 120 INDI 120 PLS 1 Stomach III 7.5 58.27 TP53 p.R248P, c.743G> C 0.97 0.00 0.9 0.983 INDI 121 INDI 121 PLS 1 Stomach I 7.5 92.85 EGFR p. G857E, c.2570G> A 0.93 0.00 0.9 0.919 INDI 122 INDI 122 PLS 1 Colorectal II 7.5 7.61 TP53 p.K372fs, c.1114delA 0.81 0.03 0.6 0.998 INDI 123 INDI 123 PLS 1 Colorectal II 7.5 20.99 EGFR p.A864T, c.2590G> A 0.89 0.01 0.6 0.996 INDI 124 INDI 124 PLS 1 Colorectal II 7.5 20.63 KRAS p. G12D, c.35G> A 4.80 0.54 34.5 1,000 INDI 125 INDI 125 PLS 1 Esophagus II 7.5 23.56 TP53 p.L194H, c.581T> A 5.32 0.72 52.6 0.999 INDI 126 INDI 126 PLS 1 Stomach III 7.5 25.59 TP53 p.K132R, c.395A> G 7.89 0.63 49.9 0.999 INDI 127 INDI 127 PLS 1 Colorectal II 7.5 23.96 TP53 p.P219H, c.656C> A 1.17 0.01 1.0 1,000 I NDI 128 INDI 128 PLS 1 Colorectal III 7.5 6.68 CTNNB1 p.S45F, c.134C> T 4.62 0.43 8.9 1,000 INDI 129 INDI 129 PLS 1 Liver II 7.5 80.95 TP53 p .H178N, c.532C> A 0.98 0.00 0.7 0.999 INDI 130 INDI 130 PLS 1 Stomach II 7.5 9.63 TP53 p.R283C, c.847C> T 0.82 0.02 0, 7 0.982 INDI 131 INDI 131 PLS 1 Stomach II 7.5 23.69 TP53 p.R158C, c.472C> T 1.02 0.04 2.6 0.993

INDI 132 INDI 132 PLS 1 Esophagus III 7.5 29.99 TP53 p.D184G, c.551A> G 1.00 0.01 1.0 0.918 INDI 133 INDI 133 PLS 1 Stomach I 7.5 16.57 TP53 p .R248Q, c.743G> A 1.04 0.04 2.0 0.990 INDI 134 INDI 134 PLS 1 Stomach III 7.5 9.09 TP53 p.G374fs, c.1120delG 0.89 0.02 0.7 0.993 INDI 135 INDI 135 PLS 1 Stomach III 7.5 52.87 TP53 p.R249S, c.747G> T 1.75 0.04 6.2 0.999 INDI 136 INDI 136 PLS 1 Colorectal II 7.5 31.41 TP53 p .H115D, c.343C> G 1.05 0.00 0.4 0.984 INDI 137 INDI 137 PLS 1 Stomach II 7.5 23.72 TP53 p.E51G, c.152A> G 1.07 0.01 0, 8 0.998 INDI 139 INDI 139 PLS 1 Stomach III 7.5 53.26 TP53 p.R158L, c.473G> T 26.38 1.79 293.2 1,000 INDI 140 INDI 140 PLS 1 Stomach III 7.5 26.63 TP53 p.L194P, c.581T> C 0.94 0.01 0.9 0.992 INDI 141 INDI 141 PLS 1 Stomach III 7.5 5.97 TP53 p.R248Q, c.743G> A 1.11 0.04 0.7 0.965 INDI 143 INDI 143 PLS 1 Colorectal II 7.5 16.33 CTNNB1 p.S45F, c.134C> T 1.52 0.03 1.3 0.998 INDI 144 INDI 144 PLS 1 Colorectal II 7.5 54 , 08 KRAS p.G12D, c.35G> A 4.12 0.24 40.6 1,000 INDI 145 INDI 145 PLS 1 Colorectal II 7.5 12.18 TP53 p.A161T, c.481G> A 1.22 0.04 1.3 0.999 INDI 146 INDI 146 PLS 1 Colorectal II 7.5 19.37 PTEN p.A126V , c.377C> T 1.08 0.01 0.5 0.934 INDI 147 INDI 147 PLS 1 Stomach II 7.5 126.95 FBXW7 p.R505H, c.1514G> A 1.01 0.03 10.9 0.953 INDI 148 INDI 148 PLS 1 Colorectal II 7.5 40.76 TP53 p.R248W, c.742C> T 1.02 0.03 3.9 0.998 INDI 150 INDI 150 PLS 1 Colorectal II 7.5 78.23 TP53 G .7578176C> T (Junction site) 189.18 2.43 585.4 1,000 INDI 151 INDI 151 PLS 1 Stomach II 7.5 37.78 TP53 p.Y220C, c.659A> G 3.51 0.46 53 , 6 1,000 INDI 152 INDI 152 PLS 1 Colorectal I 7.5 30.66 TP53 p.L194P, c.581T> C 1.21 0.01 0.8 0.988 INDI 153 INDI 153 PLS 1 Stomach II 7.5 35, 87 TP53 p.E346G, c.1037A> G 0.86 0.02 2.0 0.369 INDI 155 INDI 155 PLS 1 Colorectal II 7.5 21.12 TP53 p.F19fs, c.55delT 1.43 0.02 1 , 0 0.836 INDI 156 INDI 156 PLS 1 Colorectal III 7.5 35.66 TP53 p.P190fs, c.570delCCT 323.25 6.15 675.5 1,000 INDI 158 INDI 158 PLS 1 Colorectal I 7.5 14.91 TP53 p.C176fs, c.528insC 1.37 0.03 1.6 0.980 INDI 159 INDI 159 PLS 1 Colorectal II 7.5 6.04 TP53 p.H214Y, c.640C> T 0.61 0.02 0.3 0.852 INDI 160 INDI 160 PLS 1 Colorectal II 7 , 5 7.01 TP53 p.A138T, c.412G> A 0.83 0.03 0.7 0.890 INDI 161 INDI 161 PLS 1 Stomach II 7.5 13.98 TP53 p.A138T, c.412G> A 0 , 98 0.03 1.5 0.920 INDI 162 INDI 162 PLS 1 Stomach III 7.5 74.08 TP53 p.A189T, c.565G> A 0.95 0.01 3.0 0.998 INDI 163 INDI 163 PLS 1 Colorectal III 7.5 6.47 TP53 p.R282W, c.844C> T 1.63 0.21 4.1 0.934 INDI 164 INDI 164 PLS 1 Colorectal I 7.5 10.85 TP53 p.K372fs, c.1114delA 1 , 00 0,03 1,1 0,467 INDI 165 INDI 165 PLS 1 Colorectal I 7.5 7.25 TP53 p.M169T, c.506T> C 1.21 0.02 0.4 0.995 INDI 167 INDI 167 PLS 1 Stomach II 7.5 56.11 TP53 p.R202C, c.604C> T 0.91 0.04 7.6 0.962 INDI 168 INDI 168 PLS 1 Stomach II 7.5 33.93 TP53 G.7578555C> G (Junction site .) 1.86 0.00 0.5 1,000 INDI 169 INDI 169 PLS 1 Colorectal III 7.5 35.18 TP53 p.G374fs, c.1120delG 1.01 0.03 3.6 0.965 INDI 170 INDI 170 PLS 1 Colorectal I 7.5 28.77 TP53 p.M384I, c.1152G> A 1.24 0.01 0.6 0.992 INDI 171 INDI 171 PLS 1 Stomach III 7.5 91.76 TP53 p.G199fs, c.597insA 3.89 0.12 33.6 1,000 INDI 172 INDI 172 PLS 1 Stomach II 7.5 70.98 TP53 p.R248Q, c.743G> A 4.20 3.23 706.9 1,000 INDI 173 INDI 173 PLS 1 Stomach I 7.5 15.73 TP53 p.S215R, c.643A> C 1.54 0.03 1.5 0.999 INDI 175 INDI 175 PLS 1 Colorectal III 7.5 12.58 CDKN2A p.D84Y, c.250G> T 1.67 0.02 0.7 1,000 INDI 176 INDI 176 PLS 1 Stomach II 7.5 36.78 TP53 G.7578555C> T (Junction site) 1.34 0.02 2.0 0.997 INDI 177 INDI 177 PLS 1 Liver III 7.5 108.83 TP53 p.R249S, c. 747G> T 1.63 0.02 7.7 1,000 INDI 178 INDI 178 PLS 1 Colorectal III 7.5 12.90 TP53 p.L348M, c.1042T> A 1.12 0.02 0.8 0.967 INDI 179 INDI 179 PLS 1 Colorectal III 7.5 14,15 TP53 p.E336G, c.1007A> G 1.40 0.02 0.7 0.998 INDI 180 INDI 180 PLS 1 Colorectal III 7.5 11.31 PTEN p.R130G, c.388C> G 1.11 0.01 0.3 0.921 INDI 182 INDI 182 PLS 1 Liver III 7.5 50.25 TP53 p.A159P, c.475G> C 4.64 0.11 17.0 1,000 INDI 183 INDI 183 PLS 1 Stomach I 7.5 43.29 TP53 p.T256I, c. 767C> T 1.54 0.02 2.6 0.999 INDI 184 INDI 184 PLS 1 Esophagus II 7.5 110.40 TP53 p.H193R, c.578A> G 3.10 0.04 14.7 0.983 INDI 185 INDI 185 PLS 1 Stomach II 7.5 44.97 TP53 p.S183fs, c.549insA 41.06 1.40 194.4 1,000 INDI 186 INDI 186 PLS 1 Stomach II 7.5 86.25 GNAS p.R201C, c. 601C> T 3.32 1.20 319.7 1,000 INDI 187 INDI 187 PLS 1 Stomach I 7.5 7.29 TP53 p.R175C, c.523C> T 0.00 0.12 2.7 0.994 INDI 188 INDI 188 PLS 1 Stomach I 7.5 44.55 TP53 p.P98T, c.292C> A 0.73 0.02 2.2 0.983 INDI 189 INDI 189 PLS 1 Esophagus II 7.5 98.02 TP53 p.V274F, c.820G> T 2.09 0.02 5.5 1,000 INDI 190 INDI 190 PLS 1 Liver III 7.5 62.58 TP53 p.R249S, c.747G> T 1.26 0.01 2.3 0.988 INDI 191 INDI 191 PLS 1 Colorectal I 7.5 0.37 No detection.

NA NA NA 0.947 INDI 192 INDI 192 PLS 1 Colorectal II 7.5 30.45 TP53 p.L43M, c.127T> A 2.11 0.01 0.5 0.992 INDI 194 INDI 194 PLS 1 Colorectal II 7.5 49 , 12 TP53 p.E349 *, c.1045G> T 1.29 0.00 0.8 0.968 INDI 195 INDI 195 PLS 1 Colorectal III 7.5 14.49 TP53 p.L369P, c.1106T> C 0.97 0.03 1.3 0.966 INDI 196 INDI 196 PLS 1 Colorectal III 7.5 12.07 TP53 p.R174W, c.520A> T 0.95 0.01 0.4 0.955 INDI 197 INDI 197 PLS 1 Colorectal II 7 , 5 4.59 KRAS p.G12S, c.34G> A 1.62 0.10 1.4 0.998 INDI 198 INDI 198 PLS 1 Stomach III 7.5 10.36 TP53 p.V274F, c.820G> T 12 , 36 1.67 53.3 1,000 INDI 199 INDI 199 PLS 1 Stomach II 7.5 10.21 TP53 p.P60Q, c.179C> A 2.37 0.02 0.8 0.992 INDI 200 INDI 200 PLS 1 Breast III 7.5 56.36 TP53 p.V216M, c.646G> A 1.76 0.02 3.1 0.936 INDI 202 INDI 202 PLS 1 Colorectal II 7.5 8.48 TP53 p.R249S, c.747G> T 1.50 0.03 0.784 INDI 203 INDI 203 PLS 1 Stomach II 7.5 7.82 TP53 p.R249S, c.747G> T 2.00 0.07 1.7 0.995 INDI 205 INDI 205 PLS 1 Liver III 7.5 71.92 CTNNB1 p.S45F, c.134C> T 2.09 0.03 7.2 0.9 97 INDI 206 INDI 206 PLS 1 Stomach III 7.5 14.92 TP53 p.R174M, c.521G> T 0.89 0.01 0.5 0.957 INDI 207 INDI 207 PLS 1 Colorectal II 7.5 15.15 TP53 p.R337C, c.1009C> T 1.03 0.03 1.3 1,000 INDI 208 INDI 208 PLS 1 Liver II 7.5 69.07 TP53 p.R249S, c.747G> T 1.90 0.03 7 0.997

INDI 209 INDI 209 PLS 1 Colorectal II 7.5 13.05 TP53 p.N288S, c.863A> G 1.58 0.03 1.3 0.992 INDI 210 INDI 210 PLS 1 Stomach III 7.5 6.85 TP53 p .C135 *, c.405C> A 4.85 1.05 22.2 0.991 INDI 211 INDI 211 PLS 1 Colorectal II 7.5 13.47 TP53 p.R175H, c.524G> A 1.76 0.25 10 , 5 0.993 INDI 212 INDI 212 PLS 1 Colorectal III 7.5 7.5.94 BRAF p.V600E, c.1799T> A 33.45 2.63 72.4 1,000 INDI 213 INDI 213 PLS 1 Colorectal III 7.5 4, 15 TP53 p.R248Q, c.743G> A 1.06 0.07 0.9 0.982 INDI 214 INDI 214 PLS 1 Stomach II 7.5 15.37 TP53 p.R283H, c.848G> A 0.94 0, 03 1.5 0.935 INDI 215 INDI 215 PLS 1 Liver II 7.5 30.64 TP53 p.R196 *, c.586C> T 1.96 0.41 38.6 0.903 INDI 216 INDI 216 PLS 1 Colorectal III 7, 5 4.36 TP53 p.M237I, c.711G> A 1.26 0.08 1.1 0.981 INDI 218 INDI 218 PLS 1 Colorectal III 7.5 6.18 AKT1 p.E17K, c.49G> A 1, 71 0.06 1.1 0.900 INDI 219 INDI 219 PLS 1 Liver III 7.5 51.72 KRAS p.G12C, c.34G> T 7.72 0.57 90.9 0.994 INDI 220 INDI 220 PLS 1 Lung I 7.5 19.37 TP53 p.R273C, c.817C> T 1.82 0.17 10.0 0.982 INDI 221 IN DI 221 PLS 1 Colorectal III 7.5 4.60 NRAS p.G12D, c.35G> A 26.32 5.19 73.6 1,000 INDI 222 INDI 222 PLS 1 Colorectal II 7.5 29.31 TP53 p.G266E , c.797G> A 62.15 7.25 654.1 1,000 INDI 223 INDI 223 PLS 1 Colorectal I 7.5 12.55 TP53 p.L308Q, c.923T> A 1.07 0.01 0.3 0.501 INDI 224 INDI 224 PLS 1 Lung III 7.5 48.72 TP53 p.H179R, c.536A> G 4.74 0.20 30.3 1,000 INDI 225 INDI 225 PLS 1 Colorectal I 7.5 70.16 PIK3CA p .E542K, c.1624G> A 1.78 0.01 3.2 0.997 INDI 226 INDI 226 PLS 1 Breast III 7.5 1.91 TP53 p.A138T, c.412G> A 0.52 0.08 0, 4 0.117 INDI 227 INDI 227 PLS 1 Lung II 7.5 3.40 FBXW7 p.E471fs, c.1412insA 1.05 0.08 0.9 0.919 INDI 228 INDI 228 PLS 1 Breast II 7.5 6.28 TP53 p .R306Q, c.917G> A 0.81 0.07 1.3 0.293 INDI 229 INDI 229 PLS 1 Colorectal II 7.5 5.15 PPP2R1A p.R182W, c.544C> T 0.88 0.03 0, 5 0.968 INDI 230 INDI 230 PLS 1 Colorectal I 7.5 5.16 TP53 p.R273H, c.818G> A 1.16 0.09 1.4 0.984 INDI 231 INDI 231 PLS 1 Lung III 7.5 2.04 KRAS p.G12D, c.35G> A 24.34 9.38 59.0 0.999 INDI 233 INDI 233 PLS 1 Co lorretal I 7.5 6.54 TP53 p.G154V, c.461G> T 43.39 7.70 155.0 1,000 INDI 234 INDI 234 PLS 1 Colorectal I 7.5 7.55 TP53 p.R175H, c.524G > A 0.76 0.02 0.5 0.125 INDI 236 INDI 236 PLS 1 Stomach II 7.5 8.20 KRAS p.D57N, c.169G> A 1.00 0.07 1.7 0.117 INDI 237 INDI 237 PLS 1 Breast I 7.5 2.40 TP53 p.R202C, c.604C> T 0.70 0.11 0.8 0.434 INDI 238 INDI 238 PLS 1 Liver II 7.5 13.20 TP53 p.R249S, c .747G> T 3.85 0.93 37.6 0.999 INDI 239 INDI 239 PLS 1 Lung III 7.5 3.20 TP53 p.R202C, c.604C> T 0.92 0.05 0.5 0.987 INDI 241 INDI 241 PLS 1 Stomach III 7.5 16.09 CDKN2A p.A76T, c.226G> A 0.97 0.03 1.3 0.370 INDI 243 INDI 243 PLS 1 Colorectal I 7.5 6.33 APC p.E1309fs , c.3927delAAAGA 39.68 2.96 57.7 0.982 INDI 244 INDI 244 PLS 1 Colorectal III 7.5 4.06 KRAS p.G12C, c.34G> T 5.17 0.65 8.2 0.908 INDI 245 INDI 245 PLS 1 Colorectal III 7.5 4.36 KRAS p.Q61K, c.181C> A 7.71 1.19 16.0 0.998 INDI 246 INDI 246 PLS 1 Lung II 7.5 0.50 KRAS p.G13D , c.38G> A 0.00 0.07 0.1 0.314 INDI 247 INDI 247 PLS 1 Lung I 7.5 4.09 TP53 p.V216M, c.646G> A 2.03 0.26 3.2 0.972 INDI 248 INDI 248 PLS 1 Colorectal II 7.5 2.36 KRAS p.G12D, c.35G> A 1.09 0.08 0 , 6 0.453 INDI 250 INDI 250 PLS 1 Colorectal II 7.5 6.29 TP53 p.E286K, c.856G> A 7.41 0.32 6.2 0.985 INDI 251 INDI 251 PLS 1 Colorectal II 7.5 9, 61 TP53 p.S241C, c.722C> G 2.86 0.03 0.8 0.983 INDI 252 INDI 252 PLS 1 Lung II 7.5 13.59 TP53 G.7576927C> T (Junction site) 2.96 0 , 14 5.9 0.994 INDI 253 INDI 253 PLS 1 Colorectal I 7.5 5.84 GNAS p.L203P, c.608T> C 0.96 0.02 0.3 0.218 INDI 255 INDI 255 PLS 1 Colorectal II 7, 5 3.60 KRAS p.G13V, c.38G> T 7.72 0.57 6.4 0.950 INDI 256 INDI 256 PLS 1 Colorectal III 7.5 5.06 KRAS p.G12S, c.34G> A 9, 64 3.97 61.8 0.991 INDI 257 INDI 257 PLS 1 Lung III 7.5 8.12 KRAS p.G12R, c.34G> C 1.45 0.08 2.0 0.991 INDI 258 INDI 258 PLS 1 Colorectal I 7.5 13.10 CDKN2A p.A76V, c.227C> T 1.05 0.01 0.5 0.50 INDI 259 INDI 259 PLS 1 Colorectal II 7.5 11.59 TP53 p.P151H, c.452C> A 9.52 0.92 33.0 0.942 INDI 260 INDI 260 PLS 1 Stomach I 7.5 2.24 TP53 p.R181H, c.542 G> A 0.70 0.05 0.3 0.256 INDI 261 INDI 261 PLS 1 Breast II 7.5 2.23 CTNNB1 p.G38D, c.113G> A 0.57 0.01 0.1 0.123 INDI 262 INDI 262 PLS 1 Breast III 7.5 2.80 AKT1 p.E17K, c.49G> A 2.69 0.31 2.7 0.705 INDI 264 INDI 264 PLS 1 Lung III 7.5 1.46 TP53 p.H179R, c.536A> G 0.47 0.02 0.1 0.672 INDI 265 INDI 265 PLS 1 Breast II 7.5 2.55 TP53 p.G374fs, c.1120delG 0.46 0.08 0.6 0.117 INDI 266 INDI 266 PLS 1 Lung I 7.5 14.77 TP53 p.T230P, c.688A> C 1.29 0.03 1.2 0.930 INDI 267 INDI 267 PLS 1 Esophagus II 7.5 13.58 TP53 p.Y220C, c.659A> G 1.97 0.11 4.8 0.767 INDI 268 INDI 268 PLS 1 Colorectal III 7.5 6.66 NRAS p.Q61R, c.182A> G 7.27 3.34 68.5 0.995 INDI 269 INDI 269 PLS 1 Lung III 7.5 11.25 CDKN2A p.V51L, c.151G> C (In. exon) 0.99 0.01 0.3 0.618 INDI 270 INDI 270 PLS 1 Breast II 7.5 1.77 TP53 p.R280G, c.838A> G 0.00 0.10 0.6 0.125 INDI 271 INDI 271 PLS 1 Colorectal III 7.5 8.04 PPP2R1A p.R182W, c.544C> T 0.99 0.06 1.5 0.609 INDI 273 INDI 273 PLS 1 Lung I 7.5 3.62 TP53 p.G266 *, c.796G> T 1.74 0.28 3.1 0.998 INDI 274 INDI 274 PLS 1 Breast II 7.5 2.59 TP53 p.R283C, c.847C> T 0.00 0.10 0.8 0.125 INDI 275 INDI 275 PLS 1 Breast II 7.5 2.61 TP53 p.R282W, c.844C> T 1.27 0.14 1.1 0.335 INDI 276 INDI 276 PLS 1 Liver II 7 22.94 TP53 p.V157F, c.469G> T 74.98 5.95 420.0 1,000 INDI 277 INDI 277 PLS 1 Breast II 7.5 6.39 TP53 p.E198 *, c.592G> T 0.72 0.04 0.9 0.116 INDI 278 INDI 278 PLS 1 Ovary III 7.5 10.27 TP53 p.R273H, c.818G> A 3.76 11.68 369.5 0.984 INDI 279 INDI 279 PLS 1 Colorectal II 7.5 4.79 TP53 p .K372fs, c.1114delA 1.07 0.07 1.1 0.967 INDI 280 INDI 280 PLS 1 Breast I 7.5 3.42 TP53 p.P36S, c.106C> T 0.54 0.11 1.2 0.347 INDI 281 INDI 281 PLS 1 Colorectal I 7.5 4.49 TP53 p.E298 *, c.892G> T 0.80 0.01 0.2 0.724 INDI 283 INDI 28 3 PLS 1 Lung I 7.5 4.72 TP53 p.R306 *, c.916C> T 1.06 0.03 0.5 0.593 INDI 284 INDI 284 PLS 1 Colorectal II 7.5 17.11 APC p.E1309fs, c .3927delAAAGA 4.74 0.12 6.4 0.918

INDI 285 INDI 285 PLS 1 Lung II 7.5 4.88 TP53 p.E339 *, c.1015G> T 5.83 1.29 19.4 0.998 INDI 286 INDI 286 PLS 1 Colorectal I 7.5 1.46 TP53 p.K372fs, c.1114delA 0.91 0.06 0.3 0.620 INDI 287 INDI 287 PLS 1 Breast III 7.5 2.11 TP53 p.S94 *, c.281C> G 9.17 1.43 9, 3 0.996 INDI 288 INDI 288 PLS 1 Lung III 7.5 5.12 TP53 p.T125T, c.375G> T (Exon end) 16.13 8.37 132.1 1,000 INDI 289 INDI 289 PLS 1 Breast II 7, 5 2.03 PTEN p.A126S, c.376G> T 2.77 0.23 1.4 0.521 INDI 290 INDI 290 PLS 1 Colorectal I 7.5 2.19 TP53 p.K372fs, c.1114delA 0.59 0 , 04 0.3 0.896 INDI 291 INDI 291 PLS 1 Lung I 7.5 7.57 TP53 p.R342 *, c.1024C> T 0.69 0.04 0.9 0.804 INDI 292 INDI 292 PLS 1 Lung III 7 , 5 8.42 TP53 p.N131S, c.392A> G 1.47 0.03 0.772 INDI 293 INDI 293 PLS 1 Colorectal II 7.5 4.02 KRAS p.G12V, c.35G> T 2 , 27 0.10 1.3 0.996 INDI 295 INDI 295 PLS 1 Colorectal II 7.5 2.17 GNAS p.R201H, c.602G> A 1.45 0.20 1.3 0.939 INDI 296 INDI 296 PLS 1 Colorectal II 7.5 4.40 TP53 p.E298K, c.892G> A 0.89 0.02 0.3 0.940 INDI 297 INDI 297 PLS 1 Breast III 7.5 3.07 TP53 p.R248Q, c.743G> A 0.83 0.14 1.3 0.896 INDI 298 INDI 298 PLS 1 Colorectal I 7.5 4.58 BRAF p.T599I, c .1796C> T 0.81 0.05 0.7 0.886 INDI 299 INDI 299 PLS 1 Colorectal I 7.5 13.08 TP53 p.G199E, c.596G> A 1.21 0.04 1.4 0.924 INDI 300 INDI 300 PLS 1 Breast III 7.5 19.40 TP53 p.R174G, c.520A> G 0.86 0.03 1.5 0.917 INDI 301 INDI 301 PLS 1 Breast II 7.5 9.18 TP53 p.S241fs , c.723delTCC 3.25 0.06 1.8 0.989 INDI 302 INDI 302 PLS 1 Colorectal I 7.5 2.74 PPP2R1A p.R182W, c.544C> T 1.05 0.09 0.8 0.960 INDI 303 INDI 303 PLS 1 Colorectal I 7.5 5.30 TP53 p.R337H, c.1010G> A 0.82 0.02 0.3 0.955 INDI 304 INDI 304 PLS 1 Colorectal I 7.5 19.49 TP53 p.E258K , c.772G> A 1.12 0.01 0.4 0.997 INDI 305 INDI 305 PLS 1 Colorectal II 7.5 20.30 TP53 p.S241fs, c.723delTCC 240.28 8.37 523.1 1,000 INDI 306 INDI 306 PLS 1 Colorectal III 7.5 3.55 APC p.E1309fs, c.3927delAAAGA 123.62 36.29 396.8 1,000 INDI 307 INDI 307 PLS 1 Colorectal III 7.5 12.09 TP53 p.R202C, c .604C> T 0.94 0.06 2.4 0.944 INDI 308 INDI 308 PLS 1 Liver II 7.5 119.55 GNAS p.R201S, c.601C> A 1.61 0.01 3.8 0.974 INDI 309 INDI 309 PLS 1 Colorectal II 7.5 20.06 TP53 p.P128L, c.383C> T 0.83 0.01 0.5 0.913 INDI 310 INDI 310 PLS 1 Colorectal II 7.5 13.05 PIK3CA p.E545A, c.1634A> C 4.41 0.70 28.2 0.999 INDI 311 INDI 311 PLS 1 Colorectal III 7.5 7.53 TP53 p.K372fs, c.1114delA 0.84 0.05 1.1 0.977 INDI 312 INDI 312 PLS 1 Breast II 7.5 3.78 TP53 p.K372fs, c.1114delA 0.80 0.05 0.6 0.751 INDI 313 INDI 313 PLS 1 Esophagus II 7.5 19.47 GNAS p.R201H, c.602G> A 0.75 0.03 1.8 0.939 INDI 314 INDI 314 PLS 1 Colorectal I 7.5 3.08 TP53 p.C176fs, c.528delC 0.67 0.02 0.2 0.982 INDI 315 INDI 315 PLS 1 Breast II 7.5 1.80 TP53 p.K372fs, c. 1114delA 0.67 0.08 0.4 0.123 INDI 316 INDI 316 PLS 1 Breast II 7.5 1.25 TP53 p.P300fs, c.898delC 0.79 0.06 0.2 0.123 INDI 317 INDI 317 PLS 1 Breast II 7.5 1.29 FBXW7 p.R465H, c.1394G> A 0.40 0.07 0.3 0.116 INDI 318 INDI 318 PLS 1 Stomach III 7.5 5.16 TP53 p.K305 *, c.913A > T 21.62 1.21 19.3 0.997 INDI 319 INDI 319 PLS 1 Stomach ago I 7.5 14.30 TP53 p.T125T, c.375G> A (Exon end) 1.49 0.12 5.3 0.374 INDI 320 INDI 320 PLS 1 Colorectal II 7.5 8.18 BRAF p.V600E , c.1799T> A 2.05 0.06 1.5 1,000 INDI 321 INDI 321 PLS 1 Colorectal III 7.5 3.54 TP53 p.R158H, c.473G> A 0.47 0.05 0.6 0.836 INDI 322 INDI 322 PLS 1 Colorectal III 7.5 10.18 KRAS p.A11T, c.31G> A 1.03 0.01 0.4 0.930 INDI 323 INDI 323 PLS 1 Breast I 7.5 1.01 TP53 p .R175H, c.524G> A 0.62 0.11 0.3 0.125 INDI 324 INDI 324 PLS 1 Lung I 7.5 2.11 TP53 p.K372fs, c.1114delA 0.48 0.12 0.8 0.877 INDI 325 INDI 325 PLS 1 Colorectal III 7.5 1.63 TP53 p.A353V, c.1058C> T 0.92 0.11 0.5 0.933 INDI 326 INDI 326 PLS 1 Lung I 7.5 1.94 CDKN2A p .H83Y, c.247C> T 0.00 0.06 0.4 0.992 INDI 327 INDI 327 PLS 1 Colorectal II 7.5 2.83 TP53 p.I195N, c.584T> A 7.58 0.52 4, 6 0.954 INDI 328 INDI 328 PLS 1 Colorectal I 7.5 4.56 TP53 p.R202C, c.604C> T 1.26 0.09 1.2 0.993 INDI 329 INDI 329 PLS 1 Colorectal III 7.5 14.29 BRAF p.V600E, c.1799T> A 89.47 10.04 442.0 1,000 INDI 331 INDI 331 PLS 1A Lung I II 7.5 2.53 KRAS p.G12C, c.34G> T 56.58 10.88 84.8 1,000 INDI 332 INDI 332 PLS 1 Breast I 7.5 2.13 CTNNB1 p.T41A, c.121A> G 1.40 0.00 0.0 0.369 INDI 333 INDI 333 PLS 1A Lung II 7.5 2.17 TP53 p.R248W, c.742C> T 2.33 1.96 13.1 0.473 INDI 334 INDI 334 PLS 1 Breast I 7.5 1.36 HRAS p.G12D, c.35G> A 0.68 0.03 0.1 0.622 INDI 335 INDI 335 PLS 1A Lung I 7.5 1.61 TP53 p.R267Q, c. 800G> A 0.43 0.08 0.4 0.929 INDI 336 INDI 336 PLS 1A Lung III 7.5 5.42 TP53 G.7578176C> T (Junction site) 12.59 0.31 5.1 0.995 INDI 337 INDI 337 PLS 1 Esophagus III 7.5 4.63 TP53 p.K372fs, c.1114delA 0.90 0.04 0.6 0.235 INDI 338 INDI 338 PLS 1 Colorectal II 7.5 9.18 KRAS p.A146T, c .436G> A 1.43 0.05 1.3 0.971 INDI 339 INDI 339 PLS 1 Breast II 7.5 1.05 TP53 p.C242Y, c.725G> A 0.65 0.13 0.4 0.121 INDI 340 INDI 340 PLS 1 Colorectal III 7.5 3.43 CTNNB1 p.S37F, c.110C> T 5.67 0.39 4.2 1,000 INDI 341 INDI 341 PLS 1 Liver II 7.5 7.88 CDKN2A p.H83Y , c.247C> T 2.20 0.15 3.6 0.998 INDI 342 INDI 342 PLS 1A Lung III 5 0.42 FBXW7 p.R479Q, c.143 6G> A 0.81 0.23 0.3 0.125 INDI 343 INDI 343 PLS 1 Colorectal III 7.5 0.91 TP53 p.G244V, c.731G> T 3.44 0.34 1.0 0.573 INDI 344 INDI 344 PLS 1 Colorectal II 7.5 1.91 KRAS p.G12S, c.34G> A 2.73 0.39 2.3 1,000 INDI 346 INDI 346 PLS 1A Lung I 6.5 1.30 TP53 p.C176F, c.527G> T 2.61 0.35 1.4 0.916 INDI 347 INDI 347 PLS 1 Esophagus I 7.5 5.20 TP53 p.L137P, c.410T> C 1.16 0.05 0.9 0.650 INDI 348 INDI 348 PLS 1 Breast II 7.5 1.92 TP53 p.E339D, c.1017G> T 0.92 0.02 0.1 0.242 INDI 350 INDI 350 PLS 1 Breast II 7.5 1.04 TP53 p. R202C, c.604C> T 0.69 0.05 0.2 0.610 INDI 352 INDI 352 PLS 1 Breast III 7.5 1.45 TP53 p.R196 *, c.586C> T 0.74 0.06 0, 3 0.122 INDI 353 INDI 353 PLS 1A Lung II 7.5 2.27 TP53 p.K132N, c.396G> T 17.68 0.24 1.6 0.998 INDI 354 INDI 354 PLS 1 Colorectal III 7.5 0.47 TP53 p .S367S, c.1101C> T (Beg. exon) 0.00 0.11 0.2 0.989 INDI 355 INDI 355 PLS 1A Lung I 7.5 1.23 GNAS p.R201H, c.602G> A 1.70 0.26 1.0 0.863

INDI 356 INDI 356 PLS 1 Colorectal I 7.5 3.14 FBXW7 p.R479Q, c.1436G> A 0.75 0.05 0.5 0.883 INDI 357 INDI 357 PLS 1A Lung I 7.5 0.12 TP53 p .R158C, c.472C> T 0.00 0.08 0.0 0.580 INDI 358 INDI 358 PLS 1A Lung II 7.5 1.31 KRAS p.G12C, c.34G> T 1.22 0.12 0, 5 0.686 INDI 359 INDI 359 PLS 1 Breast I 7.5 0.67 TP53 p.R202C, c.604C> T 1.14 0.09 0.2 0.850 INDI 360 INDI 360 PLS 1A Lung II 7.5 2.58 TP53 p.E336 *, c.1006G> T 3.22 0.59 4.7 0.985 INDI 361 INDI 361 PLS 1 Breast III 7.5 4.49 TP53 p.A161S, c.481G> T 1.14 0, 05 0.6 0.324 INDI 362 INDI 362 PLS 1 Breast II 7.5 2.62 KRAS p.G13D, c.38G> A 0.78 0.06 0.5 0.304 INDI 363 INDI 363 PLS 1 Breast I 7.5 3.14 CDKN2A p.R87Q, c.260G> A 0.60 0.02 0.2 0.121 INDI 364 INDI 364 PLS 1A Lung I 7.5 0.21 GNAS p.R201H, c.602G> A 0.00 0.06 0.0 0.856 INDI 365 INDI 365 PLS 1 Colorectal II 7.5 3.06 APC p.E1309fs, c.3927delAAAGA 5.91 0.60 5.6 0.995 INDI 366 INDI 366 PLS 1 Pancreas II 7.5 42.88 TP53 p.R249S, c.747G> T 1.19 0.01 1.3 0.998 INDI 367 INDI 367 PLS 1 Liver I 7.5 13.22 C TNNB1 p.I35S, c.104T> G 8.39 0.28 11.6 0.965 INDI 368 INDI 368 PLS 1 Breast II 7.5 3.09 TP53 p.E224 *, c.670G> T 8.24 1, 42 13.5 1,000 INDI 369 INDI 369 PLS 1A Lung I 7.5 1.41 TP53 G.7576928T> C (Junction site) 0.00 0.06 0.3 0.553 INDI 371 INDI 371 PLS 1A Lung III 7, 5 1.47 KRAS p.G12V, c.35G> T 20.54 7.71 34.8 0.998 INDI 372 INDI 372 PLS 1A Lung II 7.5 4.77 TP53 p.A138S, c.412G> T 1, 58 0.03 0.5 0.995 INDI 373 INDI 373 PLS 1A Lung I 7.5 1.42 TP53 p.G245S, c.733G> A 0.92 0.09 0.4 0.842 INDI 374 INDI 374 PLS 1 Colorectal III 7 3.53 TP53 p.A159T, c.475G> A 1.00 0.04 0.4 0.956 INDI 375 INDI 375 PLS 1 Liver I 7.5 3.58 CTNNB1 p.S33Y, c.98C> A 0, 49 0.05 0.6 0.970 INDI 376 INDI 376 PLS 1 Colorectal I 7.5 3.98 TP53 p.H178N, c.532C> A 1.03 0.02 0.3 0.298 INDI 377 INDI 377 PLS 1 Colorectal II 7.5 14.70 BRAF p.V600E, c.1799T> A 3.35 0.05 2.2 1,000 INDI 378 INDI 378 PLS 1 Colorectal I 7.5 2.49 TP53 p.V274I, c.820G> A 0.72 0.06 0.5 0.155 INDI 379 INDI 379 PLS 1 Colorectal III 7.5 3.71 TP53 p.R342L, c.1025G> T 1.07 0.02 0.2 0.312 INDI 380 INDI 380 PLS 1 Colorectal II 7.5 3.57 TP53 p.N288fs, c.862delA 4.95 0.32 3.5 0.997 INDI 381 INDI 381 PLS 1A Lung III 6.5 0.32 TP53 p.R175H, c.524G> A 0.00 0.14 0.1 0.640 INDI 382 INDI 382 PLS 1 Breast II 7.5 4.29 TP53 p.N131I, c.392A> T 2.56 0.07 0.9 0.498 INDI 383 INDI 383 PLS 1A Lung III 7.5 2.65 TP53 p.Y205C, c.614A> G 2.61 0.14 1.1 0.976 INDI 384 INDI 384 PLS 1 Pancreas II 7.5 2.35 TP53 p.E346G, c.1037A> G 0.65 0.06 0.4 0.596 INDI 385 INDI 385 PLS 1 Colorectal I 7.5 4.06 TP53 p.A161T, c.481G > A 0.71 0.04 0.5 0.629 INDI 386 INDI 386 PLS 1 Colorectal III 7.5 2.80 TP53 p.P152L, c.455C> T 0.36 0.06 0.5 0.121 INDI 387 INDI 387 PLS 1 Liver I 7.5 3.79 TP53 p.P223L, c.668C> T 1.87 0.07 0.8 0.955 INDI 388 INDI 388 PLS 1 Pancreas II 7.5 7.54 TP53 p.Y220C, c .659A> G 1.41 0.07 1.6 0.992 INDI 389 INDI 389 PLS 1A Lung III 7.5 3.20 KRAS p.G12V, c.35G> T 2.35 0.25 2.5 0.992 INDI 390 INDI 390 PLS 1 Breast II 7.5 2.37 EGFR p.G863D, c.2588G> A 0.48 0.02 0.2 0.578 INDI 391 INDI 391 PLS 1 Liver II 7.5 11.50 PIK3CA p.E545K, c.1633G> A 4.02 0.24 8.7 1,000 INDI 392 INDI 392 PLS 1A Lung I 5 3.56 TP53 p.K372fs, c. 1114delA 1.16 0.07 0.8 0.326 INDI 393 INDI 393 PLS 1 Liver II 7.5 5.89 TP53 p.L265V, c.793C> G 1.70 0.03 0.5 0.911 INDI 394 INDI 394 PLS 1A Lung II 7.5 6.92 PIK3CA p.E542K, c.1624G> A 3.31 0.10 2.1 0.960 INDI 395 INDI 395 PLS 1A Lung I 7.5 4.23 TP53 p.T125T, c. 375G> T (Exon end) 3.12 0.27 3.5 0.855 INDI 396 INDI 396 PLS 1 Colorectal I 7.5 2.41 TP53 p.A86T, c.256G> A 1.08 0.09 0.7 0.965 INDI 397 INDI 397 PLS 1 Colorectal III 7.5 7.43 PIK3CA p.H1047Q, c.3141T> G 5.54 0.33 7.6 0.987 INDI 398 INDI 398 PLS 1 Colorectal II 7.5 7.40 TP53 p.Y220H, c.658T> C 1.02 0.04 1.0 0.495 INDI 400 INDI 400 PLS 1 Colorectal II 7.5 14.64 TP53 p.R175H, c.524G> A 2.94 2.96 133 , 6 0.980 INDI 402 INDI 402 PLS 1 Breast I 7.5 1.35 TP53 p.R174K, c.521G> A 0.73 0.05 0.2 0.322 INDI 403 INDI 403 PLS 1A Lung III 7.5 3, 14 TP53 p.L369P, c.1106T> C 0.79 0.03 0.3 0.569 INDI 404 INDI 404 PLS 1 Ma ma III 7.5 1.62 KRAS p.G13D, c.38G> A 0.44 0.07 0.4 0.121 INDI 405 INDI 405 PLS 1A Lung I 7.5 2.66 TP53 p.R306Q, c.917G > A 1.00 0.04 0.3 0.829 INDI 407 INDI 407 PLS 1 Breast I 7.5 3.68 KRAS p.V14I, c.40G> A 1.03 0.06 0.7 0.300 INDI 408 INDI 408 PLS 1 Breast II 7.5 2.18 TP53 p.R202C, c.604C> T 0.60 0.04 0.3 0.117 INDI 409 INDI 409 PLS 1 Breast II 7.5 5.60 TP53 p.R174G, c .520A> G 0.85 0.03 0.5 0.238 INDI 411 INDI 411 PLS 1 Breast II 7.5 1.76 TP53 p.K372fs, c.1114delA 1.07 0.10 0.5 0.312 INDI 412 INDI 412 PLS 1 Breast II 7.5 6.66 TP53 p.K305N, c.915G> T 1.04 0.01 0.3 0.945 INDI 413 INDI 413 PLS 1 Lung I 7.5 9.03 TP53 p.A119V, c .356C> T 0.80 0.01 0.4 0.877 INDI 414 INDI 414 PLS 1 Lung II 7.5 1.93 TP53 p.A189V, c.566C> T 0.76 0.08 0.4 0.620 INDI 415 INDI 415 PLS 1 Lung II 7.5 3.02 TP53 p.H193N, c.577C> A 7.07 0.51 4.7 0.979 INDI 417 INDI 417 PLS 1 Lung I 7.5 7.71 TP53 p.R337P , c.1010G> C 6.40 0.34 8.2 0.883 INDI 418 INDI 418 PLS 1 Colorectal I 7.5 3.00 HRAS p.G12D, c.35G> A 0.33 0.02 0.2 0.953 INDI 41 9 INDI 419 PLS 1 Colorectal I 7.5 10.40 TP53 p.T329I, c.986C> T 0.97 0.02 0.7 0.813 INDI 421 INDI 421 PLS 1 Colorectal II 7.5 7.89 TP53 p. E343G, c.1028A> G 1.03 0.01 0.2 0.683 INDI 422 INDI 422 PLS 1 Colorectal II 7.5 5.78 TP53 p.L257P, c.770T> C 1.20 0.02 0.4 0.994 INDI 423 INDI 423 PLS 1 Colorectal II 7.5 2.97 TP53 p.K372fs, c.1114delA 0.86 0.05 0.5 0.991 INDI 424 INDI 424 PLS 1 Breast II 7.5 4.22 TP53 p. R379C, c.1135C> T 0.97 0.04 0.5 0.413 INDI 425 INDI 425 PLS 1 Breast II 7.5 11.89 TP53 p.G374fs, c.1120delG 1.21 0.05 1.9 0.690 INDI 426 INDI 426 PLS 1 Breast II 7.5 4.78 TP53 p.C176fs, c.528insC 1.50 0.05 0.8 0.666 INDI 427 INDI 427 PLS 1 Colorectal II 7.5 6.59 PIK3CA p.N1044K, c.3132T> A 1.45 0.01 0.2 1,000 INDI 428 INDI 428 PLS 1 Colorectal III 7.5 16.72 TP53 p.R202C, c.604C> T 0.96 0.03 1.5 0.774

INDI 429 INDI 429 PLS 1 Colorectal II 7.5 21.33 TP53 p.L344P, c.1031T> C 0.94 0.01 0.9 1,000 INDI 431 INDI 431 PLS 1 Colorectal II 7.5 5.54 KRAS p .A11V, c.32C> T 0.85 0.01 0.2 0.711 INDI 433 INDI 433 PLS 1 Colorectal II 7.5 43.71 TP53 p.V173G, c.518T> G 2.27 0.01 0, 7 0.985 INDI 434 INDI 434 PLS 1 Colorectal II 7.5 2.72 TP53 p.R379C, c.1135C> T 0.82 0.04 0.4 0.817 INDI 436 INDI 436 PLS 1 Colorectal III 7.5 20.92 TP53 p.T170A, c.508A> G 0.95 0.01 0.8 0.697 INDI 437 INDI 437 PLS 1 Colorectal I 7.5 36.13 TP53 p.F134fs, c.400delT 1.22 0.01 0, 8 0.950 INDI 438 INDI 438 PLS 1 Colorectal II 7.5 17.94 TP53 p.G266E, c.797G> A 1.00 0.01 0.8 0.777 INDI 439 INDI 439 PLS 1 Colorectal III 7.5 12.99 BRAF p .F595L, c.1785T> G 14.83 0.91 36.2 1,000 INDI 440 INDI 440 PLS 1 Esophagus II 7.5 6.90 TP53 p.R273H, c.818G> A 1.15 0.15 3, 1 0.996 INDI 441 INDI 441 PLS 1 Esophagus II 7.5 32.24 TP53 p.P27H, c.80C> A 0.78 0.01 0.7 0.980 INDI 442 INDI 442 PLS 1 Breast II 7.5 6.17 KRAS p.G12R, c.34G> C 1.28 0.03 0.6 0.956 INDI 443 INDI 443 PLS 1 Breast II 7.5 4.31 TP53 p.G374fs, c.1120delG 0.89 0.03 0.4 0.878 INDI 444 INDI 444 PLS 1 Colorectal II 7.5 157.48 TP53 p.H178P, c. 533A> C 1.14 0.00 2.2 0.993 INDI 445 INDI 445 PLS 1 Breast II 7.5 5.43 TP53 p.E171fs, c.513delGACGGAG 11.87 0.69 11.6 1,000 INDI 446 INDI 446 PLS 1 Esophagus II 7.5 22.51 TP53 p.R249S, c.747G> T 2.68 0.21 14.4 0.999 INDI 447 INDI 447 PLS 1 Breast III 7.5 26.17 PIK3CA p.G1049R, c. 3145G> C 209.33 21.72 1,751.3 1,000 INDI 449 INDI 449 PLS 1 Breast III 7.5 3.51 TP53 p.T125T, c.375G> A (Exon end) 1.17 0.04 0.4 0.985 INDI 450 INDI 450 PLS 1 Esophagus II 7.5 7.28 TP53 p.A347P, c.1039G> C 0.87 0.01 0.3 0.993 INDI 452 INDI 452 PLS 1 Colorectal II 7.5 13.59 TP53 p.Y234N, c.700T> A 4.61 0.19 7.8 0.999 INDI 453 INDI 453 PLS 1 Breast I 7.5 2.26 KRAS p.G13D, c.38G> A 0.94 0.05 0 , 3 0.900 INDI 454 INDI 454 PLS 1 Breast III 7.5 2.94 TP53 p.L194R, c.581T> G 2.70 0.15 1.4 0.901 INDI 455 INDI 455 PLS 1 Breast II 7.5 3, 99 CTNNB1 p.S37F, c.110C> T 1.41 0.02 0.2 0.855 INDI 456 INDI 456 PLS 1 Neck rectal II 7.5 35.05 TP53 p.L252P, c.755T> C 1.09 0.01 1.1 0.994 INDI 457 INDI 457 PLS 1 Esophagus II 7.5 35.05 TP53 p.E221 *, c. 661G> T 58.98 1.39 150.0 1,000 INDI 458 INDI 458 PLS 1 Breast III 7.5 28.49 TP53 p.N263I, c.788A> T 1.20 0.00 0.1 0.999 INDI 459 INDI 459 PLS 1 Breast II 7.5 41.57 PIK3CA p.H1047R, c.3140A> G 399.50 60.44 7,738.7 1,000 INDI 460 INDI 460 PLS 1 Breast III 7.5 2.95 TP53 p.A307T, c.919G> A (Exon end) 0.82 0.02 0.2 0.380 INDI 461 INDI 461 PLS 1 Colorectal III 7.5 8.60 TP53 p.V172F, c.514G> T 4.56 0.40 10 , 6 0.989 INDI 462 INDI 462 PLS 1 Breast III 7.5 82.66 TP53 G.7578176C> A (Junction site) 165.23 62.32 15.866.1 1,000 INDI 463 INDI 463 PLS 1 Colorectal II 7.5 11 , 05 TP53 p.R273C, c.817C> T 3.59 2.43 82.6 1,000 INDI 464 INDI 464 PLS 1 Liver III 7.5 57.74 TP53 p.K132 *, c.394A> T 12.33 0.32 56.3 1,000 INDI 465 INDI 465 PLS 1 Esophagus II 7.5 8.91 TP53 p.Q38 *, c.112C> T 2.36 0.09 2.4 1,000 INDI 466 INDI 466 PLS 1 Colorectal II 7.5 18.81 TP53 p.T155fs, c.463insA 5.88 0.19 11.1 0.998 INDI 467 IN DI 467 PLS 1 Colorectal II 7.5 36.19 TP53 p.H380Y, c.1138C> T 0.80 0.00 0.3 0.972 INDI 468 INDI 468 PLS 1 Colorectal II 7.5 55.36 TP53 p.S261N , c.782G> A (Exon end) 1.16 0.01 1.2 0.971 INDI 469 INDI 469 PLS 1 Breast III 7.5 50.88 TP53 p.P13H, c.38C> A 1.07 0.00 0.4 0.695 INDI 470 INDI 470 PLS 1 Colorectal III 7.5 8.40 TP53 p.Q100 *, c.298C> T 3.75 0.28 7.3 0.988 INDI 471 INDI 471 PLS 1 Breast III 7.5 2.75 TP53 p.R202C, c.604C> T 0.82 0.05 0.4 0.123 INDI 472 INDI 472 PLS 1 Colorectal II 7.5 6.22 TP53 p.H179R, c.536A> G 1.07 0.02 0.3 0.991 INDI 473 INDI 473 PLS 1 Breast III 7.5 4.59 TP53 p.G374fs, c.1120delG 1.05 0.04 0.6 0.880 INDI 474 INDI 474 PLS 1 Breast II 7.5 4.29 TP53 p.G374fs, c.1120delG 0.82 0.02 0.3 0.989 INDI 475 INDI 475 PLS 1 Liver III 7.5 17.95 PIK3CA p.E81K, c.241G> A 1.64 0.01 0 , 5 0.993 INDI 476 INDI 476 PLS 1 Esophagus II 7.5 10.06 TP53 p.R249S, c.747G> T 1.39 0.04 1.3 0.996 INDI 477 INDI 477 PLS 1 Colorectal III 7.5 4, 61 TP53 p.K372fs, c.1114delA 0.86 0.04 0.5 0.928 INDI 478 INDI 478 PLS 1 Breast II 7.5 4.43 TP53 p.G374fs, c.1120delG 1.17 0.04 0.6 0.982 INDI 479 INDI 479 PLS 1 Breast III 7.5 1.67 TP53 p.R175H, c.524G> A 1.28 0.22 1.1 0.824 INDI 480 INDI 480 PLS 1 Breast III 7.5 2.63 TP53 p.K372fs, c.1114delA 0.90 0.03 0.2 0.932 INDI 482 INDI 482 PLS 1 Liver II 7.5 63.26 TP53 p.R249S, c.747G> T 2.07 0.04 8.6 0.999 INDI 483 INDI 483 PLS 1 Colorectal II 7.5 3.67 TP53 p.K372fs, c.1114delA 0 , 98 0.06 0.7 0.552 INDI 484 INDI 484 PLS 1 Colorectal II 7.5 5.56 TP53 p.R273H, c.818G> A 1.18 0.16 2.7 0.528 INDI 485 INDI 485 PLS 1 Esophagus III 7.5 9.58 TP53 p.Q331 *, c.991C> T 2.21 0.06 1.7 0.448 INDI 487 INDI 487 PLS 1 Breast II 7.5 1.30 TP53 p.K351R, c.1052A > G 0.54 0.06 0.2 0.117 INDI 488 INDI 488 PLS 1 Breast III 7.5 9.97 TP53 p.P300S, c.898C> T 0.72 0.02 0.5 0.848 INDI 489 INDI 489 PLS 1 Breast II 7.5 4.09 CTNNB1 p.G34V, c.101G> T 1.19 0.02 0.2 0.943 INDI 490 INDI 490 PLS 1 Breast III 7.5 8.63 PIK3CA p.H1047R, c .3140A> G 2.90 0.30 8.1 1,000 INDI 491 INDI 491 PLS 1 Lung I 7.5 5.99 TP53 p.A138T, c.4 12G> A 0.65 0.03 0.5 0.615 INDI 492 INDI 492 PLS 1 Lung I 7.5 2.70 TP53 p.R306 *, c.916C> T 0.69 0.06 0.5 0.572 INDI 493 INDI 493 PLS 1 Breast II 7.5 1.96 TP53 p.R202C, c.604C> T 0.70 0.29 1.7 0.116 INDI 494 INDI 494 PLS 1 Lung III 7.5 4.62 TP53 p.G244A , c.731G> C 3.64 0.14 2.0 0.997 INDI 495 INDI 495 PLS 1 Lung I 7.5 4.81 TP53 p.R174G, c.520A> G 0.65 0.03 0.5 0.122 INDI 497 INDI 497 PLS 1 Lung II 7.5 3.07 TP53 p.R175H, c.524G> A 0.97 0.12 1.2 0.50 INDI 498 INDI 498 PLS 1 Colorectal II 7 4.41 TP53 p.Y234C , c.701A> G 2.63 0.50 6.8 0.524 INDI 499 INDI 499 PLS 1 Lung I 7.5 6.84 TP53 p.R267Q, c.800G> A 0.86 0.02 0.5 0.642 INDI 501 INDI 501 PLS 1 Colorectal III 7.5 4.87 TP53 p.R282Q, c.845G> A 0.97 0.05 0.8 0.125 INDI 502 INDI 502 PLS 1 Colorectal III 7.5 7.58 TP53 p .I195T, c.584T> C 2.13 0.11 2.5 0.858 INDI 503 INDI 503 PLS 1 Lung I 7.5 3.60 TP53 p.R306Q, c.917G> A 0.77 0.07 0, 8 0.923

INDI 504 INDI 504 PLS 1 Breast III 7.5 3.70 TP53 p.K372fs, c.1114delA 0.98 0.07 0.9 0.122 INDI 505 INDI 505 PLS 1 Lung II 7.5 34.64 TP53 p.R267P , c.800G> C 4.44 0.08 8.3 1,000 INDI 506 INDI 506 PLS 1 Colorectal I 7.5 25.63 TP53 p.K372fs, c.1114insA 0.95 0.01 0.4 0.947 INDI 507 INDI 507 PLS 1 Lung II 7.5 2.75 TP53 p.R273H, c.818G> A 1.55 0.61 5.2 0.991 INDI 508 INDI 508 PLS 1 Colorectal III 6 8.18 TP53 p.P47L, c .140C> T 0.79 0.04 1.0 0.122 INDI 511 INDI 511 PLS 1 Colorectal III 7.5 4.19 TP53 p.S183fs, c.549insA 3.80 0.18 2.3 0.637 INDI 512 INDI 512 PLS 1 Breast II 7.5 2.24 TP53 p.L369P, c.1106T> C 0.44 0.06 0.4 0.761 INDI 513 INDI 513 PLS 1 Lung I 7.5 7.83 TP53 p.R273H, c .818G> A 0.91 0.19 4.6 0.502 INDI 514 INDI 514 PLS 1 Stomach I 7.5 6.42 CTNNB1 p.T41A, c.121A> G 5.54 0.04 0.8 0.991 INDI 515 INDI 515 PLS 1 Colorectal III 7.5 15.27 TP53 p.K132N, c.396G> T 4.98 0.04 2.0 0.983 INDI 516 INDI 516 PLS 1 Pancreas II 7.5 11.81 KRAS p.G12R , c.34G> C 2.13 0.10 3.5 0.999 INDI 517 INDI 517 PLS 1 Breast III 7.5 2.81 TP53 p.R110H, c.329G> A 0.55 0.09 0.8 0.588 INDI 518 INDI 518 PLS 1 Colorectal III 7.5 9.71 TP53 p.N131fs, c.392delCAA 6 , 18 0.18 5.2 0.898 INDI 519 INDI 519 PLS 1 Lung I 7.5 8.63 TP53 p.R273H, c.818G> A 1.15 0.21 5.6 0.852 INDI 521 INDI 521 PLS 1 Colorectal II 7.5 16.52 TP53 G.7579311C> A (Junction site) 1.52 0.03 1.6 0.933 INDI 522 INDI 522 PLS 1 Lung I 7.5 9.02 TP53 p.R337H, c.1010G > A 0.98 0.03 0.8 0.208 INDI 523 INDI 523 PLS 1 Colorectal I 7.5 11.24 TP53 p.V272M, c.814G> A 1.50 0.05 1.6 0.997 INDI 524 INDI 524 PLS 1 Lung III 7.5 0.15 No detection.

NA NA NA 0.982 INDI 525 INDI 525 PLS 1 Colorectal I 7.5 31.87 TP53 p.C277F, c.830G> T 3.01 0.03 3.1 0.978 INDI 526 INDI 526 PLS 1 Esophagus III 7.5 2 , 93 KRAS p.D57N, c.169G> A 0.94 0.04 0.4 0.975 INDI 527 INDI 527 PLS 1 Colorectal III 7.5 5.83 TP53 p.E349 *, c.1045G> T 1.00 0.02 0.4 0.932 INDI 528 INDI 528 PLS 1 Colorectal II 7.5 4.58 FBXW7 p.E369 *, c.1105G> T 17.54 5.50 77.6 0.950 INDI 529 INDI 529 PLS 1 Colorectal I 7, 5 4.86 KRAS p.A59E, c.176C> A 2.72 0.12 1.8 0.50 INDI 530 INDI 530 PLS 1 Breast I 7.5 3.31 TP53 p.A353S, c.1057G> T 0, 38 0.05 0.5 0.421 INDI 532 INDI 532 PLS 1 Lung I 7.5 2.94 TP53 p.R273H, c.818G> A 1.00 0.09 0.8 0.813 INDI 533 INDI 533 PLS 1 Pancreas II 7.5 4.15 TP53 p.D259N, c.775G> A 0.83 0.04 0.5 0.909 INDI 534 INDI 534 PLS 1 Stomach III 7.5 3.20 TP53 p.R333H, c.998G> A 0.88 0.05 0.5 0.421 INDI 535 INDI 535 PLS 1 Lung I 7.5 6.67 TP53 p.R202C, c.604C> T 1.00 0.04 0.8 0.966 INDI 537 INDI 537 PLS 1 Ovary I 7.5 15.56 TP53 p.M44I, c.132G> A 0.81 0.03 1.3 0.644 INDI 538 INDI 538 PLS 1 Lung III 7.5 2.10 TP53 p.R273H, c.818G> A 1.34 0.21 1.4 0.959 INDI 539 INDI 539 PLS 1 Colorectal II 7.5 8.83 PTEN p.D92A, c .275A> C 8.98 0.49 13.4 0.991 INDI 540 INDI 540 PLS 1 Breast II 7.5 8.78 TP53 p.R174G, c.520A> G 1.18 0.04 1.2 0.630 INDI 541 INDI 541 PLS 1 Breast II 7.5 2.10 TP53 p.K373E, c.1117A> G 0.58 0.05 0.3 0.571 INDI 544 INDI 544 PLS 1 Colorectal III 7.5 2.48 TP53 p.G334R , c.1000G> C 185.06 52.81 403.4 1,000 INDI 545 INDI 545 PLS 1 Lung I 7.5 7.66 TP53 p.A159T, c.475G> A 1.15 0.04 1.0 0.950 INDI 546 INDI 546 PLS 1 Lung II 7.5 9.32 TP53 p.E62G, c.185A> G 1.24 0.03 1.0 0.837 INDI 547 INDI 547 PLS 1 Stomach III 7.5 5.11 CTNNB1 p .G34E, c.101G> A 0.96 0.01 0.1 0.125 INDI 548 INDI 548 PLS 1 Breast II 7.5 9.74 TP53 p.R267Q, c.800G> A 1.08 0.03 0, 8 0.951 INDI 549 INDI 549 PLS 1 Lung III 7.5 6.09 TP53 p.R337H, c.1010G> A 0.62 0.04 0.7 0.988 INDI 550 INDI 550 PLS 1 Lung III 7.5 3.43 BRAF p.V600M, c.1798G> A 0.42 0.04 0.4 0.568 INDI 551 INDI 551 PLS 1 Colorectal II 7.5 6.37 TP53 G.7 578555C> T (Junction site) 1.48 0.04 0.9 0.866 INDI 555 INDI 555 PLS 1 Colorectal II 7.5 1.44 APC p.E1309fs, c.3927delAAAGA 8.85 0.78 3.5 0.950 INDI 558 INDI 558 PLS 1 Colorectal I 7.5 8.45 TP53 p.H368Y, c.1102C> T 3.95 0.11 2.8 0.966 INDI 559 INDI 559 PLS 1 Breast III 7.5 32.11 CTNNB1 p .S45A, c.133T> G 2.36 0.02 1.5 0.847 INDI 560 INDI 560 PLS 1 Colorectal I 7.5 6.36 TP53 p.K373R, c.1118A> G 0.74 0.02 0, 3 0.379 INDI 561 INDI 561 PLS 1 Breast III 7.5 4.60 TP53 p.E286K, c.856G> A 2.31 0.11 1.6 0.828 INDI 562 INDI 562 PLS 1 Colorectal I 7.5 32.71 TP53 p.V216M, c.646G> A 1.05 0.01 0.8 0.999 INDI 564 INDI 564 PLS 1 Colorectal I 7.5 4.17 TP53 p.R158C, c.472C> T 1.17 0.04 0.5 0.955 INDI 565 INDI 565 PLS 1 Breast III 7.5 4.43 TP53 p.K372fs, c.1114delA 1.12 0.05 0.740 INDI 566 INDI 566 PLS 1 Breast II 7.5 3.30 TP53 p.L369P, c.1106T> C 0.95 0.07 0.712 INDI 567 INDI 567 PLS 1 Colorectal II 7.5 3.64 PIK3CA p.E545K, c.1633G> A 5.37 0.89 10.0 1,000 INDI 568 INDI 568 PLS 1 Breast II 7.5 3.67 TP53 p.G154S, c.460G > A 0.68 0.05 0.6 0.342 INDI 569 INDI 569 PLS 1 Breast III 7.5 6.83 TP53 p.V173L, c.517G> T 1.20 0.05 1.1 0.339 INDI 570 INDI 570 PLS 1 Colorectal II 7.5 7.65 FBXW7 p.R505H, c.1514G> A 0.96 0.03 0.8 0.986 INDI 571 INDI 571 PLS 1 Breast III 7.5 4.55 TP53 p.R306 *, c.916C> T 0.71 0.03 0.4 0.121 INDI 572 INDI 572 PLS 1 Colorectal II 7.5 12.78 TP53 p.A276V, c.827C> T 0.89 0.03 1.2 0.834 INDI 573 INDI 573 PLS 1 Colorectal I 7.5 2.80 TP53 p.R248Q, c.743G> A 0.65 0.06 0.6 0.372 INDI 574 INDI 574 PLS 1 Colorectal III 7.5 3.20 TP53 p. G245S, c.733G> A 1.66 0.17 1.6 0.966 INDI 575 INDI 575 PLS 1 Breast II 7.5 11.69 TP53 p.M384V, c.1150A> G 1.11 0.02 0.6 0.974 INDI 577 INDI 577 PLS 1 Breast I 7.5 4.14 TP53 p.T125M, c.374C> T 0.75 0.06 0.8 0.351 INDI 578 INDI 578 PLS 1 Breast I 7.5 4.61 PIK3CA p.E542K, c.1624G> A 1.08 0.06 0.8 0.811 INDI 579 INDI 579 PLS 1 Breast I 7.5 3.43 TP53 G.7579310A> G (Junction site) 0.49 0.05 0.5 0.698 INDI 581 INDI 581 PLS 1 Colorectal II 7.5 6.91 TP53 G.7579311C> A (Junction site) 4.23 1.40 29.8 0.9 98 INDI 582 INDI 582 PLS 1 Colorectal II 7.5 23.49 TP53 p.T125M, c.374C> T 1.02 0.04 2.6 0.999 INDI 583 INDI 583 PLS 1 Colorectal II 7.5 7.26 FBXW7 p.R505H, c.1514G> A 0.88 0.03 0.6 0.979 INDI 584 INDI 584 PLS 1 Colorectal II 7.5 6.62 TP53 p.K372fs, c.1114delA 1.02 0.05 1.0 0.974

INDI 585 INDI 585 PLS 1 Esophagus III 7.5 14.36 PIK3CA p.E545K, c.1633G> A 5.64 0.25 10.9 0.999 INDI 586 INDI 586 PLS 1 Liver III 7.5 78.17 PTEN p .A126V, c.377C> T 1.07 0.01 1.5 1,000 INDI 587 INDI 587 PLS 1 Colorectal II 7.5 8.07 TP53 p.G245S, c.733G> A 0.93 0.03 0, 7 0.980 INDI 588 INDI 588 PLS 1 Breast III 7.5 8.00 TP53 p.R273H, c.818G> A 0.93 0.10 2.5 0.125 INDI 589 INDI 589 PLS 1 Breast II 7.5 10.46 TP53 G.7577156C> T (Junction site) 0.95 0.01 0.2 0.117 INDI 591 INDI 591 PLS 1 Colorectal II 7.5 10.73 TP53 p.C242Y, c.725G> A 2.10 0, 14 4.6 0.999 INDI 592 INDI 592 PLS 1 Breast II 7.5 10.67 TP53 p.A161T, c.481G> A 1.07 0.03 0.8 0.291 INDI 593 INDI 593 PLS 1 Breast II 7.5 12.94 TP53 p.G262D, c.785G> A 0.93 0.01 0.3 0.988 INDI 594 INDI 594 PLS 1 Colorectal III 7.5 41.18 PIK3CA p.H1047R, c.3140A> G 1.95 0.03 4.1 0.992 INDI 595 INDI 595 PLS 1 Esophagus II 7.5 22.74 TP53 G.7578555C> T (Junction site) 2.04 0.08 5.5 0.999 INDI 596 INDI 596 PLS 1 Breast III 7.5 4.47 TP53 p.R379H, c.1136G> A 0.87 0.05 0.6 0.539 INDI 597 I NDI 597 PLS 1 Breast II 7.5 19.59 TP53 p.K372fs, c.1114delA 0.94 0.04 2.4 0.591 INDI 598 INDI 598 PLS 1 Colorectal II 7.5 7.18 TP53 p.R175H, c .524G> A 1.25 0.07 1.6 0.974 INDI 599 INDI 599 PLS 1 Colorectal II 7.5 2.62 TP53 p.A159T, c.475G> A 0.60 0.03 0.3 0.897 INDI 600 INDI 600 PLS 1 Colorectal II 7.5 11.91 TP53 p.C135Y, c.404G> A 1.18 0.02 0.9 0.813 INDI 601 INDI 601 PLS 1 Breast III 7.5 15.18 TP53 p.R213 *, c.637C> T 5.23 9.25 432.4 1,000 INDI 602 INDI 602 PLS 1 Colorectal II 7.5 3.35 TP53 p.E204 *, c.610G> T 3.05 0.33 3, 4 0.998 INDI 603 INDI 603 PLS 1 Breast III 7.5 7.75 TP53 p.K372fs, c.1114delA 0.96 0.04 1.0 0.548 INDI 604 INDI 604 PLS 1 Breast II 7.5 3.97 TP53 p .E336G, c.1007A> G 1.06 0.03 0.4 0.948 INDI 605 INDI 605 PLS 1 Colorectal II 7.5 14.92 TP53 p.R290C, c.868C> T 0.95 0.04 1, 8 0.986 INDI 606 INDI 606 PLS 1 Breast II 7.5 12.92 TP53 p.E346V, c.1037A> T 0.77 0.01 0.4 0.681 INDI 607 INDI 607 PLS 1 Breast II 7.5 6.54 TP53 p.R174G, c.520A> G 0.88 0.04 0.767 INDI 608 INDI 608 PLS 1 Colorectal II 7 , 5 11.00 TP53 p.R202C, c.604C> T 1.00 0.05 1.6 0.964 INDI 609 INDI 609 PLS 1 Colorectal II 7.5 2.81 TP53 p.T253P, c.757A> C 1 , 88 0.05 0.4 0.998 INDI 610 INDI 610 PLS 1 Colorectal II 7.5 0.99 TP53 G.7576928T> C (Junction site) 0.75 0.14 0.4 0.965 INDI 611 INDI 611 PLS 1 Colorectal II 7.5 6.21 TP53 p.A138T, c.412G> A 0.64 0.03 0.5 0.932 INDI 612 INDI 612 PLS 1 Colorectal II 7.5 7.44 TP53 p.K372fs, c.1114delA 0.97 0.03 0.7 0.774 INDI 613 INDI 613 PLS 1 Breast II 7.5 7.56 TP53 p.K372fs, c.1114delA 1.09 0.05 1.1 0.931 INDI 614 INDI 614 PLS 1 Colorectal II 7.5 21.63 NRAS p.G12D, c.35G> A 3.65 0.12 7.8 0.999 INDI 615 INDI 615 PLS 1 Colorectal II 7.5 7.18 KRAS p.D57N, c.169G> A 1.13 0.04 0.8 0.998 INDI 617 INDI 617 PLS 1 Colorectal III 7.5 1.57 TP53 p.S367S, c.1101C> T (In. exon) 0.97 0.07 0.3 0.923 INDI 618 INDI 618 PLS 1 Colorectal III 7.5 20.27 CTNNB1 p.I35N, c.104T> A 2.36 0.00 0.3 0.977 INDI 619 INDI 619 PLS 1 Mama II 7.5 2.96 GNAS p.L203P, c.608T> C 0.44 0.02 0.2 0.125 INDI 620 INDI 620 PLS 1 Colorectal II 7.5 2.84 TP53 p.Q38R, c .113A> G 0.96 0.05 0.4 0.998 INDI 621 INDI 621 PLS 1 Colorectal II 7.5 0.75 TP53 p.R249K, c.746G> A 0.00 0.11 0.3 0.910 INDI 622 INDI 622 PLS 1 Liver III 7.5 85.94 TP53 p.R249S, c.747G> T 2.45 0.06 15.7 1,000 INDI 623 INDI 623 PLS 1 Breast III 7.5 6.71 TP53 p.R273P , c.818G> C 17.70 2.09 43.1 0.998 INDI 624 INDI 624 PLS 1 Colorectal II 7.5 4.17 TP53 p.C176Y, c.527G> A 2.00 0.31 4.0 0.986 INDI 625 INDI 625 PLS 1 Colorectal II 7.5 2.73 KRAS p.G12S, c.34G> A 3.06 0.38 3.2 0.961 INDI 626 INDI 626 PLS 1 Colorectal II 7.5 11.58 KRAS p.G12D , c.35G> A 4.47 0.43 15.4 1,000 INDI 627 INDI 627 PLS 1 Breast II 5.66 9.07 TP53 p.K372fs, c.1114delA 0.69 0.04 1.1 0.927 INDI 628 INDI 628 PLS 1 Colorectal II 5.5 6.27 TP53 p.R379H, c.1136G> A 0.74 0.05 0.9 0 , 923 INDI 629 INDI 629 PLS 1 Breast I 7.26 6.14 TP53 p.A353V, c.1058C> T 0.58 0.06 1.1 0.762 INDI 630 INDI 630 PLS 1 Breast I 7.5 3.63 CDKN2A p.R58 *, c.172C> T 0.48 0.14 1.5 0.714 INDI 631 INDI 631 PLS 1 Colorectal II 5.5 13.47 TP53 p.R156C, c.466C> T 1.04 0, 07 3.0 0.998 INDI 632 INDI 632 PLS 1 Colorectal II 6.52 27.01 TP53 p.F341I, c.1021T> A 1.36 0.01 0.7 0.998 INDI 633 INDI 633 PLS 1 Colorectal II 7.5 3.87 TP53 p.I251fs, c.753delC 1.69 0.12 1.4 0.993 INDI 635 INDI 635 PLS 1 Colorectal II 6 5.70 PTEN p.R130Q, c.389G> A 2.29 0.39 6 , 9 0.991 INDI 636 INDI 636 PLS 1 Liver III 7.5 27.51 CTNNB1 p.T41A, c.121A> G 310.96 4.60 390.0 1,000 INDI 637 INDI 637 PLS 1 Breast III 7.5 5, 95 TP53 p.R273H, c.818G> A 0.43 0.12 2.2 0.877 INDI 638 INDI 638 PLS 1 Breast I 7.5 2.81 TP53 p.R248W, c.742C> T 1.45 0, 39 3.3 0.888 INDI 639 INDI 639 PLS 1 Liver II 7.5 18.63 TP53 p.R158L, c.473G> T 4.31 0.18 10.2 1,000 INDI 640 INDI 640 PLS 1 Colorectal II 7.5 5.17 TP53 p.C141Y, c.422G> A 2.72 0.66 10.5 0.998 INDI 641 INDI 641 PLS 1 Breast III 7.18 7.46 AKT1 p.E17K, c.49G> A 1.06 0.04 0.9 0.838 INDI 643 INDI 643 PLS 1 Colorectal II 5 4.63 TP53 p.G245S, c.733G> A 1.75 0.31 4.5 0.999 INDI 644 INDI 644 PLS 1 Colorectal I 6 16.82 TP53 p.K372fs, c.1114delA 0.87 0.03 1.3 0.925 INDI 645 INDI 645 PLS 1 Liver III 7 , 5 27.37 TP53 p.R249S, c.747G> T 16.21 8.37 706.0 1,000 INDI 646 INDI 646 PLS 1 Breast II 7.5 3.10 PPP2R1A p.R182W, c.544C> T 0 , 83 0,08 0,7 0,432 INDI 647 INDI 647 PLS 1 Colorectal III 7.5 0,11 No detection.

NA NA NA 0.999 INDI 648 INDI 648 PLS 1 Colorectal III 7.5 1.21 KRAS p.G12V, c.35G> T 28.57 24.26 90.6 1,000 INDI 649 INDI 649 PLS 1 Colorectal III 7.5 0 , 39 KRAS p.G12S, c.34G> A 0.00 0.07 0.1 0.999 INDI 650 INDI 650 PLS 1 Colorectal III 7.5 1.75 TP53 p.E258G, c.773A> G 1.12 0 , 05 0,3 0,857 INDI 651 INDI 651 PLS 1 Colorectal III 3 0,30 No detection.

NA NA NA 0.881 INDI 654 INDI 654 PLS 1 Colorectal II 7.5 1.08 GNAS p.R201H, c.602G> A 0.36 0.04 0.1 0.932 INDI 655 INDI 655 PLS 1 Colorectal II 7.5 2 , 01 TP53 p.R306 *, c.916C> T 0.88 0.09 0.6 0.854 INDI 656 INDI 656 PLS 1 Breast III 7.5 5.28 TP53 p.K372fs, c.1114delA 0.91 0, 11 1.7 0.207

INDI 657 INDI 657 PLS 1 Colorectal II 7.5 0.28 KRAS p.G12A, c.35G> C 2.41 1.66 1.4 1,000 INDI 658 INDI 658 PLS 1 Colorectal I 7.5 1.11 KRAS p .G13D, c.38G> A 0.51 0.11 0.4 0.997 INDI 659 INDI 659 PLS 1 Breast II 7.5 8.16 TP53 p.K139E, c.415A> G 1.10 0.05 1, 2 0.310 INDI 660 INDI 660 PLS 1 Colorectal II 7.5 6.91 TP53 p.G59S, c.175G> A 1.13 0.02 0.3 0.955 INDI 661 INDI 661 PLS 1 Liver III 7.5 88.95 TP53 p.R249S, c.747G> T 1.58 0.02 5.8 0.989 INDI 662 INDI 662 PLS 1 Breast I 7.5 6.21 TP53 p.K351N, c.1053G> T 0.92 0.06 1.2 0.614 INDI 663 INDI 663 PLS 1 Esophagus II 7.5 29.39 TP53 p.Q331 *, c.991C> T 100.09 1.39 125.9 1,000 INDI 664 INDI 664 PLS 1 Breast I 7.5 3.55 TP53 G.7579310A> G (Junction site) 0.50 0.06 0.6 0.641 INDI 665 INDI 665 PLS 1 Esophagus III 7.5 14.81 FGFR2 p.P253R, c.758C> G 51, 21 0.35 16.1 1,000 INDI 666 INDI 666 PLS 1 Breast II 7.5 3.52 TP53 p.R267W, c.799C> T 0.38 0.07 0.7 0.831 INDI 667 INDI 667 PLS 1 Breast II 7.5 2.60 TP53 p.R306 *, c.916C> T 0.00 0.07 0.6 0.326 INDI 668 INDI 668 PLS 1 Breast III 7.5 5.33 TP53 p.K372fs, c.1114delA 0.69 0.03 0.5 0.382 INDI 669 INDI 669 PLS 1 Liver III 7.5 91.32 TP53 p.R110L, c.329G> T 2.32 0, 03 8.5 0.996 INDI 670 INDI 670 PLS 1 Colorectal III 7.5 2.88 TP53 p.G117fs, c.350insGGAC 83.06 14.28 126.9 0.999 INDI 671 INDI 671 PLS 1 Lung I 7.5 2, 63 TP53 p.A353V, c.1058C> T 0.81 0.07 0.5 0.877 INDI 672 INDI 672 PLS 1 Breast II 7.5 1.41 PPP2R1A p.A184V, c.551C> T 0.41 0, 08 0.3 0.117 INDI 673 INDI 673 PLS 1 Breast II 7.5 3.75 TP53 p.R273H, c.818G> A 1.27 0.25 2.8 0.943 INDI 674 INDI 674 PLS 1 Lung I 7.5 3.31 TP53 p.R333H, c.998G> A 1.19 0.07 0.7 0.266 INDI 675 INDI 675 PLS 1 Colorectal III 7.5 4.11 TP53 p.R181P, c.542G> C 2.74 0.10 1.2 0.991 INDI 677 INDI 677 PLS 1 Colorectal II 7.5 1.88 KRAS p.G12D, c.35G> A 1.39 0.07 0.4 0.559 INDI 678 INDI 678 PLS 1 Colorectal III 7 , 5 2.03 TP53 p.E224fs, c.670insG 13.00 1.10 6.9 0.977 INDI 679 INDI 679 PLS 1 Colorectal II 7.5 0.87 TP53 p.A159T, c.475G> A 0.68 0.03 0.1 0.116 INDI 681 INDI 681 PLS 1 Lung II 7.5 3.34 CDKN2A p.R58 *, c.172C> T 0.64 0.03 0.3 0.758 INDI 682 INDI 682 PLS 1 Colorectal III 7.5 1.95 TP53 p.C238Y, c.713G> A 2.26 0.30 1.8 0.778 INDI 683 INDI 683 PLS 1 Colorectal II 7.5 2.33 TP53 p.R158C, c.472C> T 0.89 0.03 0.2 0.589 INDI 684 INDI 684 PLS 1 Liver III 7.5 4.11 TP53 p.R181S, c.541C> A 2.33 0.11 1.3 0.806 INDI 685 INDI 685 PLS 1 Colorectal III 7.5 1.59 TP53 p.K372fs, c.1114delA 0.81 0.05 0.2 0.239 INDI 686 INDI 686 PLS 1 Pancreas II 7.5 12.55 TP53 p.R273C, c.817C> T 1.02 0.03 1.2 0.984 INDI 687 INDI 687 PLS 1 Lung III 7.5 11.57 KRAS p.G12V, c.35G> T 56.40 5.16 183.9 1,000 INDI 688 INDI 688 PLS 1 Colorectal II 7.5 3.54 KRAS p.A146T, c.436G> A 1.84 0.19 2 , 0 0.908 INDI 690 INDI 690 PLS 1 Lung III 7.5 4.42 TP53 p.K373R, c.1118A> G 0.54 0.02 0.3 0.583 INDI 691 INDI 691 PLS 1 Lung I 7.5 6, 03 KRAS p.Q61H, c.183A> C 1.12 0.04 0.8 0.914 INDI 692 INDI 692 PLS 1 Colorectal III 7.5 3.61 TP53 p.C176fs, c.528delC 0.84 0.02 0 , 3 0.955 INDI 693 INDI 693 PLS 1 Lung III 7.5 8.08 TP53 p.H193Y, c.577C> T 92.56 22.33 555.6 0.998 INDI 694 INDI 694 PLS 1 Lung I 7.5 3.26 TP53 p.R273H, c.818G> A 1.03 0.19 1.9 0.958 INDI 695 INDI 695 PLS 1 Pancreas II 7 , 5 7.79 TP53 p.A84T, c.250G> A 0.67 0.03 0.6 0.378 INDI 696 INDI 696 PLS 1 Lung II 7.5 1.71 HRAS p.G13V, c.38G> T 3 , 20 0.33 1.7 0.948 INDI 697 INDI 697 PLS 1 Breast II 7.5 3.86 TP53 p.R273H, c.818G> A 1.28 0.17 2.0 0.974 INDI 698 INDI 698 PLS 1 Ovary III 7.5 0.94 TP53 p.R158C, c.472C> T 0.41 0.10 0.3 0.947 INDI 699 INDI 699 PLS 1 Pancreas II 7.5 3.47 KRAS p.G12D, c.35G> A 3.27 0.38 4.0 0.997 INDI 700 INDI 700 PLS 1 Lung III 7.5 4.48 TP53 p.E286 *, c.856G> T 2.64 0.20 2.8 0.986 INDI 701 INDI 701 PLS 1 Colorectal II 7.5 1.54 BRAF p.V600E, c.1799T> A 3.67 0.17 0.8 0.874 INDI 702 INDI 702 PLS 1 Lung II 7.5 22.55 TP53 p.E298 *, c.892G> T 4.22 0.14 9.6 0.997 INDI 703 INDI 703 PLS 1 Lung I 7.5 2.98 TP53 G.7579310A> G (Junction site) 0.69 0.02 0.2 0.656 INDI 704 INDI 704 PLS 1 Breast II 7.5 1.94 TP53 p.R273H, c.818G> A 1.71 0.40 2.4 0.395 INDI 706 INDI 706 PLS 1 Pu lon I 7.5 2.69 TP53 p.G374fs, c.1120delG 1.26 0.05 0.4 0.993 INDI 708 INDI 708 PLS 1 Colorectal III 7.5 7.20 BRAF p.L597R, c.1790T> G 6.15 0.34 7.4 0.995 INDI 709 INDI 709 PLS 1 Colorectal II 7.5 1.71 BRAF p.V600E, c.1799T> A 2.75 0.09 0.5 0.842 INDI 710 INDI 710 PLS 1 Colorectal III 7.5 4.72 PIK3CA p.Q546R, c.1637A> G 3.28 0.16 2.4 0.977 INDI 711 INDI 711 PLS 1 Breast III 7.5 2.28 PIK3CA p.E545K, c.1633G > A 4.38 0.42 2.9 0.979 INDI 712 INDI 712 PLS 1 Breast I 7.5 4.45 TP53 p.C141Y, c.422G> A 1.34 0.11 1.5 0.354 INDI 713 INDI 713 PLS 1 Breast II 7.5 6.30 TP53 p.R273H, c.818G> A 1.28 0.23 4.4 0.342 INDI 714 INDI 714 PLS 1 Breast III 7.5 3.29 TP53 p.R175H, c .524G> A 2.30 2.15 21.8 0.956 INDI 715 INDI 715 PLS 1 Breast III 7.5 11.42 TP53 p.R158C, c.472C> T 1.64 0.20 7.0 0.987 INDI 716 INDI 716 PLS 1 Colorectal III 7.5 5.36 PTEN p.A151T, c.451G> A 1.33 0.01 0.1 0.975 INDI 717 INDI 717 PLS 1 Breast I 7.5 2.38 TP53 p.R273H , c.818G> A 1.04 0.29 2.1 0.812 INDI 718 INDI 718 PLS 1 Colorectal II 7.5 3.33 TP53 p.K372fs , c.1114delA 1.04 0.04 0.5 0.829 INDI 720 INDI 720 PLS 1 Colorectal III 7.5 4.49 KRAS p.G12D, c.35G> A 2.18 0.12 1.6 0.654 INDI 721 INDI 721 PLS 1 Mama II 7.5 3.84 PTEN p.E91fs, c.271del

GAAGACCATAACCCACCAC AGC (SEQ ID NO: 891) 0.99 0.13 1.5 0.598 INDI 722 INDI 722 PLS 1 Breast I 7.5 3.73 TP53 p.P152L, c.455C> T 0.96 0.05 0 , 5 0,201 INDI 723 INDI 723 PLS 1 Ovary III 7.5 2.80 TP53 p.S215I, c.644G> T 3.13 0.69 6.0 0.993 INDI 724 INDI 724 PLS 1 Colorectal II 7.5 5, 51 KRAS p.A11V, c.32C> T 0.90 0.02 0.3 0.230 INDI 725 INDI 725 PLS 1 Colorectal II 7.5 1.64 TP53 p.G245D, c.734G> A 0.59 0, 05 0.2 0.636 INDI 726 INDI 726 PLS 1 Colorectal I 7.5 5.49 TP53 p.R248Q, c.743G> A 1.15 0.08 1.4 0.315

INDI 729 INDI 729 PLS 1 Colorectal II 7.5 3.51 TP53 p.L252fs, c.754delTCC 2.04 0.12 1.3 0.455 INDI 731 INDI 731 PLS 1 Pancreas II 7.5 1.84 TP53 p.E339K , c.1015G> A 0.49 0.03 0.1 0.449 INDI 732 INDI 732 PLS 1 Colorectal III 7.5 2.84 TP53 p.R283C, c.847C> T 1.10 0.07 0.6 0.756 INDI 734 INDI 734 PLS 1 Pancreas II 7.5 6.23 KRAS p.G12D, c.35G> A 2.95 0.38 7.3 0.938 INDI 735 INDI 735 PLS 1 Pancreas II 7.5 2.61 TP53 p .G266V, c.797G> T 7.19 2.14 17.2 0.997 INDI 736 INDI 736 PLS 1 Lung III 7.5 4.85 TP53 p.P278T, c.832C> A 3.89 0.34 5.1 0.958 INDI 737 INDI 737 PLS 1 Stomach I 7.5 5.34 TP53 p.K372fs, c.1114delA 0.86 0.05 0.9 0.944 INDI 740 INDI 740 PLS 1 Pancreas II 7.5 13.15 TP53 p.R249S , c.747G> T 1.26 0.04 1.5 0.991 INDI 741 INDI 741 PLS 1 Pancreas III 7.5 21.92 TP53 p.K132E, c.394A> G 1.30 0.01 0.8 0.995 INDI 742 INDI 742 PLS 1 Breast II 7.5 2.70 TP53 p.F109fs, c.327delC 9.01 0.40 3.4 0.826 INDI 743 INDI 743 PLS 1 Stomach II 7.5 3.45 TP53 p.T125M , c.374C> T 0.34 0.03 0.3 0.117 INDI 745 INDI 745 PLS 1 Pancreas II 7.5 11.79 TP53 p.S241F, c.722C> T 1.06 0.02 0.9 0.953 INDI 746 INDI 746 PLS 1 Colorectal III 7.5 27.04 KRAS p.A59T, c. 175G> A 1.18 0.01 0.7 0.993 INDI 747 INDI 747 PLS 1 Colorectal III 7.5 4.74 TP53 p.K372fs, c.1114delA 1.06 0.04 0.6 0.988 INDI 749 INDI 749 PLS 1 Colorectal III 7.5 3.11 TP53 p.K372fs, c.1114delA 1.02 0.06 0.6 0.900 INDI 750 INDI 750 PLS 1 Colorectal III 7.5 0.79 KRAS p.D57N, c.169G> A 0.78 0.16 0.4 0.973 INDI 751 INDI 751 PLS 1 Colorectal III 2 1.70 TP53 p.E336 *, c.1006G> T 3.10 0.88 4.68 0.982 INDI 752 INDI 752 PLS 1 Colorectal III 7.5 7.33 TP53 p.R175H, c.524G> A 1.93 0.66 14.9 0.975 INDI 753 INDI 753 PLS 1 Esophagus III 7.5 8.37 TP53 p.Y220C, c.659A > G 4.61 3.46 89.1 0.999 INDI 754 INDI 754 PLS 1 Breast III 7.5 2.75 PIK3CA p.V344M, c.1030G> A 0.56 0.08 0.7 0.255 INDI 755 INDI 755 PLS 1 Esophagus III 7.5 12.96 TP53 p.M384V, c.1150A> G 1.26 0.03 1.2 0.996 INDI 756 INDI 756 PLS 1 Breast II 7.5 10.17 TP53 p.R267W, c .799C> T 0.94 0.03 1.0 0.543 INDI 757 INDI 757 PLS 1 Breast II 7.5 3.31 TP53 p.R248Q, c.743G> A 0.38 0.04 0.4 0.117 INDI 758 INDI 758 PLS 1 Colorectal II 7.5 7.15 TP53 p.C176fs, c.528delC 1 , 21 0,03 0,7 0,987 INDI 759 INDI 759 PLS 1 Breast III 7.5 7,72 TP53 p.R175C, c.523C> T 0,77 0,05 1,1 0,117 INDI 760 INDI 760 PLS 1 Liver III 7.5 101.29 CDKN2A p.R58 *, c.172C> T 0.85 0.02 6.9 0.997 INDI 761 INDI 761 PLS 1 Colorectal II 7.5 11.84 TP53 p.L383F, c.1147C > T 1.19 0.01 0.4 0.995 INDI 762 INDI 762 PLS 1 Esophagus II 7.5 5.18 TP53 p.R282Q, c.845G> A 0.36 0.08 1.3 0.690 INDI 763 INDI 763 PLS 1 Colorectal I 7.5 4.48 TP53 p.E62G, c.185A> G 0.86 0.02 0.3 0.824 INDI 764 INDI 764 PLS 1 Colorectal II 7.5 1.44 TP53 p.E51fs, c .151delG 8.55 1.78 7.9 0.993 INDI 765 INDI 765 PLS 1 Colorectal I 7.5 33.07 BRAF p.K601E, c.1801A> G 2.88 0.02 2.0 1,000 INDI 766 INDI 766 PLS 1 Breast II 7.5 3.86 TP53 p.P190T, c.568C> A 0.78 0.07 0.9 0.125 INDI 767 INDI 767 PLS 1 Colorectal II 7.5 1.12 KRAS p.G12D, c .35G> A 1.41 0.17 0.6 0.996 INDI 768 INDI 768 PLS 1 Colorectal II 7.5 4.62 TP53 p.R15 8C, c.472C> T 0.95 0.04 0.5 0.735 INDI 769 INDI 769 PLS 1 Colorectal II 7.5 1.51 TP53 p.I251V, c.751A> G 50.33 50.29 234.0 1,000 INDI 770 INDI 770 PLS 1 Colorectal II 7.5 5.95 APC p.E1306 *, c.3916G> T 4.97 0.16 3.0 0.970 INDI 771 INDI 771 PLS 1 Liver II 7.5 23.39 CTNNB1 p.S45A, c.133T> G 3.36 0.02 1.7 0.994 INDI 772 INDI 772 PLS 1 Colorectal II 7.5 1.89 TP53 p.A159T, c.475G> A 0.85 0.04 0.3 0.819 INDI 773 INDI 773 PLS 1 Colorectal II 7.5 2.86 TP53 p.P152L, c.455C> T 1.04 0.07 0.6 0.841 INDI 774 INDI 774 PLS 1 Colorectal I 7.5 1 , 14 KRAS p.G13D, c.38G> A 1.05 0.16 0.6 0.984 INDI 775 INDI 775 PLS 1 Liver II 7.5 105.74 TP53 p.R202C, c.604C> T 1.28 0 .10 34.2 0.999 INDI 776 INDI 776 PLS 1 Esophagus II 7.5 10.35 TP53 p.R379H, c.1136G> A 0.76 0.02 0.7 0.998 INDI 777 INDI 777 PLS 1 Colorectal II 7, 5 23.48 TP53 p.D42fs, c.124insGA 3.27 0.02 1.3 0.994 INDI 778 INDI 778 PLS 1 Liver II 7.5 16.38 CTNNB1 p.I35S, c.104T> G 4.39 0 , 08 4.2 0.999 INDI 779 INDI 779 PLS 1 Colorectal II 7.5 1.88 TP53 p.E11Q, c.31G> C 99.20 50.22 291.5 1,000 INDI 780 INDI 780 PLS 1 Colorectal II 7.5 1.78 TP53 p.R273C, c.817C> T 1.64 0.27 1.5 0.946 INDI 781 INDI 781 PLS 1 Colorectal II 7.5 0.85 BRAF p.D594G, c.1781A> G 2.44 0.52 1.4 0.965 INDI 782 INDI 782 PLS 1 Colorectal III 7.5 15.27 BRAF p.V600E, c.1799T > A 36.75 2.88 135.7 1,000 INDI 783 INDI 783 PLS 1 Colorectal III 7.5 12.35 KRAS p.G13D, c.38G> A 1.57 0.18 6.8 0.680 INDI 784 INDI 784 PLS 1 Stomach I 7.5 10.44 TP53 p.R249S, c.747G> T 1.82 0.04 1.3 0.985 INDI 785 INDI 785 PLS 1 Colorectal I 7.5 5.62 TP53 p.E346D, c .1038G> T 1.06 0.11 1.9 0.302 INDI 786 INDI 786 PLS 1 Liver I 7.5 3.23 TP53 p.C135F, c.404G> T 7.05 1.16 11.6 0.978 INDI 787 INDI 787 PLS 1 Breast II 7.5 3.28 TP53 p.V157I, c.469G> A 0.61 0.07 0.8 0.121 INDI 788 INDI 788 PLS 1 Colorectal II 7.5 9.94 KRAS p.A146V , c.437C> T 1.16 0.04 1.2 0.688 INDI 789 INDI 789 PLS 1 Breast II 7.5 3.96 TP53 p.K372fs, c.1114delA 0.87 0.05 0.6 0.117 INDI 790 INDI 790 PLS 1 Breast II 7.5 1.57 CDKN2A p.R58 *, c.172C> T 0.00 0.04 0.2 0.771 INDI 791 INDI 791 PLS 1 Breast III 7.5 1.88 TP53 p.G374fs, c.1120delG 0.46 0.07 0.4 0.320 INDI 792 INDI 792 PLS 1 Stomach I 7.5 8.98 TP53 p.A159T , c.475G> A 0.75 0.02 0.6 0.377 INDI 793 INDI 793 PLS 1 Breast II 7.5 4.67 TP53 p.K372fs, c.1114delA 1.14 0.06 0.8 0.836 INDI 794 INDI 794 PLS 1 Breast II 7.5 5.64 TP53 p.R337H, c.1010G> A 0.95 0.04 0.7 0.571 INDI 795 INDI 795 PLS 1 Breast II 7.5 3.30 TP53 p.A138T , c.412G> A 0.84 0.04 0.4 0.595 INDI 797 INDI 797 PLS 1 Stomach II 7.5 5.15 TP53 p.K372fs, c.1114delA 1.11 0.06 1.0 0.955 INDI 798 INDI 798 PLS 1 Ovary II 7.5 22.29 TP53 p.Y163C, c.488A> G 52.92 7.31 502.2 1,000 INDI 799 INDI 799 PLS 1 Stomach II 7.5 5.36 CTNNB1 p.S33Y , c.98C> A 0.85 0.03 0.4 0.239 INDI 800 INDI 800 PLS 1 Liver II 7.5 3.60 CTNNB1 p.T41A, c.121A> G 5.78 0.13 1.4 0.997 INDI 801 INDI 801 PLS 1 Stomach I 7.5 10.75 TP53 p.R158C, c.472C> T 1.00 0.05 1.8 0.293

INDI 802 INDI 802 PLS 1 Pancreas II 7.5 15.09 CDKN2A p.E88 *, c.262G> T 3.80 0.09 4.0 0.987 INDI 803 INDI 803 PLS 1 Esophagus II 7.5 2.87 PPP2R1A p.R182W, c.544C> T 0.98 0.11 1.0 0.385 INDI 805 INDI 805 PLS 1 Colorectal II 7.5 6.65 TP53 p.A159T, c.475G> A 0.81 0.05 1 , 0 0.632 INDI 806 INDI 806 PLS 1 Colorectal II 7.5 4.68 TP53 p.L369M, c.1105C> A 0.91 0.04 0.5 0.661 INDI 808 INDI 808 PLS 1 Colorectal III 7.5 16, 77 TP53 p.K372fs, c.1114delA 0.98 0.03 1.7 0.601 INDI 809 INDI 809 PLS 1 Colorectal III 7.5 4.16 TP53 p.G374fs, c.1120delG 0.43 0.05 0.7 0.213 INDI 812 INDI 812 PLS 1 Ovary II 7.5 3.11 CTNNB1 p.T41A, c.121A> G 77.58 3.18 30.5 1,000 INDI 814 INDI 814 PLS 1 Stomach I 7.5 4.26 TP53 p.L369P, c.1106T> C 0.73 0.03 0.4 0.386 INDI 815 INDI 815 PLS 1 Colorectal II 7.5 8.53 BRAF p.V600E, c.1799T> A 4.77 0.22 5 , 9 0.997 INDI 816 INDI 816 PLS 1 Colorectal II 7.5 4.72 TP53 p.A276V, c.827C> T 0.43 0.07 1.1 0.336 INDI 817 INDI 817 PLS 1 Liver III 7.5 11, 67 CTNNB1 p.I35S, c.104T> G 5.12 0.11 4.0 0.990 IND I 818 INDI 818 PLS 1 Ovary III 7.5 6.53 TP53 p.G154fs, c.460delG 41.01 2.83 56.9 1,000 INDI 819 INDI 819 PLS 1 Breast III 7.5 1.52 AKT1 p.E17K , c.49G> A 8.34 6.06 28.4 0.979 INDI 821 INDI 821 PLS 1 Colorectal III 7.5 1.69 TP53 p.S241F, c.722C> T 0.69 0.05 0.3 0.776 INDI 822 INDI 822 PLS 1 Colorectal II 7.5 6.72 TP53 p.P82L, c.245C> T 0.99 0.06 1.3 0.123 INDI 823 INDI 823 PLS 1 Breast II 7.5 5.00 TP53 p .R213Q, c.638G> A 0.97 0.12 1.8 0.121 INDI 824 INDI 824 PLS 1 Esophagus II 7.5 9.40 TP53 p.R158P, c.473G> C 24.02 1.23 35, 7 0.998 INDI 825 INDI 825 PLS 1 Colorectal III 7.5 4.98 TP53 p.E62D, c.186A> T 1.06 0.03 0.5 0.511 INDI 826 INDI 826 PLS 1 Colorectal II 7.5 3.25 TP53 p.N239S, c.716A> G 2.81 0.10 1.0 0.925 INDI 827 INDI 827 PLS 1 Colorectal III 7.5 3.01 KRAS p.A146T, c.436G> A 2.05 0.34 3.1 0.956 INDI 828 INDI 828 PLS 1 Breast II 7.5 4.03 TP53 p.K372fs, c.1114delA 0.83 0.04 0.5 0.117 INDI 829 INDI 829 PLS 1 Colorectal III 7.5 4.98 CDKN2A p.R87Q, c.260G> A 0.90 0.02 0.4 0.122 INDI 830 INDI 830 PLS 0 M ama II 7.5 7.99 TP53 p.A159T, c.475G> A 1.16 0.07 1.7 0.954 INDI 831 INDI 831 PLS 1 Breast I 7.5 11.33 TP53 p.K373R, c.1118A > G 0.99 0.03 0.9 0.175 INDI 832 INDI 832 PLS 1 Breast II 7.5 9.29 TP53 p.G154S, c.460G> A 1.36 0.10 2.7 0.831 INDI 833 INDI 833 PLS 1 Breast I 7.5 5.36 FBXW7 p.R465C, c.1393C> T 1.00 0.05 0.8 0.876 INDI 835 INDI 835 PLS 1 Colorectal III 7.5 2.80 TP53 p.G374fs, c .1120delG 1.06 0.06 0.5 0.856 INDI 837 INDI 837 PLS 1 Colorectal II 7.5 6.63 TP53 p.C176fs, c.528delC 0.91 0.03 0.7 0.609 INDI 838 INDI 838 PLS 1 Colorectal III 7.5 4.20 APC p.E1306 *, c.3916G> T 3.44 0.11 1.5 0.603 INDI 839 INDI 839 PLS 1 Breast II 7.5 3.74 TP53 p.K372fs, c. 1114delA 0.81 0.03 0.4 0.125 INDI 840 INDI 840 PLS 0 Breast II 7.5 6.27 TP53 p.A138T, c.412G> A 0.89 0.03 0.5 0.125 INDI 841 INDI 841 PLS 1 Colorectal III 7.5 7.97 TP53 p.R248W, c.742C> T 2.22 1.01 24.8 0.994 INDI 842 INDI 842 PLS 1 Breast I 7.5 3.46 TP53 p.R202C, c. 604C> T 1.26 0.14 1.5 0.826 INDI 843 INDI 843 PLS 1 Esophagus I 7.5 5.09 TP53 p.T155I, c.464C > T 0.83 0.03 0.4 0.122 INDI 844 INDI 844 PLS 1 Stomach I 7.5 3.52 TP53 p.R280T, c.839G> C 1.75 0.12 1.3 0.876 INDI 846 INDI 846 PLS 1 Colorectal II 7.5 1.84 APC p.E1309fs, c.3927delAAAGA 0.67 0.08 0.4 0.991 INDI 847 INDI 847 PLS 1 Liver III 7.5 113.59 TP53 p.H179R, c.536A > G 0.96 0.01 2.1 0.976 INDI 848 INDI 848 PLS 1 Colorectal II 7.5 1.68 TP53 p.R248Q, c.743G> A 0.61 0.06 0.3 0.876 INDI 849 INDI 849 PLS 1 Colorectal II 7.5 1.30 PPP2R1A p.R183W, c.547C> T 0.56 0.07 0.3 0.841 INDI 850 INDI 850 PLS 1 Esophagus II 7.5 8.01 TP53 p.H193P, c .578A> C 124.89 21.37 527.0 1,000 INDI 853 INDI 853 PLS 1 Esophagus II 7.5 49.44 TP53 p.H178Q, c.534C> G 1.87 0.01 1.3 0.998 INDI 854 INDI 854 PLS 1 Colorectal II 7.5 1.03 FBXW7 p.R465C, c.1393C> T 0.66 0.14 0.5 0.991 INDI 855 INDI 855 PLS 1 Colorectal II 7.5 9.62 TP53 G.7576927C > T (Junction site) 1.18 0.01 0.4 0.999 INDI 856 INDI 856 PLS 1 Breast III 7.5 5.72 KRAS p.D57N, c.169G> A 0.77 0.03 0.5 0.116 INDI 857 INDI 857 PLS 1 Breast III 7.5 6.54 TP53 p.C176G, c.526T> G 1 , 30 0.04 0.8 0.356 INDI 858 INDI 858 PLS 1 Breast I 7.5 6.21 TP53 p.A159T, c.475G> A 0.82 0.03 0.6 0.480 INDI 859 INDI 859 PLS 1 Esophagus II 7.5 15.56 TP53 p.Y234N, c.700T> A 7.28 0.59 28.3 0.992 INDI 860 INDI 860 PLS 1 Breast I 7.5 2.77 TP53 p.D49N, c.145G> A 0.40 0.08 0.721 INDI 861 INDI 861 PLS 1 Liver II 7.5 49.26 TP53 p.R249S, c.747G> T 1.32 0.02 3.3 0.997 INDI 862 INDI 862 PLS 1 Pancreas II 7.5 12.94 TP53 p.M133I, c.399G> T 1.15 0.01 0.4 0.910 INDI 864 INDI 864 PLS 1 Liver III 7.5 40.15 PTEN p.R130 *, c .388C> T 1.36 0.02 2.5 0.999 INDI 865 INDI 865 PLS 1 Breast III 7.5 3.59 TP53 p.V157I, c.469G> A 0.80 0.08 0.9 0.392 INDI 867 INDI 867 PLS 1 Esophagus II 7.5 20.52 TP53 p.R181P, c.542G> C 1.33 0.01 0.6 0.995 INDI 868 INDI 868 PLS 1 Liver III 7.5 79.15 KRAS p.A146V , c.437C> T 2.83 0.05 11.8 0.993 INDI 870 INDI 870 PLS 1 Breast II 7.5 20.48 TP53 p.R202C, c.604C> T 1.07 0.04 2.5 0.947 INDI 872 INDI 872 PLS 1 Breast III 7.5 5.17 PIK3CA p.H1047L, c.3140A> T 13.66 0.89 14.2 0.986 INDI 873 INDI 873 PLS 1 M ama III 7.5 8.85 KRAS p.A59E, c.176C> A 1.00 0.02 0.7 0.527 INDI 875 INDI 875 PLS 1 Mama II 7.5 5.66 TP53 p.R337C, c.1009C > T 0.54 0.03 0.5 0.252 INDI 876 INDI 876 PLS 1 Breast II 7.5 8.23 TP53 p.R306L, c.917G> T 0.41 0.02 0.5 0.331 INDI 877 INDI 877 PLS 1 Breast I 7.5 37.49 TP53 p.P85H, c.254C> A 1.05 0.01 0.9 0.999 INDI 878 INDI 878 PLS 1 Breast III 7.5 8.88 TP53 p.L369M, c .1105C> A 1.22 0.03 0.9 0.992 INDI 879 INDI 879 PLS 1 Breast II 7.5 5.37 TP53 p.R282W, c.844C> T 0.76 0.04 0.74040 INDI 880 INDI 880 PLS 1 Breast II 7.5 72.89 FBXW7 p.R465H, c.1394G> A 0.84 0.02 3.4 0.675 INDI 881 INDI 881 PLS 1 Breast II 7.5 18.05 HRAS p.G12S , c.34G> A 1.08 0.08 4.7 0.835 INDI 882 INDI 882 PLS 1 Breast II 7.5 21.10 TP53 p.K373E, c.1117A> G 1.00 0.01 0.8 0.779 INDI 883 INDI 883 PLS 1 Breast II 7.5 11.03 TP53 p.K372fs, c.1114delA 1.05 0.05 1.8 0.602

INDI 884 INDI 884 PLS 1 Breast II 7.5 17.62 TP53 p.Y220C, c.659A> G 12.84 11.77 638.8 0.999 INDI 885 INDI 885 PLS 1 Breast II 7.5 5.89 FBXW7 p.R505H , c.1514G> A 0.93 0.03 0.6 0.547 INDI 886 INDI 886 PLS 1 Breast II 7.5 2.52 TP53 p.K372fs, c.1114delA 0.66 0.09 0.723 INDI 887 INDI 887 PLS 1 Breast III 7.5 20.27 CDKN2A p.R87Q, c.260G> A 0.84 0.02 1.5 0.987 INDI 888 INDI 888 PLS 1 Pancreas II 7.5 8.94 TP53 p.G108S , c.322G> A 1.03 0.04 1.0 0.976 INDI 889 INDI 889 PLS 1 Colorectal II 7.5 2.44 TP53 p.P190L, c.569C> T 3.86 0.35 2.6 0.9999 INDI 892 INDI 892 PLS 1 Stomach I 7.5 4.34 TP53 p.K372fs, c.1114delA 0.90 0.03 0.4 0.561 INDI 893 INDI 893 PLS 1 Pancreas II 7.5 14.46 BRAF p.A598T , c.1792G> A 1.01 0.01 0.5 0.972 INDI 896 INDI 896 PLS 1 Lung I 7.5 3.34 TP53 p.R337L, c.1010G> T 5.64 2.20 22.6 0.864 INDI 897 INDI 897 PLS 1 Breast III 7.5 1.91 TP53 p.T155I, c.464C> T 7.02 7.02 41.2 0.993 INDI 898 INDI 898 PLS 1 Breast I 7.5 1.21 TP53 p .K372fs, c.1114delA 1.14 0.09 0.4 0.967 INDI 899 INDI 899 PLS 1 Breast I 7.5 4.02 TP53 p.A161S, c.481G> T 0.96 0.02 0.2 0.154 INDI 902 INDI 902 PLS 1 Breast III 7.5 7.00 PIK3CA p.H1047R, c.3140A> G 26.55 9.21 198.7 0.997 INDI 903 INDI 903 PLS 1 Breast I 7.5 0.56 TP53 p.E62K, c.184G> A 0.00 0.14 0.2 0.651 INDI 905 INDI 905 PLS 1 Esophagus II 7 , 5 19.99 TP53 p.K132R, c.395A> G 8.45 0.61 37.4 1,000 INDI 907 INDI 907 PLS 1 Esophagus II 7.5 18.15 TP53 p.T256fs, c.767insCACT 1.06 0.02 1.3 0.971 INDI 908 INDI 908 PLS 1 Esophagus II 7.5 41.43 TP53 p.G266V, c.797G> T 2.61 0.02 1.9 0.999 INDI 909 INDI 909 PLS 1 Esophagus II 7 , 5 22.67 TP53 p.P152L, c.455C> T 1.16 0.06 4.1 0.987 INDI 911 INDI 911 PLS 1 Esophagus II 7.5 12.33 TP53 p.A159T, c.475G> A 0 , 95 0.05 1.8 0.887 INDI 913 INDI 913 PLS 1 Esophagus III 7.5 24.11 PIK3CA p.N345K, c.1035T> A 0.89 0.01 0.6 0.835 INDI 915 INDI 915 PLS 1 Breast II 7.5 4.83 TP53 p.R273H, c.818G> A 1.32 0.22 3.2 0.328 INDI 917 INDI 917 PLS 1 Liver III 7.5 27.49 PTEN p.C136R, c.406T> C 7.49 0.18 15.4 0.997 INDI 919 INDI 919 PLS 1 Breast II 7.5 5.89 TP53 p.P152L, c.455C> T 1, 99 0.61 11.0 0.458 INDI 920 INDI 920 PLS 1 Ovary III 7.5 4.16 TP53 p.R273H, c.818G> A 1.36 0.23 2.9 0.944 INDI 921 INDI 921 PLS 1 Breast II 7.5 5.20 TP53 p.N288fs, c.864insCCCAA GTGAC (SEQ ID NO: 892) 1.25 0.08 1.2 0.458 INDI 922 INDI 922 PLS 1 Mama II 7.5 4.92 TP53 p.R273H , c.818G> A 0.90 0.14 2.1 0.125 INDI 923 INDI 923 PLS 1 Esophagus I 7.5 12.24 TP53 p.A138T, c.412G> A 1.02 0.03 1.1 0.306 INDI 924 INDI 924 PLS 1 Breast III 7.5 4.69 GNAS p.R201H, c.602G> A 1.61 0.32 4.6 0.395 INDI 926 INDI 926 PLS 1 Breast II 7.5 4.98 TP53 p .R273H, c.818G> A 1.29 0.24 3.7 0.343 INDI 927 INDI 927 PLS 1 Breast II 7.5 2.73 TP53 p.R273H, c.818G> A 1.15 0.40 3, 4 0.304 INDI 928 INDI 928 PLS 1 Stomach II 7.5 6.23 TP53 p.F338fs, c.1014delC 4.08 0.16 3.0 0.998 INDI 929 INDI 929 PLS 1 Stomach II 7.5 15.27 TP53 p .V216L, c.646G> C 1.45 0.03 1.6 0.719 INDI 930 INDI 930 PLS 1 Stomach II 7.5 5.92 TP53 p.R273H, c.818G> A 1.18 0.21 3, 8 0.672 INDI 931 INDI 931 PLS 1 Ovary III 7.5 6.52 TP53 p.R273H, c.818G> A 1.29 0.27 5.4 0.955 IND I 932 INDI 932 PLS 1 Ovary III 7.5 5.40 TP53 p.L252fs, c.754delTCC 69.94 10.81 179.8 0.999 INDI 933 INDI 933 PLS 1 Esophagus III 7.5 7.05 TP53 p.R273H , c.818G> A 1.26 0.19 4.1 0.527 INDI 935 INDI 935 PLS 1 Esophagus I 7 9.06 TP53 p.R273H, c.818G> A 1.10 0.14 4.0 0.810 INDI 936 INDI 936 PLS 1 Ovary I 7.5 8.81 TP53 p.C176fs, c.528insC 1.41 0.12 3.3 0.363 INDI 937 INDI 937 PLS 1 Esophagus III 7.5 8.18 TP53 p.R273H, c .818G> A 1.08 0.16 4.1 0.541 CSF 588 CSF 588 PLS1 Normal NA 7.5 7.34 TP53 p.K372R, c.1115A> G 0.47 0.01 0.2 0.260 CSF 589 CSF 589 PLS1 Normal NA 7.5 7.48 TP53 p.K372fs, c.1114insA 0.94 0.01 0.2 0.186 LCR 590 LCR 590 PLS1 Normal NA 7.5 8.50 TP53 p.G302fs, c.904delG 0 , 91 0.01 0.3 0.235 LCR 591 LCR 591 PLS1 Normal NA 7.5 10.50 TP53 p.R175C, c.523C> T 0.66 0.03 1.0 0.125 LCR 592 LCR 592 PLS1 Normal NA 7 , 5 5.04 TP53 p.K372fs, c.1114delA 0.95 0.04 0.6 0.117 LCR 593 LCR 593 PLS1 Normal NA 7.5 11.14 TP53 p.K372fs, c.1114delA 0.84 0.06 2.1 0.175 LCR 594 LCR 594 PLS1 Normal NA 7.5 4.84 PIK3CA p. A1046V, c.3137C> T 0.89 0.06 1.0 0.121 LCR 595 LCR 595 PLS1 Normal NA 7.5 3.73 TP53 p.K372fs, c.1114delA 1.00 0.04 0.5 0.296 LCR 596 LCR 596 PLS1 Colorectal I 7.5 2.88 TP53 p.A83T, c.247G> A 0.39 0.08 0.781 LCR 597 LCR 597 PLS1 Normal NA 7.5 4.77 TP53 p.L369P, c .1106T> C 1.07 0.05 0.7 0.355 LCR 599 LCR 599 PLS1 Normal NA 7.5 3.41 TP53 p.K372fs, c.1114delA 1.22 0.10 1.1 0.339 LCR 600 LCR 600 PLS1 Normal NA 7.5 4.65 TP53 p.R158H, c.473G> A 0.51 0.14 2.0 0.121 LCR 601 LCR 601 PLS1 Normal NA 7.5 3.19 TP53 p.L369P, c.1106T> C 1.05 0.04 0.4 0.307 LCR 602 LCR 602 PLS1 Normal NA 7.5 9.94 TP53 p.R335H, c.1004G> A 0.62 0.03 1.0 0.315 LCR 603 LCR 603 PLS1 Normal NA 7.5 2.92 KRAS p.G13D, c.38G> A 0.57 0.19 1.7 0.125 LCR 604 LCR 604 PLS1 Normal NA 7.5 7.73 HRAS p.G12D, c.35G> A 0.87 0.02 0.4 0.121 LCR 605 LCR 605 PLS1 Normal NA 7.5 5.75 KRAS p.G13D, c.38G> A 1.15 0.07 1.2 0.326 LCR 606 LCR 606 PLS1 Normal NA 7.5 6.06 KRAS p.G13D, c.38G> A 1.07 0.06 1.1 0.779 LCR 607 LCR 607 PLS1 Normal NA 7.5 8.85 KRAS p .G13D, c.38G> A 0.90 0.10 2.8 0.585 LCR 608 LCR 608 PLS1 Normal NA 7.5 3.74 TP53 p.P142L, c.425C> T 0.68 0.06 0.7 0.593 LCR 610 LCR 610 PLS1 Normal NA 7.5 2.05 TP53 p.P47L, c.140C> T 0.00 0.06 0.4 0.125 LCR 611 LCR 611 PLS1 Normal NA 7.5 2.71 KRAS p. G13D, c.38G> A 0.49 0.09 0.7 0.966 LCR 612 LCR 612 PLS1 Normal NA 7.5 4.86 TP53 p.R335H, c.1004G> A 0.36 0.07 1.1 0.116 LCR 613 LCR 613 PLS1 Normal NA 7.5 2.46 HRAS p.G12D, c.35G> A 0.41 0.07 0.5 0.117 LCR 614 LCR 614 PLS1 Normal NA 7.5 5.18 TP53 p.N200fs , c.598delCCGAGT GGAAGGAA (SEQ ID NO: 747) 0.74 0.04 0.6 0.249

LCR 616 LCR 616 PLS1 Normal NA 7.5 9.30 HRAS p.G13D, c.38G> A 0.59 0.02 0.4 0.125 LCR 617 LCR 617 PLS1 Normal NA 7.5 8.47 TP53 p.G374fs , c.1120delG 0.93 0.08 2.1 0.122 CSF 618 CSF 618 PLS1 Normal NA 7.5 3.18 KRAS p.D57N, c.169G> A 0.45 0.08 0.8 0.293 CSF 619 CSF 619 PLS1 Normal NA 7.5 5.61 KRAS p.A11V, c.32C> T 0.00 0.02 0.4 0.122 LCR 620 LCR 620 PLS1 Normal NA 7.5 5.33 FBXW7 p.G477S, c. 1429G> A 0.47 0.03 0.5 0.494 LCR 621 LCR 621 PLS1 Normal NA 7.5 2.80 TP53 p.Y234H, c.700T> C 0.31 0.11 0.9 0.117 LCR 622 LCR 622 PLS1 Normal NA 7.5 16.35 TP53 p.K372fs, c.1114delA 0.98 0.04 2.2 0.125 LCR 623 LCR 623 PLS1 Normal NA 7.5 5.34 TP53 p.R282Q, c.845G> A 0.67 0.12 1.9 0.835 LCR 624 LCR 624 PLS1 Normal NA 7.5 3.81 TP53 p.P177S, c.529C> T 0.61 0.09 1.0 0.116 LCR 625 LCR 625 PLS1 Normal NA 7.5 17.16 TP53 p.P36Q, c.107C> A 1.40 0.03 1.7 0.486 LCR 626 LCR 626 PLS1 Normal NA 7.5 3.95 TP53 p.S121Y, c.362C> A 0.59 0.06 0.8 0.603 CSF 627 CSF 627 PLS1 Normal NA 7.5 2.76 TP53 p.R290H, c.869G> A 0.00 0.10 0.9 0.861 LCR 628 LCR 628 PLS1 Normal NA 7.5 2.29 CDKN2A p.V51I, c.151G> A (In. exon) 0.00 0.08 0.6 0.117 CSF 629 CSF 629 PLS1 Normal NA 7.5 7.5 TP53 p.T118I, c.353C> T 1.06 0.06 1.3 0.639 CSF 630 CSF 630 PLS1 Normal NA 7.5 14.32 TP53 p.C182Y, c.545G> A 1.04 0.08 3.5 0.298 LCR 631 LCR 631 PLS1 Normal NA 7.5 3.87 TP53 p.R181C, c.541C> T 0.54 0.15 1.8 0.220 LCR 632 LCR 632 PLS1 Normal NA 7.5 7.22 TP53 p.R202C, c.604C> T 0.67 0.05 1.2 0.123 LCR 633 LCR 633 PLS1 Normal NA 7.5 10.48 TP53 p.L348M, c.1042T> A 0.92 0.03 0.9 0.547 LCR 634 LCR 634 PLS1 Normal NA 7.5 5.81 CDKN2A p.R87Q, c.260G> A 0.55 0.04 0.8 0.121 LCR 635 LCR 635 PLS1 Normal NA 7.5 4.11 FBXW7 p.R479 *, c.1435C> T 0.68 0.04 0.5 0.125 LCR 636 LCR 636 PLS1 Normal NA 7.5 4.08 PIK3CA p.N1044S, c.3131A> G 1.26 0.18 2.3 0.345 LCR 637 LCR 637 PLS1 Normal NA 7.5 2.56 TP53 p.P142H, c.425C> A 0.00 0.06 0.5 0.125 LCR 638 LCR 638 PLS1 Normal NA 7.5 3.27 TP53 p.L308M, c.922C> A 0.00 0.07 0.8 0.123 LCR 639 LCR 639 PLS1 Normal NA 7.5 5.42 FBXW7 p.R505H, c.1514G> A 0.63 0.04 0.7 0.222 LCR 640 LCR 640 PLS1 Normal NA 7.5 9.99 GNAS p.R2 01C, c.601C> T 0.81 0.04 1.1 0.125 LCR 641 LCR 641 PLS1 Normal NA 7.5 5.50 TP53 p.P47L, c.140C> T 0.62 0.07 1.2 0.705 LCR 642 LCR 642 PLS1 Normal NA 7.5 6.58 GNAS p.R201H, c.602G> A 0.55 0.03 0.5 0.229 LCR 643 LCR 643 PLS1 Normal NA 7.5 2.70 TP53 p.A159T , c.475G> A 0.54 0.14 1.1 0.116 LCR 644 LCR 644 PLS1 Normal NA 7.5 4.70 TP53 p.Q104R, c.311A> G 0.49 0.05 0.717 LCR 645 LCR 645 PLS1 Normal NA 7.5 8.55 GNAS p.R201H, c.602G> A 0.76 0.05 1.3 0.510 LCR 646 LCR 646 PLS1 Normal NA 7.5 7.59 GNAS p.R201H, c.602G> A 0.64 0.07 1.6 0.117 CSF 647 CSF 647 PLS1 Normal NA 7.5 7.61 TP53 p.G374fs, c.1120delG 0.89 0.07 1.5 0.166 CSF 648 CSF 648 PLS1 Normal NA 7.5 5.30 KRAS p.V14I, c.40G> A 0.41 0.05 0.8 0.122 LCR 649 LCR 649 PLS1 Normal NA 7.5 7.69 TP53 p.P177L, c.530C > T 0.62 0.05 1.2 0.125 LCR 650 LCR 650 PLS1 Normal NA 7.5 4.06 TP53 p.T170K, c.509C> A 0.00 0.08 1.0 0.747 LCR 652 LCR 652 PLS1 Normal NA 7.5 4.71 TP53 p.L299P, c.896T> C 0.00 0.05 0.7 0.222 LCR 653 LCR 653 PLS1 Normal NA 7 23.33 TP53 p.E339K, c.1015G> A 0.67 0.03 2.5 0.329 LCR 654 LCR 654 PLS1 Normal NA 7.5 3.38 TP53 p.T125T, c.375G> A (Exon end) 0.34 0.09 0, 9 0.117 LCR 655 LCR 655 PLS1 Normal NA 7.5 3.34 TP53 p.P152L, c.455C> T 0.40 0.12 1.2 0.122 LCR 658 LCR 658 PLS1 Normal NA 7.5 1.80 TP53 p .N30K, c.90C> G 0.00 0.10 0.6 0.117 LCR 659 LCR 659 PLS1 Normal NA 7.5 4.23 FBXW7 p.R505C, c.1513C> T 0.42 0.02 0.3 0.125 LCR 660 LCR 660 PLS1 Normal NA 7.5 1.72 TP53 p.R379H, c.1136G> A 0.49 0.19 1.0 0.125 LCR 661 LCR 661 PLS1 Normal NA 7.5 1.04 PIK3CA p. R88Q, c.263G> A 0.00 0.08 0.3 0.117 LCR 662 LCR 662 PLS1 Normal NA 7.5 3.35 TP53 p.L369P, c.1106T> C 0.63 0.15 1.6 0.193 LCR 663 LCR 663 PLS1 Normal NA 7.5 4.40 TP53 p.R290C, c.868C> T 1.10 0.12 1.6 0.317 LCR 665 LCR 665 PLS1 Normal NA 7.5 8.49 TP53 p.A119T , c.355G> A 0.86 0.04 1.0 0.117 CSF 666 CSF 666 PLS1 Normal NA 7.5 1.95 TP53 p.T125T, c.375G> A (Exon end) 0.00 0.03 0 , 2 0.123 LCR 667 LCR 667 PLS1 Normal NA 7.5 2.13 TP53 p.S116Y, c.347C> A 0.00 0.02 0.2 0.122 LCR 668 LCR 668 PLS1 Normal NA 7 , 5 5.91 TP53 p.R202C, c.604C> T 0.66 0.08 1.4 0.121 LCR 670 LCR 670 PLS1 Normal NA 7.5 2.93 CDKN2A p.A76T, c.226G> A 0, 94 0.15 1.3 0.125 LCR 671 LCR 671 PLS1 Normal NA 7.5 0.90 FBXW7 p.R479Q, c.1436G> A 0.00 0.15 0.4 0.123 LCR 672 LCR 672 PLS1 Normal NA 7, 5 2.56 TP53 p.T125T, c.375G> A (Exon end) 0.00 0.06 0.5 0.601 LCR 673 LCR 673 PLS1 Normal NA 7.5 2.67 TP53 p.P153L, c.458C> T 0.60 0.07 0.6 0.116 LCR 674 LCR 674 PLS1 Normal NA 7.5 5.50 TP53 p.K372fs, c.1114delA 0.91 0.05 0.8 0.179 LCR 675 LCR 675 PLS1 Normal NA 7 , 5 8.63 CDKN2A p.R58 *, c.172C> T 0.75 0.04 1.1 0.187 LCR 676 LCR 676 PLS1 Normal NA 7.5 3.34 TP53 p.R158H, c.473G> A 0 , 51 0.06 0.6 0.245 LCR 677 LCR 677 PLS1 Normal NA 7.5 12.26 TP53 p.R196Q, c.587G> A 0.82 0.04 1.7 0.156 LCR 678 LCR 678 PLS1 Normal NA 7 , 5 3.13 FBXW7 p.G477S, c.1429G> A 0.00 0.07 0.786 LCR 679 LCR 679 PLS1 Normal NA 7.5 5.80 TP53 p.V157I, c.469G> A 0, 36 0.04 0.7 0.255 CSF 680 CSF 680 PLS1 Normal NA 7.5 4.18 TP53 p.F113L, c.337T> C 4.77 0.24 3.1 0.699 CSF 681 CSF 681 PLS1 Normal NA 7.5 5.83 GNAS p.R201H, c.602G> A 0.41 0.02 0.4 0.597 LCR 682 LCR 682 PLS1 Normal NA 7.5 6.17 TP53 p.V10I, c.28G > A 0.64 0.05 0.9 0.631 LCR 683 LCR 683 PLS1 Normal NA 7.5 6.17 TP53 p.R267W, c.799C> T 1.27 0.28 5.2 0.322 LCR 684 LCR 684 PLS1 Normal NA 7.5 4.83 TP53 p.A161V, c.482C> T 0.69 0.05 0.721 LCR 685 LCR 685 PLS1 Normal NA 7.5 8.62 PTEN p.G129E, c.386G> A 0.63 0.02 0.4 0.845

LCR 686 LCR 686 PLS1 Normal NA 7.5 4.78 TP53 p.A83V, c.248C> T 0.50 0.06 0.9 0.122 LCR 687 LCR 687 PLS1 Normal NA 7.5 1.96 TP53 p.A119S , c.355G> T 0.60 0.09 0.5 0.121 CSF 688 CSF 688 PLS1 Normal NA 7.5 7.42 TP53 p.L369P, c.1106T> C 1.06 0.05 1.2 0.419 CSF 689 LCR 689 PLS1 Normal NA 7.5 1.99 FBXW7 p.E471 *, c.1411G> T 0.00 0.10 0.6 0.125 LCR 690 LCR 690 PLS1 Normal NA 7.5 6.21 TP53 p.A119V , c.356C> T 0,00 0,01 0,3 0,123 LCR 691 LCR 691 PLS1 Normal NA 7.5 3,99 TP53 p.T125T, c.375G> A (Exon end) 0,53 0,09 1 , 1 0.123 LCR 692 LCR 692 PLS1 Normal NA 7.5 3.42 TP53 p.K372fs, c.1114delA 0.76 0.06 0.770 LCR 693 LCR 693 PLS1 Normal NA 7.5 2.57 TP53 p. R175H, c.524G> A 0.48 0.16 1.2 0.116 LCR 694 LCR 694 PLS1 Normal NA 7.5 1.54 CDKN2A p.R58 *, c.172C> T 0.57 0.18 0.9 0.122 LCR 697 LCR 697 PLS1 Normal NA 7.5 12.02 GNAS p.R201H, c.602G> A 1.08 0.03 1.2 0.308 LCR 698 LCR 698 PLS1 Normal NA 7.5 6.49 FBXW7 p. E471fs, c.1412delA 0.74 0.04 0.8 0.328 LCR 699 LCR 699 PLS1 Normal NA 7.5 0.90 CTNNB1 p.S33C, c.98C> G 0, 00 0.06 0.2 0.123 LCR 700 LCR 700 PLS1 Normal NA 7.5 3.76 TP53 p.P300fs, c.898delC 0.71 0.02 0.3 0.755 LCR 701 LCR 701 PLS1 Normal NA 7.5 1 , 86 TP53 p.R110C, c.328C> T 0.49 0.06 0.4 0.630 LCR 702 LCR 702 PLS1 Normal NA 7.5 3.95 TP53 p.R202C, c.604C> T 0.76 0, 04 0.5 0.968 LCR 703 LCR 703 PLS1 Normal NA 7.5 0.65 KRAS p.V14I, c.40G> A 0.00 0.14 0.3 0.117 LCR 704 LCR 704 PLS1 Normal NA 7.5 7, 32 TP53 p.T125M, c.374C> T 1.05 0.05 1.1 1,000 LCR 705 LCR 705 PLS1 Normal NA 7.5 5.14 GNAS p.R201C, c.601C> T 1.24 0.11 1.8 0.485 LCR 706 LCR 706 PLS1 Normal NA 7.5 10.62 CDKN2A p.V82M, c.244G> A 0.80 0.04 1.3 0.192 LCR 707 LCR 707 PLS1 Normal NA 7.5 3.90 TP53 p.P222L, c.665C> T 0.51 0.10 1.2 0.216 LCR 709 LCR 709 PLS1 Normal NA 7.5 2.36 TP53 p.N131T, c.392A> C 0.00 0.14 1 , 0 0.400 LCR 710 LCR 710 PLS1 Normal NA 7.5 7.20 TP53 p.T150K, c.449C> A 1.02 0.02 0.5 0.758 LCR 711 LCR 711 PLS1 Normal NA 7.5 2.26 TP53 p.M237K, c.710T> A 0.69 0.09 0.6 0.123 LCR 712 LCR 712 PLS1 Normal NA 7.5 11.51 TP53 p.A161V, c.482C> T 0.6 9 0.03 1.2 0.121 CRL 713 CRL 713 PLS1 Normal NA 7.5 9.78 PTEN p.A137T, c.409G> A 1.14 0.02 0.7 0.61 CRL 714 CRL 714 PLS1 Normal NA 7, 5 1.93 TP53 p.D49N, c.145G> A 0.55 0.26 1.5 0.123 LCR 715 LCR 715 PLS1 Normal NA 7.5 0.50 TP53 p.R156C, c.466C> T 0.00 0.15 0.2 0.122 LCR 716 LCR 716 PLS1 Normal NA 7.5 5.21 TP53 p.A119V, c.356C> T 0.74 0.01 0.2 0.538 LCR 717 LCR 717 PLS1 Normal NA 7.5 1.27 TP53 p.A119T, c.355G> A 0.00 0.01 0.1 0.50 LCR 718 LCR 718 PLS1 Normal NA 7.5 3.12 TP53 p.P47L, c.140C> T 0.52 0 , 10 0.9 0.523 LCR 719 LCR 719 PLS1 Normal NA 7.5 3.19 TP53 p.R306 *, c.916C> T 0.56 0.03 0.3 0.125 LCR 720 LCR 720 PLS1 Normal NA 7.5 3.36 TP53 p.R283C, c.847C> T 0.51 0.07 0.7 0.166 LCR 721 LCR 721 PLS1 Normal NA 7.5 5.91 CDKN2A p.R58 *, c.172C> T 0.66 0.02 0.4 0.517 LCR 722 LCR 722 PLS1 Normal NA 7.5 5.16 TP53 p.R335H, c.1004G> A 0.80 0.03 0.5 0.377 LCR 723 LCR 723 PLS1 Normal NA 7.5 5.22 TP53 p.R202C, c.604C> T 0.98 0.02 0.3 0.117 LCR 724 LCR 724 PLS1 Normal NA 7.5 0.83 TP53 p.T118I, c.353C> T 0.00 0 , 04 0.1 0.122 LCR 725 LCR 725 PLS1 Normal NA 7.5 2.85 TP53 p.S303G, c.907A> G 0.77 0.02 0.2 0.184 LCR 726 LCR 726 PLS1 Normal NA 7.5 1, 45 TP53 p.R267Q, c.800G> A 0.58 0.11 0.5 0.564 LCR 727 LCR 727 PLS1 Normal NA 7.5 1.80 PPP2R1A p.R182W, c.544C> T 0.52 0.08 0.4 0.125 CRL 728 CRL 728 PLS1 Normal NA 7.5 1.82 TP53 p.R174G, c.520A> G 0.53 0.08 0.4 0.303 CRL 729 CRL 729 PLS1 Normal NA 7.5 1.61 TP53 p.P153L, c.458C> T 0.64 0.05 0.3 0.122 LCR 730 LCR 730 PLS1 Normal NA 7.5 2.75 TP53 p.R273C, c.817C> T 0.44 0.07 0 , 6 0.117 CRL 731 CRL 731 PLS1 Normal NA 7.5 1.45 TP53 p.S94P, c.280T> C 0.60 0.06 0.3 0.294 CRL 732 CRL 732 PLS1 Normal NA 7.5 0.76 TP53 p.T125T, c.375G> A (Exon end) 0.31 0.04 0.1 0.125 CSF 733 CSF 733 PLS1 Normal NA 7.5 3.81 TP53 p.R337H, c.1010G> A 0.61 0 , 03 0.4 0.305 LCR 734 LCR 734 PLS1 Normal NA 7.5 3.43 TP53 G.7577017A> G (Junction site) 0.74 0.03 0.4 0.377 LCR 736 LCR 736 PLS1 Normal NA 7.5 2.97 TP53 p.L369P, c.1106T> C 0.67 0.06 0.5 0.404 LCR 737 LCR 737 PLS1 Normal NA 7.5 2.65 PPP2R1A p. P179L, c.536C> T 0.68 0.07 0.5 0.863 LCR 738 LCR 738 PLS1 Normal NA 7.5 7.94 TP53 p.D228E, c.684C> A 0.69 0.04 1.0 0.591 LCR 739 LCR 739 PLS1 Normal NA 7.5 1.95 TP53 p.P219S, c.655C> T 0.00 0.09 0.6 0.390 LCR 740 LCR 740 PLS1 Normal NA 7.5 37.74 TP53 p.P309S , c.925C> T 0.46 0.06 7.4 0.411 CSF 741 CSF 741 PLS1 Normal NA 7.5 100.42 TP53 p.V274A, c.821T> C 0.60 0.05 14.3 0.374 CSF 742 LCR 742 PLS1 Normal NA 7.5 5.67 GNAS p.R201H, c.602G> A 0.30 0.02 0.4 0.860 LCR 743 LCR 743 PLS1 Normal NA 7.5 4.22 TP53 p.E298K, c.892G> A 0.48 0.03 0.4 0.603 CSF 745 CSF 745 PLS1 Normal NA 7.5 1.30 PPP2R1A p.R183Q, c.548G> A 0.00 0.05 0.2 0.680 CSF 746 CRL 746 PLS1 Normal NA 7.5 25.18 TP53 p.R175H, c.524G> A 0.77 0.06 5.0 0.553 CRL 747 CRL 747 PLS1 Normal NA 7.5 1.78 TP53 p.A161T, c .481G> A 0.76 0.04 0.2 0.327 LCR 749 LCR 749 PLS1 Normal NA 7.5 1.96 TP53 p.R337H, c.1010G> A 0.00 0.05 0.3 0.221 LCR 750 LCR 750 PLS1 Normal NA 7.5 1.36 APC p.R1450 *, c.4348C> T 0.00 0.06 0.2 0.851 LCR 751 LCR 751 PLS1 Normal NA 7.5 2.10 T P53 p.R158H, c.473G> A 0.74 0.06 0.4 0.125 LCR 752 LCR 752 PLS1 Normal NA 7.5 9.31 TP53 p.G302W, c.904G> T 0.91 0.01 0 , 3 0.855 LCR 753 LCR 753 PLS1 Normal NA 7.5 1.52 TP53 p.D48N, c.142G> A 0.66 0.06 0.3 0.193 LCR 754 LCR 754 PLS1 Normal NA 7.5 2.21 TP53 p.T125M, c.374C> T 0.80 0.03 0.2 0.121 LCR 755 LCR 755 PLS1 Normal NA 7.5 1.39 TP53 p.R158H, c.473G> A 0.00 0.08 0, 3 0.125 LCR 757 LCR 757 PLS1 Normal NA 7.5 1.62 TP53 p.T125M, c.374C> T 0.40 0.21 1.0 0.387 LCR 758 LCR 758 PLS1 Normal NA 7.5 67.40 TP53 p .R283H, c.848G> A 0.49 0.06 12.9 0.473

LCR 759 LCR 759 PLS1 Normal NA 7.5 1.97 TP53 p.R283H, c.848G> A 0.48 0.06 0.4 0.736 LCR 760 LCR 760 PLS1 Normal NA 7.5 0.65 TP53 p.L114M , c.340T> A 0.00 0.08 0.1 0.401 CSF 761 CSF 761 PLS1 Normal NA 7.5 0.94 TP53 p.E298K, c.892G> A 0.45 0.08 0.2 0.117 CSF 762 LCR 762 PLS1 Normal NA 7.5 135.67 TP53 p.V203M, c.607G> A 0.82 0.02 7.1 0.117 LCR 763 LCR 763 PLS1 Normal NA 7 39.88 TP53 p.S303N, c. 908G> A 0.95 0.05 5.9 0.121 LCR 764 LCR 764 PLS1 Normal NA 7.5 71.68 TP53 p.P300fs, c.898delC 0.85 0.03 7.4 0.184 LCR 765 LCR 765 PLS1 Normal NA 7.5 64.69 TP53 p.R202C, c.604C> T 1.26 0.09 17.3 0.339 LCR 766 LCR 766 PLS1 Normal NA 7.5 6.85 TP53 p.R342 *, c.1024C> T 0 , 77 0.08 1.7 0.116 LCR 768 LCR 768 PLS1 Normal NA 7.5 4.67 GNAS p.R201H, c.602G> A 0.46 0.05 0.786 LCR 769 LCR 769 PLS1 Normal NA 7 , 5 4.49 TP53 p.R202C, c.604C> T 0.95 0.06 0.9 0.338 LCR 770 LCR 770 PLS1 Normal NA 7.5 3.25 TP53 p.P152L, c.455C> T 0, 43 0.07 0.721 CSF 771 CSF 771 PLS1 Normal NA 7.5 100.85 KRAS p.D57N, c.169G> A 0.40 0.03 8.7 0, 388 LCR 772 LCR 772 PLS1 Normal NA 7.5 7.79 TP53 p.C176fs, c.528insC 0.94 0.06 1.5 0.121 LCR 773 LCR 773 PLS1 Normal NA 7.5 5.23 FBXW7 p.R367 * , c.1099C> T 0.65 0.05 0.8 0.156 CSF 774 CSF 774 PLS1 Normal NA 7.5 7.43 TP53 p.R202C, c.604C> T 0.75 0.03 0.7 600 CSF 775 LCR 775 PLS1 Normal NA 7.5 5.50 TP53 p.A307T, c.919G> A (Exon end) 0.99 0.02 0.3 0.935 LCR 776 LCR 776 PLS1 Normal NA 7.5 2.82 TP53 p.K372fs, c.1114delA 0.71 0.06 0.6 0.117 CSF 777 CSF 777 PLS1 Normal NA 7.5 4.78 TP53 p.A161T, c.481G> A 0.65 0.05 0.711 LCR 778 LCR 778 PLS1 Normal NA 7.5 3.11 TP53 p.E336K, c.1006G> A 0.56 0.04 0.3 0.209 LCR 779 LCR 779 PLS1 Normal NA 7.5 11.52 CTNNB1 p.S33P , c.97T> C 0.97 0.01 0.3 0.721 CSF 780 CSF 780 PLS1 Normal NA 7.5 5.49 TP53 p.R202C, c.604C> T 0.87 0.06 1.0 0.122 CSF 781 LCR 781 PLS1 Normal NA 7.5 3.34 TP53 p.E11K, c.31G> A 0.44 0.11 1.1 0.125 LCR 782 LCR 782 PLS1 Normal NA 7.5 3.10 TP53 p.R248Q, c.743G> A 0.44 0.13 1.2 0.220 LCR 783 LCR 783 PLS1 Normal NA 7.5 4.26 TP53 p.K373R, c.1118A> G 1 , 06 0.05 0.7 0.73 LCR 784 LCR 784 PLS1 Normal NA 7.5 4.30 TP53 p.T118I, c.353C> T 1.13 0.05 0.738 LCR 785 LCR 785 PLS1 Normal NA 7 , 5 6.71 TP53 p.L369P, c.1106T> C 0.89 0.05 1.0 0.827 LCR 786 LCR 786 PLS1 Normal NA 7.5 5.05 TP53 p.E298K, c.892G> A 0, 65 0.02 0.3 0.333 CRL 812 CRL 812 PLS1 Pancreas II 7.5 31.37 TP53 p.R249S, c.747G> T 2.19 0.10 9.8 0.999 CRL 814 CRL 814 PLS1 Pancreas II 7, 5 5.46 TP53 p.K372fs, c.1114delA 1.09 0.05 0.9 0.711 NL 918 NL PLS 918 Normal NA 7.5 3.67 TP53 G.7577018C> T (Junction site) 0.00 0 , 03 0.3 0.117 NL 919 NL PLS 919 Normal NA 7.5 1.98 TP53 p.R248Q, c.743G> A 0.85 0.07 0.5 0.875 NL 920 NL PLS 920 Normal NA 7.5 2 , 89 TP53 p.R333H, c.998G> A 0.89 0.07 0.6 0.122 NL 921 NL PLS 921 Normal NA 7.5 0.88 TP53 p.V157I, c.469G> A 0.00 0, 09 0.2 0.125 NL 922 NL PLS 922 Normal NA 7.5 3.69 TP53 p.C242Y, c.725G> A 0.55 0.03 0.3 0.116 NL 923 NL PLS 923 Normal NA 7.5 1, 25 TP53 p.R248Q, c.743G> A 0.00 0.03 0.1 0.610 NL 924 NL PLS 924 Normal NA 7.5 0.87 TP53 p.S313G, c.937A> G 1.18 0.1 5 0.4 0.335 NL 925 NL PLS 925 Normal NA 7.5 3.08 TP53 p.R181C, c.541C> T 0.38 0.02 0.2 0.121 NL 930 NL PLS 930 Normal NA 7.5 2, 79 TP53 p.R175H, c.524G> A 0.59 0.07 0.6 0.192 NL 931 NL PLS 931 Normal NA 7.5 6.06 TP53 G.7576927C> T (Junction site) 1.00 0, 01 0.2 0.300 NL 932 NL PLS 932 Normal NA 7.5 0.57 CTNNB1 p.A43T, c.127G> A 0.47 0.05 0.1 0.116 NL 938 NL PLS 938 Normal NA 7.5 2, 43 KRAS p.G12A, c.35G> C 0.42 0.03 0.2 0.117 NL 939 NL PLS 939 Normal NA 7.5 4.19 TP53 p.K351R, c.1052A> G 0.70 0.05 0.6 0.599 NL 940 NL PLS 940 Normal NA 7.5 2.27 TP53 p.R379H, c.1136G> A 0.54 0.05 0.4 0.117 NL 941 NL PLS 941 Normal NA 7.5 3.08 TP53 p.P309S, c.925C> T 0.85 0.09 0.9 0.116 NL 942 NL PLS 942 Normal NA 7.5 13.09 TP53 p.R379P, c.1136G> C 0.91 0.01 0 , 3 0.660 NL 943 NL PLS 943 Normal NA 7.5 2.60 KRAS p.G60D, c.179G> A 0.48 0.04 0.3 0.122 NL 944 NL PLS 944 Normal NA 7.5 1.99 TP53 p.R282Q, c.845G> A 0.83 0.09 0.5 0.341 NL 945 NL PLS 945 Normal NA 7.5 2.20 CDKN2A p.E88D, c.264G> T 0.70 0.02 0, 1 0.843 NL 946 NL PLS 94 6 Normal NA 7.5 1.61 TP53 p.R181C, c.541C> T 0.53 0.13 0.7 0.382 NL 947 NL PLS 947 Normal NA 7.5 11.63 TP53 p.A86T, c.256G > A 0.90 0.02 0.7 0.558 NL 948 NL PLS 948 Normal NA 7.5 2.42 TP53 p.W91G, c.271T> G 0.00 0.05 0.3 0.125 NL 949 NL PLS 949 Normal NA 7.5 3.08 TP53 p.V274G, c.821T> G 0.83 0.04 0.4 0.117 NL 950 NL PLS 950 Normal NA 7.5 10.82 TP53 p.P60L, c.179C> T 1.15 0.02 0.8 0.320 NL 951 NL PLS 951 Normal NA 7.5 3.64 TP53 p.R333H, c.998G> A 0.67 0.06 0.7 0.166 NL 952 NL PLS 952 Normal NA 7.5 3.19 TP53 p.G226D, c.677G> A 0.67 0.05 0.5 0.230 NL 964 NL PLS 964 Normal NA 7.5 3.43 TP53 p.P278R, c.833C> G 0.92 0.06 0.7 0.255 NL 965 NL PLS 965 Normal NA 7.5 3.49 TP53 p.A161V, c.482C> T 1.10 0.03 0.3 0.612 NL 1160 NL PLSA 1160 Normal NA 7.5 5.60 TP53 p.L348S, c.1043T> C 1.00 0.06 1.1 0.677 NL 1163 NL PLSA 1163 Normal NA 7.5 5.59 TP53 p.A138T, c.412G> A 0 , 73 0.03 0.6 0.125 NL 1164 NL PLSA 1164 Normal NA 7.5 6.27 TP53 p.Q354H, c.1062G> T 1.10 0.02 0.5 0.319 NL 1165 NL PLSA 1165 Normal NA 7 , 5 5.04 TP53 p .L369M, c.1105C> A 1.04 0.03 0.5 0.303 NL 1166 NL PLSA 1166 Normal NA 7.5 4.96 TP53 p.K373N, c.1119G> T 0.91 0.01 0.2 0.117 NL 1167 NL PLSA 1167 Normal NA 7.5 3.00 TP53 p.G374fs, c.1120delG 1.24 0.06 0.5 0.329 NL 1168 NL PLSA 1168 Normal NA 7.5 2.17 PTEN p.D153N, c.457G> A 0.60 0.03 0.2 0.121 NL 1169 NL PLSA 1169 Normal NA 7.5 3.98 TP53 p.A276V, c.827C> T 0.71 0.08 0.9 0.125 NL 1170 NL PLSA 1170 Normal NA 7.5 2.15 TP53 p.C176fs, c.528insC 0.68 0.07 0.4 0.125

NL 1171 NL PLSA 1171 Normal NA 7.5 2.66 TP53 p.G374S, c.1120G> A 0.87 0.04 0.3 0.122 NL 1172 NL PLSA 1172 Normal NA 7.5 6.58 TP53 p.G374fs , c.1120delG 1.26 0.05 1.0 0.334 NL 1173 NL PLSA 1173 Normal NA 7.5 5.93 TP53 p.R174M, c.521G> T 0.89 0.03 0.6 0.125 NL 1174 NL PLSA 1174 Normal NA 7.5 2.60 TP53 p.A138T, c.412G> A 0.48 0.04 0.3 0.122 NL 1175 NL PLSA 1175 Normal NA 7.5 4.11 TP53 p.A161V, c. 482C> T 0.91 0.04 0.5 0.121 NL 1176 NL PLSA 1176 Normal NA 7.5 7.64 PIK3CA p.E81K, c.241G> A 0.95 0.02 0.5 0.125 NL 1177 NL PLSA 1177 Normal NA 7.5 7.08 TP53 p.G374fs, c.1120insG 1.29 0.02 0.4 0.338 NL 1178 NL PLSA 1178 Normal NA 7.5 2.18 TP53 p.K372fs, c.1114delA 0, 66 0.03 0.2 0.123 NL 1179 NL PLSA 1179 Normal NA 7.5 1.86 TP53 p.R283H, c.848G> A 1.07 0.10 0.6 0.316 NL 1180 NL PLSA 1180 Normal NA 7, 5 3.46 TP53 p.K372fs, c.1114delA 0.80 0.03 0.4 0.121 NL 1181 NL PLSA 1181 Normal NA 7.5 1.80 TP53 p.R202C, c.604C> T 1.06 0, 17 1.0 0.633 NL 1182 NL PLSA 1182 Normal NA 7.5 4.79 PPP2R1A p.A184V, c.551C> T 1.16 0, 06 0.9 0.322 NL 1183 NL PLSA 1183 Normal NA 7.5 3.63 TP53 p.P36S, c.106C> T 1.02 0.07 0.8 0.297 NL 1184 NL PLSA 1184 Normal NA 7.5 4, 58 CTNNB1 p.G34R, c.100G> C 0.80 0.01 0.1 0.357 NL 1185 NL PLSA 1185 Normal NA 7.5 2.85 TP53 p.K373E, c.1117A> G 0.58 0.01 0.1 0.125 NL 1186 NL PLSA 1186 Normal NA 7.5 3.38 TP53 p.R306 *, c.916C> T 0.70 0.02 0.2 0.117 NL 1187 NL PLSA 1187 Normal NA 7.5 1, 63 TP53 p.K372fs, c.1114delA 0.62 0.04 0.2 0.117 NL 1188 NL PLSA 1188 Normal NA 7.5 1.61 FBXW7 p.R505H, c.1514G> A 0.67 0.06 0, 3 0.316 NL 1189 NL PLSA 1189 Normal NA 7.5 3.02 TP53 p.R273H, c.818G> A 0.94 0.09 0.9 0.317 NL 1190 NL PLSA 1190 Normal NA 7.5 6.88 TP53 p .R379H, c.1136G> A 0.68 0.04 0.8 0.121 NL 1191 NL PLSA 1191 Normal NA 7.5 5.24 GNAS p.R201H, c.602G> A 1.16 0.06 1.0 0.320 NL 1192 NL PLSA 1192 Normal NA 7.5 5.67 TP53 p.E336G, c.1007A> G 0.75 0.01 0.2 0.125 NL 1193 NL PLSA 1193 Normal NA 7.5 6.97 TP53 p. R267W, c.799C> T 0.96 0.05 1.0 0.125 NL 1194 NL PLSA 1194 Normal NA 7.5 3.12 TP53 p.E298K, c.892G> A 0.6 5 0.10 0.9 0.334 NL 1195 NL PLSA 1195 Normal NA 7.5 7.64 TP53 p.A84T, c.250G> A 0.93 0.02 0.4 0.311 NL 1196 NL PLSA 1196 Normal NA 7, 5 6.18 CDKN2A p.S56N, c.167G> A 1.06 0.02 0.5 0.309 NL 1197 NL PLSA 1197 Normal NA 7.5 4.67 TP53 p.K372fs, c.1114delA 1.07 0, 05 0.7 0.489 NL 1198 NL PLSA 1198 Normal NA 7.5 2.74 TP53 p.P89L, c.266C> T 0.52 0.03 0.3 0.125 NL 1199 NL PLSA 1199 Normal NA 7.5 5, 06 TP53 p.A86T, c.256G> A 0.66 0.03 0.4 0.125 NL 1200 NL PLSA 1200 Normal NA 7.5 2.12 GNAS p.R201H, c.602G> A 1.09 0.15 1.0 0.316 NL 1201 NL PLSA 1201 Normal NA 7.5 2.12 TP53 p.A129T, c.385G> A 0.61 0.05 0.3 0.125 NL 1202 NL PLSA 1202 Normal NA 7.5 3.17 TP53 G.7576928T> C (Junction site) 0.53 0.03 0.3 0.117 NL 1203 NL PLSA 1203 Normal NA 7.5 6.06 TP53 p.F341S, c.1022T> C 1.18 0.02 0.4 0.322 NL 1204 NL PLSA 1204 Normal NA 7.5 8.29 KRAS p.G13D, c.38G> A 0.88 0.02 0.6 0.125 NL 1205 NL PLSA 1205 Normal NA 7.5 1.48 TP53 p.K373R, c.1118A> G 0.85 0.08 0.4 0.125 NL 1206 NL PLSA 1206 Normal NA 7.5 9.14 TP53 p.R273H, c.818G > A 1.17 0.15 4.1 0.323 NL 1207 NL PLSA 1207 Normal NA 7.5 0.13 No detection.

NA NA NA 0.125 NL 1208 NL PLSA 1208 Normal NA 7.5 3.99 TP53 p.A189T, c.565G> A 0.82 0.02 0.3 0.125 NL 1209 NL PLSA 1209 Normal NA 7.5 7.98 TP53 p.R202C, c.604C> T 1.05 0.05 1.1 0.302 NL 1210 NL PLSA 1210 Normal NA 7.5 3.60 TP53 p.R337H, c.1010G> A 0.40 0.03 0 , 3 0.123 NL 1211 NL PLSA 1211 Normal NA 7.5 3.60 TP53 p.I254F, c.760A> T 1.06 0.08 0.9 0.310 NL 1212 NL PLSA 1212 Normal NA 7.5 3.21 TP53 p.R342 *, c.1024C> T 0.95 0.08 0.8 0.121 NL 1213 NL PLSA 1213 Normal NA 7.5 5.61 PPP2R1A p.R183W, c.547C> T 0.76 0.03 0 , 4 0.122 NL 1214 NL PLSA 1214 Normal NA 7.5 2.82 TP53 p.R283C, c.847C> T 0.65 0.04 0.4 0.121 NL 1215 NL PLSA 1215 Normal NA 7.5 1.75 TP53 p.R273H, c.818G> A 0.78 0.08 0.4 0.122 NL 1216 NL PLSA 1216 Normal NA 7.5 3.06 TP53 p.P36S, c.106C> T 0.31 0.05 0, 5 0.565 NL 1217 NL PLSA 1217 Normal NA 7.5 1.42 TP53 p.R175L, c.524G> T 0.00 0.09 0.4 0.121 NL 1218 NL PLSA 1218 Normal NA 7.5 6.73 GNAS p .R201C, c.601C> T 1.18 0.05 1.0 0.320 NL 1219 NL PLSA 1219 Normal NA 7.5 9.54 TP53 p.S260T, c.778T> A 0.89 0.01 0.3 0.117 NL 1220 NL PLSA 1220 Normal NA 7.5 4.83 TP53 p.P309S, c.925C> T 0.73 0.04 0.6 0.117 NL 1221 NL PLSA 1221 Normal NA 7.5 3.08 CDKN2A p.H83Y, c.247C> T 0.00 0.04 0.3 0.125 NL 1222 NL PLSA 1222 Normal NA 7.5 3.49 TP53 p.L369P, c.1106T> C 0.71 0 , 06 0.6 0.121 NL 1223 NL PLSA 1223 Normal NA 7.5 3.21 TP53 p.R335H, c.1004G> A 0.68 0.07 0.721 NL 1224 NL PLSA 1224 Normal NA 7.5 6 , 25 PTEN p.F154fs, c.461delT 0.80 0.03 0.5 0.116 NL 1225 NL PLSA 1225 Normal NA 7.5 5.68 TP53 p.R174G, c.520A> G 0.68 0.04 0 , 7 0.123 NL 1226 NL PLSA 1226 Normal NA 7.5 2.18 TP53 p.K372fs, c.1114delA 0.89 0.05 0.4 0.117 NL 1227 NL PLSA 1227 Normal NA 7.5 4.17 TP53 p. Q192H, c.576G> C 0.75 0.01 0.2 0.125 NL 1228 NL PLSA 1228 Normal NA 7.5 1.53 TP53 p.A159T, c.475G> A 0.43 0.09 0.4 0.117 NL 1229 NL PLSA 1229 Normal NA 7.5 4.69 PPP2R1A p.R183Q, c.548G> A 0.89 0.06 0.8 0.125 NL 1230 NL PLSA 1230 Normal NA 7.5 1.63 TP53 p.R202C , c.604C> T 0.81 0.10 0.5 0.125 NL 1231 NL PLSA 1231 Normal NA 7.5 5.06 TP53 p.K372fs, c.1114 delA 0.86 0.05 0.7 0.2121 NL 1232 NL PLSA 1232 Normal NA 7.5 5.50 TP53 p.L93P, c.278T> C 0.83 0.03 0.5 0.117 NL 1233 NL PLSA 1233 Normal NA 7.5 3.84 TP53 p.R202C, c.604C> T 1.37 0.11 1.3 0.355 NL 1234 NL PLSA 1234 Normal NA 7.5 5.01 TP53 p.K372fs, c.1114delA 0, 91 0.05 0.7 0.21 NL 1235 NL PLSA 1235 Normal NA 7.5 3.19 TP53 p.P222L, c.665C> T 0.87 0.15 1.4 0.575 NL 1236 NL PLSA 1236 Normal NA 7, 5 5.35 TP53 p.L369P, c.1106T> C 0.68 0.03 0.5 0.122

NL 1237 NL PLSA 1237 Normal NA 7.5 11.50 TP53 p.G374fs, c.1120delG 0.78 0.02 0.8 0.121 NL 1238 NL PLSA 1238 Normal NA 7.5 7.90 HRAS p.G12C, c .34G> T 1.16 0.01 0.2 0.320 NL 1239 NL PLSA 1239 Normal NA 7.5 3.06 TP53 p.A353V, c.1058C> T 0.54 0.06 0.6 0.121 NL 1240 NL PLSA 1240 Normal NA 7.5 5.08 TP53 p.A189V, c.566C> T 0.93 0.04 0.6 0.258 NL 1241 NL PLSA 1241 Normal NA 7.5 7.23 TP53 p.Y236C, c. 707A> G 1.16 0.03 0.7 0.532 NL 1242 NL PLSA 1242 Normal NA 7.5 5.15 KRAS p.V14I, c.40G> A 0.57 0.03 0.4 0.123 NL 1243 NL PLSA 1243 Normal NA 7.5 5.93 PPP2R1A p.A184V, c.551C> T 0.62 0.04 0.6 0.121 NL 1244 NL PLSA 1244 Normal NA 7.5 9.27 TP53 p.P85T, c.253C > A 1.16 0.02 0.5 0.321 NL 1245 NL PLSA 1245 Normal NA 7.5 11.82 TP53 p.A276V, c.827C> T 1.05 0.03 1.0 0.309 NL 1246 NL PLSA 1246 Normal NA 7.5 6.82 TP53 p.A138T, c.412G> A 0.85 0.03 0.5 0.123 NL 1247 NL PLSA 1247 Normal NA 7.5 5.34 TP53 p.R306Q, c.917G> A 1.39 0.08 1.3 0.727 NL 1248 NL PLSA 1248 Normal NA 7.5 6.67 TP53 p.A353V, c.1058C> T 0.91 0.04 0.9 0.121 NL 1249 NL PLSA 1249 Normal NA 7.5 3.68 TP53 p.R290C, c.868C> T 0.29 0.03 0.3 0.123 NL 1250 NL PLSA 1250 Normal NA 7.5 4.29 TP53 p.T125M , c.374C> T 0.76 0.07 1.0 0.248 NL 1251 NL PLSA 1251 Normal NA 7.5 1.46 TP53 p.R335H, c.1004G> A 0.79 0.07 0.3 0.121 NL 1252 NL PLSA 1252 Normal NA 7.5 0.85 TP53 p.S260fs, c.778insT 0.00 0.09 0.2 0.374 NL 1253 NL PLSA 1253 Normal NA 7.5 1.16 KRAS p.G13D, c. 38G> A 0.45 0.06 0.2 0.121 NL 1254 NL PLSA 1254 Normal NA 7.5 1.68 TP53 p.R273H, c.818G> A 0.96 0.27 1.4 0.584 NL 1255 NL PLSA 1255 Normal NA 7.5 1.06 TP53 p.R273H, c.818G> A 0.90 0.61 2.0 0.297 NL 1256 NL PLSA 1256 Normal NA 7.5 3.88 TP53 p.R273H, c.818G > A 0.89 0.09 1.0 0.117 NL 1257 NL PLSA 1257 Normal NA 7.5 1.85 TP53 p.G262D, c.785G> A 0.62 0.08 0.4 0.341 NL 1258 NL PLSA 1258 Normal NA 7.5 1.21 TP53 p.R273H, c.818G> A 1.11 0.53 2.0 0.324 NL 1259 NL PLSA 1259 Normal NA 7.5 1.62 TP53 p.R290C, c.868C> T 0.00 0.04 0.2 0.604 NL 1260 NL PLSA 1260 Normal NA 7.5 0.03 No detection.

NA NA NA 0.116 NL 1291 NL PLSA 1291 Normal NA 7.5 5.28 TP53 p.E298K, c.892G> A 0.61 0.03 0.4 0.117 NL 1292 NL PLSA 1292 Normal NA 7.5 2.31 TP53 p.R273H, c.818G> A 1.45 0.27 1.9 0.376 NL 1293 NL PLSA 1293 Normal NA 7.5 2.12 TP53 p.S260fs, c.779insC 1.16 0.06 0.4 0.514 NL 1294 NL PLSA 1294 Normal NA 7.5 1.27 TP53 p.R273H, c.818G> A 1.73 0.47 1.8 0.416 NL 1295 NL PLSA 1295 Normal NA 7.5 2.70 TP53 p. R337C, c.1009C> T 0.37 0.03 0.2 0.117 NL 1296 NL PLSA 1296 Normal NA 7.5 4.08 FBXW7 p.R465H, c.1394G> A 0.31 0.04 0.5 0.116 NL 1297 NL PLSA 1297 Normal NA 7.5 3.27 TP53 p.K372fs, c.1114delA 0.86 0.04 0.4 0.117 NL 1298 NL PLSA 1298 Normal NA 7.5 2.88 TP53 p.L93P, c .278T> C 0.92 0.05 0.5 0.122 NL 1299 NL PLSA 1299 Normal NA 7.5 4.87 TP53 p.F134V, c.400T> G 0.93 0.02 0.3 0.125 NL 1300 NL PLSA 1300 Normal NA 7.5 4.81 TP53 p.C176fs, c.528insC 0.36 0.03 0.5 0.242 NL 1301 NL PLSA 1301 Normal NA 7.5 1.98 TP53 p.R175C, c.523C> T 0.47 0.12 0.7 0.573 NL 1302 NL PLSA 1302 Normal NA 7.5 8.45 FBXW7 p.R505L, c.1514G> T 1.18 0.04 1.0 0.833 NL 1303 NL PLSA 1303 Normal NA 7.5 23.63 TP53 p.L348S, c.1043T> C 0.98 0.02 1.6 0.122 NL 1304 NL PLSA 1304 Normal NA 7.5 13.29 TP53 p.K372fs, c.1114delA 0.96 0.06 2.4 0.117 NL 1305 NL PLSA 1305 Normal NA 7.5 6.96 TP53 p.A189V, c.566C> T 0, 72 0.02 0.4 0.192 NL 1306 NL PLSA 1306 Normal NA 7.5 3.66 TP53 p.G279R, c.835G> C 1.78 0.10 1.2 0.409 NL 1307 NL PLSA 1307 Normal NA 7, 5 2.63 TP53 p.E287D, c.861G> T 0.47 0.03 0.3 0.125 NL 1308 NL PLSA 1308 Normal NA 7.5 8.50 TP53 p.F134L, c.400T> C 0.77 0.01 0.3 0.121 NL 1309 NL PLSA 1309 Normal NA 7.5 5.78 TP53 p.R342 *, c.1024C> T 0.57 0.02 0.3 0.125 NL 1310 NL PLSA 1310 Normal NA 7, 5 0.12 No detection

NA NA NA 0.414 NL 1311 NL PLSA 1311 Normal NA 7.5 0.39 TP53 p.M133I, c.399G> T 0.00 0.12 0.1 0.553 NL 1312 NL PLSA 1312 Normal NA 7.5 0.33 TP53 p.E11K, c.31G> A 0.00 0.10 0.1 0.121 NL 1313 NL PLSA 1313 Normal NA 7.5 0.80 TP53 p.P301T, c.901C> A 0.00 0.08 0 , 2 0.117 NL 1314 NL PLSA 1314 Normal NA 7.5 3.34 TP53 p.A353V, c.1058C> T 0.62 0.06 0.6 0.125 NL 1315 NL PLSA 1315 Normal NA 7.5 3.83 TP53 p.R273H, c.818G> A 0.66 0.09 1.0 0.576 NL 1316 NL PLSA 1316 Normal NA 7.5 26.99 TP53 p.A353V, c.1058C> T 0.97 0.03 2, 1 0.117 NL 1317 NL PLSA 1317 Normal NA 7.5 6.83 TP53 p.K372fs, c.1114insA 0.63 0.02 0.4 0.362 NL 1318 NL PLSA 1318 Normal NA 7.5 2.49 TP53 p.R273H , c.818G> A 0.70 0.12 0.9 0.121 NL 1319 NL PLSA 1319 Normal NA 7.5 5.24 GNAS p.R201H, c.602G> A 0.83 0.05 0.8 0.116 NL 1320 NL PLSA 1320 Normal NA 7.5 4.61 TP53 p.R306 *, c.916C> T 0.81 0.05 0.7 0.255 NL 1321 NL PLSA 1321 Normal NA 7.5 1.52 TP53 p.P177H , c.530C> A 0.58 0.08 0.4 0.121 NL 1322 NL PLSA 1322 Normal NA 7.5 2.85 TP53 p.Y234C, c.701A> G 0.57 0.07 0.6 0.125 NL 1323 NL PLSA 1323 Normal NA 7.5 0.09 No detection.

NA NA NA 0.121 NL 1324 NL PLSA 1324 Normal NA 7.5 0.02 No detection.

NA NA NA 0.123 NL 1325 NL PLSA 1325 Normal NA 7.5 0.08 No detection.

NA NA NA 0.331 NL 1326 NL PLSA 1326 Normal NA 7.5 6.36 EGFR p.E868G, c.2603A> G 1.37 0.09 1.8 0.356 NL 1327 NL PLSA 1327 Normal NA 7.5 0.08 No detec.

NA NA NA 0.122 NL 1328 NL PLSA 1328 Normal NA 7.5 2.14 TP53 p.K372fs, c.1114delA 0.78 0.05 0.3 0.125 NL 1329 NL PLSA 1329 Normal NA 7.5 1.05 TP53 p .E339G, c.1016A> G 0.78 0.03 0.1 0.125 NL 1330 NL PLSA 1330 Normal NA 7.5 1.62 TP53 p.R273H, c.818G> A 0.97 0.18 0.9 0.121 NL 1331 NL PLSA 1331 Normal NA 7.5 1.04 TP53 p.A138T, c.412G> A 0.68 0.06 0.2 0.122 NL 1332 NL PLSA 1332 Normal NA 7.5 2.01 TP53 p. L264P, c.791T> C 0.91 0.03 0.2 0.117

NL 1333 NL PLSA 1333 Normal NA 7.5 3.52 TP53 p.A161V, c.482C> T 0.85 0.03 0.3 0.123 NL 1334 NL PLSA 1334 Normal NA 7.5 3.12 TP53 p.G154S , c.460G> A 1.16 0.04 0.4 0.489 NL 1335 NL PLSA 1335 Normal NA 7.5 2.00 TP53 p.R306 *, c.916C> T 1.15 0.08 0.5 0.849 NL 1336 NL PLSA 1336 Normal NA 7.5 1.44 TP53 p.A138T, c.412G> A 1.22 0.07 0.3 0.325 NL 1337 NL PLSA 1337 Normal NA 7.5 2.29 TP53 p.Q375 *, c.1123C> T 0.90 0.02 0.1 0.122 NL 1338 NL PLSA 1338 Normal NA 7.5 2.59 TP53 p.M384V, c.1150A> G 0.91 0.03 0.3 0.188 NL 1339 NL PLSA 1339 Normal NA 7.5 2.72 CDKN2A p.V82M, c.244G> A 0.77 0.03 0.2 0.117 NL 1340 NL PLSA 1340 Normal NA 7.5 3.30 TP53 p.P250S , c.748C> T 0.77 0.02 0.2 0.125 NL 1341 NL PLSA 1341 Normal NA 7.5 2.13 TP53 p.R202C, c.604C> T 0.88 0.06 0.4 0.125 NL 1342 NL PLSA 1342 Normal NA 7.5 1.86 TP53 p.R273H, c.818G> A 0.99 0.09 0.5 0.205 NL 1343 NL PLSA 1343 Normal NA 7.5 1.22 TP53 p.R273H, c.818G> A 0.65 0.10 0.4 0.122 NL 1344 NL PLSA 1344 Normal NA 7.5 1.63 TP53 G.7577156C> G (Junction site) 0.77 0.01 0, 1 0.125 NL 1345 NL PLSA 1345 Normal NA 7.5 0.75 TP53 p.G108S, c.322G> A 0.57 0.15 0.4 0.125 NL 1346 NL PLSA 1346 Normal NA 7.5 1.31 TP53 p .K373R, c.1118A> G 0.74 0.03 0.1 0.346 NL 1347 NL PLSA 1347 Normal NA 7.5 2.67 TP53 p.A189V, c.566C> T 0.85 0.02 0.2 0.343 NL 1348 NL PLSA 1348 Normal NA 7.5 1.94 TP53 p.E336G, c.1007A> G 0.82 0.02 0.1 0.125 NL 1349 NL PLSA 1349 Normal NA 7.5 4.77 TP53 p. R202C, c.604C> T 0.82 0.04 0.5 0.117 NL 1350 NL PLSA 1350 Normal NA 7.5 2.11 TP53 p.L369P, c.1106T> C 0.77 0.03 0.2 0.397 NL 1351 NL PLSA 1351 Normal NA 7.5 1.86 TP53 p.E258D, c.774A> T 1.10 0.05 0.3 0.317 NL 1352 NL PLSA 1352 Normal NA 7.5 1.12 TP53 p.K373R , c.1118A> G 0.83 0.03 0.1 0.125 NL 1353 NL PLSA 1353 Normal NA 7.5 3.05 TP53 p.L130P, c.389T> C 1.09 0.02 0.2 0.593 NL 1354 NL PLSA 1354 Normal NA 7.5 7.82 TP53 p.P300T, c.898C> A 1.04 0.00 0.1 0.299 NL 1355 NL PLSA 1355 Normal NA 7.5 1.15 TP53 G.7576928T> C (Junction site) 0.87 0.04 0.1 0.116 NL 1356 NL PLSA 1356 Normal NA 7.5 2.61 EGFR p.A864T, c.2590G> A 1, 15 0.02 0.1 0.327 NL 1357 NL PLSA 1357 Normal NA 7.5 1.41 TP53 p.L369P, c.1106T> C 0.88 0.08 0.3 0.121 NL 1358 NL PLSA 1358 Normal NA 7, 5 1.83 TP53 p.A138T, c.412G> A 1.14 0.05 0.3 0.553 NL 1359 NL PLSA 1359 Normal NA 7.5 0.72 TP53 p.R283C, c.847C> T 0.38 0.05 0.1 0.570 NL 1360 NL PLSA 1360 Normal NA 7.5 0.89 TP53 p.R283H, c.848G> A 0.74 0.07 0.2 0.125 NL 1361 NL PLSA 1361 Normal NA 7.5 1.59 TP53 p.D281G, c.842A> G 0.70 0.05 0.3 0.123 NL 1362 NL PLSA 1362 Normal NA 7.5 0.47 TP53 p.R333H, c.998G> A 0.29 0 , 06 0.1 0.122 NL 1363 NL PLSA 1363 Normal NA 7.5 0.83 TP53 p.E346G, c.1037A> G 0.39 0.05 0.1 0.125 NL 1364 NL PLSA 1364 Normal NA 7.5 1 , 82 TP53 p.S261S, c.783T> C (Beg. exon) 1.10 0.03 0.2 0.441 NL 1365 NL PLSA 1365 Normal NA 7.5 1.58 TP53 p.K370E, c.1108A> G 1.17 0.03 0.1 0.325 NL 1366 NL PLSA 1366 Normal NA 7.5 1.98 TP53 p.R335C, c.1003C> T 0.65 0.03 0.2 0.125 NL 1367 NL PLSA 1367 Normal NA 7.5 1.81 TP53 p.A138V, c.413C> T 1.47 0.04 0.2 0.383 NL 1368 NL PLSA 1368 Normal NA 7.5 3.16 TP53 p.P36S, c.106C> T 0.95 0.06 0.6 0.121 NL 1369 NL PLSA 1369 Normal NA 7.5 3.21 FBXW7 p.R505H, c.1514G> A 1.00 0.03 0.3 0.618 NL 1370 NL PLSA 1370 Normal NA 7.5 1.65 TP53 p.R273H, c.818G> A 1.03 0.11 0.6 0.304 NL 1371 NL PLSA 1371 Normal NA 7.5 1.20 TP53 p.R283H, c.848G> A 0.58 0.03 0.1 0.116 NL 1372 NL PLSA 1372 Normal NA 7.5 1.19 KRAS p.A11V, c.32C> T 0.66 0.05 0.2 0.125 NL 1373 NL PLSA 1373 Normal NA 7.5 7.04 TP53 p.L194P, c.581T> C 1 , 10 0.01 0.1 0.311 NL 1374 NL PLSA 1374 Normal NA 7.5 1.77 TP53 p.R337C, c.1009C> T 0.79 0.05 0.3 0.117 NL 1375 NL PLSA 1375 Normal NA 7 , 5 1.51 TP53 p.C135Y, c.404G> A 0.50 0.02 0.1 0.117 NL 1376 NL PLSA 1376 Normal NA 7.5 1.53 TP53 p.A161T, c.481 G> A 0.79 0.04 0.2 0.121 NL 1377 NL PLSA 1377 Normal NA 7.5 2.06 TP53 p.R202C, c.604C> T 1.14 0.06 0.3 0.319 NL 1378 NL PLSA 1378 Normal NA 7.5 5.39 FBXW7 p.R465C, c.1393C> T 0.99 0.07 1.2 0.122 NL 1379 NL PLSA 1379 Normal NA 7.5 1.79 KRAS p.A146T, c.436G > A 0.84 0.02 0.1 0.125 NL 1380 NL PLSA 1380 Normal NA 7.5 1.80 TP53 p.A159T, c.475G> A 0.69 0.06 0.3 0.125 NL 1381 NL PLSA 1381 Normal NA 7.5 1.94 TP53 p.A159T, c.475G> A 0.59 0.04 0.2 0.123 NL 1382 NL PLSA 1382 Normal NA 7.5 2.05 TP53 p.P36S, c.106C> T 1.05 0.04 0.3 0.305 NL 1383 NL PLSA 1383 Normal NA 7.5 1.85 TP53 p.A86T, c.256G> A 0.65 0.02 0.1 0.337 NL 1384 NL PLSA 1384 Normal NA 7.5 1.93 FBXW7 p.E369 *, c.1105G> T 1.01 0.02 0.1 0.301 NL 1385 NL PLSA 1385 Normal NA 7.5 1.36 TP53 p.A189V, c.566C> T 0.57 0.03 0.1 0.366 NL 1386 NL PLSA 1386 Normal NA 7.5 1.73 TP53 p.R202C, c.604C> T 0.97 0.09 0.5 0.122 NL 1387 NL PLSA 1387 Normal NA 7.5 3.35 TP53 p.A189fs, c.566delC 1.17 0.01 0.1 0.489 NL 1388 NL PLSA 1388 Normal NA 7.5 1.66 TP53 p.R202C, c.604 C> T 1.06 0.06 0.3 0.303 NL 1389 NL PLSA 1389 Normal NA 7.5 1.41 TP53 p.R209fs, c.627delA 1.56 0.08 0.3 0.388 NL 1390 NL PLSA 1390 Normal NA 7.5 4.87 EGFR p.L858R, c.2573T> G 1.66 0.06 0.9 0.403 NL 1391 NL PLSA 1391 Normal NA 7.5 1.25 TP53 p.G244D, c.731G> A 0.83 0.04 0.1 0.590 NL 1392 NL PLSA 1392 Normal NA 7.5 1.50 TP53 p.A138T, c.412G> A 0.60 0.03 0.2 0.123 NL 1393 NL PLSA 1393 Normal NA 7.5 5.66 TP53 p.A276V, c.827C> T 0.95 0.04 0.781 NL 1394 NL PLSA 1394 Normal NA 7.5 1.38 TP53 p.P60L, c.179C> T 0 , 65 0.03 0.1 0.191 NL 1395 NL PLSA 1395 Normal NA 7.5 0.58 TP53 p.T125M, c.374C> T 0.55 0.13 0.2 0.117 NL 1396 NL PLSA 1396 Normal NA 7 , 5 1.06 HRAS p.G13D, c.38G> A 0.68 0.03 0.1 0.117 NL 1397 NL PLSA 1397 Normal NA 7.5 2.60 TP53 p.L369P, c.1106T> C 1, 09 0.05 0.4 0.320 NL 1398 NL PLSA 1398 Normal NA 7.5 2.79 TP53 p.R202C, c.604C> T 0.81 0.03 0.3 0.123

NL 1399 NL PLSA 1399 Normal NA 7.5 3.11 TP53 p.A189V, c.566C> T 0.75 0.03 0.3 0.121 NL 1400 NL PLSA 1400 Normal NA 7.5 3.46 TP53 p.A138T , c.412G> A 0.86 0.05 0.5 0.307 NL 1401 NL PLSA 1401 Normal NA 7.5 6.61 TP53 p.R248Q, c.743G> A 0.61 0.03 0.5 0.116 NL 1402 NL PLSA 1402 Normal NA 7.5 1.98 TP53 p.G245S, c.733G> A 0.61 0.05 0.3 0.125 NL 1403 NL PLSA 1403 Normal NA 7.5 6.82 TP53 p.K372fs, c.1114delA 1.07 0.05 1.0 0.311 NL 1404 NL PLSA 1404 Normal NA 7.5 3.03 TP53 p.L369P, c.1106T> C 0.55 0.05 0.4 0.125 NL 1405 NL PLSA 1405 Normal NA 7.5 4.49 TP53 p.R342Q, c.1025G> A 0.57 0.02 0.3 0.123 NL 1406 NL PLSA 1406 Normal NA 7.5 6.08 TP53 p.V10I, c.28G > A 1.12 0.04 0.741 0.841 NL 1407 NL PLSA 1407 Normal NA 7.5 2.94 BRAF p.T599I, c.1796C> T 0.89 0.05 0.5 0.125 NL 1408 NL PLSA 1408 Normal NA 7.5 3.29 TP53 p.R181H, c.542G> A 0.34 0.06 0.6 0.125 NL 1409 NL PLSA 1409 Normal NA 7.5 4.96 TP53 p.R306Q, c.917G> A 0.73 0.06 1.0 0.390 NL 1410 NL PLSA 1410 Normal NA 7.5 1.49 TP53 p.R290H, c.869G> A 0.00 0.04 0.2 0.219 NL 1411 NL PLSA 1411 Normal NA 7.5 3.13 TP53 p.R282W, c.844C> T 0.36 0.05 0.5 0.116 NL 1412 NL PLSA 1412 Normal NA 7.5 6.22 TP53 p.V217M , c.649G> A 0.60 0.02 0.4 0.299 NL 1413 NL PLSA 1413 Normal NA 7.5 11.01 TP53 p.A86T, c.256G> A 0.81 0.02 0.7 0.233 NL 1414 NL PLSA 1414 Normal NA 7.5 3.18 TP53 p.R335C, c.1003C> T 1.31 0.07 0.7 0.345 NL 1425 NL PLSA 1425 Normal NA 7.5 1.28 TP53 p.L348M, c.1042T> A 0.65 0.04 0.2 0.121 NL 1426 NL PLSA 1426 Normal NA 7.5 1.48 TP53 p.R335H, c.1004G> A 0.58 0.02 0.1123 NL 1427 NL PLSA 1427 Normal NA 7.5 1.81 FBXW7 p.R505C, c.1513C> T 0.85 0.03 0.1 0.125 NL 1428 NL PLSA 1428 Normal NA 7.5 2.08 TP53 p.P300fs, c .898delC 0.63 0.04 0.2 0.553 NL 1429 NL PLSA 1429 Normal NA 7.5 1.05 KRAS p.V14I, c.40G> A 0.61 0.05 0.2 0.121 NL 1430 NL PLSA 1430 Normal NA 7.5 2.09 TP53 p.R110C, c.328C> T 1.07 0.05 0.3 0.307 NL 1431 NL PLSA 1431 Normal NA 7.5 3.13 TP53 G.7577018C> T (Junction site .) 0.91 0.02 0.2 0.219 NL 1432 NL PLSA 1432 Normal NA 7.5 2.16 GNAS p.R201H, c.602G> A 0.83 0.04 0.3 0.122 NL 1433 NL PLSA 1433 Normal NA 7.5 2.31 TP53 p.R158H, c.473G> A 0.61 0.05 0.4 0.125 NL 1434 NL PLSA 1434 Normal NA 7.5 3.00 TP53 p.G244A, c.731G> C 0.99 0.01 0.1 0.125 NL 1435 NL PLSA 1435 Normal NA 7.5 2.82 TP53 p.R283H, c.848G> A 0.68 0.02 0 , 1 0.125 NL 1436 NL PLSA 1436 Normal NA 7.5 0.93 TP53 p.R202C, c.604C> T 0.52 0.08 0.2 0.237 NL 1437 NL PLSA 1437 Normal NA 7.5 0.77 TP53 p.G279E, c.836G> A 0.97 0.09 0.2 0.117 NL 1438 NL PLSA 1438 Normal NA 7.5 2.90 TP53 p.R202C, c.604C> T 0.87 0.04 0, 4 0.122 NL 1439 NL PLSA 1439 Normal NA 7.5 1.65 TP53 p.R273H, c.818G> A 0.81 0.13 0.7 0.177 NL 1440 NL PLSA 1440 Normal NA 7.5 5.15 TP53 p .L369P, c.1106T> C 0.80 0.03 0.5 0.117 NL 1441 NL PLSA 1441 Normal NA 7.5 1.05 TP53 p.R290C, c.868C> T 0.74 0.15 0.5 0.125 NL 1442 NL PLSA 1442 Normal NA 7.5 1.11 HRAS p.G13D, c.38G> A 0.43 0.03 0.1 0.117 NL 1443 NL PLSA 1443 Normal NA 7.5 1.71 TP53 p. M384V, c.1150A> G 1.10 0.05 0.2 0.317 NL 1444 NL PLSA 1444 Normal NA 7.5 1.87 TP53 p.P300S, c.898C> T 1.06 0.03 0.2 0.816 NL 1445 NL PLSA 1445 Normal NA 7.5 2.20 TP53 p.A129V, c.386C> T 0.97 0.02 0.2 0.117 NL 1482 NL PLSA 1482 Normal NA 7.5 1.74 TP53 p. A189V, c.566C> T 0.63 0.05 0.2 0.125 NL 1483 NL PLSA 1483 Normal NA 7.5 1.78 TP53 p.P36S, c.106C> T 0.65 0.05 0.3 0.575 NL 1484 NL PLSA 1484 Normal NA 7.5 4.03 TP53 p.T329A, c.985A> G 1.42 0.07 0.8 0.357 NL 1485 NL PLSA 1485 Normal NA 7 2.80 TP53 p.R337H, c .1010G> A 0.90 0.09 0.8 0.117 NL 1486 NL PLSA 1486 Normal NA 7.5 2.60 TP53 p.A189V, c.566C> T 0.85 0.04 0.3 0.125 NL 1487 NL PLSA 1487 Normal NA 7.5 2.87 TP53 p.M133T, c.398T> C 1.21 0.09 0.8 0.338 NL 1488 NL PLSA 1488 Normal NA 7.5 3.72 TP53 p.R196Q, c. 587G> A 0.56 0.04 0.5 0.116 NL 1489 NL PLSA 1489 Normal NA 7.5 2.70 TP53 p.S261G, c.781A> G 1.06 0.07 0.6 0.818 NL 1490 NL PLSA 1490 Normal NA 7.5 2.21 TP53 p.R202C, c.604C> T 0.52 0.10 0.7 0.255 NL 1491 NL PLSA 1491 Normal NA 7.5 3.50 TP53 p.R181H, c.542G > A 1.18 0.06 0.6 0.325 NL 1492 NL PLSA 1492 Normal NA 7.5 2.83 FBXW7 p.R465C, c.1393C> T 0.75 0.06 0.5 0.12 3 NL 1493 NL PLSA 1493 Normal NA 7 6.78 TP53 p.S371F, c.1112C> T 1.00 0.02 0.3 0.297 NL 1494 NL PLSA 1494 Normal NA 7.5 1.99 TP53 p.P36S, c.106C> T 0.77 0.04 0.3 0.116 NL 1495 NL PLSA 1495 Normal NA 7.5 2.78 PPP2R1A p.R183W, c.547C> T 1.32 0.11 0.9 0.355 NL 1496 NL PLSA 1496 Normal NA 7.5 7.53 TP53 p.A353V, c.1058C> T 1.04 0.04 0.9 0.413 NL 1497 NL PLSA 1497 Normal NA 7.5 3.37 TP53 p.R273H, c .818G> A 0.67 0.07 0.7 0.177 NL 1498 NL PLSA 1498 Normal NA 7.5 5.73 TP53 p.A86T, c.256G> A 0.88 0.02 0.4 0.125 NL 1499 NL PLSA 1499 Normal NA 7.5 5.03 TP53 p.L369P, c.1106T> C 1.04 0.06 0.9 0.308 NL 1500 NL PLSA 1500 Normal NA 7.5 21.02 TP53 p.R333H, c. 998G> A 0.88 0.07 4.3 0.193 NL 1501 NL PLSA 1501 Normal NA 7.5 7.00 TP53 p.R202C, c.604C> T 0.70 0.05 1.1 0.125 NL 1502 NL PLSA 1502 Normal NA 7.5 11.11 TP53 p.A189T, c.565G> A 0.70 0.01 0.4 0.123 NL 1503 NL PLSA 1503 Normal NA 7.5 5.63 TP53 p.M169I, c.507G > T 0.90 0.03 0.5 0.117 NL 1504 NL PLSA 1504 Normal NA 7.5 2.51 TP53 p.A138T, c.412G> A 1.00 0.11 0.9 0.11 7 NL 1505 NL PLSA 1505 Normal NA 7.5 3.53 TP53 p.A353V, c.1058C> T 1.14 0.07 0.8 0.457 NL 1506 NL PLSA 1506 Normal NA 7.5 4.43 TP53 p. R110C, c.328C> T 1.38 0.10 1.3 0.509 NL 1507 NL PLSA 1507 Normal NA 7.5 4.87 TP53 p.A276V, c.827C> T 1.12 0.05 0.7 0.31 NL 1508 NL PLSA 1508 Normal NA 7.5 5.26 TP53 p.R174G, c.520A> G 0.87 0.04 0.6 0.125 NL 1509 NL PLSA 1509 Normal NA 7.5 3.99 TP53 p.H168Y , c.502C> T 1.22 0.02 0.3 0.328 NL 1510 NL PLSA 1510 Normal NA 7.5 4.36 FBXW7 p.R505H, c.1514G> A 1.05 0.03 0.4 0.300

NL 1511 NL PLSA 1511 Normal NA 7.5 4.09 TP53 p.Q167R, c.500A> G 1.05 0.02 0.3 0.514 NL 1512 NL PLSA 1512 Normal NA 7.5 3.48 TP53 p.A86E , c.257C> A 0.80 0.03 0.3 0.121 NL 1513 NL PLSA 1513 Normal NA 7.5 6.15 TP53 p.A138T, c.412G> A 0.82 0.03 0.5 0.185 NL 1514 NL PLSA 1514 Normal NA 7.5 2.99 PPP2R1A p.R183Q, c.548G> A 1.14 0.07 0.6 0.329 NL 1515 NL PLSA 1515 Normal NA 7.5 3.68 GNAS p.R201H, c.602G> A 0.46 0.01 0.1 0.238 NL 1516 NL PLSA 1516 Normal NA 7.5 4.02 TP53 p.A138T, c.412G> A 0.63 0.03 0.4 0.117 NL 1517 NL PLSA 1517 Normal NA 7.5 3.34 TP53 p.L369P, c.1106T> C 1.00 0.04 0.4 0.305 NL 1518 NL PLSA 1518 Normal NA 7.5 5.74 TP53 p.A353V, c .1058C> T 1.11 0.05 0.8 0.443 NL 1519 NL PLSA 1519 Normal NA 7.5 4.30 KRAS p.D57N, c.169G> A 0.85 0.02 0.3 0.122 NL 1520 NL PLSA 1520 Normal NA 7.5 6.00 TP53 p.R202C, c.604C> T 0.99 0.06 1.1 0.125 NL 1521 NL PLSA 1521 Normal NA 7.5 4.06 TP53 p.A353V, c. 1058C> T 1.07 0.04 0.5 0.314 NL 1522 NL PLSA 1522 Normal NA 7.5 2.73 TP53 p.E51G, c.152A> G 0.70 0.04 0.4 0.116 N L 1523 NL PLSA 1523 Normal NA 7.5 9.04 PIK3CA p.Q546H, c.1638G> T 1.06 0.01 0.3 0.314 NL 1524 NL PLSA 1524 Normal NA 7.5 11.25 TP53 p.S376P , c.1126T> C 1.11 0.01 0.4 0.600 NL 1525 NL PLSA 1525 Normal NA 7.5 9.45 TP53 p.A353V, c.1058C> T 1.00 0.03 0.8 0.550 NL 1526 NL PLSA 1526 Normal NA 7.5 9.48 TP53 p.C176Y, c.527G> A 0.94 0.02 0.5 0.117 NL 1527 NL PLSA 1527 Normal NA 7.5 7.99 TP53 p.A138T, c.412G> A 1.10 0.07 1.8 0.296 NL 1528 NL PLSA 1528 Normal NA 7.5 6.24 TP53 p.C141Y, c.422G> A 1.03 0.03 0.6 0.301 NL 1529 NL PLSA 1529 Normal NA 7.5 8.39 TP53 p.A189fs, c.566delC 1.40 0.01 0.2 0.814 NL 1530 NL PLSA 1530 Normal NA 7.5 3.93 TP53 p.C176fs, c.528delC 0.68 0.04 0.5 0.421 NL 1531 NL PLSA 1531 Normal NA 7.5 2.30 TP53 p.G262D, c.785G> A 0.80 0.04 0.3 0.116 NL 1532 NL PLSA 1532 Normal NA 7.5 7.77 TP53 p.H179Q, c.537T> A 1.68 0.07 1.7 0.409 NL 1533 NL PLSA 1533 Normal NA 7.5 3.21 TP53 p.M384V, c.1150A> G 1 , 09 0.03 0.3 0.295 NL 1534 NL PLSA 1534 Normal NA 7.5 3.15 TP53 p.Q38R, c.113A> G 1.21 0.05 0.5 0 , 490 NL 1535 NL PLSA 1535 Normal NA 7.5 6.29 PPP2R1A p.R183W, c.547C> T 0.84 0.02 0.4 0.229 NL 1536 NL PLSA 1536 Normal NA 7.5 2.80 TP53 p .A189V, c.566C> T 0.82 0.02 0.2 0.125 NL 1537 NL PLSA 1537 Normal NA 7.5 3.02 TP53 p.P36L, c.107C> T 0.98 0.04 0.3 0.125 NL 1538 NL PLSA 1538 Normal NA 7.5 9.57 TP53 p.R175H, c.524G> A 1.03 0.07 2.2 0.297 NL 1539 NL PLSA 1539 Normal NA 7.5 10.01 TP53 p. H179Q, c.537T> A 0.97 0.03 0.8 0.425 NL 1540 NL PLSA 1540 Normal NA 7.5 2.95 TP53 p.R213 *, c.637C> T 0.70 0.08 0.8 0.123 NL 1541 NL PLSA 1541 Normal NA 7.5 7.27 TP53 p.A353V, c.1058C> T 1.03 0.04 0.8 0.617 NL 1542 NL PLSA 1542 Normal NA 7.5 4.84 TP53 p. A353V, c.1058C> T 0.90 0.04 0.5 0.213 NL 1543 NL PLSA 1543 Normal NA 7.5 2.89 TP53 p.S37P, c.109T> C 0.67 0.03 0.3 0.338 NL 1544 NL PLSA 1544 Normal NA 7.5 4.45 TP53 p.A189V, c.566C> T 0.76 0.02 0.2 0.117 NL 1545 NL PLSA 1545 Normal NA 7.5 8.20 TP53 p.R213 *, c.637C> T 0.73 0.05 1.2 0.451 NL 1546 NL PLSA 1546 Normal NA 7.5 8.19 TP53 p.R174G, c.520A> G 1.20 0.04 0.9 0 , 338 NL 1547 NL PLSA 1547 Normal NA 7.5 3.18 TP53 p.A189V, c.566C> T 0.71 0.02 0.2 0.246 NL 1548 NL PLSA 1548 Normal NA 7.5 2.59 CTNNB1 p .T42I, c.125C> T 0.64 0.02 0.1 0.125 NL 1549 NL PLSA 1549 Normal NA 7.5 3.90 FBXW7 p.R505H, c.1514G> A 0.90 0.05 0.5 0.731 NL 1550 NL PLSA 1550 Normal NA 7.5 6.28 TP53 p.R273H, c.818G> A 0.84 0.08 1.5 0.542 NL 1551 NL PLSA 1551 Normal NA 7.5 5.38 TP53 p. A353V, c.1058C> T 0.65 0.03 0.6 0.123 NL 1552 NL PLSA 1552 Normal NA 7.5 6.18 PIK3CA p.E545Q, c.1633G> C 0.80 0.02 0.3 0.415 NL 1553 NL PLSA 1553 Normal NA 7.5 4.75 TP53 p.S99P, c.295T> C 1.03 0.02 0.3 0.307 NL 1554 NL PLSA 1554 Normal NA 7.5 6.13 TP53 p.L369P , c.1106T> C 0.97 0.03 0.5 0.117 NL 1555 NL PLSA 1555 Normal NA 7.5 5.38 TP53 p.P222S, c.664C> T 1.15 0.04 0.6 0.435 NL 1556 NL PLSA 1556 Normal NA 7.5 2.93 TP53 p.K372fs, c.1114delA 1.06 0.06 0.5 0.305 NL 1557 NL PLSA 1557 Normal NA 7.5 2.63 TP53 p.A161V, c. 482C> T 1.05 0.10 0.8 0.673 NL 1558 NL PLSA 1558 Normal NA 7.5 3.85 TP53 p.P301T, c.901C> A 1.01 0.04 0.5 0.455 NL 1559 NL PLSA 1559 Normal NA 7.5 6.18 TP53 p.S185G, c.553A> G 1.12 0.03 0.6 0.518 NL 1560 NL PLSA 1560 Normal NA 7.5 7.00 TP53 p.R202C, c.604C> T 1.18 0.12 2.6 0.847 NL 1561 NL PLSA 1561 Normal NA 7.5 4.78 TP53 p.A138T, c.412G> A 1.10 0.07 1 , 0 0.472 NL 1562 NL PLSA 1562 Normal NA 7.5 8.41 CDKN2A p.A86fs, c.257delC 1.34 0.02 0.4 0.976 NL 1563 NL PLSA 1563 Normal NA 7.5 6.71 PPP2R1A p. A184V, c.551C> T 0.84 0.03 0.6 0.399 NL 1564 NL PLSA 1564 Normal NA 7.5 1.55 TP53 p.C135Y, c.404G> A 2.19 0.27 1.3 0.485 NL 1565 NL PLSA 1565 Normal NA 7.5 2.15 TP53 p.R267Q, c.800G> A 0.51 0.09 0.6 0.121 NL 1566 NL PLSA 1566 Normal NA 7.5 3.57 FBXW7 p.R367 *, c.1099C> T 0.95 0.06 0.7 0.305 NL 1567 NL PLSA 1567 Normal NA 7.5 2.40 CTNNB1 p.T42I, c.125C> T 0.77 0.02 0.1 0.116 NL 1568 NL PLSA 1568 Normal NA 7.5 3.59 TP53 p.L348S, c.1043T> C 0.55 0.02 0.2 0.207 NL 1569 NL PLSA 1569 Normal NA 7.5 4.89 TP53 p.R273H , c.818G> A 1.10 0.12 1.7 0.616 NL 1570 NL PLSA 1570 Normal NA 7.5 7.36 TP53 p.L369Q, c.1106T> A 1, 02 0.01 0.3 0.295 NL 1571 NL PLSA 1571 Normal NA 7.5 2.64 GNAS p.L203P, c.608T> C 1.05 0.02 0.2 0.303 NL 1572 NL PLSA 1572 Normal NA 7, 5 4.46 TP53 p.T170A, c.508A> G 0.96 0.02 0.3 0.125 NL 1573 NL PLSA 1573 Normal NA 7.5 5.18 TP53 p.K101fs, c.301delA 0.85 0, 01 0.2 0.195 NL 1574 NL PLSA 1574 Normal NA 7.5 13.15 TP53 p.A189V, c.566C> T 0.97 0.02 1.0 0.554 NL 1575 NL PLSA 1575 Normal NA 7.5 7, 39 TP53 p.R174G, c.520A> G 0.99 0.03 0.6 0.333 NL 1576 NL PLSA 1576 Normal NA 7.5 3.74 TP53 p.R283H, c.848G> A 0.94 0.04 0.4 0.121

NL 1577 NL PLSA 1577 Normal NA 7.5 1.36 TP53 p.S20 *, c.59C> A 0.87 0.03 0.1 0.543 NL 1578 NL PLSA 1578 Normal NA 7.5 2.14 TP53 p. R202C, c.604C> T 0.99 0.11 0.7 0.177 NL 1579 NL PLSA 1579 Normal NA 7.5 1.51 TP53 p.R333H, c.998G> A 0.49 0.22 1.0 0.256 NL 1580 NL PLSA 1580 Normal NA 7.5 1.53 TP53 p.R202C, c.604C> T 0.69 0.13 0.6 0.242 NL 1581 NL PLSA 1581 Normal NA 7.5 6.46 TP53 p.R267Q , c.800G> A 0.78 0.02 0.4 0.125 NL 1582 NL PLSA 1582 Normal NA 7.5 1.78 TP53 p.R181H, c.542G> A 0.84 0.17 0.9 0.197 NL 1583 NL PLSA 1583 Normal NA 7.5 4.88 KRAS p.A11T, c.31G> A 0.90 0.02 0.3 0.334 NL 1584 NL PLSA 1584 Normal NA 7.5 1.83 TP53 p.A138T, c.412G> A 0.96 0.10 0.6 0.117 NL 1585 NL PLSA 1585 Normal NA 7.5 3.32 TP53 p.A83T, c.247G> A 0.51 0.03 0.3 0.117 NL 1586 NL PLSA 1586 Normal NA 7.5 1.67 TP53 p.R282Q, c.845G> A 0.45 0.06 0.3 0.382 NL 1587 NL PLSA 1587 Normal NA 7.5 2.55 TP53 p.P92L, c .275C> T 1.13 0.02 0.2 0.719 NL 1588 NL PLSA 1588 Normal NA 7.5 2.44 TP53 p.R342 *, c.1024C> T 0.82 0.09 0.7 0.58 NL 1589 N L PLSA 1589 Normal NA 7.5 4.50 TP53 p.M169T, c.506T> C 1.35 0.02 0.3 0.666 NL 1590 NL PLSA 1590 Normal NA 7.5 3.19 TP53 p.Q100R, c .299A> G 0.52 0.03 0.2 0.123 NL 1591 NL PLSA 1591 Normal NA 7.5 2.13 TP53 p.K351N, c.1053G> T 0.64 0.05 0.3 0.188 NL 1592 NL PLSA 1592 Normal NA 7.5 2.94 TP53 p.R202C, c.604C> T 1.09 0.06 0.5 0.320 NL 1593 NL PLSA 1593 Normal NA 7.5 2.35 TP53 p.R273H, c. 818G> A 0.82 0.14 1.0 0.122 NL 1594 NL PLSA 1594 Normal NA 7.5 2.09 TP53 p.P300S, c.898C> T 0.51 0.04 0.3 0.117 NL 1595 NL PLSA 1595 Normal NA 7.5 3.70 APC p.R1450 *, c.4348C> T 0.94 0.04 0.4 0.396 NL 1596 NL PLSA 1596 Normal NA 7.5 6.14 TP53 p.Q104R, c. 311A> G 1.10 0.02 0.4 0.321 NL 1597 NL PLSA 1597 Normal NA 7.5 2.78 HRAS p.S17G, c.49A> G 0.91 0.00 0.0 0.125 NL 1598 NL PLSA 1598 Normal NA 7.5 9.90 TP53 p.A84V, c.251C> T 0.92 0.01 0.4 0.238 NL 1599 NL PLSA 1599 Normal NA 7.5 4.42 TP53 G.7579311C> T (Site junction) 0.95 0.03 0.3 0.116 NL 1600 NL PLSA 1600 Normal NA 7.5 5.45 FBXW7 p.R505H, c.1514G> A 0.90 0.03 0.6 0.338 NL 1601 NL PLSA 1601 Normal NA 7.5 3.56 TP53 p.R196 *, c.586C> T 0.49 0.03 0.3 0.318 NL 1602 NL PLSA 1602 Normal NA 7.5 3.75 TP53 p.R273H , c.818G> A 0.93 0.07 0.8 0.242 NL 1603 NL PLSA 1603 Normal NA 7.5 3.53 TP53 p.A189V, c.566C> T 0.88 0.03 0.3 0.125 NL 1604 NL PLSA 1604 Normal NA 7.5 2.06 TP53 p.R273H, c.818G> A 0.45 0.08 0.5 0.331 NL 1605 NL PLSA 1605 Normal NA 7.5 2.51 FBXW7 p.R465C, c.1393C> T 0.53 0.03 0.2 0.125 NL 1606 NL PLSA 1606 Normal NA 7.5 1.80 TP53 p.R267Q, c.800G> A 0.87 0.05 0.3 0.121 NL 1607 NL PLSA 1607 Normal NA 7.5 2.47 TP53 p.R273H, c.818G> A 0.74 0.16 1.2 0.381 NL 1608 NL PLSA 1608 Normal NA 7.5 4.78 TP53 p.P309S, c .925C> T 0.90 0.03 0.4 0.121 NL 1609 NL PLSA 1609 Normal NA 7.5 3.27 TP53 p.G108S, c.322G> A 1.13 0.10 1.0 0.301 NL 1610 NL PLSA 1610 Normal NA 7.5 3.49 TP53 p.R290C, c.868C> T 0.77 0.03 0.4 0.121 NL 1611 NL PLSA 1611 Normal NA 7.5 34.53 TP53 p.H179Q, c. 537T> A 0.82 0.01 1.1 0.538 NL 1612 NL PLSA 1612 Normal NA 7.5 2.52 TP53 p.K373R, c.1118A> G 0.89 0.03 0.3 0.125 NL 1613 NL PLSA 1613 Normal NA 7.5 3.33 FBXW7 p.R505C, c.1513C> T 0.83 0.04 0.4 0.117 NL 1614 NL PLSA 1614 Normal NA 7.5 3.86 TP53 p.A138T, c.412G> A 0.76 0.03 0.3 0.125 NL 1615 NL PLSA 1615 Normal NA 7.5 4.06 TP53 p.H115Y, c.343C> T 0.89 0.03 0.4 0.125 NL 1616 NL PLSA 1616 Normal NA 7.5 17.87 TP53 p.R342 *, c.1024C> T 0.88 0.04 2.5 0.815 NL 1617 NL PLSA 1617 Normal NA 7.5 2.80 TP53 p.R273H, c. 818G> A 0.85 0.14 1.2 0.121 NL 1618 NL PLSA 1618 Normal NA 7.5 3.05 TP53 p.A88V, c.263C> T 0.84 0.02 0.2 0.116 NL 1619 NL PLSA 1619 Normal NA 7.5 5.09 TP53 p.R306Q, c.917G> A 0.83 0.05 0.8 0.200 NL 1620 NL PLSA 1620 Normal NA 7.5 7.50 TP53 p.E298K, c.892G > A 1.03 0.04 1.0 0.301 NL 1621 NL PLSA 1621 Normal NA 7.5 2.27 TP53 p.R273H, c.818G> A 0.93 0.06 0.4 0.117 NL 1622 NL PLSA 1622 Normal NA 7.5 4.74 CTNNB1 p.S45A, c.133T> G 2.21 0.04 0.5 0.480 NL 1623 NL PLSA 1623 Normal NA 7.5 3.29 TP53 p.R249M, c.746G> T 0.81 0.12 1.2 0.122 NL 1624 NL PLSA 1624 Normal NA 7.5 0.00 No detection.

NA NA NA 0.121 NL 1625 NL PLSA 1625 Normal NA 7.5 0.00 No detection.

NA NA NA 0.117 NL 1626 NL PLSA 1626 Normal NA 7.5 0.00 No detection.

NA NA NA 0.125 NL 1627 NL PLSA 1627 Normal NA 7.5 0.06 No detection.

NA NA NA 0.192 NL 1628 NL PLSA 1628 Normal NA 7.5 0.00 No detection.

NA NA NA 0.116 NL 1629 NL PLSA 1629 Normal NA 7.5 0.00 No detection.

NA NA NA 0.187 NL 1630 NL PLSA 1630 Normal NA 7.5 0.00 No detection.

NA NA NA 0.121 NL 1631 NL PLSA 1631 Normal NA 7.5 0.00 No detection.

NA NA NA 0.123 NL 1632 NL PLSA 1632 Normal NA 7.5 0.04 No detection.

NA NA NA 0.117 NL 1633 NL PLSA 1633 Normal NA 7.5 0.00 No detection.

NA NA NA 0.122 NL 1634 NL PLSA 1634 Normal NA 7.5 0.04 No detection.

NA NA NA 0.180 NL 1635 NL PLSA 1635 Normal NA 6 0.81 No detection.

NA NA NA 0.335 NL 1636 NL PLSA 1636 Normal NA 7.5 0.21 No detection.

NA NA NA 0.125 NL 1637 NL PLSA 1637 Normal NA 7.5 0.00 No detection.

NA NA NA 0.125 NL 1638 NL PLSA 1638 Normal NA 7.5 0.00 No detection.

NA NA NA 0.310 NL 1639 NL PLSA 1639 Normal NA 7.5 0.39 No detection.

NA NA NA 0.187 NL 1640 NL PLSA 1640 Normal NA 7.5 0.04 No detection.

NA NA NA 0.121 NL 1641 NL PLSA 1641 Normal NA 7.5 0.01 No detection.

NA NA NA 0.123 NL 1642 NL PLSA 1642 Normal NA 7.5 0.38 No detection.

NA NA NA 0.175

NL 1643 NL PLSA 1643 Normal NA 7.5 0.40 No detection.

NA NA NA 0.122 NL 1644 NL PLSA 1644 Normal NA 7.5 0.15 No detection.

NA NA NA 0.222 NL 1645 NL PLSA 1645 Normal NA 7.5 0.03 No detection.

NA NA NA 0.117 NL 1646 NL PLSA 1646 Normal NA 7.5 0.07 No detection.

NA NA NA 0.125 NL 1647 NL PLSA 1647 Normal NA 7.5 0.05 No detection.

NA NA NA 0.125 NL 1648 NL PLSA 1648 Normal NA 7.5 13.03 TP53 p.R202C, c.604C> T 1.04 0.06 2.5 0.306 NL 1649 NL PLSA 1649 Normal NA 7.5 5.00 TP53 p.R202C, c.604C> T 1.10 0.06 1.0 0.747 NL 1650 NL PLSA 1650 Normal NA 7.5 4.22 TP53 p.G112D, c.335G> A 0.90 0.06 0 , 0.125 NL 1651 NL PLSA 1651 Normal NA 7.5 4.49 TP53 p.A189V, c.566C> T 1.07 0.04 0.5 0.311 NL 1652 NL PLSA 1652 Normal NA 7.5 4.36 TP53 p.A138T, c.412G> A 1.07 0.04 0.5 0.317 NL 1653 NL PLSA 1653 Normal NA 7.5 4.65 TP53 p.C176fs, c.528delC 0.86 0.03 0.4 0.568 NL 1654 NL PLSA 1654 Normal NA 7.5 4.77 TP53 p.L369P, c.1106T> C 0.99 0.02 0.3 0.121 NL 1655 NL PLSA 1655 Normal NA 7.5 3.19 TP53 p.A276V , c.827C> T 0.68 0.05 0.5 0.720 NL 1656 NL PLSA 1656 Normal NA 7.5 7.47 TP53 p.S215N, c.644G> A 1.05 0.02 0.4 0.522 NL 1657 NL PLSA 1657 Normal NA 7.5 3.06 GNAS p.R201H, c.602G> A 0.66 0.05 0.5 0.320 NL 1658 NL PLSA 1658 Normal NA 7.5 4.21 PPP2R1A p.R182W, c.544C> T 0.74 0.02 0.3 0.324 NL 1659 NL PLSA 1659 Normal NA 7.5 6.67 PPP2R1A p.R183Q, c.548G> A 1.00 0.05 1.1 0.300 NL 1660 NL PLSA 1660 Normal NA 7.5 0.05 No detection.

NA NA NA 0.333 NL 1661 NL PLSA 1661 Normal NA 7.5 0.12 No detection.

NA NA NA 0.334 NL 1662 NL PLSA 1662 Normal NA 7.5 0.52 HRAS p.G13D, c.38G> A 0.21 0.00 0.0 0.121 NL 1663 NL PLSA 1663 Normal NA 7.5 1.86 TP53 G.7578175A> G (Junction site) 0.79 0.04 0.2 0.116 NL 1664 NL PLSA 1664 Normal NA 7.5 7.61 TP53 p.A88G, c.263C> G 1.05 0.01 0.1 0.308 NL 1665 NL PLSA 1665 Normal NA 7.5 3.86 PPP2R1A p.R183W, c.547C> T 0.80 0.05 0.6 0.471 NL 1666 NL PLSA 1666 Normal NA 7.5 4.86 TP53 p.A353V, c.1058C> T 1.06 0.04 0.6 0.304 NL 1667 NL PLSA 1667 Normal NA 7.5 4.81 TP53 p.A138T, c.412G> A 1.10 0.04 0 , 6 0.313 NL 1668 NL PLSA 1668 Normal NA 7.5 5.56 TP53 p.L369P, c.1106T> C 1.08 0.04 0.6 0.309 NL 1669 NL PLSA 1669 Normal NA 7.5 5.42 TP53 p.K372fs, c.1114delA 0.96 0.04 0.7 0.577 NL 1701 NL PLSA 1701 Normal NA 7.5 1.94 TP53 p.Q331R, c.992A> G 0.95 0.02 0.1 0.528 NL 1702 NL PLSA 1702 Normal NA 7.5 2.96 TP53 p.P47L, c.140C> T 0.80 0.03 0.3 0.214 NL 1703 NL PLSA 1703 Normal NA 7.5 2.23 TP53 p.A83T , c.247G> A 1.05 0.02 0.1 0.713 NL 1704 NL PLSA 1704 Normal NA 7.5 2.81 TP53 p.M384V, c.1150 A> G 1.29 0.03 0.2 0.338 NL 1705 NL PLSA 1705 Normal NA 7.5 2.54 TP53 p.R306Q, c.917G> A 0.96 0.05 0.4 0.117 NL 1706 NL PLSA 1706 Normal NA 7.5 1.28 TP53 p.G187D, c.560G> A (In. exon) 1.02 0.02 0.1 0.308 NL 1707 NL PLSA 1707 Normal NA 7.5 1.97 TP53 p.P222S, c.664C> T 1.13 0.03 0.2 0.314 NL 1708 NL PLSA 1708 Normal NA 7.5 4.38 TP53 p.H179R, c.536A> G 2.19 0.04 0.5 0.576 NL 1709 NL PLSA 1709 Normal NA 7.5 3.30 PIK3CA p.H1047R, c.3140A> G 1.73 0.05 0.5 0.566 NL 1710 NL PLSA 1710 Normal NA 7.5 2.96 TP53 p.R337H, c.1010G> A 0.78 0.02 0.2 0.323 NL 1711 NL PLSA 1711 Normal NA 7.5 1.19 TP53 p.A161T, c.481G> A 0.94 0.02 0.1 0.123 NL 1712 NL PLSA 1712 Normal NA 7.5 2.86 TP53 p.V203M, c.607G> A 1.16 0.03 0.3 0.305 NL 1713 NL PLSA 1713 Normal NA 7.5 3.33 TP53 G.7579311C> T (Junction site) 0.97 0.02 0.2 0.178 NL 1714 NL PLSA 1714 Normal NA 7.5 3.13 TP53 p.Q38R, c.113A> G 0.89 0.02 0.2 0.121 NL 1715 NL PLSA 1715 Normal NA 7.5 1.15 TP53 G.7579311C> T (Junction site. ) 0.76 0.03 0.1 0.187 NL 1716 NL PLSA 1716 Normal NA 7.5 2.45 TP53 p.R267W, c.799C> T 0.82 0.06 0.5 0.125 NL 1717 NL PLSA 1717 Normal NA 7.5 1.11 TP53 p.A84V, c.251C> T 1.31 0.03 0.1 0.353 NL 1718 NL PLSA 1718 Normal NA 7.5 0.95 HRAS p.G13D, c.38G> A 1.33 0.03 0.1 0.349 NL 1719 NL PLSA 1719 Normal NA 7.5 1.31 TP53 p.A161T, c.481G> A 0.63 0.04 0 , 2 0.121 NL 1720 NL PLSA 1720 Normal NA 7.5 3.02 TP53 p.A189V, c.566C> T 0.92 0.04 0.3 0.444 NL 1721 NL PLSA 1721 Normal NA 7.5 1.76 TP53 p.H168R, c.503A> G 0.97 0.04 0.2 0.123 NL 1722 NL PLSA 1722 Normal NA 7.5 2.62 TP53 p.A353V, c.1058C> T 0.85 0.03 0, 2 0.125 NL 1723 NL PLSA 1723 Normal NA 7.5 2.90 TP53 p.G245D, c.734G> A 0.87 0.02 0.1 0.116 NL 1724 NL PLSA 1724 Normal NA 7.5 2.31 TP53 p .A138T, c.412G> A 1.02 0.05 0.4 0.599 NL 1725 NL PLSA 1725 Normal NA 7.5 0.04 No detection.

NA NA NA 0.624 NL 1726 NL PLSA 1726 Normal NA 7.5 0.04 No detection.

NA NA NA 0.123 NL 1727 NL PLSA 1727 Normal NA 7.5 0.19 TP53 p.R196 *, c.586C> T 0.95 0.07 0.0 0.123 NL 1728 NL PLSA 1728 Normal NA 7.5 0, 11 No detection

NA NA NA 0.735 NL 1729 NL PLSA 1729 Normal NA 7.5 0.03 No detection.

NA NA NA 0.322 NL 1730 NL PLSA 1730 Normal NA 7.5 0.01 No detection.

NA NA NA 0.599 NL 1731 NL PLSA 1731 Normal NA 7.5 0.03 No detection.

NA NA NA 0.236 NL 1732 NL PLSA 1732 Normal NA 7.5 0.06 No detection.

NA NA NA 0.125 NL 1733 NL PLSA 1733 Normal NA 7.5 0.05 No detection.

NA NA NA 0.125 NL 1734 NL PLSA 1734 Normal NA 7.5 0.04 No detection.

NA NA NA 0.155 NL 1735 NL PLSA 1735 Normal NA 7.5 0.06 No detection.

NA NA NA 0.187 NL 1736 NL PLSA 1736 Normal NA 7.5 0.05 No detection.

NA NA NA 0.564 NL 1737 NL PLSA 1737 Normal NA 7.5 0.07 No detection.

NA NA NA 0.183 NL 1738 NL PLSA 1738 Normal NA 7.5 0.08 No detection.

NA NA NA 0.175 NL 1739 NL PLSA 1739 Normal NA 7.5 0.03 No detection.

NA NA NA 0.122

NL 1740 NL PLSA 1740 Normal NA 7.5 0.18 No detection.

NA NA NA 0.121 NL 1741 NL PLSA 1741 Normal NA 7.5 0.09 No detection.

NA NA NA 0.117 NL 1742 NL PLSA 1742 Normal NA 7.5 0.02 No detection.

NA NA NA 0.125 NL 1743 NL PLSA 1743 Normal NA 7.5 0.05 No detection.

NA NA NA 0.125 NL 1744 NL PLSA 1744 Normal NA 7.5 0.11 No detection.

NA NA NA 0.116 NL 1745 NL PLSA 1745 Normal NA 7.5 0.19 EGFR p.L858Q, c.2573T> A 0.00 0.04 0.0 0.154 NL 1746 NL PLSA 1746 Normal NA 7.5 0.08 No detec.

NA NA NA 0.344 NL 1747 NL PLSA 1747 Normal NA 7.5 0.07 No detection.

NA NA NA 0.121 NL 1748 NL PLSA 1748 Normal NA 7.5 0.13 No detection.

NA NA NA 0.182 NL 1749 NL PLSA 1749 Normal NA 7.5 2.78 TP53 p.G226D, c.677G> A 1.11 0.04 0.3 0.323 NL 1750 NL PLSA 1750 Normal NA 7.5 0.10 No detec.

NA NA NA 0.117 NL 1751 NL PLSA 1751 Normal NA 7.5 2.09 TP53 p.A159T, c.475G> A 0.84 0.03 0.2 0.116 NL 1752 NL PLSA 1752 Normal NA 7.5 0.29 TP53 p.R196Q, c.587G> A 1.07 0.10 0.1 0.303 NL 1753 NL PLSA 1753 Normal NA 7.5 3.67 TP53 p.Q38R, c.113A> G 1.37 0.02 0 , 3 0.810 NL 1754 NL PLSA 1754 Normal NA 7.5 0.75 TP53 p.A86E, c.257C> A 0.71 0.04 0.1 0.116 NL 1755 NL PLSA 1755 Normal NA 7.5 0.14 None detect

NA NA NA 0.122 NL 1756 NL PLSA 1756 Normal NA 7.5 3.75 PTEN p.A148T, c.442G> A 1.20 0.03 0.3 0.332 NL 1757 NL PLSA 1757 Normal NA 7.5 0.26 KRAS p.A146T, c.436G> A 0.00 0.04 0.0 0.116 NL 1758 NL PLSA 1758 Normal NA 7.5 1.35 FBXW7 p.R465H, c.1394G> A 1.19 0.07 0 , 3 0.554 NL 1759 NL PLSA 1759 Normal NA 7.5 0.77 FBXW7 p.R465C, c.1393C> T 0.90 0.04 0.1 0.122 NL 1760 NL PLSA 1760 Normal NA 7.5 0.15 TP53 p.R156C, c.466C> T 1.25 0.11 0.1 0.337 NL 1761 NL PLSA 1761 Normal NA 7.5 0.15 CTNNB1 p.S37Y, c.110C> A 0.00 0.07 0, 0 0.117 NL 1762 NL PLSA 1762 Normal NA 7.5 0.47 TP53 p.E298K, c.892G> A 0.75 0.04 0.1 0.297 NL 1763 NL PLSA 1763 Normal NA 7.5 0.26 No detection .

NA NA NA 0.237 NL 1764 NL PLSA 1764 Normal NA 7.5 0.42 APC p.N1455fs, c.4364delA 0.00 0.05 0.1 0.116 NL 1765 NL PLSA 1765 Normal NA 7.5 0.46 TP53 p .T230I, c.689C> T 0.00 0.07 0.1 0.125 NL 1766 NL PLSA 1766 Normal NA 7.5 0.29 TP53 p.R158H, c.473G> A 1.19 0.13 0.1 0.327 NL 1767 NL PLSA 1767 Normal NA 7.5 0.19 TP53 p.P222S, c.664C> T 0.00 0.08 0.0 0.226 NL 1768 NL PLSA 1768 Normal NA 7.5 0.46 TP53 p. R335C, c.1003C> T 1.16 0.06 0.1 0.332 NL 1769 NL PLSA 1769 Normal NA 7.5 1.01 TP53 p.C238Y, c.713G> A 0.87 0.03 0.1 0.125 NL 1770 NL PLSA 1770 Normal NA 7.5 1.90 TP53 p.P142L, c.425C> T 1.10 0.02 0.1 0.428 NL 1771 NL PLSA 1771 Normal NA 7.5 1.67 TP53 p.R335C , c.1003C> T 0.81 0.04 0.2 0.245 NL 1772 NL PLSA 1772 Normal NA 7.5 1.43 TP53 p.R283H, c.848G> A 0.86 0.03 0.1 0.178 NL 1773 NL PLSA 1773 Normal NA 7.5 0.47 TP53 p.R158H, c.473G> A 0.00 0.05 0.1 0.117 NL 1774 NL PLSA 1774 Normal NA 7.5 0.29 FBXW7 p.R465C, c.1393C> T 0.00 0.05 0.0 0.319 NL 1775 NL PLSA 1775 Normal NA 7.5 0.20 TP53 p.R158G, c.472C> G 0, 00 0.06 0.0 0.125 NL 1776 NL PLSA 1776 Normal NA 7.5 0.18 TP53 p.V157A, c.470T> C 0.00 0.05 0.0 0.123 NL 1777 NL PLSA 1777 Normal NA 7, 5 0.23 TP53 p.R196Q, c.587G> A 0.00 0.11 0.1 0.240 NL 1778 NL PLSA 1778 Normal NA 7.5 0.10 No detection.

NA NA NA 0.117 NL 1779 NL PLSA 1779 Normal NA 7.5 0.19 TP53 p.R156H, c.467G> A 0.00 0.05 0.0 0.198 NL 1780 NL PLSA 1780 Normal NA 7.5 0.27 EGFR p.A864T, c.2590G> A 0.00 0.03 0.0 0.125 NL 1781 NL PLSA 1781 Normal NA 7.5 0.79 KRAS p.V14I, c.40G> A 0.25 0.01 0 , 0.121 NL 1782 NL PLSA 1782 Normal NA 7.5 0.24 TP53 p.V157F, c.469G> T 1.65 0.06 0.0 0.405 NL 1783 NL PLSA 1783 Normal NA 7.5 0.35 TP53 p.R248fs, c.743delG 0.00 0.03 0.0 0.122 NL 1784 NL PLSA 1784 Normal NA 7.5 0.18 TP53 p.M160I, c.480G> T 0.00 0.05 0.0 0.122 NL 1785 NL PLSA 1785 Normal NA 7.5 0.34 CTNNB1 p.A43T, c.127G> A 0.82 0.05 0.0 0.117 NL 1786 NL PLSA 1786 Normal NA 7.5 1.96 BRAF p.T599I , c.1796C> T 0.92 0.02 0.1 0.125 NL 1787 NL PLSA 1787 Normal NA 7.5 0.23 EGFR p.L858Q, c.2573T> A 0.00 0.05 0.0 0.236 NL 1788 NL PLSA 1788 Normal NA 7.5 1.58 TP53 p.S261I, c.782G> T (Exon end) 1.14 0.02 0.1 0.436 NL 1789 NL PLSA 1789 Normal NA 7.5 3.36 TP53 p.C242S, c.725G> C 1.44 0.02 0.2 0.670 NL 1790 NL PLSA 1790 Normal NA 7.5 0.29 TP53 G.7573009C> T (Junction site) 0.00 0.04 0.0 0.121 NL 1791 NL PLSA 1791 Normal NA 7.5 0.55 TP53 p.L369M, c.1105C> A 1.08 0.04 0.1 0.313 NL 1792 NL PLSA 1792 Normal NA 7.5 0.69 CTNNB1 p.T41I, c.122C> T 1.26 0.02 0.0 0.338 NL 1793 NL PLSA 1793 Normal NA 7.5 1.36 TP53 p.R202C, c .604C> T 0.93 0.03 0.1 0.345 NL 1794 NL PLSA 1794 Normal NA 7.5 2.12 TP53 p.M40V, c.118A> G 1.36 0.01 0.154 NL 1795 NL PLSA 1795 Normal NA 7.5 1.12 CTNNB1 p.S33Y, c.98C> A 0.67 0.02 0.1 0.240 NL 1796 NL PLSA 1796 Normal NA 7.5 0.41 TP53 p.P47L, c. 140C> T 0.55 0.11 0.1 0.248 NL 1797 NL PLSA 1797 Normal NA 7.5 1.09 KRAS p.G60D, c.179G> A 0.63 0.02 0.1 0.221 NL 1798 NL PLSA 1798 Normal NA 7.5 1.79 TP53 p.V272M, c.814G> A 0.76 0.04 0.2 0.116 NL 1799 NL PLSA 1799 Normal NA 7.5 2.29 TP53 p.A189V, c.566C > T 0.83 0.03 0.2 0.234 NL 1800 NL PLSA 1800 Normal NA 7.5 3.81 TP53 p.P219L, c.656C> T 0.80 0.02 0.2 0.123 NL 1801 NL PLSA 1801 Normal NA 7.5 1.06 TP53 p.P82L, c.245C> T 0.35 0.04 0.1 0.331 NL 1802 NL PLSA 1802 Normal NA 7.5 2.36 TP53 p.A276V, c.827C> T 0.67 0.03 0.2 0.125 NL 1803 NL PLSA 1803 Normal NA 7.5 0.95 TP53 p.C176fs, c.528delC 0.59 0.04 0.1 0.125 NL 1804 NL PLSA 1804 Normal NA 7.5 1.17 PTEN p.R142Q, c.425G> A 0.82 0.08 0.3 0.322 NL 1805 NL PLSA 1805 Normal NA 7.5 0.52 CDKN2A p.R87Q, c.260G > A 0.53 0.10 0.2 0.176

NL 1806 NL PLSA 1806 Normal NA 7.5 1.71 TP53 p.S15G, c.43A> G 0.58 0.01 0.1 0.125 NL 1807 NL PLSA 1807 Normal NA 7.5 2.26 PTEN p.G129R , c.385G> A 0.65 0.01 0.1 0.125 NL 1808 NL PLSA 1808 Normal NA 7.5 2.03 TP53 p.P300S, c.898C> T 0.82 0.02 0.1 0.306 NL 1809 NL PLSA 1809 Normal NA 7.5 0.81 TP53 p.R174W, c.520A> T 0.50 0.02 0.1 0.117 NL 1810 NL PLSA 1810 Normal NA 7.5 2.91 FBXW7 p.R465H, c.1394G> A 0.72 0.06 0.5 0.125 NL 1811 NL PLSA 1811 Normal NA 7.5 1.26 TP53 p.A138V, c.413C> T 0.68 0.07 0.3 0.121 NL 1812 NL PLSA 1812 Normal NA 7.5 0.63 TP53 p.G245S, c.733G> A 0.35 0.04 0.1 0.596 NL 1813 NL PLSA 1813 Normal NA 7.5 0.95 TP53 p.M384I, c .1152G> A 0.48 0.04 0.1 0.121 NL 1814 NL PLSA 1814 Normal NA 7.5 1.83 TP53 p.A138T, c.412G> A 1.13 0.04 0.2 0.322 NL 1815 NL PLSA 1815 Normal NA 7.5 3.45 TP53 p.E180G, c.539A> G 0.77 0.02 0.2 0.116 NL 1816 NL PLSA 1816 Normal NA 7.5 0.83 TP53 p.A83T, c. 247G> A 0.66 0.06 0.1 0.121 NL 1817 NL PLSA 1817 Normal NA 7.5 2.16 TP53 p.T329A, c.985A> G 0.81 0.03 0.2 0.125 NL 1 818 NL PLSA 1818 Normal NA 7.5 6.00 TP53 p.Q38R, c.113A> G 0.82 0.01 0.3 0.125 NL 1819 NL PLSA 1819 Normal NA 7.5 1.74 NRAS p.G12D, c.35G> A 0.71 0.02 0.1 0.117 NL 1820 NL PLSA 1820 Normal NA 7.5 0.84 TP53 p.Q136 *, c.406C> T 0.40 0.04 0.1 0.125 NL 1821 NL PLSA 1821 Normal NA 7.5 1.46 TP53 p.R342Q, c.1025G> A 0.50 0.04 0.2 0.117 NL 1822 NL PLSA 1822 Normal NA 7.5 1.00 TP53 p.R273C, c.817C> T 0.58 0.07 0.2 0.121 NL 1823 NL PLSA 1823 Normal NA 7.5 2.03 GNAS p.L203P, c.608T> C 0.92 0.02 0.1 0.125 NL 1824 NL PLSA 1824 Normal NA 7.5 1.55 PPP2R1A p.R183W, c.547C> T 0.62 0.04 0.2 0.125 NL 1825 NL PLSA 1825 Normal NA 7.5 1.44 TP53 p.C277Y, c .830G> A 0.64 0.05 0.2 0.122 NL 1826 NL PLSA 1826 Normal NA 7.5 1.75 TP53 p.Q38H, c.114A> T 0.45 0.02 0.1 0.805 NL 1827 NL PLSA 1827 Normal NA 7.5 0.79 TP53 p.R174W, c.520A> T 0.57 0.05 0.1 0.125 NL 1828 NL PLSA 1828 Normal NA 7.5 2.33 TP53 p.R202C, c. 604C> T 0.78 0.07 0.5 0.116 NL 1829 NL PLSA 1829 Normal NA 7.5 1.41 TP53 p.A353V, c.1058C> T 0.70 0.05 0.2 0.121 NL 1830 NL PLSA 1830 Normal NA 7.5 1.96 TP53 p.A159T, c.475G> A 0.74 0.03 0.2 0.125 NL 1831 NL PLSA 1831 Normal NA 7.5 1.90 TP53 p.G245S, c .733G> A 0.67 0.02 0.1 0.710 NL 1832 NL PLSA 1832 Normal NA 7.5 0.88 TP53 p.C176Y, c.527G> A 0.31 0.03 0.1123 NL 1833 NL PLSA 1833 Normal NA 7.5 1.23 PIK3CA p.N1044S, c.3131A> G 0.66 0.07 0.3 0.587 NL 1834 NL PLSA 1834 Normal NA 7.5 0.57 HRAS p.G12S, c. 34G> A 0.39 0.02 0.0 0.125 NL 1835 NL PLSA 1835 Normal NA 7.5 1.31 TP53 p.L369P, c.1106T> C 0.64 0.06 0.2 0.117 NL 1836 NL PLSA 1836 Normal NA 7.5 2.26 TP53 p.R333H, c.998G> A 0.62 0.03 0.2 0.125 NL 1837 NL PLSA 1837 Normal NA 7.5 2.75 TP53 p.C176fs, c.528delC 0.86 0.02 0.1 0.253 NL 1838 NL PLSA 1838 Normal NA 7.5 1.19 TP53 p.S99P, c.295T> C 0.68 0.04 0.1 0.117 NL 1839 NL PLSA 1839 Normal NA 7.5 3.38 TP53 p.R202C, c.604C> T 1.03 0.05 0.5 0.309 NL 1840 NL PLSA 1840 Normal NA 7.5 0.92 TP53 p.R174G, c.520A> G 0 , 64 0.04 0.1123 NL 1841 NL PLSA 1841 Normal NA 7.5 1.14 TP53 G.7579310A> G (Junction site) 0.31 0.03 0.1 0.12 1 NL 1842 NL PLSA 1842 Normal NA 7.5 1.70 TP53 p.S33fs, c.98delC 1.01 0.03 0.1 0.305 NL 1843 NL PLSA 1843 Normal NA 7.5 3.5 TP53 p.R306 * , c.916C> T 0.81 0.04 0.4 0.122 NL 1844 NL PLSA 1844 Normal NA 7.5 6.53 TP53 G.7579311C> T (Junction site) 0.78 0.01 0.3 0.184 NL 1845 NL PLSA 1845 Normal NA 7.5 3.97 BRAF p.V600E, c.1799T> A 2.42 0.06 0.7 0.515 NL 1846 NL PLSA 1846 Normal NA 7.5 1.57 TP53 p.R202C , c.604C> T 0.86 0.03 0.1 0.125 NL 1847 NL PLSA 1847 Normal NA 7.5 1.18 TP53 p.A86T, c.256G> A 0.82 0.06 0.2 0.122 NL 1848 NL PLSA 1848 Normal NA 7.5 0.67 PPP2R1A p.R182W, c.544C> T 0.00 0.07 0.2 0.123 NL 1849 NL PLSA 1849 Normal NA 7.5 1.65 TP53 p.G262S, c.784G> A 0.67 0.07 0.3 0.125 NL 1850 NL PLSA 1850 Normal NA 7.5 1.36 TP53 p.L130F, c.388C> T 0.72 0.03 0.1 0.121 NL 1851 NL PLSA 1851 Normal NA 7.5 2.20 KRAS p.V14I, c.40G> A 0.71 0.02 0.1 0.125 NL 1852 NL PLSA 1852 Normal NA 7.5 4.27 TP53 p.L348V, c .1042T> G 0.97 0.01 0.1 0.116 NL 1853 NL PLSA 1853 Normal NA 7.5 0.66 GNAS p.L203P, c.608T> C 0.40 0.04 0 , 1 0.117 NL 1854 NL PLSA 1854 Normal NA 7.5 0.59 GNAS p.L203P, c.608T> C 0.50 0.03 0.1 0.125 NL 1855 NL PLSA 1855 Normal NA 7.5 2.50 TP53 p.A353V, c.1058C> T 0.79 0.04 0.3 0.125 NL 1856 NL PLSA 1856 Normal NA 7.5 3.11 TP53 p.I255F, c.763A> T 0.99 0.05 0, 5 0.191 NL 1857 NL PLSA 1857 Normal NA 7.5 4.26 TP53 p.E339G, c.1016A> G 0.81 0.01 0.2 0.117 NL 1858 NL PLSA 1858 Normal NA 7.5 1.14 TP53 p .L369P, c.1106T> C 0.69 0.05 0.2 0.234 NL 1859 NL PLSA 1859 Normal NA 7.5 2.20 TP53 p.A276V, c.827C> T 1.15 0.04 0.3 0.325 NL 1860 NL PLSA 1860 Normal NA 7.5 3.32 TP53 p.A189V, c.566C> T 1.06 0.03 0.3 0.803 NL 1861 NL PLSA 1861 Normal NA 7.5 3.09 BRAF p. V600A, c.1799T> C 0.78 0.02 0.2 0.116 NL 1862 NL PLSA 1862 Normal NA 7.5 2.06 TP53 p.E258 *, c.772G> T 0.84 0.01 0.1 0.125 NL 1863 NL PLSA 1863 Normal NA 7.5 7.63 KRAS p.A146T, c.436G> A 0.87 0.01 0.3 0.116 NL 1864 NL PLSA 1864 Normal NA 7.5 3.46 KRAS p. A146T, c.436G> A 1.09 0.02 0.2 0.317 NL 1865 NL PLSA 1865 Normal NA 7.5 2.79 FBXW7 p.R465C, c.1393C> T 0.53 0.02 0.1 0.116 NL 1866 NL PLSA 1866 Normal NA 7.5 2.17 TP53 p.S376P, c.1126T> C 1.12 0.02 0.1 0.324 NL 1867 NL PLSA 1867 Normal NA 7.5 0.89 TP53 p.D186N, c.556G> A 0.91 0.09 0.2 0.122 NL 1868 NL PLSA 1868 Normal NA 7.5 1.29 CDKN2A p.A76T, c.226G> A 0.49 0.05 0 , 2 0.125 NL 1869 NL PLSA 1869 Normal NA 7.5 0.49 KRAS p.G12A, c.35G> C 0.33 0.03 0.1 0.121 NL 1870 NL PLSA 1870 Normal NA 7.5 0.77 TP53 p.G302R, c.904G> A 0.67 0.03 0.1 0.123 PANC 669 PANC 669 PLS 1 Pancreas II 7.5 16.28 TP53 p.C176fs, c.528delC 1.12 0.03 1.4 0.982

PANC 670 PANC 670 PLS 1 Pancreas II 7.5 3.29 KRAS p.G12V, c.35G> T 0.81 0.07 0.7 0.560 PANC 671 PANC 671 PLS 1 Pancreas II 7.5 27.73 KRAS p .G12V, c.35G> T 1.69 0.04 3.0 0.994 PANC 672 PANC 672 PLS 1 Pancreas II 7.5 8.17 TP53 p.A159T, c.475G> A 1.08 0.03 0, 8 0.982 PANC 673 PANC 673 PLS 1 Pancreas II 7.5 4.94 KRAS p.G12V, c.35G> T 1.09 0.10 1.5 0.423 PANC 674 PANC 674 PLS 1 Pancreas II 7.5 7.53 KRAS p.G12D, c.35G> A 1.19 0.04 1.0 0.993 PANC 675 PANC 675 PLS 1 Pancreas II 7.5 3.97 TP53 p.F109V, c.325T> G 19.28 2.16 26.4 1,000 PANC 676 PANC 676 PLS 1 Pancreas II 7.5 5.18 GNAS p.R201H, c.602G> A 1.19 0.05 0.777 PANC 677 PANC 677 PLS 1 Pancreas II 7.5 14 , 44 TP53 p.C238Y, c.713G> A 2.91 0.60 26.5 1,000 PANC 678 PANC 678 PLS 1 Pancreas II 7.5 14.28 FBXW7 p.R367 *, c.1099C> T 0.66 0.02 1.0 0.473 PANC 679 PANC 679 PLS 1 Pancreas II 7.5 7.76 TP53 p.K372fs, c.1114delA 0.75 0.07 1.7 0.605 PANC 680 PANC 680 PLS 1 Pancreas II 7.5 5.40 TP53 p.K372fs, c.1114delA 1.22 0.08 1.3 0.822 PANC 757 PANC 757 PLS 1 Pâ ncreas II 7.5 13.14 TP53 p.K372fs, c.1114delA 0.85 0.05 2.2 0.221 PANC 758 PANC 758 PLS 1 Pancreas II 7.5 3.49 TP53 p.L369M, c.1105C> A 0.71 0.03 0.3 0.679 PANC 759 PANC 759 PLS 1 Pancreas II 7.5 11.66 KRAS p.G12V, c.35G> T 0.98 0.02 0.8 0.832 PANC 760 PANC 760 PLS 1 Pancreas II 7.5 1.97 BRAF p.T599I, c.1796C> T 0.43 0.04 0.2 0.872 PANC 761 PANC 761 PLS 1 Pancreas II 7.5 2.01 TP53 p.P309S, c.925C > T 0.63 0.01 0.1 0.904 PANC 762 PANC 762 PLS 1 Pancreas II 7.5 10.33 KRAS p.G12R, c.34G> C 1.90 0.05 1.6 1,000 PANC 764 PANC 764 PLS 1 Pancreas II 7.5 9.08 TP53 p.C176Y, c.527G> A 1.15 0.03 0.789 PANC 765 PANC 765 PLS 1 Pancreas II 7.5 12.46 KRAS p.G12D, c .35G> A 2.14 0.07 2.6 1,000 PANC 766 PANC 766 PLS 1 Pancreas I 7 8.57 TP53 p.C176fs, c.528delC 1.13 0.03 0.7 0.995 PANC 767 PANC 767 PLS 1 Pancreas II 7.5 1.71 TP53 p.V173L, c.517G> T 1.19 0.03 0.2 0.457 PANC 768 PANC 768 PLS 1 Pancreas II 7.5 1.77 TP53 p.L348S, c.1043T > C 0.89 0.03 0.2 0.125 PANC 769 PANC 769 PLS 1 Pancreas II 7.5 2.84 TP53 p.A16 1T, c.481G> A 0.90 0.02 0.2 0.804 PANCA 1001 PANCA 1001 PLS 1 Pancreas II 6 4.44 TP53 p.G245D, c.734G> A 0.81 0.04 0.6 0.980 PANCA 1004 PANCA 1004 PLS 1 Pancreas II 7 3.08 TP53 p.K139N, c.417G> T 2.97 0.20 1.9 0.994 PANCA 1005 PANCA 1005 PLS 1 Pancreas II 7.5 3.90 TP53 p.R333H, c.998G> A 0.89 0.04 0.5 0.993 PANCA 1006 PANCA 1006 PLS 1 Pancreas II 7.5 109.97 TP53 p.G374fs, c.1120delG 1.03 0.03 11.0 0.9999 PANCA 1008 PANCA 1008 PLS 1 Pancreas II 7.5 6.96 GNAS p.R201H, c.602G> A 1.44 0.16 3.5 0.911 PANCA 1009 PANCA 1009 PLS 1 Pancreas II 7.5 2.59 TP53 p.I195F, c.583A> T 11.87 1.73 13.8 1,000 PANCA 1010 PANCA 1010 PLS 1 Pancreas I 7.5 4.18 TP53 G.7577156C> T (Junction site) 0.88 0.06 0.725 PANCA 1011 PANCA 1011 PLS 1 Pancreas II 7.5 14.89 KRAS p.G12D, c.35G> A 1.84 0.06 2.8 0.999 PANCA 1012 PANCA 1012 PLS 1 Pancreas II 7.5 3.04 TP53 p .P47L, c.140C> T 0.42 0.05 0.5 0.794 PANCA 1013 PANCA 1013 PLS 1 Pancreas III 7.5 12.68 TP53 p.P47L, c.140C> T 1.09 0.04 1, 6 0.989 PANCA 1014 PANCA 1014 PLS 1 Pancreas II 7.5 25.75 TP53 p.R283P, c.848G> C 2.66 0.06 4.7 0.997 PANCA 1015 PANCA 1015 PLS 1 Pancreas III 7.5 3.57 TP53 p.R283H, c.848G> A 0.44 0.12 1.3 0.968 PANCA 1016 PANCA 1016 PLS 1 Pancreas I 7.5 3.82 GNAS p.R201S, c.601C> A 0.55 0.05 0.6 0.884 PANCA 1018 PANCA 1018 PLS 1 Pancreas II 7.5 10.34 TP53 p.R202C, c.604C> T 1.04 0.04 1.3 1,000 PANCA 1020 PANCA 1020 PLS 1 Pancreas II 7.5 8.66 TP53 p.R202C, c. 604C> T 1.02 0.07 1.7 0.999 PANCA 1022 PANCA 1022 PLS 1 Pancreas II 7.5 13.86 TP53 p.C176fs, c.528delC 1.15 0.03 1.2 0.994 PANCA 1024 PANCA 1024 PLS 1 Pancreas II 7.5 1.49 TP53 p.A159T, c.475G> A 0.61 0.08 0.4 0.117 PANCA 1025 PANCA 1025 PLS 1 Pancreas III 7.5 3.82 KRAS p.G12D, c. 35G> A 3.81 1.17 13.8 0.988 PANCA 1028 PANCA 1028 PLS 1 Pancreas III 7.5 3.06 TP53 p.A159T, c.475G> A 1.19 0.05 0.5 0.910 PANCA 1030 PANCA 1030 PLS 1 Pancreas II 7.5 12.93 TP53 p.I255N, c.764T> A 2.95 0.10 4.1 0.999 PANCA 1031 PANCA 1031 PLS 1 Pancreas II 7.5 1.97 TP53 p.R202C, c.604C> T 0.90 0.08 0.5 0.978 PANCA 1034 PANCA 1034 PLS 1 Pancreas II 7.5 3.16 TP53 p.A88fs, c.263delC 0.00 0.06 0.6 0.969 PANCA 1036 PANCA 1036 PLS 1 Pancreas II 7.5 2.21 TP53 p.R379S, c. 1135C> A 0.00 0.16 1.1 0.402 PANCA 1037 PANCA 1037 PLS 1 Pancreas II 7.5 29.96 TP53 p.P177R, c.530C> G 1.49 0.02 2.0 0.857 PANCA 1039 PANCA 1039 PLS 1 Pancreas II 7.5 6.89 TP53 p.K372fs, c.1114delA 0.68 0.05 1.1 0.693 PANCA 1040 PANCA 1040 PLS 1 Pancreas II 7.5 10.00 TP53 p.P300fs, c. 898delC 1.30 0.03 0.9 0.987 PANCA 1042 PANCA 1042 PLS 1 Pancreas II 7.5 6.70 KRAS p.G12V, c.35G> T 2.44 0.18 3.8 0.50 PANCA 1043 PANCA 1043 PLS 1 Pancreas II 7.5 2.41 TP53 p.A159T, c.475G> A 0.55 0.07 0.5 0.998 PANCA 1044 PANCA 1044 PLS 1 Pancreas II 7.5 4.25 KRAS p.G12R, c. 34G> C 2.91 0.24 3.1 1,000 PANCA 1047 PANCA 1047 PLS 1 Pancreas II 7 2.13 TP53 p.P151fs, c.451insC 0.00 0.07 0.4 0.824 PANCA 1048 PANCA 1048 PLS 1 Pancreas III 7.5 77.12 TP53 p.R174G, c.520A> G 0.98 0.02 4.4 0.929 PANCA 1050 PANCA 1050 PLS 1 Pancreas II 7.5 37.90 CTNNB1 p.G34R, c.100G> A 1.06 0.01 1.6 0, 947 PANCA 1051 PANCA 1051 PLS 1 Pancreas II 7.5 26.98 TP53 p.K372fs, c.1114delA 1.03 0.04 3.2 0.993 PANCA 1052 PANCA 1052 PLS 1 Pancreas II 7.5 4.34 TP53 p. R283C, c.847C> T 0.60 0.16 2.1 0.406 PANCA 1053 PANCA 1053 PLS 1 Pancreas II 7 22.34 TP53 p.E336G, c.1007A> G 1.10 0.02 1.2 0.977 PANCA 1054 PANCA 1054 PLS 1 Pancreas II 7.5 8.34 TP53 p.R202C, c.604C> T 0.41 0.03 0.7 0.866 PANCA 1055 PANCA 1055 PLS 1 Pancreas II 7 1.92 TP53 p.R156H, c.467G> A 0.00 0.06 0.3 0.122 PANCA 1056 PANCA 1056 PLS 1 Pancreas II 7.5 8.77 TP53 p.I232N, c.695T> A 1.37 0.11 3.0 0.997 PANCA 1057 PANCA 1057 PLS 1 Pancreas II 7.5 2.44 TP53 p.R175C, c.523C> T 0.00 0.07 0.6 0.284 PANCA 1058 PANCA 1058 PLS 1 Pancreas II 7.5 7.76 TP53 p. R202C, c.604C> T 0.91 0.05 1.1 0.957 PANCA 1059 PANCA 1059 PLS 1 Pancreas II 7.5 3.11 KRAS p.Q61H, c.183A> T 0.78 0.11 1.1 0.877 PANCA 1061 PANCA 1061 PLS 1 Pancreas II 7.5 17.39 TP53 p.S241fs, c.722delC 5.83 0.27 14.6 0.999 PANCA 1063 PANCA 1063 PLS 1 Pancreas II 7.5 2.67 KRAS p.Q61H, c.183A> C 0.00 0.12 1.0 0.701

PANCA 1149 PANCA 1149 PLS 1 Pancreas II 4 19.28 TP53 p.A138T, c.412G> A 1.03 0.04 2.4 0.905 PANCA 1152 PANCA 1152 PLS 1 Pancreas I 3 11.79 TP53 p.R273H, c .818G> A 1.39 0.42 15.4 0.690 PANCA 1153 PANCA 1153 PLS 1 Pancreas II 7.5 10.42 TP53 p.E346D, c.1038G> T 0.98 0.02 0.5 0.877 PANCA 1155 PANCA 1155 PLS 1 Pancreas II 7.5 9.02 KRAS p.G12D, c.35G> A 2.69 0.11 2.9 1.000 PANCA 1156 PANCA 1156 PLS 1 Pancreas II 7.5 1.60 TP53 p.A161T , c.481G> A 0.75 0.03 0.2 0.224 PAP 938 PAP 938 PLS 1 Ovary III 5 6.59 TP53 p.S127fs, c.380delC 60.45 18.18 369.1 1,000 PAP 939 PAP 939 PLS 1 Ovary III 5 10.83 TP53 p.Y163C, c.488A> G 252.57 43.92 1,464.8 1,000 PAP 940 PAP 940 PLS 1 Ovary III 5 11.54 TP53 p.Y163C, c.488A> G 20.13 2.42 86.1 1,000 PAP 941 PAP 941 PLS 1 Ovary III 5 9.21 BRAF p.V600E, c.1799T> A 8.02 0.36 10.3 1,000 PAP 944 PAP 944 PLS 1 Ovary III 5 4.35 KRAS p.G12D, c.35G> A 1.65 0.24 3.3 0.998 PAP 945 PAP 945 PLS 1 Ovary III 5 10.63 TP53 p.D259V, c.776A> T 260.70 9 , 47 310.1 1,000 PAP 946 PAP 946 PLS 1 Ovary III 5 5.59 TP53 p.S241F, c.722C> T 3.22 0.29 5.1 1,000 PAP 947 PAP 947 PLS 1 Ovary III 5 6.81 TP53 p.H168Q, c.504C> G 10, 49 0.24 5.0 0.999 PAP 949 PAP 949 PLS 1 Ovary III 5 13.59 TP53 G.7576852C> T (Junction site) 3.31 0.13 5.6 1,000 PAP 950 PAP 950 PLS 1 Ovary III 5 10.15 TP53 p.L130F, c.388C> T 15.94 1.96 61.3 1,000 PAP 951 PAP 951 PLS 1 Ovary III 5 4.12 TP53 p.G245D, c.734G> A 1.51 0, 05 0.6 0.836 PAP 953 PAP 953 PLS 1 Ovary III 5 6.60 TP53 p.F341fs, c.1022delGTGGGC

GTGAGCGCTTCGAGATGTT (SEQ ID NO: 748) 2.03 0.03 0.5 0.998 PAP 954 PAP 954 PLS 1 Ovary III 5 33.14 TP53 p.R175H, c.524G> A 6.20 23.40 2,388.5 1,000 PAP 955 PAP 955 PLS 1 Ovary III 5 5.75 TP53 p.T155N, c.464C> A 9.22 0.45 8.0 1.000 PAP 956 PAP 956 PLS 1 Ovary III 5 22.89 TP53 p.P278S, c .832C> T 210.57 52.34 3,690.7 1,000 PAP 957 PAP 957 PLS 1 Ovary III 5 5.85 TP53 p.M340fs, c.1018delTCGAGA 1.91 0.07 1.2 0.983 PAP 959 PAP 959 PLS 1 Ovary III 5 4.72 TP53 p.E336fs, c.1006insGAGCGCTTC 10.55 0.60 8.7 1,000 PAP 961 PAP 961 PLS 1 Ovary III 5 21.78 TP53 p.T211A, c.631A> G 9.98 0 , 44 29.7 1,000 PAP 962 PAP 962 PLS 1 Ovary III 5 13.61 TP53 p.V218G, c.653T> G 7.50 0.31 13.0 0.992 PAP 974 PAP 974 PLS 1 Ovary I 5 8.88 TP53 p.V216L, c.646G> C 56.77 4.58 125.3 1,000 POPE 1330 POPE 1330 PLS 1 Ovary III 3.5 29.23 BRAF p.G596R, c.1786G> C 17.73 1.00 90, 3 1,000 PAPA 1331 PAPA 1331 PLS 1 Ovary III 3.5 45.45 TP53 p.R306 *, c.916C> T 3.70 2.96 414.6 1,000 PAPA 1332 PAPA 1332 PLS 1 Ovary I 3.5 61, 19 TP53 p.K372 fs, c.1114delA 1.18 0.07 12.8 0.971 PAPA 1333 PAPA 1333 PLS 1 Ovary III 3.5 19.89 TP53 p.A353V, c.1058C> T 0.92 0.04 2.6 0.994 PAPA 1334 PAPA 1334 PLS 1 Ovary III 3.5 17.60 TP53 p.R267P, c.800G> C 10.16 1.36 73.8 0.999 PAPA 1335 PAPA 1335 PLS 1 Ovary III 3.5 8.71 TP53 p.F341fs, c.1023delC 24.76 2.93 78.4 0.999 POPE 1336 POPE 1336 PLS 1 Ovary I 3.5 29.40 CTNNB1 p.S33Y, c.98C> A 26.13 11.72 1.061.3 1,000 POPE 1339 POPE 1339 PLS 1 Ovary I 3,5 52,87 CTNNB1 p.S33P, c.97T> C 169,01 6,15 1,001,5 1,000 PAPA 1342 PAPA 1342 PLS 1 Ovary III 3,5 21,31 TP53 p.L264fs, c.792delA 35.67 4.77 312.9 1,000 POPE 1343 POPE 1343 PLS 1 Ovary III 3.5 89.38 TP53 p.S127F, c.380C> T 6.48 2.09 574.1 1,000 POPE 1344 POPE 1344 PLS 1 Ovary III 3.5 17.07 TP53 p.C176fs, c.528delC 1.07 0.02 1.2 0.871 POPE 1345 POPE 1345 PLS 1 Ovary III 3.5 39.86 TP53 p.P177R, c.530C> G 70.80 2.00 245.1 1,000 PAPA 1346 PAPA 1346 PLS 1 Ovary III 3.5 42.94 TP53 p.R249G, c.745A> G 40.97 15.62 2,066.3 1,000 PAPA 1347 PAPA 1347 PLS 1 Ovari o I 3.5 30.11 PIK3CA p.H1047R, c.3140A> G 3.00 0.18 16.3 1,000 PAPA 1348 PAPA 1348 PLS 1 Ovary III 3.5 47.69 TP53 p.R273L, c.818G > T 4.71 0.19 28.5 0.999 POPE 1349 POPE 1349 PLS 1 Ovary III 3.5 24.47 TP53 p.S241F, c.722C> T 35.28 6.38 480.6 1,000 POPE 1350 POPE 1350 PLS 1 Ovary II 3.5 5.73 CDKN2A p.R87Q, c.260G> A 0.49 0.05 0.8 0.941 PAPA 1353 PAPA 1353 PLS 1 Ovary I 3.5 6.55 TP53 p.P300fs, c .898delC 0.97 0.03 0.6 0.920 PAPA 1354 PAPA 1354 PLS 1 Ovary I 3.5 22.83 TP53 p.R249G, c.745A> G 3.84 0.74 51.9 0.990 PAPA 1355 PAPA 1355 PLS 1 Ovary III 3.5 64.51 TP53 G.7579311C> G (Junction site) 10.11 1.88 374.1 0.999 PAPA 1356 PAPA 1356 PLS 1 Ovary II 3.5 13.71 TP53 p.R282W, c.844C> T 0.73 0.05 2.2 1,000 PAPA 1357 PAPA 1357 PLS 1 Ovary III 3.5 19.81 TP53 G.7576927C> T (Junction site) 1.42 0.03 1.6 1,000

[1238] * Coordinates refer to the release of hg19 from the human reference genome (Genome Reference Consortium GRCh37, Feb 2009). CancerSEEK Positive Negative Test Result

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[1239] Table 4. Concentrations of protein biomarker tested in plasma samples from cancer patients and healthy controls. AJCC AFP Angiopoietin-2 AXL CA125 CA15-3 CA19-9 CD44 CEA CYFRA 21-1 DKK1 Endoglobin FGF2 Folistatin Galectin-3 Patient ID # Sample ID # Tumor type Stage (pg / ml) (pg / ml) (pg / ml) (pg / ml) (pg / ml) ( U / ml) (U / ml) (U / ml) (ng / ml) (pg / ml) (pg / ml) (ng / ml) (pg / ml) (pg / ml) (pg / ml) ( ng / ml) CRC 455 CRC 455 PLS 1 Colorectal I 1583.45 5598.50 3621.04 5.09 19.08 * 2.742 9.81 540.1 * 323.109 0.78 2882.65 92.02 2144.33 11 , 19 CRC 456 CRC 456 PLS 1 Colorectal I * 119.218 20936.35 2772.96 7.27 10.04 40.91 27.57 5.902.4 * 323.109 0.77 3921.77 164.06 1646.26 9.9 CRC 457 CRC 457 PLS 1 Colorectal II 4365.53 2350.93 4120.77 * 0.809 16.96 * 2.742 14.59 973.8 1976.94 0.9 2410.16 154.77 2486.88 11.61 CRC 458 CRC 458 PLS 1 Colorectal II * 119.218 1604.34 2029.96 5.39 8.31 * 2.742 7.78 2.027.5 * 323.109 0.64 1284.96 227.57 829.43 4.8 CRC 459 CRC 459 PLS 1 Colorectal II 801.3 2087.57 2069.17 * 0.809 11.73 * 2.742 12.21 614.5 * 323.109 0.78 2552.72 134.72 2168.23 8.92 CRC 460 CRC 460 PLS 1 Colorectal II * 119.218 1147.55 2054.9 * 0.809 5.49 39 , 51 6.97 1,242.3 * 323.109 0.75 2459.5 123.75 1341.11 6.25 CRC 461 CRC 461 PLS 1 Colorectal I 1975.13 1900.89 3499.99 6.33 16.65 * 2.742 12.21 755.77 3329.43 0.86 1566.22 172.95 1765.41 7.67 CRC 462 CRC 462 PLS 1 Colorectal I 992.17 1783.54 2478.39 * 0.809 11.44 * 2.742 11, 23 478.98 * 323.109 0.71 1574.6 145.02 1484.83 8.25 CRC 463 CRC 463 PLS 1 Colorectal I 1039.85 1103.02 954.25 5.7 6.23 36.31 * 1.367 895 , 66 1976.94 0.59 1513.17 205.52 790.19 5.63 CRC 464 CRC 464 PLS 1 Colorectal III 6002.13 1942.35 1692.65 * 0.809 7.29 * 2.742 10.87 818.81 * 323,109 0.64 2392.78 177.27 1229.47 14.81 CRC 465 CRC 465 PLS 1 Colorectal II 772.64 2371.75 4878.84 6.02 6.25 45.91 17.2 2.038.8 2129.61 0.69 1931.79 99.02 1291.93 14.62 CRC 466 CRC 466 PLS 1 Colorectal III 868.13 1198.96 2375.93 * 0.809 15.11 * 2.742 11.64 672.19 * 323.109 0.81 2581.94 177.27 1831.13 10.06 CRC 467 CRC 467 PLS 1 Colorectal III * 119.218 2747.35 2370.46 * 0.809 7.98 19.32 11.05 22.271.3 * 323.109 0.98 2687.53 111.94 1246.24 11.95 CRC 468 CRC 4 68 PLS 1 Colorectal III 2387.08 1597.46 1777.87 * 0.809 24.41 * 2.742 9.29 8.587.4 * 323.109 0.72 1917.63 123.75 2564.16 6.09 CRC 469 CRC 469 PLS 1 Colorectal II 3003.1 764.67 1089.96 * 0.809 11.00 * 2.742 10.74 911.83 * 323.109 1.3 2280.16 111.94 1341.11 7.26 CRC 470 CRC 470 PLS 1 Colorectal II 1545.28 1343.05 1018.57 14.64 17.44 26.84 7.37 3,352.6 3181.21 0.81 1796.21 213.06 1476.98 14.21 CRC 471 CRC 471 PLS 1 Colorectal III 1182.88 761, 26 752.44 5.09 26.84 26.03 * 1.367 2.209.4 * 323.109 0.62 1376.69 189.77 910.64 8.42 CRC 472 CRC 472 PLS 1 Colorectal II 4189.1 2778.72 2637 , 05 * 0.809 26.23 115.84 7.49 17219.93 * 323.109 0.72 1994.19 123.75 819.68 10.3 CRC 473 CRC 473 PLS 1 Colorectal II 1573.91 1260.68 1909.47 * 0.809 13.18 * 2.742 9.77 2.167.7 2129.61 0.73 1613.75 111.94 980.12 8.65 CRC 474 CRC 474 PLS 1 Colorectal III * 119.218 1075.63 1500.21 * 0.809 11 , 85 * 2,742 8.49 537.1 * 323.109 0.74 1711.85 123.75 559.01 8.1 CRC 475 CRC 475 PLS 1 Colorectal I 4149.94 901.22 428.41 6.64 7.34 * 2,742 8.18 * 74.59 * 323.109 0.68 1931.79 159.46 1139.99 11.37 CRC 476 CRC 476 PLS 1 Colorectal II * 119.218 2066.81 2341.33 * 0.809 9.54 * 2.742 7.49 2,696.2 3550 , 97 0.96 1616.55 172.95 984.69 8.99 CRC 477 CRC 477 PLS 1 Colorectal II 2291.16 1935.44 2079.89 * 0.809 13.79 * 2.742 17.39 * 74.59 * 323.109 0, 94 2125.25 111.94 1867.27 8.13 CRC 478 CRC 478 PLS 1 Colorectal III 2396.68 2170.66 1713.47 * 0.809 12.30 * 2.742 14.1 3910.80 * 323.109 0.69 * 26 , 35 201.67 625.11 0.7 CRC 479 CRC 479 PLS 1 Colorectal II * 119.218 2198.37 3323.7 * 0.809 20.40 23.39 10.99 4.489.4 * 323.109 0.69 2699.29 111 , 94 1113.95 6.25 CRC 480 CRC 480 PLS 1 Colorectal III * 119.218 2174.12 2625.93 * 0.809 4.21 * 2.742 8.57 * 74.59 * 323.109 0.87 1176.64 123.75 891 , 78 6.49 CRC 481 CRC 481 PLS 1 Colorectal II 7022.24 2413.41 2256.06 * 0.809 19.57 292.07 8.26 2,110.5 * 323,109 0.72 731.81 111.94 1043.36 10.02 CRC 482 CRC 482 PLS 1 Colorectal III * 119.218 624.96 970.28 * 0.809 12.51 19.11 * 1.367 1.753.1 * 323.109 0.69 1532.71 273.79 952.55 2.75 CRC 483 CRC 483 PLS 1 Colorectal I * 119.218 2073.73 1703.06 * 0.809 10.84 * 2.742 10.77 1006.64 * 323.109 0.71 1588.58 111.94 1065.61 3.89 CRC 484 CRC 484 PLS 1 Colorectal I 1965.57 2517.64 2203.7 * 0.809 12.01 123.22 10.68 2.127.7 * 323.109 0.75 1672.57 149.95 1052.28 5.17 CRC 486 CRC 486 PLS 1 Colorectal I 9728.86 679.45 1231.3 5.7 19.68 17.68 * 1.367 2.046.3 3918.28 0.58 1265.52 326.05 352.26 5.46

CRC 487 CRC 487 PLS 1 Colorectal I 1850.84 2219.16 2936.38 8.53 21.77 18.91 8.79 1113.21 * 323.109 1.02 1818.75 111.94 1254.59 6.41 CRC 488 CRC 488 PLS 1 Colorectal II * 119.218 1945.80 1671.88 5.7 3.35 * 2.742 9.5 3.004.8 * 323.109 0.78 1154.42 145.02 1433.55 4.58 CRC 489 CRC 489 PLS 1 Colorectal II * 119.218 1377.39 2361.35 48.49 5.11 88.34 * 1.367 8.477.7 2883.42 0.78 1254.41 134.72 1324.79 7.02 CRC 490 CRC 490 PLS 1 Colorectal III * 119.218 1044.83 2104.92 * 0.809 6.23 * 2.742 11.61 1.651.3 * 323.109 0.77 1432.35 99.02 1935.22 3.92 CRC 491 CRC 491 PLS 1 Colorectal III 858, 58 1952.71 3585.82 5.39 11.60 39.31 15.92 1.479.4 * 323.109 0.78 2005.56 99.02 998.34 7.54 CRC 492 CRC 492 PLS 1 Colorectal III * 119.218 4254 , 90 841.52 57.91 21.49 18.91 11.79 8487.66 * 323.109 0.73 466.66 279.93 435.56 15.7 CRC 493 CRC 493 PLS 1 Colorectal II 3516.3 720, 35 2592.62 5.09 27.44 65.15 8.45 4806.52 * 323.109 0.57 1337.76 111.94 755.25 8.14 CRC 494 CRC 494 PLS 1 Colorectal II * 119.218 1542.39 1462 , 8 * 0.809 10.13 * 2 .742 19.18 560.7 * 323.109 0.6 1813.12 * 14.089 943.3 6.84 CRC 495 CRC 495 PLS 1 Colorectal I * 119.218 400.65 3010.15 * 0.809 15.15 * 2.742 * 1.367 * 74.59 * 323.109 0.78 1566.22 84.54 1152.93 8.59 CRC 496 CRC 496 PLS 1 Colorectal I * 119.218 3040.55 3585.82 * 0.809 4.90 66.53 16.16 1099.78 * 323.109 0.63 2145.26 92.02 2506.28 11.24 CRC 497 CRC 497 PLS 1 Colorectal III 782.2 4173.41 7223.76 5.09 13.52 * 2.742 23.89 15.904.1 * 323.109 0.77 3064.83 181.51 887.04 8.27 CRC 498 CRC 498 PLS 1 Colorectal III 1030.32 1907.80 2187.49 5.39 24.17 124.19 10.01 578.6 * 323.109 0 , 71 2056.77 181.51 1341.11 7.47 CRC 499 CRC 499 PLS 1 Colorectal I * 119.218 567.11 1181.73 * 0.809 7.29 * 2.742 8.41 * 74.59 * 323.109 1.09 813 , 86 164.06 1204.16 4.7 CRC 500 CRC 500 PLS 1 Colorectal I 8295.07 2587.19 4479.12 * 0.809 7.07 * 2.742 18.56 812.47 * 323.109 0.76 1387.82 145 , 02 1034.42 12.59 CRC 501 CRC 501 PLS 1 Colorectal I * 119.218 1212.67 1990.86 * 0.809 24.57 * 2.742 17.22 895.7 2281.58 1.08 1555.04 164.06 1668.84 12.54 CRC 502 CRC 502 PLS 1 Colorectal I * 119.218 1243.53 2212.72 * 0.809 7.70 24.00 * 1.367 1479.41 * 323.109 0.65 2148.13 154.77 1308.4 10.42 CRC 503 CRC 503 PLS 1 Colorectal I * 119,218 1852.56 2237.99 * 0.809 5.48 * 2.742 10.99 737.0 * 323.109 0.84 590.99 * 14.089 760.28 7.74 CRC 504 CRC 504 PLS 1 Colorectal III 4140.16 4684 , 89 2083.46 7.58 9.38 57.83 * 1.367 79.678.8 3918.28 0.77 1215.52 168.55 1357.35 15.29 CRC 505 CRC 505 PLS 1 Colorectal III * 119.218 1473.60 1289.49 * 0.809 6.15 16.45 10.24 5435.06 * 323.109 0.64 1151.64 84.54 1208.39 4.65 CRC 506 CRC 506 PLS 1 Colorectal III * 119.218 1449.54 877.83 * 0.809 14.10 * 2.742 12.46 1,150.3 * 323,109 0.69 2308.97 99.02 943.3 6.63 CRC 507 CRC 507 PLS 1 Colorectal I * 133.814 992.76 3019.58 5.24 9 , 09 * 2.745 11.04 4524.32 * 328.934 0.84 584.2 110.68 978.25 3.92 CRC 508 CRC 508 PLS 1 Colorectal I * 133.814 2548.80 4892.6 * 0.83 6.26 * 2.745 27.13 2.287.1 * 328.934 0.88 1618.37 * 14.113 864.9 8.79 CRC 509 CRC 509 PLS 1 Colorectal I * 133.814 1316.41 1718.73 * 0.83 12.64 * 2.745 16.99 577.4 * 328.934 1.03 2110.06 110.68 418.05 4.7 CRC 510 CRC 510 PLS 1 Colorectal I 905.21 1746.69 2083.3 4.98 4.01 * 2.745 15, 63 1503.59 * 328.934 0.74 1841.97 84.68 1012.2 0.86 CRC 511 CRC 511 PLS 1 Colorectal II * 133.814 1586.62 1950.29 8.31 6.68 * 2.745 15.76 * 74.812 * 328.934 0.73 1004 150.94 185.72 4.36 CRC 512 CRC 512 PLS 1 Colorectal II 1513.35 14107.58 2111.12 * 0.83 12.44 * 2.745 22.1 13.183.2 * 328.934 1 , 05 1895.31 98.45 2113.67 12.2 CRC 513 CRC 513 PLS 1 Colorectal II * 133.814 2733.88 3591.1 * 0.83 52.72 * 2.745 51.76 608.3 * 328.934 1.47 3102.55 84.68 1491.11 3.63 CRC 514 CRC 514 PLS 1 Colorectal II 2500.76 1096.07 2947.15 * 0.83 35.69 22.57 16.26 2,960.4 * 328.934 1.15 1432.17 * 14,113 1286.89 7.07 CRC 515 CRC 515 PLS 1 Colorectal II 843.61 1799.04 2730.25 * 0.83 4.31 * 2.745 14.35 1751.77 * 328.934 0.83 1747, 51 84.68 1037.41 4.95 CRC 516 CRC 516 PLS 1 Colorectal II * 133.814 2311.34 3267.52 * 0.83 3.18 * 2.745 27.4 906.9 * 328.934 0.8 2140.06 84 , 68 1371,35 2.66 CRC 517 CRC 517 PLS 1 Neck rectal II 1221.64 694.14 2963.24 * 0.83 3.23 * 2.745 11.93 476.2 * 328.934 0.9 1691.64 * 14,113 569.68 0.53 CRC 518 CRC 518 PLS 1 Colorectal III * 133.814 1531.17 4413.2 5.77 8.25 19.81 17.73 1.385.1 1973.6 0.85 2576.62 * 14,113 801.71 7.44 CRC 519 CRC 519 PLS 1 Colorectal III 2075.04 867 , 21 1498.01 * 0.83 16.17 * 2.745 11.55 582.53 * 328.934 1.06 1208 110.68 943.88 0.26 CRC 520 CRC 520 PLS 1 Colorectal III * 133.814 1857.99 1274, 4 * 0.83 5.07 * 2.745 14.94 603.1 * 328.934 1.09 * 28.66 110.68 600.29 13.33 CRC 521 CRC 521 PLS 1 Colorectal III * 133.814 1436.71 2146.92 * 0.83 4.08 * 2.745 13.98 2.482.4 * 328.934 0.7 1841.97 * 14.113 995.27 4.19 CRC 522 CRC 522 PLS 1 Colorectal II * 133.814 870.42 1521.61 4.98 11.69 * 2.745 19.81 * 74.812 * 328.934 1.89 2152.07 * 14.113 1446.61 3.26 CRC 523 CRC 523 PLS 1 Colorectal III * 133.814 2087.64 2077.33 6.84 16.20 * 2.745 11.49 885.0 * 328.934 0.7 1516.3 * 14.113 * 20.74 3.53 CRC 524 CRC 524 PLS 1 Colorectal II * 133.814 970.19 2346.15 9.53 4.37 21.47 15, 55 1,420.4 * 328,934 0, 73 1927.97 141.81 769.47 0.67 CRC 525 CRC 525 PLS 1 Colorectal III * 133.814 6809.77 2288.32 * 0.83 12.92 * 2.745 19.21 17.514.4 * 328.934 0.62 2092 , 08 98.45 383.14 1.43 CRC 526 CRC 526 PLS 1 Colorectal III * 133.814 774.14 719.4 4.98 6.81 * 2.745 11.93 466.2 * 328.934 0.71 764.47 84 , 68 838.02 5.76 CRC 527 CRC 527 PLS 1 Colorectal III * 133.814 851.14 2890.86 * 0.83 18.76 51.46 14.98 5.106.8 * 328.934 1.24 2290.69 84, 68 630.37 6.03 CRC 528 CRC 528 PLS 1 Colorectal II * 133.814 1044.38 2150.9 * 0.83 7.11 * 2.745 16.95 755.4 * 328.934 0.65 1630.08 132.15 610 , 37 7.23 CRC 529 CRC 529 PLS 1 Colorectal II * 133.814 1151.06 2226.54 * 0.83 5.42 * 2.745 18.7 665.50 * 328.934 0.8 1516.3 110.68 620.4 2.48 CRC 530 CRC 530 PLS 1 Colorectal II * 133.814 1799.04 2822.56 * 0.83 11.95 * 2.745 26.56 2584.50 * 328.934 0.77 2068.13 * 14.113 1454.06 4.89 CRC 531 CRC 531 PLS 1 Colorectal III * 133.814 854.36 2684.13 * 0.83 9.78 * 2.745 10.57 2.242.0 * 328.934 0.78 1630.08 98.45 474.1 7.86 INDI 001 INDI 001 PLS 1 Lung II * 130,232 40 50.94 2561.78 * 0.795 5.27 * 2.633 25.95 * 73.413 * 1033.785 2.11 1211.27 106.93 993.91 8.31 INDI 002 INDI 002 PLS 1 Lung II * 130.232 1204.80 918.55 5.49 19.86 22.76 11.51 1959.21 * 1033.785 1.01 985.45 143.4 542.59 3.98 INDI 003 INDI 003 PLS 1 Lung III 5337 1034.19 2507 , 12 169.14 36.28 476.96 18.42 34.562.2 * 1033.785 0.7 2205.23 * 13.973 1274.11 5.84 INDI 004 INDI 004 PLS 1 Lung I * 130.232 2203.07 2782, 18 * 0.795 11.09 149.33 17.29 1885.07 * 1033.785 0.8 1817.61 83.84 1131.28 7.55 INDI 005 INDI 005 PLS 1 Lung I * 130.232 1860.67 4122.82 * 0.795 5, 36 * 2.633 9.71 885.64 * 1033.785 0.75 873.48 * 13.973 774.77 2.92 INDI 007 INDI 007 PLS 1 Lung II * 130.232 513.82 1853.65 9.85 32.40 22 , 76 21.65 91090.52 * 1033.785 0.68 1975.85 106.93 542.59 3.83 INDI 009 INDI 009 PLS 1 Lung II * 130.232 1200.19 442.58 8.39 16.50 * 2.633 10.53 * 73.413 * 1033.785 0.74 837.8 106.93 626.68 2.31 INDI 010 INDI 010 PLS 1 Lung II * 130.232 2376.25 391.11 39.67 16.84 307.79 10.42 * 73.413 * 1033.785 0.99 1066.47 * 13.973 1162.95 5.07 INDI 011 INDI 011 PLS 1 Lung I * 130.232 1177.16 1290.04 * 0.795 8.15 17.62 19.32 1085.49 * 1033.785 0.6 1729.53 158.9 576.84 5.49 INDI 012 INDI 012 PLS 1 Lung I * 130.232 3217.17 1446.5 * 0.795 20.36 * 2.633 11.97 512.7 * 1033.785 0.77 1864.07 83.84 626.68 5.39 INDI 013 INDI 013 PLS 1 Lung III 908.66 1567.71 2326.22 * 0.795 20.52 * 2.633 18.62 617.83 * 1033.785 0.96 1466.85 126.31 471.17 4.67 INDI 014 INDI 014 PLS 1 Lung II * 130.232 3647.90 2251.89 * 0.795 9.11 * 2.633 14.73 2.303.1 * 1033.785 0.92 1471.44 126.31 1053.71 3.05 INDI 015 INDI 015 PLS 1 Lung I * 130.232 2102.72 4967.26 * 0.795 32.57 29.87 26.27 1155.54 * 1033.785 1.09 1480.62 126.31 1007.33 5.08 INDI 016 INDI 016 PLS 1 Lung III 1710.54 1508.10 1511.27 * 0.795 16.73 47.86 11.72 34,150.7 * 1033.785 1.11 1604.8 126.31 618.49 4.57 INDI 017 INDI 017 PLS 1 Lung I * 130,232 2011.43 2406.08 6.93 23.44 * 2.633 19.03 1226.57 * 1033.785 0.85 2455.01 106.93 939.47 6.73 INDI 018 INDI 018 PLS 1 Lung III * 130.232 1663.92 2912 16 , 33 13.42 16.06 20.32 44341.86 * 1033.785 1.35 1632.47 83.84 855.2 6.48 INDI 022 INDI 022 PLS 1 Lung III * 130.232 3185.41 970.09 * 0.795 16.74 * 2.633 17.37 787.3 * 1033.785 0.95 779.98 126.31 876.58 5.35 INDI 023 INDI 023 PLS 1 Lung II * 130.232 1434.68 2223.79 * 0.795 32.73 * 2.633 18.13 2.267.1 * 1033.785 0.86 2469.19 * 13.973 759.77 7.37 INDI 024 INDI 024 PLS 1 Lung II 2331.68 4544.00 2494.13 * 0.795 11.08 * 2.633 32.6 1353.09 * 1033.785 1.46 1845.48 * 13.973 * 72.59 4.21 INDI 025 INDI 025 PLS 1 Lung I 1323.96 2631.16 1703.17 6.57 9.62 * 2.633 10.15 4.045.1 * 1033.785 0.94 1655.55 126.31 966.85 6.87 INDI 026 INDI 026 PLS 1 Lung I 4433.47 881.57 1231.5 * 0.795 72.06 * 2.633 11 , 56 923.20 * 1033.785 0.9 1457.68 143.4 1286.2 7.7 INDI 027 INDI 027 PLS 1 Colorectal II 5170.73 1874.38 2223.79 * 0.795 3.92 * 2.633 17.96 2.205 , 8 * 1033,785 0.78 2106.73 * 13.973 1000.63 5.5 INDI 028 INDI 028 PLS 1 Colorectal II 2518 918.61 1591.01 * 0.795 7.25 * 2.633 18.38 1.085.5 * 1033 , 785 0.75 1416.44 83.84 * 72.59 4.59 INDI 029 INDI 029 PLS 1 Colorectal II 1110.73 1718.86 1076.72 * 0.795 25.76 * 2.633 20.16 2862.66 * 1033.785 0.79 2711.21 * 13.973 364.62 3.25 INDI 030 INDI 030 PLS 1 Colorectal II 4699.57 4887.50 4748.93 4.77 19.16 22.76 26.66 2965.77 * 1033.785 1 1878.02 126.31 1945.45 4.54 INDI 031 INDI 031 PLS 1 Colorectal II 3240.13 2553.81 738.43 4.77 16.92 * 2.633 21.26 2008.99 * 1033.785 0.87 1152.33 264.43 1261.96 6.12 INDI 032 INDI 032 PLS 1 Colorectal III 1722.98 1741.75 2023.6 * 0.795 12.60 16.58 14.32 1680.98 * 1033.785 0.76 2720.73 83.84 2451.7 4.36 INDI 034 INDI 034 PLS 1 Colorectal III * 130.232 3226.24 2137.28 * 0.795 23.09 * 2.633 44.71 4,899.1 * 1033.785 0.81 1512.76 106.93 1188.03 9.81 INDI 035 INDI 035 PLS 1 Colorectal II 1223.77 2703.92 1264.23 * 0.795 3.22 * 2.633 22.87 479.9 * 1033.785 1.22 771.1 106.93 626.68 4.49 INDI 036 INDI 036 PLS 1 Colorectal I 1273.9 2626.61 1851.17 * 0.795 20.20 * 2.633 18.46 572.3 * 1033.785 0.75 2988.22 83.84 * 72.59 4.83 INDI 037 INDI 037 PLS 1 Colorectal I * 130,232 216 6.59 2016.06 * 0.795 70.12 52.30 18.99 3252.07 * 1033.785 1.2 1947.87 * 13.973 1219.06 5.32 INDI 038 INDI 038 PLS 1 Colorectal II 1047.76 1034 , 19 2711.04 * 0.795 15.60 * 2.633 16.9 * 73.413 * 1033.785 0.96 1873.37 186.6 1695 4.14 INDI 039 INDI 039 PLS 1 Colorectal III 6200.29 1103.41 2147, 43 10.22 4.69 24.80 11.41 1,244.5 * 1033.785 0.94 335.03 186.6 658.99 9.3 INDI 040 INDI 040 PLS 1 Colorectal I 1959.14 639.98 501 , 08 * 0.795 14.65 * 2.633 20.24 1235.51 * 1033.785 1.01 642.98 186.6 2250.23 6.56

INDI 042 INDI 042 PLS 1 Colorectal I 3841.1 1952.06 1154.87 * 0.795 12.85 * 2.633 18.71 1.894.9 * 1033.785 0.86 1320.49 83.84 1684.24 6.29 INDI 043 INDI 043 PLS 1 Colorectal II * 130,232 1498.92 1516.09 9.12 5.95 * 2.633 11.67 7170.88 * 1033.785 0.8 3122.62 274.05 568.36 2.98 INDI 044 INDI 044 PLS 1 Colorectal II 1518.46 1461.82 1169.82 46.46 13.71 * 2.689 13.58 * 77.55 * 319.819 0.88 1466.89 * 13.517 536.43 7.93 INDI 045 INDI 045 PLS 1 Colorectal III * 159.986 1516.92 1048.57 17.46 20.25 * 2.689 12.2 11828.89 2725.41 1.32 1663.27 206.36 678.86 51.61 INDI 046 INDI 046 PLS 1 Colorectal II * 159.986 2307.35 1406.29 8.14 17.49 * 2.689 11.98 2628.56 2413.21 0.73 892.07 144.85 1315.85 4.04 INDI 047 INDI 047 PLS 1 Colorectal II * 159.986 2794, 99 495.64 5.18 8.48 * 2.689 12.62 2746.89 4936.36 0.724.28 236.7 573.27 7.51 INDI 048 INDI 048 PLS 1 Breast II * 130.232 2075.34 2512 , 31 20.63 41.03 * 2.633 19.84 11062.71 * 1033.785 0.85 1669.41 83.84 1619.03 5.95 INDI 049 INDI 049 PLS 1 Breast III 2965.8 1462.22 1439 33 8 , 39 15.80 * 2.633 10.74 579.79 * 1033.785 0.73 1265.82 * 13.973 1007.33 2.17 INDI 050 INDI 050 PLS 1 Breast II * 130.232 471.62 949.87 8.39 10.62 * 2.633 11.46 * 73.413 * 1033.785 0.81 1859.42 106.93 525.13 2.17 INDI 051 INDI 051 PLS 1 Breast III * 130.232 3801.92 2406.08 64.31 18, 70 24.29 16.64 2390.97 * 1033.785 0.84 1034.92 143.4 1237.52 6.11 INDI 052 INDI 052 PLS 1 Breast II * 130.232 3003.83 2890.74 * 0.795 5.61 * 2.633 20.16 1271.43 * 1033.785 1.08 2228.72 83.84 1112.1 2.86 INDI 053 INDI 053 PLS 1 Breast II 946.68 1974.90 2369.96 5.85 25.91 * 2.633 14.92 1533.81 * 1033.785 1.03 2266.33 126.31 1060.26 6.26 INDI 054 INDI 054 PLS 1 Breast II * 130.232 2025.13 2512.31 * 0.795 6.54 * 2.633 23.66 * 73.413 * 1033.785 0.92 1443.93 * 13.973 1462.35 3.24 INDI 055 INDI 055 PLS 1 Breast II * 130.232 244.85 1910.88 5.85 11.07 * 2.633 15.55 982.30 * 1033.785 0.69 208.39 244.28 507.41 4.3 INDI 056 INDI 056 PLS 1 Breast II * 130.232 2685.73 1035.68 * 0.795 13.70 * 2.633 13.57 2835, 66 * 1033,785 1.01 1554.14 83.84 576.84 4.44 INDI 057 IN DI 057 PLS 1 Breast III * 130,232 1255,43 1351,35 * 0,795 8.64 * 2,633 16,94 1,137.9 * 1033,785 0.91 1016.91 83.84 1020.67 6.6 INDI 058 INDI 058 PLS 1 Breast I * 130.232 1158.73 777.45 6.21 21.80 * 2.633 12.71 1984.06 * 1033.785 0.72 1297.69 106.93 452.61 2.45 INDI 059 INDI 059 PLS 1 Breast II 5870.64 1204.30 1704.54 10.97 20.18 22.69 16.16 1235.59 * 319.819 1.02 2353.93 129.61 678.86 9.13 INDI 060 INDI 060 PLS 1 Breast II 1752.17 1491.48 1012.87 6.68 7.44 * 2.689 15.13 * 77.55 2087.71 0.9 1019.3 183.89 831.88 6.76 INDI 061 INDI 061 PLS 1 Breast II * 159.986 3285.53 877.09 * 0.816 6.19 17.01 22.86 1.432.2 2413.21 0.95 1931.89 158.79 2515.41 8.31 INDI 062 INDI 062 PLS 1 Breast II * 159.986 699.44 224.04 * 0.816 4.13 * 2.689 9.15 * 77.55 3026.76 1.3 2368.34 272.84 1014.25 7.25 INDI 063 INDI 063 PLS 1 Breast III * 159.986 1754, 98 2058.55 * 0.816 27.02 * 2.689 16.08 3726.45 * 319.819 0.95 1188.56 195.4 1833.6 9.2 INDI 064 INDI 064 PLS 1 Breast II 4248.51 1904.33 1646, 63 5.75 17.16 20.34 12.67 777.0 2725.41 0.77 1814.95 289.5 6 1337.92 8.09 INDI 065 INDI 065 PLS 1 Breast II * 159.986 1162.25 1483.01 * 0.816 20.48 26.39 20.25 1497.89 5189.04 4.2716.45 137.42 734 , 88 8.19 INDI 066 INDI 066 PLS 1 Breast III * 159.986 1720.90 1704.54 * 0.816 7.56 * 2.689 16.33 1443.17 3603.35 1.27 1733.72 206.36 1617.74 9.57 INDI 067 INDI 067 PLS 1 Breast III * 159.986 2596.10 1829.71 * 0.816 16.31 * 2.689 8.34 777.0 * 319.819 1.08 1628.1 112.55 1849.2 12.61 INDI 068 INDI 068 PLS 1 Breast II * 159,986 927.78 1163.32 * 0.816 10.00 * 2.689 7.29 * 77.55 * 319.819 1.14 874.91 206.36 762.29 5.98 INDI 069 INDI 069 PLS 1 Breast II * 159.986 2346.07 3383.08 * 0.816 8.42 * 2.689 21.72 2.136.9 * 319.819 1.21 271.82 281.29 1964.66 11.19 INDI 070 INDI 070 PLS 1 Breast III 4529.89 2816.64 832.55 * 0.816 30.22 31.24 16.66 733.2 21984.63 1.01 837.2 195.4 1609.54 4.72 INDI 071 INDI 071 PLS 1 Breast II * 159.986 2591, 79 1372.85 * 0.816 5.58 * 2.689 17.11 1.006.5 * 319.819 1.88 1133.19 183.89 1239.73 18.16 INDI 072 INDI 072 PLS 1 Breast II 2367.68 1559.34 1172.42 * 0.816 7.72 * 2.689 18.68 3030.32 2252.31 1.01 1638.65 255.28 1802.25 8.53 INDI 073 INDI 073 PLS 1 Breast III 1171.83 1449.12 2509.64 * 0.816 12.97 34.53 17.46 1,366.6 2087,71 1,53 1974,53 * 13,517 1543,36 4 INDI 074 INDI 074 PLS 1 Pancreas II 1823,22 2228,39 2167,67 25,55 33,61 2136,83 20,09 4,158.0 7562.35 0.81 2078.57 99.23 1217.75 4.71 INDI 075 INDI 075 PLS 1 Ovary III 2302.37 1018.56 2655.01 113.65 13.91 * 2.624 23.8 1,102.6 9240.89 0.97 1890.23 112.06 1618.34 4.68 INDI 076 INDI 076 PLS 1 Esophagus II 1989.46 2829.63 1660.39 * 0.816 23.02 31.91 16.16 5507.57 2877.32 0 , 62 1091.73 158.79 * 77 11.73 INDI 077 INDI 077 PLS 1 Liver II 7843.86 1644.30 2868.99 9.57 12.85 41.97 21.06 3.104.4 4152.37 0, 66 3313.49 171.73 326.66 4.71 INDI 078 INDI 078 PLS 1 Liver II 2030.92 852.82 762.79 * 0.816 21.49 26.39 13.78 1.783.3 5189.04 0.64 1882.22 171.73 561.09 7.67 INDI 079 INDI 079 PLS 1 Liver II * 159.986 4155.29 1302.39 * 0.816 22.27 * 2.689 11.76 1.552.7 * 319.819 0.63 2174.35 * 13,517 511.34 9.7 INDI 080 INDI 080 PLS 1 Fig I 7417.29 937.12 7194.25 * 0.795 5.79 * 2.633 17.12 978.1 * 1033.785 0.82 2431.37 * 13.973 1020.67 4.63 INDI 081 INDI 081 PLS 1 Colorectal II * 130.232 3620, 72 2076.54 * 0.795 25.54 22.76 15.72 1.899.9 * 1033.785 0.82 2892.49 83.84 1033.94 6.28 INDI 082 INDI 082 PLS 1 Stomach II * 130.232 812.04 3366.34 * 0.795 18.90 * 2.633 21.46 1850.70 * 1033.785 0.72 1416.44 83.84 452.61 2.87 INDI 083 INDI 083 PLS 1 Stomach II 4467.66 3137.66 811 , 64 * 0.816 11.76 * 2.689 16.08 4042.02 3318.97 0.66 1327.41 137.42 821.32 10.55 INDI 084 INDI 084 PLS 1 Stomach II 49 326.37 2432.19 1198.5 14.42 20.98 46.09 13.39 1290.18 14369.30 0.57 1501.86 226.96 2618.59 9.79 INDI 085 INDI 085 PLS 1 Stomach I * 159.986 1166.46 631.07 * 0.816 8.67 * 2.689 11.87 3195.82 4679.62 0.55 1896.4 206.36 391.81 6.53 INDI 086 INDI 086 PLS 1 Stomach I * 159.986 4471.57 2747.31 * 0.816 22, 93 * 2.689 37.16 2533.06 3318.97 1.13 1938.99 171.73 894.22 7.95 INDI 087 INDI 087 PLS 1 Stomach I * 159.986 1153.85 362.66 19.46 5.40 30 , 57 11.92 1,772.3 3027 0.6 1600 206.36 1043.41 7.12 INDI 089 INDI 089 PLS 1 Colorectal II * 159.986 836.19 737.34 * 0.816 6.42 29.20 11.53 * 77.55 4679.62 1, 29 2804.14 313.33 805.39 7.87 INDI 090 INDI 090 PLS 1 Colorectal II * 159.986 2114.09 778.61 * 0.816 9.94 * 2.689 11.18 * 77.55 3880.89 1.02 570 , 82 171.73 1359.86 5.81 INDI 092 INDI 092 PLS 1 Stomach II * 159.986 2596.10 1120.55 * 0.816 5.28 * 2.689 14.4 1970.77 2087.71 0.99 1825.56 129 , 61 964.93 6.29 INDI 093 INDI 093 PLS 1 Colorectal I * 159.986 2131.25 3662.82 9.92 41.44 * 2.689 19.26 1.093.8 11116.5 1.06 1617.56 183.89 974, 86 11.95 INDI 094 INDI 094 PLS 1 Colorectal I * 159.986 3800.44 2320.73 14.59 7.56 * 2.689 28.33 2583.58 3603.35 1.67 1758.42 272.84 2354 7 INDI 095 INDI 095 PLS 1 Stomach II * 159,986 1250.61 2574.64 7.05 9.68 * 2.689 13.78 490.38 3318.97 1.55 1765.48 289.56 1355.48 9.02 INDI 096 INDI 096 PLS 1 Colorectal I * 159.986 1831.74 586.52 5.18 4.98 * 2.689 * 1.35 787.95 6866.41 0.87 1931.89 206.36 1072.27 3.46 INDI 097 INDI 097 PLS 1 Colorectal III 947.92 3016.05 3819.22 25.37 14.95 43.17 36.23 2,956.4 2725.41 1.45 3068.78 264.17 1394.68 6.63 INDI 098 INDI 098 PLS 1 Colorectal II * 159.986 4436.39 783.49 * 0.816 11.71 * 2.689 8.11 820.78 * 319.819 1.08 796.1 112.55 1175.68 5.27 INDI 100 INDI 100 PLS 1 Colorectal III * 159.986 2872, 95 1154.22 * 0.816 2.94 * 2.689 10.46 1.028.3 * 319.819 1.54 892.07 195.4 1584.85 11.13 INDI 101 INDI 101 PLS 1 Colorectal III * 159.986 1989.86 1277.28 * 0.816 5.67 * 2.689 10.34 1,148.3 6635.12 0.99 898.93 112.55 419.45 8.17 INDI 102 INDI 102 PLS 1 Colorectal III * 159.986 2165.57 1707.3 * 0.816 10 , 35 * 2,689 16.74 523.7 * 319.819 2.97 3039.25 343.07 1625.92 7.22 INDI 103 INDI 103 PLS 1 Stomach II 4169.69 1237.98 884.55 * 0.816 4.44 * 2.689 11.87 601.17 3462.07 2.14 926.41 183.89 3271.93 11.64 INDI 104 INDI 104 PLS 1 Stomach III 390 698.43 2509.79 924.51 * 0.816 8.39 * 2.689 15 , 26 19,906.4 * 319.819 2.02 2567.27 305.56 548.82 11.53 INDI 107 INDI 107 PLS 1 Colorectal II 4026.98 3940.50 2259.77 * 0.816 5.22 * 2.689 14.49 19 59.72 2413.21 1.6 2375.55 144.85 1147.83 9.18 INDI 108 INDI 108 PLS 1 Colorectal II * 159.986 1925.70 173.03 7.41 1.88 17.01 9.28 634.3 38779.71 1.34 1917.69 255.28 2047.72 13.45 INDI 109 INDI 109 PLS 1 Colorectal III * 159.986 3011.71 1112.8 5.93 31.24 * 2.689 13.63 2.136.9 3880.89 0 , 71 2958.22 144.85 341.54 6.26 INDI 110 INDI 110 PLS 1 Stomach I * 159.986 1246.40 1621.91 * 0.816 3.39 * 2.689 10.46 3.013.3 * 319.819 1.61 1889, 31 * 13.517 2047.72 8.46 INDI 111 INDI 111 PLS 1 Stomach II * 159.986 1720.90 967.26 * 0.816 10.66 * 2.689 12.2 886.38 * 319.819 0.79 2253.23 * 13.517 924, 77 10.32 INDI 112 INDI 112 PLS 1 Esophagus II 4930.94 1917.15 751.87 * 0.816 44.94 * 2.689 15.99 941.0 * 319.819 1.44 1355.25 144.85 756.84 13.81 INDI 113 INDI 113 PLS 1 Colorectal II * 159.986 2242.87 740.97 * 0.816 10.56 99.93 9.69 120.958.6 * 319.819 0.86 1868.05 171.73 1115.01 5.79 INDI 116 INDI 116 PLS 1 Stomach I * 159.986 5053.37 1656.26 * 0.816 16.79 * 2.689 21.56 * 77.55 * 319.819 0.65 1445.93 * 13.517 1257.79 5.79 INDI 119 I NDI 119 PLS 1 Esophagus I * 159.986 1406.80 307.22 187.03 ** 54.442 224.73 12.73 2.303.8 11016.84 1.16 1924.79 216.86 767.73 13.13 INDI 120 INDI 120 PLS 1 Stomach III * 159.986 3717.36 3379.98 7.41 6.44 * 2.689 15.82 804.37 * 319.819 0.91 1237.08 * 13.517 632.76 91.98 INDI 121 INDI 121 PLS 1 Stomach I * 159.986 1263.25 2816.3 8.5 6.59 * 2.689 12.52 689.25 * 319.819 1.55 940.15 144.85 1337.92 4.58 INDI 122 INDI 122 PLS 1 Colorectal II 145090.58 * * 9999.94 1967.61 11.32 31.75 * 2.689 10.21 656.27 * 319.819 1.16 1882.22 343.07 2529.26 19.07 INDI 123 INDI 123 PLS 1 Colorectal II * 159.986 654, 06 1379.53 18.46 17.85 35.81 14.76 4183.26 3603.35 1.37 830.34 226.96 1004.46 19.3 INDI 124 INDI 124 PLS 1 Colorectal II * 159.986 4014.97 1189, 36 50.41 12.58 * 2.689 29.04 3.494.7 3026.76 0.97 570.82 158.79 934.87 16.91 INDI 125 INDI 125 PLS 1 Esophagus II 3640.27 2414.96 888.29 * 0.816 10.33 * 2.689 8.93 557.0 2725.41 0.95 1404.04 158.79 377.71 11.61 INDI 126 INDI 126 PLS 1 Stomach III * 159.986 3102.90 982.43 * 0.816 19.49 * 2.689 12.2 1.629.4 * 319.819 1.16 1303.06 * 13.517 964.93 13.92 INDI 127 INDI 127 PLS 1 Colorectal II * 159.986 3037.76 1857.72 * 0.816 25.92 * 2.689 11.98 2.606.1 * 319.819 1.03 2006.55 158.79 740.4 29.53 INDI 128 INDI 128 PLS 1 Colorectal III 5345.11 2847.97 864.37 12.06 4.11 147.22 18 , 95 14,732.4 11,119.95 * 0.107 1627.35 217.27 2133.98 8.17 INDI 129 INDI 129 PLS 1 Liver II ** 100101.482 2242.08 1047.07 6.29 9.58 50.35 8, 98 1530.86 6047.91 1.54 2512.5 290.33 910.59 12.48

INDI 130 INDI 130 PLS 1 Stomach II * 168,118 1317.90 609.74 11.48 9.22 * 2.915 13.18 936.72 4204.12 * 0.107 1841.6 250.9 793.8 6.31 INDI 131 INDI 131 PLS 1 Stomach II 1015.37 2163.90 2232.8 86.55 15.37 21.56 15.96 664.1 4624.15 1.38 1247.64 116.99 820.42 14.52 INDI 132 INDI 132 PLS 1 Esophagus III * 168.118 1765.49 2866 5.61 4.30 * 2.915 7.94 1.331.4 * 347.375 * 0.107 1386.8 105.39 996.74 45.32 INDI 133 INDI 133 PLS 1 Stomach I * 168.118 2259 , 46 1581.43 * 0.845 11.45 * 2.915 8.9 1.233.4 2203 * 0.107 1677.92 * 15.378 947.96 24.05 INDI 134 INDI 134 PLS 1 Stomach III * 168.118 2612.15 1962.2 * 0.845 18.72 34.28 15.67 2672.50 * 347.375 * 0.107 1588.48 105.39 497.5 36.54 INDI 135 INDI 135 PLS 1 Stomach III * 168.118 2272.50 2893.2 101.49 336.25 19.24 11.75 2.222.7 8555 * 0.107 125.29 177.94 739.25 7.24 INDI 136 INDI 136 PLS 1 Colorectal II 1960.36 2992.32 1168.99 7.25 15.68 * 2.915 8 , 34 2,130.0 * 347,375 * 0.107 1062.73 146.25 638.87 32.61 INDI 137 INDI 137 PLS 1 Stomach II 29205.8 2233.39 2869.2 7.25 12.56 * 2.915 16.44 79.953.9 * 347.375 * 0.107 1970.52 * 15.378 638.87 5.95 INDI 139 INDI 139 PLS 1 Stomach III * 168.118 1834.63 2226.63 26.87 16.90 * 2.915 9.22 ** 56071.331 722034 1.32 2150.68 191.89 1385.14 74.85 INDI 140 INDI 140 PLS 1 Stomach III 3829.74 2931.06 1261.22 14.4 4.84 * 2.915 10.29 910 , 88 * 347,375 * 0.107 1947.06 127.5 1436.33 15.04 INDI 141 INDI 141 PLS 1 Stomach III 2244.47 2042.43 553.16 6.15 8.03 * 2.915 11.55 957.5 * 347.375 * 0.107 2793.05 * 15.378 793.8 5.09 INDI 143 INDI 143 PLS 1 Colorectal II * 168.118 1253.63 1525.68 24.07 12.64 * 2.915 9.06 576.7 * 347.375 * 0.107 1008, 95 * 15.378 1091.09 7.01 INDI 144 INDI 144 PLS 1 Colorectal II * 168.118 1674.85 290.09 24.69 20.14 115.33 8.47 8.796.4 * 347.375 * 0.107 1101.19 127.5 353.44 24.73 INDI 145 INDI 145 PLS 1 Colorectal II * 168.118 848.75 343.48 16.18 8.09 30.82 11.07 3,657.4 * 347,375 * 0.107 924.58 127.5 711.26 8.95 INDI 146 INDI 146 PLS 1 Colorectal II * 168.118 3005.46 772.86 10.48 4.47 * 2.915 10.72 4.435.2 * 347.375 1.34 1479.79 105 , 39 623.93 11.28 INDI 147 INDI 147 PLS 1 Stomach II 3115.12 2189.95 743.55 * 0.845 11.11 168.89 11.58 2.041.7 * 347.375 * 0.107 1705.17 127.5 419 , 33 28,06 INDI 148 INDI 148 PLS 1 Colorectal II 2673,94 3246,46 2896,4 8,92 23,57 * 2,915 18,48 1901,61 4204,12 1,27 985,92 177,94 885, 27 48.8 INDI 150 INDI 150 PLS 1 Colorectal II * 168.118 2129.17 1329.92 * 0.845 5.65 167.18 7.14 ** 56071.331 13239.38 1.18 916.91 191.89 * 76 , 56 76.28 INDI 151 INDI 151 PLS 1 Stomach II 5187.97 1800.05 3362.79 17.07 13.40 * 2.915 8.56 193.910.0 * 347.375 1.24 1093.5 162.85 480.65 84, 07 INDI 152 INDI 152 PLS 1 Colorectal I 16 759.56 692.28 5505.36 * 0.845 4.89 26.20 22.48 1.051.7 * 347.375 * 0.107 2611.15 105.39 711.26 4.8 INDI 153 INDI 153 PLS 1 Stomach II * 168.118 984.68 2174.3 5.34 2.93 * 2.915 14.44 * 76.457 * 347.375 1.57 1572.94 105.39 401.03 140.43 INDI 155 INDI 155 PLS 1 Colorectal II * 168.118 6069.42 3747.51 * 0.845 * 0.236 * 2.915 9.62 567.1 * 347.375 1.49 771.67 * 15.378 1008.75 78.02 INDI 156 INDI 156 PLS 1 Colorectal III 1042.06 1236.51 2155.87 * 0.845 20.69 117.04 15.1 50.868.3 24724.77 * 0.107 2437.61 * 15.378 1364.42 47.81 INDI 158 INDI 158 PLS 1 Colorectal I * 168.118 1403, 71 2051.85 8.36 3.25 * 2.915 8.69 586.3 * 347.375 * 0.107 1865.02 * 15.378 1613.98 10.29 INDI 159 INDI 159 PLS 1 Colorectal II * 168.118 4082.35 2999.14 * 0.845 7.92 * 2.915 10.72 * 76.457 * 347.375 1.1 2607.2 * 15.378 1415.96 7.86 INDI 160 INDI 160 PLS 1 Colorectal II * 168.118 1895.18 2030.53 * 0.845 7.02 * 2.915 11.51 1342.38 * 347.375 1.36 1771.39 146.25 1395.45 6.47 INDI 161 INDI 161 PLS 1 Stomach II * 168.118 4064.71 1819.03 * 0.845 6.05 * 2.915 15.36 1.158 , 1 * 347,375 1.45 897.77 127.5 1296.01 5.98 INDI 162 INDI 162 PLS 1 Stomach III * 168.118 3914.80 6484.12 * 0.845 14.68 * 2.915 9.06 664.10 * 347.375 1.39 901.6 105.39 2146.6 10.46 INDI 163 INDI 163 PLS 1 Colorectal III * 168,118 925.15 1750.26 * 0.845 12.69 * 2.915 7.14 703.5 * 347.375 * 0.107 1093, 5 * 15.378 2184.27 7.38 INDI 164 INDI 164 PLS 1 Colorectal I * 168.118 1903.83 2738.53 * 0.845 2.93 * 2.915 7.85 813.76 * 347.375 1.59 1232.2 146.25 1979.62 7.23 INDI 165 INDI 165 PLS 1 Colorectal I * 168.118 2957.31 1997.08 * 0.845 4.13 * 2.915 11, 21 1.677.6 * 347.375 * 0.107 1043.51 154.77 4165.56 4.6 INDI 167 INDI 167 PLS 1 Stomach II * 168.118 1232.23 1268.35 5.07 5.78 * 2.915 14.33 1364.35 * 347.375 * 0.107 2343.14 * 15.378 2100.14 18.85 INDI 168 INDI 168 PLS 1 Stomach II * 168.118 3119.33 3441.52 * 0.845 5.33 98.26 11.35 2.944.9 * 347.375 1.6 1131.98 177.94 793.8 66.23 INDI 169 INDI 169 PLS 1 Colorectal III * 168.118 486.48 1711.55 5.61 7.45 * 2.915 9.77 2.428.9 * 347.375 1.34 1904.08 162.85 570.25 47.29 INDI 170 INDI 170 PLS 1 Colorectal I * 168.118 937.90 1391.88 14.69 9.38 * 2.915 9.22 813.8 * 347.375 1.47 206.32 * 15.378 247 , 38 51.64 INDI 171 INDI 171 PLS 1 Stomach III 9498.91 1986.09 935.24 6.15 21.48 329.76 9.62 10.006.2 3776.79 1.16 2009.65 271.24 353 , 44 39.74 INDI 172 INDI 172 PLS 1 Stomach II * 168.118 2939.81 2001.64 59.78 18.40 9615.69 11.88 24.029.0 21718.99 * 0.107 2410.05 162.85 846, 64 67.34 INDI 173 INDI 173 PLS 1 Stomach I * 168.118 1438.07 2372.22 5.61 6.01 * 2.915 12.07 15.825.7 * 347.375 1.33 3281.75 * 15.378 3877.33 34.66 INDI 175 INDI 175 PLS 1 Colorectal III 3813.23 4563.92 5085.46 27.5 18.81 * 2.915 26.97 1.677.6 11299.06 1.68 400.41 105.39 1020.71 53.94 INDI 176 INDI 176 PLS 1 Stomach II * 168.118 4705.55 1616.77 5.61 21.84 52.64 11.71 2.623.5 * 347.375 1.12 407.92 92.27 739.25 35.45 INDI 177 INDI 177 PLS 1 Liver III ** 100,101,482 1696.42 2181.98 8.36 10.60 40.03 10 978.3 3777 1.32 1201.34 228.97 343.56 32.99 INDI 178 INDI 178 PLS 1 Colorectal III 2519.15 2185.61 1084.71 * 0.845 13.16 27.35 8.03 1.126.0 * 347.375 1.72 695.47 162.85 247.38 5.19 INDI 179 INDI 179 PLS 1 Colorectal III * 168,118 1666.23 446.05 11.48 6.49 19.24 7.28 1,458.3 3776.79 1.44 1031.99 280.93 1181.75 46.7 INDI 180 INDI 180 PLS 1 Colorectal III * 168.118 278.81 1913.81 8.22 2.73 * 2.915 9.92 1.700.4 * 347.375 * 0.107 1915.8 162.85 910.59 12.35 INDI 182 INDI 182 PLS 1 Liver III 3879.35 3866 , 33 26 04.03 5.61 22.78 23.30 14.47 1.694.7 18146.52 * 0.107 2103.63 105.39 1038.51 10.36 INDI 183 INDI 183 PLS 1 Stomach I * 168.118 1856.25 2269, 86 11.77 3.60 * 2.915 12.48 773.4 * 347.375 1.99 1670.13 146.25 * 76.56 95.28 INDI 184 INDI 184 PLS 1 Esophagus II 1177.11 3611.02 1696.69 9.77 4.90 * 2.915 9.81 864.7 * 347.375 1.42 1441.02 127.5 1877.98 79.36 INDI 185 INDI 185 PLS 1 Stomach II * 168.118 2808.63 2265.23 18.88 7.20 * 2.915 11.48 335,550.1 2668.03 2.22 1880.64 105.39 1176.18 68.66 INDI 186 INDI 186 PLS 1 Stomach II * 168.118 2581.62 1900.23 16.18 10, 37 * 2,915 8.12 ** 56071.331 * 347.375 1.49 1301.71 198.52 480.65 42.98 INDI 187 INDI 187 PLS 1 Stomach I * 168.118 1326.47 1383.21 6.42 2.96 * 2.915 11.48 1711.75 * 347.375 * 0.107 2052.71 * 15.378 291.97 9.23 INDI 188 INDI 188 PLS 1 Stomach I 1015.37 8540.58 2372.22 6.69 3.94 * 2.915 7, 38 491.35 2895.27 1.25 1001.27 * 15.378 463.52 70.15 INDI 189 INDI 189 PLS 1 Esophagus II * 168.118 2450.89 288.92 6.29 4.74 * 2.915 * 1.372 576.7 3776.79 1.64 642.25 217.27 1604.36 92.26 INDI 190 INDI 190 PLS 1 Liver III * 134.509 3110.53 2786.58 5.07 10.15 18.38 9.78 618.6 * 336.028 1.91 1105.4 * 14.024 670.34 23.85 INDI 191 INDI 191 PLS 1 Colorectal I * 134.509 3595.17 1842.57 * 0.821 7.33 * 2.73 11.54 1473.58 * 336.028 1.52 1911.55 170.71 1082.47 30, 79 INDI 192 INDI 192 PLS 1 Colorectal II 2506.97 1653.10 4757.01 * 0.821 12.12 * 2.73 18.19 1.648.0 3526 2.17 3065.73 177.18 1058.33 30.62 INDI 194 INDI 194 PLS 1 Colorectal II * 134.509 1438.86 1377.23 * 0.821 13.43 * 2.73 10.27 540.9 * 336.028 2.49 * 26.403 116.7 637.41 29.43 INDI 195 INDI 195 PLS 1 Colorectal III * 134.509 1424.21 2078.27 15.81 8.81 17.39 9.68 1016.44 * 336.028 1.57 1613.18 189.51 569.62 46.97 INDI 196 INDI 196 PLS 1 Colorectal III * 134.509 1160.87 2237.14 6.2 7.83 * 2.73 8.83 1658.10 2467.28 1.3 1514.11 201.16 1030.5 50.22 INDI 197 INDI 197 PLS 1 Colorectal II * 134.509 2130.90 1496.19 7.36 3.43 * 2.73 11.75 5.719.9 2106.97 2.21 1347.2 287.53 789.07 52.95 INDI 198 INDI 198 PLS 1 Stomach III ** 98718,787 2122.02 6817.13 * 0.821 22.96 * 2.73 * 1.364 8.698,2 2106.97 0.82 109.47 116.7 471.29 7.39 INDI 199 INDI 199 PLS 1 Stomach II 3007.38 1977.14 1648.03 * 0.821 5.17 18.38 12.28 1.035.2 * 336.028 1.38 901.3 134.22 364.89 8.87 INDI 200 INDI 200 PLS 1 Breast III * 134.509 506.98 1358.05 * 0.821 8.87 * 2.73 7.64 706.4 * 336.028 1.26 1687.89 201.16 * 96.087 59.19 INDI 202 INDI 202 PLS 1 Colorectal II * 134.509 518.67 3104.56 5.92 6.49 92.78 8.83 3.904.1 3351.56 0.96 1543.77 233.01 301.96 6.35 INDI 203 INDI 203 PLS 1 Stomach II * 134.509 2326.63 2794.41 * 0.821 3, 58 324.93 10.27 * 76.419 * 336.028 1.69 1528.93 149.81 861.02 7.76 INDI 205 INDI 205 PLS 1 Liver III 6066.84 3998.62 3274.1 * 0.821 17.51 17.39 27.97 1.548.0 11917.86 1.21 2794.24 96.2 1474.26 13.93 INDI 206 INDI 206 PLS 1 Stomach III * 134.509 3291.72 2392.52 * 0.821 2.59 * 2.73 10.6 809.46 * 336.028 1.22 426.15 164.02 815.84 23.4 INDI 207 INDI 207 PLS 1 Colorectal II * 134.509 2279.12 2194.48 5.35 4.65 29.13 * 1.364 34.340.2 3526 1, 33 157.64 242.81 100 9.45 11.49 INDI 208 INDI 208 PLS 1 Liver II 195 444.75 2696.21 5918.39 7.07 14.34 21.35 11.83 859.4 19067 1.1 967.33 270.39 1113, 22 8.94 INDI 209 INDI 209 PLS 1 Colorectal II 22535.23 2290.99 2177.04 * 0.821 9.98 * 2.73 9.78 614.2 * 336.028 1.54 1652.98 * 14.024 569.62 18 , 52 INDI 210 INDI 210 PLS 1 Stomach III * 134.509 1650.16 2361.41 * 0.821 6.36 26.72 10.74 549.5 * 336.028 0.92 2693.95 * 14.024 480.52 9.15 INDI 211 INDI 211 PLS 1 Colorectal II * 134.509 2720.14 784.54 * 0.821 11.54 * 2.73 10.92 1.311.9 * 336.028 1.19 1873.65 * 14.024 2797.46 12.58 INDI 212 INDI 212 PLS 1 Colorectal III * 134.509 1770.75 1312.05 * 0.821 5.81 16.38 * 1.364 1.021.1 3176.33 1.01 1206.4 116.7 2689.27 9.97 INDI 213 INDI 213 PLS 1 Colorectal III * 134.509 2582.68 1503.87 * 0.821 9.43 * 2.73 8.83 1.063.3 4045.81 1.47 3980.45 96.2 1648.9 8.34 INDI 214 INDI 214 PLS 1 Stomach II * 134.509 1856.18 1490.43 * 0.821 2.64 * 2.73 23 * 76.419 4900.90 2.12 1737.89 96.2 1788.52 14.62 INDI 215 INDI 215 PLS 1 Liver II 18789.42 1026, 51 1580, 72 * 0.821 64.61 17.39 12.83 877.7 * 336.028 0.93 789.12 149.81 872.18 9.33 INDI 216 INDI 216 PLS 1 Colorectal III * 134.509 1324.64 1848.36 * 0.821 6, 13 30.57 15.92 2,141.2 8060.05 1.84 2344.09 212.24 789.07 8.79 INDI 218 INDI 218 PLS 1 Colorectal III * 134.509 425.07 1634.56 * 0.821 2.47 19 , 38 6.82 * 76.419 3526 1.72 242.62 370.07 838.54 8.25 INDI 219 INDI 219 PLS 1 Liver III * 134.509 2015.55 2739.63 * 0.821 2.92 * 2.73 8, 08 * 76,419 2467.28 1.18 3173.11 96.2 534.62 5.64 INDI 220 INDI 220 PLS 1 Lung I 2965.8 2399.03 786.16 12.43 4.18 * 2.633 20.48 1.366 .8 * 1033.785 0.73 542.22 106.93 1020.67 5.25

INDI 221 INDI 221 PLS 1 Colorectal III * 130.232 2248.67 1836.29 41.6 23.16 16.06 17.07 26.173.8 * 1033.785 1.12 1985.18 83.84 1547.11 3.2 INDI 222 INDI 222 PLS 1 Colorectal II * 130.232 2940.24 2152.51 10.58 20.43 17.62 37.33 37.253.1 * 1033.785 0.86 2162.98 244.28 1007.33 5.3 INDI 223 INDI 223 PLS 1 Colorectal I 2667.14 677.25 849.76 10.22 33.85 * 2.633 15.46 490.8 * 1033.785 0.77 2559.18 106.93 585.27 4.75 INDI 224 INDI 224 PLS 1 Lung III * 130.232 5077.25 2390.59 50.87 16.46 * 2.633 16.51 965.3 * 1033.785 0.98 438.04 * 13.973 1530.32 6.33 INDI 225 INDI 225 PLS 1 Colorectal I * 130.232 2189.39 708.3 6.93 8.71 159.25 30.71 2.664.3 * 1033.785 1.01 2658.88 126.31 1780.12 4.89 INDI 226 INDI 226 PLS 1 Breast III * 130,232 1663.92 1417.85 8.57 16.08 * 2.633 14.73 516.41 * 1033.785 0.76 1206.73 199.23 593.65 3.28 INDI 227 INDI 227 PLS 1 Lung II * 130.232 2485.54 2285.17 * 0.795 21.75 * 2.633 20.56 1.362.2 * 1033.785 0.83 1521.95 * 13.973 1853.21 4.89 INDI 228 INDI 228 PLS 1 Breast II 845.14 1131 , 08 1499.24 * 0 , 795 28.65 * 2.633 12.76 1353.09 * 1033.785 0.73 2398.3 * 13.973 690.65 4.03 INDI 229 INDI 229 PLS 1 Colorectal II * 130.232 941.75 2106.88 16.7 16.18 * 2.633 13.43 3969.27 * 1033.785 1.08 1752.68 * 13.973 610.26 3.64 INDI 230 INDI 230 PLS 1 Colorectal I 5055.85 1544.79 2246.78 * 0.795 24, 34 * 2.633 24.03 3,692.3 * 1033.785 0.88 2069.29 96 953.2 4.92 INDI 231 INDI 231 PLS 1 Lung III * 130.232 1411.72 970.09 8.2 10.98 17.62 17.2 7,733.9 * 1033.785 0.95 1147.8 244.28 980.42 4.98 INDI 233 INDI 233 PLS 1 Colorectal I * 130.232 2912.99 1417.85 30.67 45.38 * 2.633 22.26 2.878.9 10264.95 1.01 1850.13 126.31 1345.99 3.14 INDI 234 INDI 234 PLS 1 Colorectal I 1499.85 934.44 3290.78 5.4 18.30 * 2.624 29.98 2048.43 2904 , 45 1.45 1811.03 227.98 1065.72 4.78 INDI 236 INDI 236 PLS 1 Stomach II * 130.232 1066.50 1710.53 * 0.795 5.52 * 2.633 13.86 549.76 * 1033.785 0.74 520.43 83.84 752.22 3.96 INDI 237 INDI 237 PLS 1 Breast I * 154.94 1574.84 1674.31 * 0.816 13.05 * 2.737 14.91 3336.38 * 331.366 0, 82 2614.85 106.15 572.89 5 , 41 INDI 238 INDI 238 PLS 1 Liver II 3889.22 4371.08 1125.69 15.18 27.72 73.69 12.66 1,284.1 5598 0.75 4549.13 216.16 818.73 5.29 INDI 239 INDI 239 PLS 1 Lung III 9644.2 5326.65 3690.17 12.38 9.63 * 2.737 30.4 3797.63 * 331.366 0.91 3159.07 147.26 1439.1 6.72 INDI 241 INDI 241 PLS 1 Stomach III * 154.94 1396.65 1917.82 * 0.816 13.51 * 2.737 19.44 524.13 * 331.366 0.97 1594.55 106.15 1572.27 3.84 INDI 243 INDI 243 PLS 1 Colorectal I 2540.54 2459.77 3683.33 5.11 23.11 * 2.624 33.47 1,247.0 * 315.949 0.9 818.84 123.81 1458.36 11.59 INDI 244 INDI 244 PLS 1 Colorectal III 2380.99 2589.82 5575.16 * 0.817 24.53 * 2.624 31.36 3.829.3 2805.47 0.87 2974.45 112.06 1522.32 5.03 INDI 245 INDI 245 PLS 1 Colorectal III 2433, 83 1073.37 2965.44 * 0.817 18.84 21.80 39.49 2.114.3 3003.07 0.83 2349.24 112.06 1118.92 7.72 INDI 246 INDI 246 PLS 1 Lung II * 154, 94 2974.73 3152.02 6.72 18.76 20.78 23.99 2656.90 * 331.366 0.85 2069.14 * 14.107 1048.83 4.86 INDI 247 INDI 247 PLS 1 Lung I 1342.4 1570 , 51 2392.38 * 0.816 16.27 * 2.737 23.38 2.299.0 * 331.366 1.41 2548.94 * 14.107 1350.87 9.09 INDI 248 INDI 248 PLS 1 Colorectal II 3411.52 1241.20 3249.7 * 0.817 32.39 * 2.624 21.57 2,239.0 1895.7 0.88 2393.41 99.23 1605.95 5.5 INDI 250 INDI 250 PLS 1 Colorectal II 1987.56 4218.73 2032.99 5.4 9.26 18, 57 29.98 2.892.7 * 315.949 0.88 1529.11 84.9 618.39 7.52 INDI 251 INDI 251 PLS 1 Colorectal II 2504.82 2235.63 3112.92 * 0.817 5.41 * 2.624 31, 93 12,282.7 * 315,949 0.91 778.42 112.06 935.4 9.42 INDI 252 INDI 252 PLS 1 Lung II 2826.15 2633.29 1997.12 * 0.816 86.17 * 2.737 19.85 * 77.037 7477 1.77 2274.02 128.6 1701.09 7.29 INDI 253 INDI 253 PLS 1 Colorectal I 2139 2492.29 3606.34 * 0.817 25.89 * 2.624 21 897.10 1998.75 0.98 2459, 82 99.23 735.83 11.16 INDI 255 INDI 255 PLS 1 Colorectal II 3093.82 3109.40 2521.99 * 0.817 16.81 16.96 17.07 1.753.2 3394.29 0.83 1674.92 99, 23 612.29 4.19 INDI 256 INDI 256 PLS 1 Colorectal III 2980.6 2036.57 4306.88 * 0.817 60.78 18.17 23.8 3.721.2 2904.45 2.55 1440.75 129.37 695.65 13.64 INDI 257 INDI 257 PLS 1 Lung III * 154.94 6493.46 3958.73 18.18 17.82 16.98 29.02 10.666.9 4121.95 1.21 2405.61 147.26 1070.7 5.29 INDI 258 INDI 258 PLS 1 Colorectal I * 126.474 556.21 3003.65 8.17 14.26 * 2.624 15.6 657.9 4542 0.76 1167.97 197.94 457.12 4.15 INDI 259 INDI 259 PLS 1 Colorectal II 4997.86 1007.59 2194.65 * 0.817 6.21 * 2.624 30.25 770.9 4541.87 0.88 1592.99 173.01 618.39 3.53 INDI 260 INDI 260 PLS 1 Stomach I 2692.17 3289.73 873.35 14.52 14.49 * 2.737 9.13 964.23 * 331.366 0.95 1116.84 178.29 1208.82 4.65 INDI 261 INDI 261 PLS 1 Breast II * 154 , 94 2953.41 1917.82 5.13 23.77 * 2.737 16.64 477.61 * 331.366 0.91 1679.36 128.6 1037.83 5.83 INDI 262 INDI 262 PLS 1 Breast III * 154.94 1055 , 53 896.12 * 0.816 18.12 52.88 18.42 2.064.0 * 331.366 0.65 1205.42 * 14.107 515.9 3.24 INDI 264 INDI 264 PLS 1 Lung III 2178.73 1353.08 1202 , 14 * 0.816 ** 196.241 23.93 14.31 2332.96 * 331.366 0.88 708.37 147.26 775.88 3.95 INDI 265 INDI 265 PLS 1 Breast II 3292.66 2552.04 3131.98 * 0.816 10.05 * 2.737 21.52 1066.5 1 * 331.366 0.93 1795.06 106.15 812.67 4.15 INDI 266 INDI 266 PLS 1 Lung I * 154.94 1985.47 900.68 * 0.816 16.41 * 2.737 17.08 466.1 * 331.366 0 , 7 445.78 204.36 1370.67 3.89 INDI 267 INDI 267 PLS 1 Esophagus II 5319.71 3328.00 2414.38 * 0.816 18.18 * 2.737 22.55 * 77.037 * 331.366 0.66 3174, 66 * 14.107 1380.53 4.68 INDI 268 INDI 268 PLS 1 Colorectal III * 126.474 1069.71 4102.31 * 0.817 6.22 * 2.624 23.13 4,666.1 2304.69 1.06 1209.64 134.74 562.54 6.04 INDI 269 INDI 269 PLS 1 Lung III * 154.94 2269.25 2441.28 46.05 18.33 * 2.737 16.9 2083.04 * 331.366 0.95 1876.09 * 14.107 1285, 69 4.37 INDI 270 INDI 270 PLS 1 Breast II * 154.94 1570.51 1033.47 * 0.816 20.82 * 2.737 18.72 1346.09 * 331.366 0.85 2444.33 * 14.107 1270.47 4, 65 INDI 271 INDI 271 PLS 1 Colorectal III * 126.474 2286.26 3381.22 16.01 5.86 * 2.624 15.45 823.1 * 315.949 0.76 4242.37 145.03 575.14 7.34 INDI 273 INDI 273 PLS 1 Lung I * 154.94 1509.73 1655.32 5.13 14.41 29.00 23.76 3,315.7 2190 0.63 1517.47 227.33 1048.83 7 INDI 274 INDI 274 PLS 1 Breast II 2014.65 1626.88 2304.59 * 0.816 13.05 * 2.737 27.58 747.90 * 331.366 0.68 1286.31 128.6 1015.68 4.74 INDI 275 INDI 275 PLS 1 Breast II * 154, 94 1353.08 1288.23 * 0.816 15.81 * 2.737 21.96 938.9 * 331.366 0.64 2452.08 * 14.107 1140.59 4.18 INDI 276 INDI 276 PLS 1 Liver II ** 99069.977 4235 , 63 4061.6 6.4 17.94 24.44 27.55 1.489.3 * 331.366 1.23 4012.44 147.26 1395.26 4.95 INDI 277 INDI 277 PLS 1 Breast II 3412.18 2936.36 3618 , 92 * 0.816 18.82 * 2.737 25.32 686.96 * 331.366 0.86 2042.1 * 14.107 818.73 3.21 INDI 278 INDI 278 PLS 1 Ovary III * 126.474 3278.81 4622.76 1217.96 108, 24 25.03 28.79 674.9 5659 2.24 754.91 235.06 925.08 7.77 INDI 279 INDI 279 PLS 1 Colorectal II 1569.23 1452.37 3003.65 * 0.817 14.64 * 2.624 32.6 * 76.03 * 315.949 0.9 1426.65 99.23 524.03 6.52 INDI 280 INDI 280 PLS 1 Breast I * 154.94 1825.88 2358.21 * 0.816 18.52 * 2.737 13 , 11 6659.72 * 331.366 0.57 1795.06 128.6 744.69 5.71 INDI 281 INDI 281 PLS 1 Colorectal I * 126,474 949.08 2916.34 5.97 10.24 * 2.624 40.61 * 76.03 2203.21 0.99 1836.19 154 , 78 429.24 6.95 INDI 283 INDI 283 PLS 1 Lung I * 154.94 2654.66 1570.05 * 0.816 13.01 19.71 23.61 2.543.2 * 331.366 0.81 2424.97 * 14.107 1182.76 7.11 INDI 284 INDI 284 PLS 1 Colorectal II 1868.52 2651.31 1487.51 * 0.824 3.56 * 2.781 24.4 8.648,0 * 327.606 1.19 2588.17 * 15.64 1644, 42 6.09 INDI 285 INDI 285 PLS 1 Lung II 1527.73 1596.53 1445.11 21.71 9.85 * 2.737 18.8 30.535.7 7565.62 0.77 1556 128.6 1129.95 5, 73 INDI 286 INDI 286 PLS 1 Colorectal I 906.77 1565.06 2205.44 * 0.817 8.69 * 2.624 20.41 890.01 * 315.949 0.79 1447.8 99.23 463.99 7.38 INDI 287 INDI 287 PLS 1 Breast III 1682.38 1700.50 1433.36 8.96 26.07 20.78 9.53 37.524.4 3171.34 1.12 1086.02 243.12 1086.99 7.91 INDI 288 INDI 288 PLS 1 Lung III 1328.01 5115.33 2202.51 113.04 ** 196.241 47.47 30.05 2.927.3 33810.21 2.21 1294.01 221.82 1525.24 9.38 INDI 289 INDI 289 PLS 1 Breast II 1626.35 1335.64 665.65 * 0.816 5.22 * 2.737 16.24 * 77.037 3554.71 0.76 2351.41 106.15 976.46 1.46 INDI 290 INDI 290 PLS 1 Colorectal I 790.34 5320.30 5916.5 * 0, 817 26.87 * 2.624 38.41 2475.64 * 315.949 0.85 2064.03 99.23 648.55 10.99 INDI 291 INDI 291 PLS 1 Lung I 2719 1046.74 1253.28 * 0.816 10.43 * 2.737 13.49 4107.85 * 331.366 0.96 970.46 128.6 700.13 5.9 INDI 292 INDI 292 PLS 1 Lung III * 154.94 3854.37 1551.14 * 0.816 11.54 * 2.737 21 4.237.6 * 331.366 0.92 2003.48 106.15 * 74.42 7.02 INDI 293 INDI 293 PLS 1 Colorectal II 2549.49 4477.89 3260.65 5.83 13.08 17.77 33.21 26.271, 8 2203 0.88 1696.35 99.23 1263.68 5.72 INDI 295 INDI 295 PLS 1 Colorectal II 1707.99 3059.31 2400.29 * 0.821 3.43 30.09 * 1.364 25.046.6 * 336.028 1 , 24 1232.99 164.02 1672.46 5.45 INDI 296 INDI 296 PLS 1 Colorectal II 1258.19 4183.13 2904.14 6.78 3.33 17.39 * 1.368 688.73 * 336.028 1.5 722 233 , 01 1157.11 4.09 INDI 297 INDI 297 PLS 1 Breast III * 134.509 839.74 1067.1 * 0.821 21.15 16.38 9.83 914.40 * 336.028 1.24 2256.27 116.7 1498 , 78 6.63 INDI 298 INDI 298 PLS 1 Colorectal I * 134.509 4411.69 3337.32 * 0.821 7.25 * 2.73 13.36 * 76.419 * 336.028 1.7 1583.39 177.18 586.83 5 , 91 INDI 299 IN DI 299 PLS 1 Colorectal I 3420.77 6774.65 3366.99 * 0.821 8.39 * 2.73 8.01 733.1 2556.7 1.79 2528.72 134.22 1005.93 12.27 INDI 300 INDI 300 PLS 1 Breast III * 134.509 348.87 1734.64 * 0.821 9.76 33.43 9.32 1215.40 * 336.028 1.23 1469.74 134.22 894.34 4.88 INDI 301 INDI 301 PLS 1 Breast II * 134,509 2514,09 1126,37 11,06 16,35 * 2,73 16,95 1,234,6 * 336,028 0,96 1071,92 * 14,024 1136,93 9,15 INDI 302 INDI 302 PLS 1 Colorectal I * 134.509 4380.74 2606.79 * 0.821 8.08 * 2.73 6.96 997.8 * 336.028 0.91 2662.38 149.81 645.69 7.07 INDI 303 INDI 303 PLS 1 Colorectal I 4931 , 01 3802.59 5720.18 5.63 7.66 23.32 22.76 1488.41 * 336.028 1.52 1964.75 195.41 2265.75 6.07 INDI 304 INDI 304 PLS 1 Colorectal I * 134.509 7292.68 2618.5 * 0.821 14.00 * 2.73 13.94 1.390.0 * 336.028 1.36 1876.17 116.7 1406.07 7.59 INDI 305 INDI 305 PLS 1 Colorectal II * 134.509 3586, 04 1371.47 * 0.821 6.94 33.91 9.73 14.690.7 6578.56 1.5 2458.31 164.02 938.1 6.38 INDI 306 INDI 306 PLS 1 Colorectal III * 134.509 5476.43 2390 , 57 26.49 6.80 * 2.73 9.27 93.510.4 6661.5 1.16 1742.9 134.22 2234.07 12 INDI 307 INDI 307 PLS 1 Colorectal III * 134.509 4691.17 4319.42 * 0.821 9.01 * 2.73 7.95 719.77 2106.97 1.47 929.55 * 14.024 1130.17 6.91 INDI 308 INDI 308 PLS 1 Liver II ** 98718.787 1829.65 1421.34 40.28 16.26 * 2.73 11.15 644 , 7 2106.97 1.18 2636.11 201.16 * 96.087 43.87 INDI 309 INDI 309 PLS 1 Colorectal II 2757.97 1210.56 624.42 6.2 6.07 23.81 14.08 3933, 21 2287.68 1.68 2375.17 164.02 * 96.087 16.16

INDI 310 INDI 310 PLS 1 Colorectal II * 134.509 6764.87 3467.96 23.42 6.86 * 2.73 15.83 1.587.9 6412.44 1.56 3081.81 * 14.024 1252.94 46.07 INDI 311 INDI 311 PLS 1 Colorectal III * 134.509 1155.02 277.76 * 0.821 8.24 27.20 * 1.364 8076.31 2106.97 1.19 1712.87 177.18 * 96.087 55.56 INDI 312 INDI 312 PLS 1 Breast II 1101.89 1055.71 2283.71 * 0.821 16.30 * 2.73 12.4 601.17 7404.47 1.04 2107.38 222.84 414.47 33.6 INDI 313 INDI 313 PLS 1 Esophagus II 1882.49 848.49 2305.07 7.36 22.76 * 2.73 9.16 768.91 3000.34 0.93 1136.57 149.81 842.3 25.53 INDI 314 INDI 314 PLS 1 Colorectal I * 134.509 1564.98 2035.71 8.82 3.65 18.38 10.65 566.63 3264.04 0.9 1657.97 189.51 * 96.087 41.2 INDI 315 INDI 315 PLS 1 Breast II * 134.509 682.19 2194.48 * 0.821 9.08 * 2.73 11.96 786.90 5239.60 1.02 5472.55 177.18 354.69 5.88 INDI 316 INDI 316 PLS 1 Breast II * 134.509 1067.39 1588.41 * 0.821 18.13 * 2.73 14.89 * 76.419 * 336.028 0.92 1660.46 * 14.024 516.82 6.14 INDI 317 INDI 317 PLS 1 Breast II * 134.509 1137 , 49 4001.89 * 0.821 5.06 * 2.73 15.09 1688.34 * 336.028 0.79 1538.82 * 14.024 * 96.087 10.79 INDI 318 INDI 318 PLS 1 Stomach III 975.05 4311.83 1377.06 10.26 21.91 275.78 15.79 30.584.3 2783.08 0.67 2103.91 178.29 890.2 3.73 INDI 319 INDI 319 PLS 1 Stomach I * 154.94 2415.04 4333.12 5.76 16.01 20.25 31 469.9 * 331.366 1.08 1024.39 204.36 326.21 9.58 INDI 320 INDI 320 PLS 1 Colorectal II 3219.96 1544.99 4789.23 9.88 3.79 * 2,688 29.57 12,114.0 2981.75 1.59 562.62 253.52 650.06 5.24 INDI 321 INDI 321 PLS 1 Colorectal III 13904.55 1879.58 2166.45 111.49 12.57 * 2.688 19.95 978.30 1984.23 0 , 86 1944.44 180.85 1042.89 0.9 INDI 322 INDI 322 PLS 1 Colorectal III 3705.16 1745.08 3693.3 * 0.819 7.61 25.82 31.92 957.5 2981.75 1, 26 941.24 282.31 859.63 2.48 INDI 323 INDI 323 PLS 1 Breast I * 154.94 1418.41 1309.23 * 0.816 4.90 * 2.737 13.35 * 77.037 * 331.366 0.61 1035, 95 178.29 627.71 4.57 INDI 324 INDI 324 PLS 1 Lung I * 154.94 1261.44 1598.44 * 0.816 16.44 * 2.737 14.4 4032.52 * 331.366 0.69 1186.17 138 , 29 493.89 4.46 INDI 325 INDI 325 PLS 1 Neck Rectal III 3439.28 342.14 4967.71 8.77 15.27 * 2.688 20.39 1120.64 2981.75 1.02 984.63 201.81 1067.05 2.01 INDI 326 INDI 326 PLS 1 Lung I * 154.94 701.80 470.49 5.61 30.58 22.36 18.38 9033.13 * 331.366 0.88 1995.76 237.97 725.73 4.78 INDI 327 INDI 327 PLS 1 Colorectal II * 147.882 1560.39 3846.14 6.3 11.91 * 2.688 18.5 1.703.9 2502.31 0.8 2015.9 220.56 981.39 1.91 INDI 328 INDI 328 PLS 1 Colorectal I 2169 , 31 1398.55 2915.87 99.6 7.28 * 2.688 14.93 953.4 * 330.705 0.95 1564.49 220.56 255.15 2.21 INDI 329 INDI 329 PLS 1 Colorectal III 5114.01 2021.63 4190.88 8.55 13.69 70.89 27.01 2.083.6 71827.39 2.39 1941.2 201.81 1269.44 5.85 INDI 331 INDI 331 PLS 1A Lung III * 166.572 4950 , 43 1323.05 6.1 4.38 * 2.781 24.92 6.718.2 2252.24 0.98 1300.84 161.07 * 78.14 2.22 INDI 332 INDI 332 PLS 1 Breast I 4895.14 1422 , 77 2818.08 * 0.816 16.17 * 2.737 20.3 * 77.037 * 331.366 0.91 1825.92 * 14.107 1290.75 4.86 INDI 333 INDI 333 PLS 1A Lung II * 166.572 2299.29 2109.42 * 0.824 8.86 * 2.781 17.42 * 76.889 * 327.606 1.02 1 386.68 * 15.64 981.84 3.54 INDI 334 INDI 334 PLS 1 Breast I 1973.44 1448.87 1452.16 * 0.816 6.27 * 2.737 14.78 739.74 * 331.366 0.64 1798, 92 163.59 842.82 3.5 INDI 335 INDI 335 PLS 1A Lung I 3292.88 1976.13 1638.22 * 0.824 48.42 * 2.781 26.46 4261.12 * 327.606 1.09 1541.42 115, 58 1581.5 5.32 INDI 336 INDI 336 PLS 1A Lung III * 166.572 857.79 713.04 6.1 5.72 * 2.771 22.32 1.084.5 * 327.606 0.65 939.71 * 15.64 417.51 3.32 INDI 337 INDI 337 PLS 1 Esophagus III * 154.94 2226.32 2865.21 8.96 31.91 27.50 21.68 2045.02 * 331.366 0.96 2801.15 106.15 732.07 4.23 INDI 338 INDI 338 PLS 1 Colorectal II 3304.7 1004.42 4713.05 24.71 18.21 * 2.688 28.23 1.040.9 3433.08 0.92 2484.86 220.56 1114.69 3 , 27 INDI 339 INDI 339 PLS 1 Breast II * 154.94 2705.93 1367.69 * 0.816 9.25 * 2.737 23.34 2410.81 * 331.366 1.01 3104.53 106.15 706.56 5.23 INDI 340 INDI 340 PLS 1 Colorectal III 3967.29 408.84 1263.58 24.92 8.89 * 2.688 22.15 636.4 4865.43 0.93 762.78 455.84 879.33 2.14 INDI 341 INDI 341 PLS 1 Liver II * 154.94 3021.60 1555.87 9.93 40.03 3446.30 13.74 1.453.3 8180 0.78 2065.28 369.52 530.38 5.53 INDI 342 INDI 342 PLS 1A Lung III * 166.572 1553.70 270.52 5.01 156.65 * 2.781 23.15 2451.24 * 327.606 0.71 2601.6 105.47 562.96 5.85 INDI 343 INDI 343 PLS 1 Colorectal III 5082.7 2795.03 4198.55 8.1 20, 09 * 2,688 23.81 1.637.8 * 330.705 1.16 2139.78 180.85 1275.01 1.84 INDI 344 INDI 344 PLS 1 Colorectal II 5859.15 2588.59 2634 24.09 20.51 77.70 31.14 12.819.2 3433.08 0.98 1663.75 156.67 826.35 1.41 INDI 346 INDI 346 PLS 1A Lung I * 166.572 424.08 517.75 * 0.824 14.40 * 2.781 22.04 608, 2 * 327,606 0.97 1154.93 161.07 585.83 4.11 INDI 347 INDI 347 PLS 1 Esophagus I 1255.73 3400.25 2706.76 * 0.816 17.62 * 2.737 13.01 634.7 * 331.366 0, 84 1332.53 147.26 1310.91 5.32 INDI 348 INDI 348 PLS 1 Breast II * 154.94 1200.23 1574.78 9.28 32.43 19.17 19.97 821.92 * 331.366 0, 91 1424.99 * 14.107 936.6 4.26 INDI 350 INDI 350 PLS 1 Breast II * 154.94 1230.84 918.93 5.61 16.73 * 2.737 19.85 650.74 * 331.366 0.73 1378 , 76 248.15 1081.57 6.26 INDI 352 IND I 352 PLS 1 Breast III * 154.94 1326.92 4551.88 * 0.816 15.90 * 2.737 23.45 2145.07 * 331.366 0.74 2812.8 106.15 456.29 6.79 INDI 353 INDI 353 PLS 1A Lung II * 166.572 772.79 229.52 * 0.824 3.47 * 2.781 28.27 461.3 * 327.606 1.33 601.93 * 15.64 667.1 7.23 INDI 354 INDI 354 PLS 1 Colorectal III 5519 2489.12 2368.6 8.77 12.68 17.58 30.71 2750.33 2981.75 0.88 1602.86 320.3 2023.77 1.22 INDI 355 INDI 355 PLS 1A Lung I * 166.572 1056 , 34 1646.31 * 0.824 8.49 * 2.781 25.65 1.283 * 327.606 0.93 2189 * 15.64 328.31 5.26 INDI 356 INDI 356 PLS 1 Colorectal I 5735.69 1614.28 819, 68 * 0.819 22.43 * 2.688 27.77 928.57 1984.23 0.98 1341.93 156.67 1541.74 3.23 INDI 357 INDI 357 PLS 1A Lung I * 166.572 1767.21 1682.79 * 0.824 25.08 24.73 19.24 608.21 * 327,606 0.92 1056.1 231.82 270.37 5.83 INDI 358 INDI 358 PLS 1A Lung II * 166.572 1027.96 554.38 6.24 9, 95 * 2,781 21,4 1,269.7 * 327,606 0.67 1887.35 161.07 * 78.14 4.61 INDI 359 INDI 359 PLS 1 Breast I 4802.4 1064.32 1111.82 7.35 13.30 * 2.737 19.18 826.1 * 331.366 0.63 1988.03 * 14.107 667.6 5.67 INDI 360 INDI 360 PLS 1A Lung II * 166.572 2099.65 2271.23 * 0.824 21.08 * 2.781 30.1 719.0 * 327.606 1.59 2035.56 * 15 , 64 724 4.38 INDI 361 INDI 361 PLS 1 Breast III 2096.83 3076.97 1017.39 7.35 6.93 * 2.737 23.34 * 77.037 * 331.366 0.750509 106.15 993.35 4, 08 INDI 362 INDI 362 PLS 1 Breast II 1959.68 2774.26 889.29 * 0.816 7.47 * 2.737 13.54 586.99 * 331.366 0.83 1344.09 147.26 544.69 4.78 INDI 363 INDI 363 PLS 1 Breast I * 154.94 3429.99 4651.3 6.56 11.81 26.49 32.82 1363.88 * 331.366 0.83 2781.73 106.15 1320.94 9.5 INDI 364 INDI 364 PLS 1A Lung I * 166.572 2204.21 1825.62 5.28 6.40 * 2.781 28.22 3434.55 * 327.606 0.88 3353.24 115.58 467.79 3.37 INDI 365 INDI 365 PLS 1 Colorectal II 1799.72 919.16 950.21 * 0.819 7.00 25.16 20.17 1.385.2 6149.65 1.14 1526.18 295.56 * 77.47 2.48 INDI 366 INDI 366 PLS 1 Pancreas II 2173.1 2383.88 2186.55 18.56 18.86 172.97 53.77 1.673.9 3588.09 1.08 2997.04 274.74 925.08 5.01 INDI 367 INDI 367 PLS 1 Liver I 5 266.58 1257.07 2365.53 15.52 35.0 0 33.42 26.89 1.471.3 * 331.366 0.86 3182.46 * 14.107 1380.53 3.91 INDI 368 INDI 368 PLS 1 Breast II * 154.94 3926.43 5671.81 8.32 5.13 92.00 36.66 ** 56207.571 * 331.366 0.91 2715.72 128.6 1320.94 8.28 INDI 369 INDI 369 PLS 1A Lung I * 166.572 2432.45 960.84 12.7 74.46 * 2,781 35.32 4554.64 * 327,606 0.73 2013.71 * 15.64 * 78.14 8 INDI 371 INDI 371 PLS 1A Lung III * 166.572 2827.43 1445.66 47.51 29.87 53.38 22.88 2.635.4 10275.15 0.84 1266.51 161.07 709.94 3.75 INDI 372 INDI 372 PLS 1A Lung II 2062.31 4969.54 2084.35 * 0.824 6.06 * 2.781 22, 21 1,215.0 * 327,606 0.78 1378.09 147.81 508.23 3.22 INDI 373 INDI 373 PLS 1A Lung I * 166.572 838.90 2750.42 * 0.824 18.61 17.41 25.85 6310.62 * 327,606 0.84 2202.2 * 15.64 724 5.1 INDI 374 INDI 374 PLS 1 Colorectal III 1488.62 841.57 1916.89 7.43 7.64 44.92 33.16 24236.29 4274.50 1.05 2025.66 253.52 701.32 2.03 INDI 375 INDI 375 PLS 1 Liver I 11030.57 4675.66 2957.2 169.14 28.84 * 2.737 17.3 4615.26 * 331.366 1.94 3847 , 4 * 14.107 1590.92 8.37 INDI 376 INDI 376 PLS 1 Colorectal I * 138.956 800.55 2757.32 * 0.815 13.15 * 2.744 20.14 500.9 * 323.192 0.56 2079.62 * 14.413 729.21 4.15 INDI 377 INDI 377 PLS 1 Colorectal II 6373 , 14 720.35 2675.29 18.04 5.12 * 2.744 24.26 1.087.6 4734.29 1.15 1267.85 734.55 1115.93 4.88 INDI 378 INDI 378 PLS 1 Colorectal I * 138.956 3124.88 12247.31 * 0.815 46.85 * 2.744 39.79 2024.99 * 323.192 0.99 1940.72 99.02 710.11 9.46 INDI 379 INDI 379 PLS 1 Colorectal III 8497.3 850.61 2005.28 * 0.815 7.84 * 2.744 63.88 * 77.473 * 323.192 0.69 3125.56 128.85 610.75 4.45 INDI 380 INDI 380 PLS 1 Colorectal II * 138.956 780.51 6167.15 * 0.815 7.08 * 2.744 59.24 2.030.3 2535.48 0.8 1764.91 166.55 * 84.63 5.77 INDI 381 INDI 381 PLS 1A Lung III * 166.572 1146.24 666.15 8.62 11 , 59 * 2.781 19.61 1825.47 * 327.606 1.04 831.3 * 15.64 328.31 2.68 INDI 382 INDI 382 PLS 1 Breast II 4418.48 1501.04 1891.44 * 0.816 25.59 * 2.737 22.2 1.381.7 * 331.366 1 2312.71 106.15 1015.68 3.74 INDI 383 INDI 383 PLS 1A Lung III * 166.572 1278.81 749.39 10.06 15.37 * 2.781 30.5 6 570.9 * 327.606 1.05 1090.5 231.82 364.94 5.27 INDI 384 INDI 384 PLS 1 Pancreas II 5598.93 1561.43 2611.55 * 0.817 19.22 * 2.624 12.76 1295, 85 * 315,949 1.04 1199.21 84.9 * 71.31 3.18 INDI 385 INDI 385 PLS 1 Colorectal I * 138.956 1526.22 4966.31 * 0.815 40.28 * 2.744 72.63 1004.15 * 323.192 0.6 1703.17 * 14.413 3532.45 7.25 INDI 386 INDI 386 PLS 1 Colorectal III * 138.956 740.41 2977.23 * 0.815 4.47 * 2.744 29.56 958.35 * 323.192 0.62 997, 21 166.55 366.62 4.66 INDI 387 INDI 387 PLS 1 Liver I * 154.94 3349.25 2875.14 * 0.816 6.63 * 2.737 21.88 1.621.0 * 331.366 1.01 2614.85 216 , 16 1772.98 4.57 INDI 388 INDI 388 PLS 1 Pancreas II 1522.87 941.76 3039.14 6.55 12.50 26.65 21.64 3.749.3 4730 0.92 1370.38 99.23 660.45 6.62 INDI 389 INDI 389 PLS 1A Lung III 17193.3 2589.44 912.01 28.03 20.54 48.87 22.32 869.0 7678 1.05 2145.06 503.3 1633, 99 6.49 INDI 390 INDI 390 PLS 1 Breast II * 154.94 865.99 1917.82 * 0.816 55.18 * 2.737 27.73 1884.75 * 331.366 0.83 1394.17 * 14.107 1224.35 3.95 INDI 391 INDI 391 PLS 1 Liver II * 154.94 4980.07 2328.95 24.93 15.98 18.08 20.79 2.548.1 1988 1.07 2847.77 163.59 2003.38 6.58 INDI 392 INDI 392 PLS 1A Lung I 1354.34 1226.72 1441.68 * 0.824 20.57 * 2.781 17.63 * 76.889 * 327.606 0.731.06 115.58 400.27 4.65 INDI 393 INDI 393 PLS 1 Liver II ** 99069.977 4430 , 32 1740.91 5.45 50.39 19.71 17.04 955.8 * 331.366 0.71 2999.41 359.1 1092.39 2.99

INDI 394 INDI 394 PLS 1A Lung II * 166.572 1700.76 3719.06 * 0.824 39.57 * 2.781 22.77 574.3 * 327.606 1.56 2263.88 * 15.64 467.79 4.79 INDI 395 INDI 395 PLS 1A Lung I 1429.31 1236.19 1715.3 * 0.824 13.04 * 2.781 27.74 1.910.9 2061.47 0.9 624.33 184.36 1285.89 4.17 INDI 396 INDI 396 PLS 1 Colorectal I 4454.42 1675.93 4294.27 * 0.815 17.23 * 2.744 28.08 * 77.473 * 323.192 0.64 1068.31 * 14.413 1438.63 4.11 INDI 397 INDI 397 PLS 1 Colorectal III * 138.956 1100, 48 3628.65 * 0.815 6.75 * 2.744 29.91 58.578.6 * 323.192 0.58 1359.26 202.76 2290.1 4.45 INDI 398 INDI 398 PLS 1 Colorectal II * 138.956 1579.45 2300.24 * 0.815 23.20 * 2.744 36.78 788.2 * 323.192 0.79 1132.57 99.02 1640.41 4.76 INDI 400 INDI 400 PLS 1 Colorectal II * 138.956 3131.62 8347.78 6.52 13 , 84 * 2,744 148,44 3,869.6 * 323,192 0.8 1768.34 * 14.413 2601.61 6.79 INDI 402 INDI 402 PLS 1 Breast I 2341.76 2002.71 3866.41 * 0.816 19.68 20, 25 20.59 3336.38 * 331.366 0.91 3843.48 155.66 471.47 4.73 INDI 403 INDI 403 PLS 1A Lung III * 166.572 1577.42 1358 , 5 * 0.824 4.83 * 2.781 20.7 684.00 * 327.606 0.87 1343.75 * 15.64 1086.57 3.22 INDI 404 INDI 404 PLS 1 Breast III * 154.94 2560.59 3573, 19 5.76 4.00 * 2.737 13.64 2002.39 * 331.366 1 1949.42 106.15 674.15 3.52 INDI 405 INDI 405 PLS 1A Lung I * 166.572 1715.00 2981.23 * 0.824 8, 18 * 2.781 24.55 1211.15 * 327.606 1.27 1287.97 * 15.64 681.49 4.15 INDI 407 INDI 407 PLS 1 Breast I * 154.94 3795.01 3368.22 * 0.816 5.74 * 2.737 18.97 508.6 * 331.366 0.65 1633.1 128.6 1290.75 6.51 INDI 408 INDI 408 PLS 1 Breast II * 154.94 3187.64 1051.87 5.92 6.59 * 2.737 21.52 764.27 * 331.366 0.71 1656.23 * 14.107 1092.39 6.43 INDI 409 INDI 409 PLS 1 Breast II 1654.39 967.52 3049.46 10.26 22.21 23.41 17.12 715.31 * 331.366 0.75 1301.72 106.15 884.33 3.67 INDI 411 INDI 411 PLS 1 Breast II 10065.51 2019.93 2880.11 * 0.816 30.64 * 2.737 19.89 1.708.2 * 331.366 0.75 1825.92 118 763.47 4.86 INDI 412 INDI 412 PLS 1 Breast II 7616.1 1243.96 1617.38 12.22 16.53 58.19 13.4 3.269.2 2587 0.783 , 06 257.94 1310.91 5.83 INDI 413 INDI 413 PLS 1 Lung I * 154.94 2569.15 1755.21 95.89 10.43 * 2.737 9.19 590.95 * 331.366 0.98 1818.21 * 14.107 1124.62 4.44 INDI 414 INDI 414 PLS 1 Lung II * 154.94 1313.83 1051.87 * 0.816 16.30 * 2.737 16.46 2040.27 * 331.366 0.95 854.87 106.15 913.52 0.88 INDI 415 INDI 415 PLS 1 Lung II 63012.68 1230.84 2141.94 * 0.816 34.89 * 2.737 20.02 1.218.2 * 331.366 0.89 2208.28 * 14.107 1156.48 5.58 INDI 417 INDI 417 PLS 1 Lung 1779.95 2080.19 3433 , 88 * 0.816 10.57 19.71 17.78 723.4 * 331.366 1.24 623.49 128.6 1054.32 3.68 INDI 418 INDI 418 PLS 1 Colorectal I * 134.509 3473.57 565.36 * 0.821 7 , 63 * 2.73 14.49 706.44 * 336.028 1.33 1376.52 157.06 1492.66 12.07 INDI 419 INDI 419 PLS 1 Colorectal I * 134.509 1888.61 7113.41 10.31 6, 29 * 2.73 19.61 498.42 8386.27 1.36 1105.4 * 14.024 754.17 8.29 INDI 421 INDI 421 PLS 1 Colorectal II * 134.509 3340.15 1258.42 8.82 12.80 * 2.73 12.28 1.995.3 * 336.028 1.04 * 26.403 201.16 1233.34 5.45 INDI 422 INDI 422 PLS 1 Colorectal II * 134.509 2618.51 3754.14 * 0.821 1.81 * 2, 73 11.83 1.892.1 3088.4 3 1.89 1707.87 217.6 1153.75 10.32 INDI 423 INDI 423 PLS 1 Colorectal II * 134.509 3857.64 6834.25 6.78 2.58 * 2.73 11.62 1678.25 2645.88 1 , 19 761.28 189.51 934.48 8.79 INDI 424 INDI 424 PLS 1 Breast II 12620.43 1647.22 2056.98 5.07 4.38 * 2.73 7.89 579.55 * 336.028 1 , 24 1048.06 311.77 702.69 5.88 INDI 425 INDI 425 PLS 1 Breast II 3365.77 1307.08 3165.61 * 0.821 6.10 * 2.73 16.86 532.4 * 336.028 1, 19 2276.9 164.02 864.74 5.63 INDI 426 INDI 426 PLS 1 Breast II * 134.509 2048.06 1471.23 * 0.821 6.94 * 2.73 7.76 * 76.419 * 336.028 2.12 2993, 55 * 14.024 595.36 8.85 INDI 427 INDI 427 PLS 1 Colorectal II * 134.509 5966.93 2450.88 10.61 6.88 32.96 20.58 26.090.8 * 336.028 2.89 1110.19 233, 01 1226.78 7.68 INDI 428 INDI 428 PLS 1 Colorectal III * 117,693 3595.17 5059.3 6.52 5.13 36.54 9.73 34148.35 * 311.017 1.83 430.56 134.25 734 , 52 6.45 INDI 429 INDI 429 PLS 1 Colorectal II 1940.15 5072.71 6684.75 44.35 3.20 18.38 14.59 2983.16 * 336.028 2.18 1095.83 116.7 1642, 99 6.99 INDI 431 INDI 431 PLS 1 Colorectal II * 134.509 5091.55 3712.31 5.07 8.24 16.38 9.88 662.29 * 336.028 1.25 752.02 116.7 475.92 6.82 INDI 433 INDI 433 PLS 1 Colorectal II * 134.509 2033, 28 7364.24 29.93 10.41 44.71 25.78 473.2 * 336.028 1.41 1337.44 116.7 1103 4.33 INDI 434 INDI 434 PLS 1 Colorectal II 940.72 6108.05 5034, 72 * 0.821 2.09 * 2.73 11.36 1210.60 * 336.028 1.07 1533.88 * 14.024 * 96.087 9.24 INDI 436 INDI 436 PLS 1 Colorectal III * 134.509 4072.34 700.65 * 0.821 6 , 63 * 2.73 6.82 1258.70 * 336.028 1.11 1206.4 * 14.024 603.85 4.86 INDI 437 INDI 437 PLS 1 Colorectal I * 134.509 1641.34 1906.25 * 0.821 13.68 * 2.73 8.83 481.6 2106.97 2.1 1692.89 170.71 1180.5 6.05 INDI 438 INDI 438 PLS 1 Colorectal II * 134.509 2190.14 1365.72 * 0.821 5.77 * 2 , 73 9.88 566.63 * 336.028 2.11 768.23 116.7 1327.11 3.44 INDI 439 INDI 439 PLS 1 Colorectal III 2168.78 1468.17 2857.09 5.07 4.16 58, 09 9.88 662.3 * 336.028 1.95 673.73 116.7 1123.4 10.8 INDI 440 INDI 440 PLS 1 Esophagus II * 134.509 3888.25 673.97 * 0.821 22.13 * 2.73 11 , 32 1,253.9 * 336,028 1.43 1163.01 * 14.024 742.4 8.06 INDI 441 INDI 441 PLS 1 Esophagus II 2147.81 1852.64 1850.37 * 0.83 4.94 17.24 12.29 1239.39 2639.08 1.29 1942.16 159.22 2426 , 62 27.17 INDI 442 INDI 442 PLS 1 Breast II * 147.82 1370.17 1984.28 * 0.83 12.57 * 2.755 13.3 * 75.938 * 325.95 1.13 951.61 159.22 279.99 16.72 INDI 443 INDI 443 PLS 1 Breast II 1409.49 1546.58 1363.58 * 0.83 24.97 * 2.755 15.25 3974.29 2055.05 0.96 1776.29 86.07 764.01 11.66 INDI 444 INDI 444 PLS 1 Colorectal II 11637.12 616.77 2310.12 9.32 30.59 46.90 22.06 46.050.8 2924.55 1.35 1113.16 204.08 773.24 14.16 INDI 445 INDI 445 PLS 1 Breast II 964.78 460.45 1736.21 6.83 10.49 31.79 17.46 4,655.0 4572.73 2.28 * 26.781 180.46 469.13 5.23 INDI 446 INDI 446 PLS 1 Esophagus II 3276.17 4986.42 1556.16 6.83 77.24 * 2.755 22.93 4.776.1 2349 1.27 2744.94 159.22 1242.17 11.16 INDI 447 INDI 447 PLS 1 Breast III 12912.11 1414.25 2344.22 3269.27 ** 172.378 4333.59 29.28 195.227.2 685439.58 1.52 1932.36 403.56 854.25 7.87 INDI 449 INDI 449 PLS 1 Breast III * 147.82 701.29 1192.15 7.14 10.78 * 2.755 11.75 11.343.6 6315 1.81 2154.23 180.46 1158.43 8.65 INDI 450 INDI 450 PLS 1 Esophagus II 1797.94 5264.97 2511.45 * 0.83 12.57 * 2.755 11 , 72 3019.36 4304.34 1.08 1052.18 104.91 578.03 13.18 INDI 452 INDI 452 PLS 1 Colorectal II * 147.82 472.95 2200.74 * 0.83 22.81 * 2.755 17.54 1.079.5 1956 1.17 2075.01 120.79 800.63 9.01 INDI 453 INDI 453 PLS 1 Breast I * 147.82 1036.86 1134.9 6.83 31.80 * 2.755 14.0 6237 , 27 * 325.95 0.77 2897.15 * 14.345 990.72 6.77 INDI 454 INDI 454 PLS 1 Breast III * 147.82 1149.97 948.31 15.75 18.79 31.79 9.63 2,468.3 8,161.17 1.8 876.94 225.41 668.91 8.05 INDI 455 INDI 455 PLS 1 Breast II * 147.82 1685.33 1752.26 5.6 23.70 * 2.755 10.08 720.0 * 325.95 1.02 1099.07 175.38 530.23 5.48 INDI 456 INDI 456 PLS 1 Colorectal II * 147.82 560.46 596.15 28.13 3.39 491.12 * 1,372 3,493.0 1955.7 1.7 1382.41 217.11 * 21.901 12.63 INDI 457 INDI 457 PLS 1 Esophagus II 5986.73 3693.14 4970.74 21.92 13.43 55.15 22.82 3.584, 6 9225.61 1.99 970.29 309.69 1596.48 15.31 INDI 458 INDI 458 PLS 1 Breast III * 1 47.82 867.39 3189.86 24.95 46.88 250.07 19.12 5.406.4 21058 1.28 1131.95 190.23 385.95 23.34 INDI 459 INDI 459 PLS 1 Breast II 1059, 74 11 586.41 3113.15 6.83 27.22 32.47 18.54 * 75.938 194407.31 1.17 2228.83 217.11 1019.48 10.4 INDI 460 INDI 460 PLS 1 Mama III * 147.82 2137 , 18 1452.76 20.02 11.09 19.01 11.87 490.93 * 325.95 2.55 2253.77 303.44 818.68 4.9 INDI 461 INDI 461 PLS 1 Colorectal III * 147, 82 785.87 1796.85 * 0.83 7.32 * 2.755 13.19 7,231.7 2734.67 1.01 1197.84 194.95 800.63 7.29 INDI 462 INDI 462 PLS 1 Mama III 1200, 69 7249.36 3087.61 2759.3 172.48 471.50 27.71 209.102.0 ** 245954.599 2.05 4200.57 233.44 1047.9 15.53 INDI 463 INDI 463 PLS 1 Colorectal II * 147.82 1282.05 1206.45 18.75 4.06 39.56 7.32 21.051.1 2639.08 1.61 1679.44 284.02 385.95 7.21 INDI 464 INDI 464 PLS 1 Liver III 5135.34 773.34 2831.24 855.37 107.96 123.01 12.17 190.437.5 3669.08 1.24 2169.13 104.91 398.28 18.14 INDI 465 INDI 465 PLS 1 Esophagus II * 147.82 1382.77 1495.55 27.34 8.23 26.66 12.61 6.735.5 * 325.95 1.17 127 8.1 104.91 480.48 9.85 INDI 466 INDI 466 PLS 1 Colorectal II * 147.82 6360.32 1991.43 * 0.83 7.28 35.86 10.88 3.095.2 * 325.95 1.03 3511.55 * 14.345 593.59 5.75 INDI 467 INDI 467 PLS 1 Colorectal II 1786.6 920.72 753.16 7.14 10.25 * 2.755 17.21 3918.15 * 325.95 1.57 1356.29 199.56 373.44 36.01 INDI 468 INDI 468 PLS 1 Colorectal II 1615.92 1093.41 1005.83 6.83 14.05 16.53 11.43 7,822.7 3484.98 1.56 2278.75 233.44 1279.43 11.8 INDI 469 INDI 469 PLS 1 Breast III 1581.65 3370.32 2138.07 15.44 25.11 * 2.755 12.66 1576.53 * 325.95 1.2 1240.29 113.13 220.08 14.89 INDI 470 INDI 470 PLS 1 Colorectal III * 147.82 1118.54 2222.25 * 0.83 8.24 * 2.755 18.48 2.584.8 * 325.95 1 , 81 795.41 95.97 541 10.08 INDI 471 INDI 471 PLS 1 Breast III * 147.82 363.70 1179.63 * 0.83 13.03 * 2.755 8.56 1410.34 1955.70 1, 61 534.35 190.23 170.07 5.95 INDI 472 INDI 472 PLS 1 Colorectal II 3231.59 3622.75 2407.1 25.42 14.10 * 2.755 16.52 8.040.3 * 325.95 1, 51 1256.82 277.29 598.73 6.21 INDI 473 INDI 473 PLS 1 Breast III * 147.82 698.16 1846.8 45.02 32.21 * 2.755 * 1.372 698.7 20766 1.08 1798.15 199.56 265.58 8.94 INDI 474 INDI 474 PLS 1 Breast II * 147.82 917.58 2184.62 10.88 34, 31 * 2,755 14.08 7014.30 2924.55 1.52 2154.23 225.41 629.22 7.31 INDI 475 INDI 475 PLS 1 Liver III * 147.82 926.99 2313.71 7.77 19, 18 * 2,755 14,31 1,162.8 * 325.95 2.42 1487.28 199.56 491.72 11.89 INDI 476 INDI 476 PLS 1 Esophagus II 3365.32 3290.53 3450.36 * 0.83 25 , 42 * 2,755 14.13 1.826.9 11860 1.12 2409.21 * 14.345 818.68 11.85 INDI 477 INDI 477 PLS 1 Colorectal III * 147.82 2952.76 1388.56 24.15 5.08 * 2,755 9.67 1379.01 * 325.95 0.95 1413.34 * 14.345 321.47 3.73 INDI 478 INDI 478 PLS 1 Breast II 3198.16 2612.64 2068.29 * 0.83 23.03 * 2.755 16.5 2.680.3 * 325.95 1.38 1735.06 199.56 446.06 8.28 INDI 479 INDI 479 PLS 1 Breast III * 147.82 2746.05 1111.63 23.19 9.83 43.90 10.08 1.433.9 3299.59 0.83 907.26 199.56 132.89 4.6 INDI 480 INDI 480 PLS 1 Breast III * 147.82 1452.05 1766.53 14.97 12, 67 55.80 12.05 1773.94 * 325.95 0.94 1415.72 113.13 422.46 4.85 INDI 482 INDI 482 PLS 1 Liver II 108 284.29 1697.95 211.14 * 0.83 20.24 39.89 8.82 3.342.8 3113 0.91 1841.96 86.07 410.44 9.99 INDI 483 INDI 483 PLS 1 Colorectal II * 147.82 1395.36 1195.73 * 0.83 6.99 * 2.755 12.72 * 75.938 * 325.95 1.08 1645.66 170.15 791.55 9.53 INDI 484 INDI 484 PLS 1 Colorectal II * 138.956 1270.15 4069.31 * 0.815 7.98 * 2.744 25.13 675.5 * 323.192 1.36 2003.11 * 14.413 821.48 3.85

INDI 485 INDI 485 PLS 1 Esophagus III 3491.78 1331.28 2382.61 * 0.816 7.46 * 2.737 15.92 2.474.4 * 331.366 0.72 2057.55 * 14.107 614.19 3.43 INDI 487 INDI 487 PLS 1 Breast II 3743.59 1002.76 1015.09 5.45 11.19 * 2.737 18.97 813.65 * 331.366 0.75 654.36 * 14.107 2116.75 3.87 INDI 488 INDI 488 PLS 1 Breast III 1218.96 2174.92 851.58 * 0.831 23.39 * 2.782 18.24 908.12 * 325.874 0.66 1233.04 228.84 1091.53 6.03 INDI 489 INDI 489 PLS 1 Breast II * 137.024 1758.08 1054.45 7.03 14.97 * 2.782 13.52 6.618.8 * 325.874 0.71 2363.74 189.5 347.47 4.01 INDI 490 INDI 490 PLS 1 Mama III 1703.14 1961, 97 4358.56 308.89 22.39 39.23 24.63 5.876.0 * 325.874 0.75 3419.83 120.99 1091.53 5.86 INDI 491 INDI 491 PLS 1 Lung I 1425.93 441.16 1114.83 6.08 15.80 * 2.782 15.71 938.89 * 325.874 0.79 1708.11 * 14.53 1115.81 9.41 INDI 492 INDI 492 PLS 1 Lung I 5958.7 2132.28 1537 , 79 * 0.831 10.90 * 2.782 * 1.36 794.20 * 325.874 0.83 1544.45 100.18 1004.46 7.06 INDI 493 INDI 493 PLS 1 Breast II 2058.19 5637.11 374.39 * 0.831 27.74 * 2.782 14.7 1664 , 00 * 325,874 0,57 1797,56 100,18 398,13 4,07 INDI 494 INDI 494 PLS 1 Lung III * 137,024 1045,01 2472,02 13,76 29,68 * 2,782 13,07 45,376.4 * 325.874 0.76 1637.39 120.99 641.2 10 INDI 495 INDI 495 PLS 1 Lung I * 137.024 4357.56 1507.38 6.4 49.48 18.59 22.13 1549.28 * 325.874 0.74 2265.81 159.59 1940.62 4.03 INDI 497 INDI 497 PLS 1 Lung II 1485.43 756.49 621.15 8.32 13.21 31.64 11.85 2366.48 2815.65 0.76 2198.12 200.1 1029.69 8.51 INDI 498 INDI 498 PLS 1 Colorectal II 1503.63 860.62 2977.23 * 0.815 7.73 * 2.744 15.13 823.5 * 323.192 0.7 2030.9 179.41 1368.46 4.14 INDI 499 INDI 499 PLS 1 Lung I 1434.39 1563.21 933.16 4.99 7.76 * 2.782 * 1.36 1251.45 * 325.874 0.63 2025.55 152 , 8 1029.69 6.58 INDI 501 INDI 501 PLS 1 Colorectal III 3069.7 1143.74 6481.12 * 0.815 19.94 * 2.744 46.49 684.06 * 323.192 0.64 2303.89 * 14.413 959, 98 3.74 INDI 502 INDI 502 PLS 1 Colorectal III 5616.93 733.73 1036.91 * 0.815 14.56 * 2.744 26.36 3.163.0 * 323.192 0.68 2156.43 * 14.413 400.66 6.57 INDI 503 INDI 503 PLS 1 Lung I 1276.03 865.00 581.22 * 0.831 22.85 * 2.782 14.37 1924.61 * 325.874 0.72 1655.99 100.18 832.3 6.07 INDI 504 INDI 504 PLS 1 Breast III * 137.024 806.54 1458.51 5.14 11.67 * 2.782 13.47 682.72 2389.58 0.8 737.87 262.26 1079.3 5.52 INDI 505 INDI 505 PLS 1 Lung II * 137.024 6193, 41 552.21 * 0.831 15.81 * 2.782 18.2 474.5 * 325.874 0.71 1018.61 100.18 2996.68 6.56 INDI 506 INDI 506 PLS 1 Colorectal I * 145.246 1194.55 3462.32 * 0.826 23.65 * 2.756 44.77 543.74 * 327.082 0.95 1081.31 151.16 928.31 9.35 INDI 507 INDI 507 PLS 1 Lung II 7850.82 1149.97 1170.61 * 0.831 5 , 05 * 2,782 15,63 2,770,1 * 325,874 0,7 2074,27 120,99 * 79,85 4,45 INDI 508 INDI 508 PLS 1 Colorectal III 905,9 1820,76 1906,33 * 0,826 15,07 * 2.756 28.38 472.44 * 327.082 0.67 2283.03 126.77 749.51 6.54 INDI 511 INDI 511 PLS 1 Colorectal III * 145.246 1517.49 1701.29 * 0.826 15.08 * 2.756 20.03 487 , 8 * 327,082 0.83 2167.73 104.23 724.47 4.14 INDI 512 INDI 512 PLS 1 Breast II * 137.024 957.01 2406.39 13.25 12.22 * 2.782 20.32 545.17 * 325.874 0.7 1831, 14 120.99 1121.84 5.14 INDI 513 INDI 513 PLS 1 Lung I 2528.84 1800.51 881.88 9.29 69.60 19.85 15.32 2646.54 1955.25 0.62 1540, 73 178.21 245.76 10.33 INDI 514 INDI 514 PLS 1 Stomach I * 137.024 898.44 1592.02 * 0.831 6.44 * 2.782 11.63 950.5 * 325.874 0.86 959.51 * 14, 53 1837.02 4.28 INDI 515 INDI 515 PLS 1 Colorectal III 1848.54 1257.07 1568.48 * 0.826 18.91 * 2.756 34.82 1.492.2 * 327.082 0.71 683.6 112.33 305, 43 4.67 INDI 516 INDI 516 PLS 1 Pancreas II 920 2351.35 1960.39 14.44 5.10 627.86 24.22 2.557.1 * 315.949 0.68 1890.23 154.78 1335.79 4, 26 INDI 517 INDI 517 PLS 1 Breast III * 137.024 2894.29 2655.64 * 0.831 25.47 * 2.782 17.55 1079.41 * 325.874 0.76 2613.21 111.22 846.12 6.27 INDI 518 INDI 518 PLS 1 Colorectal III 1716.98 5538.12 3611.54 * 0.826 10.31 * 2.756 34.61 1.192.1 * 327.082 0.87 2888.57 95.29 1169.17 5.97 INDI 519 INDI 519 PLS 1 Lung I 5020.97 1377.33 1000.89 * 0.831 45.11 * 2.782 13.22 1.511.4 * 325.874 0.76 1140.57 100.18 818.38 6.37 INDI 521 INDI 521 PLS 1 Colorectal II 1195.65 120 3.47 2484.76 * 0.826 11.07 * 2.756 34.14 626.3 * 327.082 0.83 2132.07 151.16 640.68 3.88 INDI 522 INDI 522 PLS 1 Lung I 2067.53 2349.94 3090.3 * 0.831 12.76 * 2.782 16.22 * 77.625 * 325.874 0.89 2590.47 87.18 1029.69 6.92 INDI 523 INDI 523 PLS 1 Colorectal I 2202.18 7846.46 2435.1 8 , 93 19.46 49.54 44.36 2.798.0 * 327.082 0.74 1437.19 112.33 984.98 5.21 INDI 524 INDI 524 PLS 1 Lung III 2266.26 1141.56 2342.72 12, 92 15.74 19.85 15.76 697.44 8995.64 0.91 893.02 489.89 1528.6 7.74 INDI 525 INDI 525 PLS 1 Colorectal I * 145.246 1634.89 3991.44 * 0.826 27 , 21 * 2.756 35.74 756.8 * 327.082 0.96 2467.08 112.33 882.01 4.28 INDI 526 INDI 526 PLS 1 Esophagus III 1615.09 2448.28 3409.96 5.14 33.82 * 2.782 17.86 34275.61 * 325.874 0.65 2018.06 100.18 656.8 5.45 INDI 527 INDI 527 PLS 1 Colorectal III * 145.246 1061.06 1797.18 * 0.826 10.39 * 2.756 24.89 * 76.193 * 327.082 0.91 1607.16 * 14.2 973.74 5.15 INDI 528 INDI 528 PLS 1 Colorectal II 1027.61 2034.86 4611.46 * 0.826 11.57 * 2.756 54.52 562.6 * 327,082 0.74 2187.57 * 14.2 1450.77 4.9 INDI 529 INDI 529 PLS 1 Colorectal I * 145.246 1342.14 2644.87 * 0.826 9.23 * 2.756 20.8 1.544.7 * 327.082 0.76 1391.02 104.23 * 83.65 4.42 INDI 530 INDI 530 PLS 1 Breast I * 137.024 590.15 1164.03 7.35 12.79 * 2.782 12.61 682.72 * 325.874 0.64 1284.86 200.1 509, 03 6.51 INDI 532 INDI 532 PLS 1 Lung I * 137.024 1242.49 1536.1 5.14 20.98 * 2.782 16.55 1.223.1 * 325.874 0.81 2228.19 120.99 846.12 4, 57 INDI 533 INDI 533 PLS 1 Pancreas II 1356.49 1982.24 3170.34 * 0.817 97.70 88.85 30.72 3296.32 3972.46 0.85 1564.57 84.9 846.1 8.42 INDI 534 INDI 534 PLS 1 Stomach III 855.1 1550.52 1337.9 * 0.831 4.55 * 2.782 14.83 * 77.625 * 325.874 0.66 2051.78 100.18 873.43 7.53 INDI 535 INDI 535 PLS 1 Lung I * 137.024 1504.01 2374.53 5.77 9.28 * 2.782 18.13 10.476.5 * 325.874 0.76 1218.24 * 14.53 543.47 7.37 INDI 537 INDI 537 PLS 1 Ovary I 2276.34 3664.28 675.82 5.11 4.35 * 2.624 14.22 * 76.03 2203.21 0.72 1064.32 145.03 517.5 6.05 INDI 538 INDI 538 PLS 1 Lung III 2668.01 1221.45 1062.59 * 0.831 22.38 2 1.53 22.98 6.944.8 * 325.874 0.82 1136.88 * 14.53 473.45 3.09 INDI 539 INDI 539 PLS 1 Colorectal II 2067.39 2932.80 2093.09 * 0.826 13.94 * 2.756 38.28 2.054.3 * 327.082 0.88 3330.19 112.33 945.45 8.06 INDI 540 INDI 540 PLS 1 Breast II 1099.1 2021.54 2116.03 6.4 7.94 22.80 14.37 1.119.0 * 325.874 1.08 3450.7 100.18 1004.46 6.35 INDI 541 INDI 541 PLS 1 Breast II * 137.024 540.40 1147.61 * 0.831 32.35 * 2.782 17.35 1411.30 * 325.874 0.6 1935.74 120.99 832.3 5.65 INDI 544 INDI 544 PLS 1 Colorectal III * 145.246 1743.59 1104.84 * 0.826 5.78 * 2.756 26.19 1.447.6 * 327.082 0, 77 1417.94 161.87 450.95 5.95 INDI 545 INDI 545 PLS 1 Lung I * 137.024 1040.82 1931.6 * 0.831 16.14 * 2.782 17.2 3.742.1 * 325.874 1.03 1678.32 120.99 991 , 73 5.19 INDI 546 INDI 546 PLS 1 Lung II * 137.024 515.56 3215.95 5.14 31.45 * 2.782 19.16 1.150.9 * 325.874 0.82 2548.82 * 14.53 1646.18 7.87 INDI 547 INDI 547 PLS 1 Stomach III 2578.31 1453.31 1955.86 * 0.831 23.95 * 2.782 15.37 1.047.9 * 325.874 0.85 2352.43 * 14.53 1720.76 7, 39 INDI 548 IN DI 548 PLS 1 Breast II 5871.66 5950.28 5148.67 6.24 44.71 * 2.782 20.39 1.968.8 * 325.874 1.08 3667.38 * 14.53 1620.96 10.52 INDI 549 INDI 549 PLS 1 Lung III * 137.024 3221.53 4232.61 5.77 7.83 * 2.782 21.62 606.19 * 325.874 1.38 1730.46 100.18 886.94 7.84 INDI 550 INDI 550 PLS 1 Lung III * 137.024 735.66 1742.11 5.14 28.41 23.22 14.7 1629.84 * 325.874 1.28 1229.34 * 14.53 818.38 5.75 INDI 551 INDI 551 PLS 1 Colorectal II * 145.246 915.07 1136.17 * 0.826 7.85 * 2.756 28.21 1.711.5 * 327.082 0.83 1942.73 95.29 1132.2 6.28 INDI 555 INDI 555 PLS 1 Colorectal II * 145.246 1934 , 51 2702.86 * 0.826 5.10 * 2.756 26.73 5.581.2 * 327.082 0.8 1611.04 95.29 6143.61 3.82 INDI 558 INDI 558 PLS 1 Colorectal I * 147.82 2285.99 4468.14 6.21 11.83 20.06 29.85 1,278.0 * 325.95 1.52 2243.79 134.8 578.03 5.69 INDI 559 INDI 559 PLS 1 Breast III 3209.3 2355, 70 1479.5 6.52 9.26 * 2.755 12.86 1.786.1 2252 0.94 1014.72 190.23 434.33 4.57 INDI 560 INDI 560 PLS 1 Colorectal I 1165.6 2358.87 3312, 55 * 0.83 11.49 * 2.755 13.72 842.37 * 3 25.95 1.62 1323.08 120.79 2754.5 8.81 INDI 561 INDI 561 PLS 1 Breast III * 147.82 986.63 281.27 * 0.83 13.27 * 2.755 * 1.372 1.034.4 * 325.95 1.34 1000.68 86.07 764.01 4.66 INDI 562 INDI 562 PLS 1 Colorectal I 1012.37 5524.73 2892.92 * 0.83 3.80 * 2.755 11.47 1.505, 0 2543.03 1.29 2858.33 159.22 1055.95 7.2 INDI 564 INDI 564 PLS 1 Colorectal I * 147.82 5609.29 3009.26 * 0.83 9.16 * 2.755 9.31 * 75.938 * 325.95 1.36 1010.04 * 14.345 932.08 7.31 INDI 565 INDI 565 PLS 1 Breast III 23124.6 2631.69 2446.66 * 0.83 4.39 * 2.755 11.96 930, 3 2925 1.25 1458.62 * 14.345 398.28 3.76 INDI 566 INDI 566 PLS 1 Breast II * 147.82 867.39 694.95 5.29 7.17 * 2.755 11.24 583.22 * 325 , 95 1.42 2020.76 147.5 629.22 4.93 INDI 567 INDI 567 PLS 1 Colorectal II * 147.82 ** 10000.597 5403.35 7.77 6.08 27.00 19.45 47.542 , 6 1955.7 1.44 1275.73 86.07 786.99 8.33 INDI 568 INDI 568 PLS 1 Breast II * 147.82 1307.22 1566.85 * 0.83 3.15 * 2.755 11.47 989.56 * 325.95 2.18 1444.31 159.22 535.63 5.65 INDI 569 INDI 569 PLS 1 Breast III * 147.82 8993.64 1565.07 * 0.83 5.97 * 2.755 13.14 846.0 2349.47 1.34 1439.54 225.41 629.22 4.4 INDI 570 INDI 570 PLS 1 Colorectal II * 147.82 6353.76 5497.27 15.6 5.83 * 2.755 16.19 524.86 * 325.95 2.43 1221.41 190.23 480.48 5.12 INDI 571 INDI 571 PLS 1 Breast III * 147.82 1074.56 1673, 81 * 0.83 4.22 * 2.755 11.18 600.53 * 325.95 1.4 1391.92 134.8 649.2 4.16 INDI 572 INDI 572 PLS 1 Colorectal II * 147.82 785.87 955.51 * 0.83 7.36 * 2.755 7.32 504.46 * 325.95 1.18 369.34 134.8 1119.49 3.35 INDI 573 INDI 573 PLS 1 Colorectal I * 147.82 1093 , 41 1474.15 * 0.83 4.63 * 2.755 * 1.372 748.58 * 325.95 1.3 459.26 104.91 668.91 4.38 INDI 574 INDI 574 PLS 1 Colorectal III 928.93 839 , 17 481.58 * 0.83 9.78 * 2.755 6.94 * 75.938 * 325.95 1.49 335.89 170.15 698.02 4.36 INDI 575 INDI 575 PLS 1 Breast II 940.89 939 , 54 1260.05 * 0.83 19.91 * 2.755 20.21 922.9 * 325.95 1.28 1458.62 180.46 562.29 6.35 INDI 577 INDI 577 PLS 1 Breast I 1524.42 735.74 564.87 5.9 12.90 17.24 17.13 3483.84 4572.73 1.2 734.88 190.23 588.42 6.28 INDI 578 INDI 578 PLS 1 Breast I * 147.82 1871.59 1030.96 * 0.83 6.14 * 2.755 10.85 1.216.3 4033.71 1.02 1164.87 170.15 360.76 3.46 INDI 579 INDI 579 PLS 1 Breast I * 147, 82 1036.86 2802.24 102.9 17.30 18.65 9.19 2793.98 3112.82 1.06 1706.02 104.91 678.68 7.86 INDI 581 INDI 581 PLS 1 Colorectal II 3131.3 1975 , 87 2914.71 * 0.83 30.92 * 2.755 10.5 3.575.4 1955.7 1.01 1155.46 * 14.345 279.99 6.32

INDI 582 INDI 582 PLS 1 Colorectal II * 147.82 773.34 1995 * 0.83 5.64 23.55 10.29 307.683.1 * 325.95 1.42 1518.37 86.07 398.28 11, 53 INDI 583 INDI 583 PLS 1 Colorectal II * 147.82 1011.74 2376.55 * 0.83 10.88 * 2.755 8.47 4390.63 * 325.95 1.7 1961.77 113.13 608.97 30.24 INDI 584 INDI 584 PLS 1 Colorectal II * 147.82 2266.99 789.44 * 0.83 15.32 * 2.755 12.29 2.412.6 6825.06 1.19 830.34 159.22 567, 56 22.74 INDI 585 INDI 585 PLS 1 Esophagus III * 147.82 1439.45 3415.4 9.17 32.84 39.89 16.32 2.344.3 13264.5 1.06 2097.26 134.8 279 , 99 17.03 INDI 586 INDI 586 PLS 1 Liver III 3075.57 4389.52 4595.43 60.62 17.49 22.16 17.31 642.4 * 325.95 1.12 688.32 185.41 524.8 3.83 INDI 587 INDI 587 PLS 1 Colorectal II * 147.82 1963.23 2012.87 8.7 11.33 * 2.755 28.07 3139.93 * 325.95 1.11 1075.62 104.91 2084.34 8, 95 INDI 588 INDI 588 PLS 1 Breast III 2091.58 279.55 1299.34 * 0.83 13.86 * 2.755 10.47 993.28 * 325.95 0.89 879.28 159.22 220.08 5 , 87 INDI 589 INDI 589 PLS 1 Breast II * 147.82 651.19 1490.2 6.21 8.80 * 2.755 8.99 1640.59 4750.51 1.16 890.93 241.24 265.58 10.99 INDI 591 INDI 591 PLS 1 Colorectal II 964.29 14137.98 2533.36 4.88 12.47 * 2.715 8.71 2.745.2 * 312.793 2.15 1461.11 136.9 783.88 6.34 INDI 592 INDI 592 PLS 1 Breast II * 157.823 1186.21 2114.61 * 0.803 13.53 26.20 13.24 1.582.4 1969.27 0 , 7 1348.46 136.9 525.92 7.68 INDI 593 INDI 593 PLS 1 Breast II 5498.88 1996.57 6601.21 * 0.803 7.91 35.88 14.61 4664.83 19350.78 1, 27 1910.94 187.1 711.31 9.57 INDI 594 INDI 594 PLS 1 Colorectal III * 157.823 2005.41 1738.26 * 0.803 3.98 * 2.715 13.47 1.059.3 * 312.793 1.13 1759.83 88.71 408.3 7.19 INDI 595 INDI 595 PLS 1 Esophagus II * 157.823 4522.14 1616.6 10.04 7.91 31.22 12.84 827.0 31071.18 0.76 1133.07 260 , 68 141.32 8.94 INDI 596 INDI 596 PLS 1 Breast III * 157.823 781.20 876 5.65 11.31 18.94 9.46 1727.12 2337.96 0.87 797.96 291.04 510 , 48 3.83 INDI 597 INDI 597 PLS 1 Breast II * 157.823 1540.09 1949.88 * 0.803 13.31 * 2.715 12.84 786.38 * 312.793 0.76 443.09 171.97 305.58 9, 96 INDI 598 INDI 598 PLS 1 Colorectal II * 157,823 680.26 1761 * 0.803 7.02 * 2.715 12.34 3.279.3 1969.27 1.68 904.47 115.42 816.75 6.08 INDI 599 INDI 599 PLS 1 Colorectal II * 157.823 1053.00 1469.04 * 0.803 7.62 17.43 11.95 27378.02 3069.79 0.98 696.46 171.97 797.62 13.88 INDI 600 INDI 600 PLS 1 Colorectal II * 157.823 1025.46 1243.64 7.21 5.95 * 2.715 12.08 1.457.5 1969.27 1.12 1852.54 232.68 613.18 6.26 INDI 601 INDI 601 PLS 1 Breast III 2840.2 377.48 2134.45 4.88 164.45 181 , 80 12.64 3.015.0 99452.06 0.77 1040.78 136.9 283.94 5.28 INDI 602 INDI 602 PLS 1 Colorectal II * 157.823 688.18 1925 7.21 5.37 * 2.715 * 1.372 3,055.3 3433.43 1.44 1168.86 376.66 283.94 6.5 INDI 603 INDI 603 PLS 1 Breast III * 157.823 427.46 1558 15.54 30.56 26.20 12.76 9343.74 * 312.793 0.67 1654.72 171.97 395.05 7.08 INDI 604 INDI 604 PLS 1 Breast II * 157.823 1127.73 1778.52 * 0.803 3.30 * 2.715 12.26 533.7 3614.76 0 , 94 1310.04 291.04 84.84 8.5 INDI 605 INDI 605 PLS 1 Colorectal II * 157.823 1130.51 401.53 22.31 10.42 * 2.715 15.17 706.05 1969.27 1.01 619.69 201.12 671.38 12.89 INDI 60 6 INDI 606 PLS 1 Breast II * 157.823 483.13 2550.06 * 0.803 16.00 * 2.715 15.46 1827.83 2153.88 1.09 1266.33 214.27 865.39 5.42 INDI 607 INDI 607 PLS 1 Breast II * 157.823 2174.05 748.3 7.21 3.75 * 2.715 11.04 * 75 * 312.793 0.85 1567.73 136.9 913.28 5.18 INDI 608 INDI 608 PLS 1 Colorectal II * 157.823 513.79 1705.1 * 0.803 5.61 * 2.715 8.02 4,920.7 1876.76 1.19 627.99 226.7 3297.36 6.84 INDI 609 INDI 609 PLS 1 Colorectal II * 157.823 861 , 86 2710.51 4.88 15.29 39.00 12.26 6.163.2 13753.05 1.44 1010.85 214.27 965.7 16.72 INDI 610 INDI 610 PLS 1 Colorectal II * 157.823 612, 06 539.32 5.39 6.81 * 2.715 * 1.372 921.39 2521.55 2.09 * 27.581 318.81 395.05 10.34 INDI 611 INDI 611 PLS 1 Colorectal II 2887.94 2575.70 1554, 57 * 0.803 5.78 * 2.715 10.3 5281.79 2153.88 1.56 1288.15 364.92 714.14 12.44 INDI 612 INDI 612 PLS 1 Colorectal II * 157.823 1058.52 273.14 * 0.803 4.23 * 2.715 9.2 798.53 2704.70 1.53 981.05 271.14 682.86 7.95 INDI 613 INDI 613 PLS 1 Breast II * 157.823 387.43 393.13 5.9 4, 70 21.22 13.2 806.6 * 312.793 1.27 784.34 214.27 860.02 11.68 INDI 614 INDI 614 PLS 1 Colorectal II * 157.823 1400.25 1797.83 * 0.803 7.91 * 2.715 16.59 1.386.9 2613.18 1.45 1387.07 271.14 1399.12 20.06 INDI 615 INDI 615 PLS 1 Colorectal II * 157.823 1278.70 839.88 * 0.803 3.76 * 2.715 10.52 2.622.1 1969.27 2.15 1299.08 115.42 1192.81 15.06 INDI 617 INDI 617 PLS 1 Colorectal III * 157.823 2575.70 3183.73 * 0.803 5.47 * 2.715 10.94 734.03 * 312.793 1.59 1269.96 155.42 535 , 12 6.96 INDI 618 INDI 618 PLS 1 Colorectal III 1025.73 1902.57 1416.4 * 0.803 6.98 97.36 8.64 2.079.0 4696.88 1.34 780.94 88.71 1919, 36 10.6 INDI 619 INDI 619 PLS 1 Breast II * 157.823 1208.57 921.86 * 0.803 3.61 * 2.715 8.64 * 75 * 312.793 0.85 1547.05 136.9 312.7 4.75 INDI 620 INDI 620 PLS 1 Colorectal II * 157.823 6995.37 3547.17 * 0.803 4.49 * 2.715 19.49 5315.95 1969.27 1.15 2100.2 115.42 171.4 5.73 INDI 621 INDI 621 PLS 1 Colorectal II * 157.823 1727.60 1303.63 * 0.803 17.76 * 2.715 8.79 3340.86 * 312.793 1.01 1554.56 155.42 223.78 4.78 INDI 622 INDI 622 PLS 1 Liver III 43779.36 5141.16 2350.85 6.95 7.74 * 2.715 12.68 10.872.1 4337 1.01 1530.17 155.42 547.33 8.2 INDI 623 INDI 623 PLS 1 Breast III * 157.823 2779.03 1001.73 133.24 10.49 34.32 12.08 762.2 6482.74 0.99 1115.23 171.97 595.42 11.52 INDI 624 INDI 624 PLS 1 Colorectal II * 140.254 1583 , 05 3134.87 * 0.835 5.95 * 2.794 27.83 * 77.894 * 328.815 1.05 585 136.53 1210.95 7.65 INDI 625 INDI 625 PLS 1 Colorectal II * 157.823 1124.96 1527.12 * 0.803 8.62 21.22 9.2 827.0 3614.76 1.4 861.33 207.8 816.75 10.66 INDI 626 INDI 626 PLS 1 Colorectal II * 157.823 2515.31 3154.87 6.55 16 , 70 248.03 9.59 246.925.4 3614.76 1.82 691.42 271.14 2260.07 15.28 INDI 627 INDI 627 PLS 1 Breast II * 157.823 284.44 2060.67 5.13 14, 81 * 2.715 9.89 2715.58 * 312.793 2.73 150.48 352.86 886.76 9.55 INDI 628 INDI 628 PLS 1 Colorectal II * 157.823 727.89 2812.06 * 0.803 5.75 29.67 12.26 1008.82 * 312.793 1.27 1242.75 291.04 1417.87 8.42 INDI 629 INDI 629 PLS 1 Breast I * 157.823 135.21 1030.72 * 0.803 10.95 21.22 7.5 1457, 47 * 312.793 1.57 679.69 22 6.7 728.23 8.06 INDI 630 INDI 630 PLS 1 Breast I * 157.823 733.21 1672.05 * 0.803 8.15 18.56 7.68 690.13 * 312.793 1.08 1168.86 249.83 501.16 6.79 INDI 631 INDI 631 PLS 1 Colorectal II * 157.823 1990.69 1910.8 * 0.803 5.04 * 2.715 14.58 12.657.8 5055.63 0.99 1926.58 226.7 636.63 9.18 INDI 632 INDI 632 PLS 1 Colorectal II 10,404.07 1791.55 3166.41 10.18 12.77 44.09 33.51 2.154 648 1.87 784.34 413.79 280.29 20, 38 INDI 633 INDI 633 PLS 1 Colorectal II * 157.823 651.31 1157.75 * 0.803 5.72 * 2.715 * 1.372 7.004.1 2887.44 0.9 423.79 201.12 659.86 7.38 INDI 635 INDI 635 PLS 1 Colorectal II 1565.62 977.45 4881.67 * 0.835 2.59 * 2.794 29.08 * 77.894 2235.86 1.6 1892.48 184.28 978.23 6.84 INDI 636 INDI 636 PLS 1 Liver III * 157.823 861.86 902.84 32.12 7.24 * 2.715 7.76 892.5 * 312.793 0.94 821.87 115.42 996.77 7.88 INDI 637 INDI 637 PLS 1 Breast III * 157.823 377.48 1340.5 8.01 21.66 22.75 9.46 4510.64 5055.63 0.73 659.63 238.53 200.08 5.01 INDI 638 INDI 638 PLS 1 Breast I * 157.823 554.95 515.95 5.13 7.37 * 2.715 9 , 59 * 75 * 312.793 1.22 871.66 136.9 298.42 7.28 INDI 639 INDI 639 PLS 1 Liver II 270 818.88 3199.48 3172.18 6.42 12.44 202.88 18.64 4.303.3 5055.63 0.69 2603.5 384.32 805.84 10.8 INDI 640 INDI 640 PLS 1 Colorectal II 1119.86 1245.40 4094.83 * 0.835 7.81 * 2.794 22.64 6.140, 5 4810.03 1.76 1535.06 230.22 * 74.45 11.51 INDI 641 INDI 641 PLS 1 Breast III 990.48 2395.00 182.92 5.39 15.88 25.05 10.41 1.887 , 8 * 312.793 0.99 1414.76 291.04 84.84 11.08 INDI 643 INDI 643 PLS 1 Colorectal II 1017.91 1847.75 3647.31 * 0.835 4.87 * 2.794 18.95 2.983.5 2627.86 1.27 1588.46 175.65 4135.49 10.93 INDI 644 INDI 644 PLS 1 Colorectal I 1587.36 1618.68 5010.57 * 0.835 13.11 * 2.794 27.45 912.76 3533.32 1, 39 1995.84 192.57 964.55 4.43 INDI 645 INDI 645 PLS 1 Liver III ** 99634.823 2733.37 2992.17 5.13 7.81 * 2.715 17.99 1.768.2 2796.12 1.48 * 27.581 260.68 99.75 23.57 INDI 646 INDI 646 PLS 1 Breast II 1685.86 1462.86 1240.32 * 0.803 21.62 35.88 9.65 851.46 2704.70 0.84 720 88 , 71 1323.4 9.22 INDI 647 INDI 647 PLS 1 Colorret al III 1132.83 905.13 5567.25 * 0.835 12.24 * 2.794 44.04 29915.23 7758.3 1.14 1780.43 112.47 471.48 7.39 INDI 648 INDI 648 PLS 1 Colorectal III * 140.254 2772.48 2535.98 23.64 4.56 207.39 12.67 27.548.3 162084.89 3.87 1941.88 480.52 555.18 6.62 INDI 649 INDI 649 PLS 1 Colorectal III * 140.254 856.82 4675.17 12.97 5.55 * 2.794 19.49 8447.92 * 328.815 2.27 935.79 266.56 3202.77 10.06 INDI 650 INDI 650 PLS 1 Colorectal III * 140.254 674, 96 3568.68 6.06 1.71 18.38 23.06 1.635.6 * 328.815 0.69 822.61 125.04 908.83 3.06 INDI 651 INDI 651 PLS 1 Colorectal III 1251.74 901.10 1702.63 * 0.835 4.08 17.73 49.31 1219.27 * 328.815 1.27 2054.38 157.18 2991.11 14.19 INDI 654 INDI 654 PLS 1 Colorectal II * 140.254 1709.62 3685.32 * 0.835 4.24 * 2.794 40.51 1194.55 * 328.815 1 918.34 166.64 431.93 18.36 INDI 655 INDI 655 PLS 1 Colorectal II * 140.254 925.23 1726.04 7.26 2.85 * 2.794 27.39 * 77.894 * 328.815 1.21 1419.64 184.28 894.65 5.72 INDI 656 INDI 656 PLS 1 Breast III 7958.52 1003.48 1375.84 9.36 33.52 * 2.715 11.81 8 92.50 * 312.793 0.92 1339.29 115.42 323.3 4.22 INDI 657 INDI 657 PLS 1 Colorectal II * 140.254 2099.59 2012.87 12.12 11.07 211.24 13.15 102,115, 2 3790.07 0.71 1370.92 200.57 411.49 4.78 INDI 658 INDI 658 PLS 1 Colorectal I * 140.254 1934.42 2375.01 9.31 30.35 * 2.794 27.58 1764.61 1972 , 89 0.35 1499.51 223.11 706.82 6.29 INDI 659 INDI 659 PLS 1 Breast II * 157.823 669.72 551.06 5.65 4.05 18.94 9.59 549.1 2338 0 , 74 580.01 249.83 319.78 5.64 INDI 660 INDI 660 PLS 1 Colorectal II * 140.254 2959.39 2296.05 * 0.835 3.16 * 2.794 24.98 1.62.4 * 328.815 1.23 735, 9 98.38 865.95 14.57 INDI 661 INDI 661 PLS 1 Liver III * 157.823 1669.67 1443.53 5.65 20.63 23.51 8.1 734.0 5771 0.66 1319.16 260, 68 276.63 4.96 INDI 662 INDI 662 PLS 1 Breast I * 157.823 2533.41 2190.5 * 0.803 5.87 20.46 10.19 3146.54 3976.53 0.94 863.05 226.7 645 , 37 9.88 INDI 663 INDI 663 PLS 1 Esophagus II * 157.823 7594.14 3692.82 6.16 8.23 * 2.715 13.04 5.706.8 3795.79 0.6 2659.93 187.1 127.89 9.5 INDI 664 INDI 664 PLS 1 Breast I * 157.823 1838.21 929, 8 * 0.803 5.01 * 2.715 9.07 666.33 3976.53 0.82 745.32 155.42 150.08 4.05 INDI 665 INDI 665 PLS 1 Esophagus III 1820.38 1287.14 2431.7 6 , 95 9.16 212.62 8.49 183.408.1 * 312.793 1.06 981.05 146.45 411.6 11.48 INDI 666 INDI 666 PLS 1 Breast II * 157.823 274.81 486.95 5.39 13.65 * 2.715 7.11 1546.52 * 312.793 0.78 935.7 226.7 276.63 2.46

INDI 667 INDI 667 PLS 1 Breast II 1645.08 1844.05 2925.6 * 0.803 8.77 * 2.715 12.21 3238.30 * 312.793 0.8 1809.93 155.42 485.51 5.93 INDI 668 INDI 668 PLS 1 Breast III 1614.69 1287.14 2710.51 * 0.803 22.36 * 2.715 11.14 24867.67 2153.88 0.89 2266.71 115.42 1202.61 6.88 INDI 669 INDI 669 PLS 1 Liver III ** 99634.823 1180.63 2394.9 * 0.803 17.08 * 2.715 12.13 3.927.0 4517.16 0.89 2013.01 155.42 472.89 3.93 INDI 670 INDI 670 PLS 1 Colorectal III 3597 , 73 1360.09 1179.71 * 0.826 15.45 120.83 24.32 170.479.1 7666.56 0.69 2806.93 95.29 1407 4.77 INDI 671 INDI 671 PLS 1 Lung I 975.04 669 , 07 1914.29 * 0.831 17.02 * 2.782 12.81 5282.07 * 325.874 0.95 1240.45 * 14.53 846.12 4.85 INDI 672 INDI 672 PLS 1 Breast II * 137.024 1415.31 1660.07 * 0.831 11.59 * 2.782 13.27 908.12 * 325.874 1.68 1514.74 166.06 985.34 4.65 INDI 673 INDI 673 PLS 1 Breast II 9636.03 1673.29 2224.69 10.27 31.91 * 2.782 20.22 1.403.0 * 325.874 1.14 2959.34 200.1 1017.11 8.22 INDI 674 INDI 674 PLS 1 Lung I 4436.25 2392.69 2088.08 5.46 11.83 * 2,782 1 8.87 3.427.0 * 325.874 1.21 1678.32 120.99 1631.07 7.21 INDI 675 INDI 675 PLS 1 Colorectal III * 145.246 5476.12 4829.46 77.28 14.02 * 2.756 35.33 952.9 * 327.082 0.84 2745.88 95.29 2318.86 8.14 INDI 677 INDI 677 PLS 1 Colorectal II 2722.56 884.22 3378.51 * 0.826 4.08 18.20 12.11 616, 6 2396.41 1.35 1077.51 198.86 * 83.65 6.08 INDI 678 INDI 678 PLS 1 Colorectal III * 145.246 1752.66 2768.92 * 0.826 9.57 29.13 31.97 29.535.4 * 327,082 0.71 2410.91 218.84 798.65 4.32 INDI 679 INDI 679 PLS 1 Colorectal II 1080.33 1007.86 1908.87 * 0.826 5.96 * 2.756 19.08 1015.32 * 327.082 0 , 72 1805.61 161.87 984.98 4.05 INDI 681 INDI 681 PLS 1 Lung II 1367.1 931.90 1389.7 13.93 31.77 * 2,782 16.8 2156.62 * 325.874 0.94 1262.65 178.21 1636.11 5.47 INDI 682 INDI 682 PLS 1 Colorectal III 1082.37 1819.42 2455.89 5.55 5.51 * 2.781 23.97 4.441.9 * 327.606 0.71111, 99 * 15.64 1181.88 5.4 INDI 683 INDI 683 PLS 1 Colorectal II * 166.572 1473.10 1593.81 * 0.824 8.39 * 2.781 28.8 1187.91 * 327.606 0.86 1416.74 * 15 , 64 724 5.41 INDI 684 INDI 684 PLS 1 Liver III 1545.57 2243.18 2741.66 * 0.831 22.06 * 2.782 17.59 * 77.625 2604 0.65 1760.27 166.06 966.04 6.14 INDI 685 INDI 685 PLS 1 Colorectal III * 166.572 2308.80 3077.75 * 0.824 21.32 * 2.781 28.89 2442.12 * 327.606 1.02 1722.6 * 15.64 2144.34 6.67 INDI 686 INDI 686 PLS 1 Pancreas II 1195.94 2014 , 84 2962.71 55.59 28.81 674.89 18.68 1.805.1 2203.21 0.85 1757.19 197.94 707.23 6.09 INDI 687 INDI 687 PLS 1 Lung III * 137.024 4044, 19 3989.33 50.86 51.94 * 2.782 15.37 * 77.625 4461.54 0.92 922.57 100.18 1256.6 4.03 INDI 688 INDI 688 PLS 1 Colorectal II 3468.12 1056.34 2103 , 15 * 0.824 15.75 * 2.781 25.03 1.301.1 * 327.606 0.85 2022.45 * 15.64 724 4.52 INDI 690 INDI 690 PLS 1 Lung III 1738.71 2191.98 2222.93 41, 83 14.64 * 2.782 * 1.36 3326.83 10016.75 0.98 3215.87 138.02 1222.13 6.94 INDI 691 INDI 691 PLS 1 Lung I 914.54 982.14 1641.33 5, 77 16.74 19.01 13.71 577.4 4259.89 1.08 1629.95 312.65 1175.44 9.16 INDI 692 INDI 692 PLS 1 Colorectal III 2326.29 1016.23 3737.38 8, 68 30.19 19.76 2 4.91 18359.27 2786.53 1.17 1128.09 * 14.319 646.74 10.95 INDI 693 INDI 693 PLS 1 Lung III 8876.44 2452.56 1083.78 9.62 10.00 19.01 16 , 26 * 77.625 11482.78 1.14 1444.22 * 14.53 1740.39 6.14 INDI 694 INDI 694 PLS 1 Lung I * 137.024 1749.59 1431.61 * 0.831 7.58 * 2.782 13.17 1.071 , 5 2603.54 1.12 3466.14 237.63 832.3 4.68 INDI 695 INDI 695 PLS 1 Pancreas II 1231.9 2651.21 3534.9 5.97 177.76 17.36 33.01 1247.04 3780.79 0.75 555.82 112.06 1114.12 6.5 INDI 696 INDI 696 PLS 1 Lung II * 137.024 990.52 1943.73 7.67 6.44 * 2.782 17.97 1.494.6 * 325.874 0, 85 2594.26 189.5 839.22 8.09 INDI 697 INDI 697 PLS 1 Breast II 4621.21 9101.36 1836.61 10.6 39.22 * 2.782 13.71 1.867.5 16299.76 1.44 3319.66 254.31 8126.49 4.36 INDI 698 INDI 698 PLS 1 Ovary III 2478.13 2351.35 2782.84 105.92 17.09 47.65 23.98 * 76.03 * 315.949 0.73 1625, 01 99.23 867.44 5.06 INDI 699 INDI 699 PLS 1 Pancreas II 960.01 1248.49 4398.23 5.4 6.73 42.00 37.22 2.201.5 * 315.949 1.34 1846.99 105.8 689.84 6.31 INDI 700 INDI 700 PLS 1 Lung III 1864.75 2157.86 1750.68 7.03 16.48 * 2.782 14.83 8.422.4 * 325.874 0.72 1470.19 152.8 1379.4 7.39 INDI 701 INDI 701 PLS 1 Colorectal II 1431, 41 2112.60 1898.63 * 0.817 23.70 * 2.624 20.54 2.106.1 * 315.949 0.86 937.56 145.03 802.73 5.31 INDI 702 INDI 702 PLS 1 Lung II 1349.07 1797, 39 2736.58 * 0.817 21.68 * 2.624 24.69 770.9 2203.21 0.99 1023.08 99.23 851.45 9.17 INDI 703 INDI 703 PLS 1 Lung I 1027.8 4017.14 1179.93 * 0.817 34.26 * 2.624 27.29 1717.45 3684.57 1.13 1258.39 99.23 612.29 4.89 INDI 704 INDI 704 PLS 1 Breast II * 137.024 2396.96 2163.28 7.03 20, 65 25.74 11.18 1,111.1 1955.25 0.84 700.91 210.13 378.25 3.45 INDI 706 INDI 706 PLS 1 Lung I 1268.18 1459.65 5838.09 5.83 19, 16 33,92 38,46 722,7 * 315,949 0,7 1451,33 189,9 292,7 7,72 INDI 708 INDI 708 PLS 1 Colorectal III 1087,66 1197,54 4235 * 0,835 11,26 * 2,794 43 , 94 83,595.9 * 328,815 0.81 2525.63 98.38 714.78 3.04 INDI 709 INDI 709 PLS 1 Colorectal II 1742.64 2582.60 2019.54 * 0.817 5.20 * 2.624 30.99 802 , 2 * 315,949 1.1 1800.25 112.06 153 4.98 5.98 INDI 710 INDI 710 PLS 1 Colorectal III 11516.96 1408.72 6067.84 5.69 15.74 19.38 50 1.905.8 * 315.949 0.88 1458.39 84.9 1807.78 4.71 INDI 711 INDI 711 PLS 1 Breast III * 137.024 1082.77 367.08 10.93 22.00 * 2.782 20.63 11.612.3 5259.02 1.59 1455.35 237.63 846.12 5, 46 INDI 712 INDI 712 PLS 1 Breast I * 137.024 1647.87 2070.63 * 0.831 14.61 * 2.782 11.85 474.5 * 325.874 1.3 1984.37 100.18 775.92 10.61 INDI 713 INDI 713 PLS 1 Breast II 825.79 2089.67 1827.99 * 0.831 5.33 * 2.782 17.04 * 77.625 * 325.874 0.95 1496.17 100.18 702.58 5.08 INDI 714 INDI 714 PLS 1 Breast III * 137.024 1461.76 2943.46 * 0.831 36.90 * 2.782 25.57 847.1 * 325.874 0.95 2503.43 * 14.53 1279.34 9.49 INDI 715 INDI 715 PLS 1 Mama III * 137.024 673, 23 1955.86 98.43 68.17 * 2.782 13.81 6.975.2 127666 1.28 970.59 189.5 293.02 8.75 INDI 716 INDI 716 PLS 1 Colorectal III 2540.54 2763.11 2687, 63 * 0.817 25.15 * 2.624 32.5 1,284.6 7652 0.87 1861.39 112.06 730.15 6.71 INDI 717 INDI 717 PLS 1 Breast I * 137.024 1491.33 3137.58 * 0.831 23, 59 * 2.782 1 4.51 635.2 * 325.874 0.83 1551.88 * 14.53 900.35 7.23 INDI 718 INDI 718 PLS 1 Colorectal II * 140.254 1610.76 2873 * 0.835 17.65 * 2,794 38.57 1,261.9 * 328.815 0.55 2072.41 125.04 784.54 4.78 INDI 720 INDI 720 PLS 1 Colorectal III 1238.34 1507.77 2238.23 * 0.835 12.19 * 2.794 26.17 2.416.5 * 328.815 0, 51 1428.51 175.65 1383.66 3.55 INDI 721 INDI 721 PLS 1 Breast II 1858.45 1805.18 2511.45 * 0.824 22.77 * 2.781 26.46 507.36 * 327.606 0.96 2153, 85 * 15.64 578.25 3.82 INDI 722 INDI 722 PLS 1 Breast I 6430.66 2679.86 2358.02 7.35 29.20 82.55 27.15 1611.84 4111.76 0.96 3053 , 98 173.16 911.88 3.54 INDI 723 INDI 723 PLS 1 Ovary III * 166.572 2732.22 2275.45 134.63 140.67 * 2.781 26.86 670.1 10617.8 0.74 2255.06 * 15 , 64 1000.61 2.42 INDI 724 INDI 724 PLS 1 Colorectal II * 140.254 585.56 3320.23 * 0.835 11.95 * 2.794 17.36 611.50 * 328.815 0.72 1905.95 125.04 807.13 3 , 22 INDI 725 INDI 725 PLS 1 Colorectal II * 140.254 1253.38 3582.23 * 0.835 11.32 * 2.794 28.52 872.76 * 328.815 0.81 1490.62 * 14.391 894.65 5.2 INDI 726 IN DI 726 PLS 1 Colorectal I * 140.254 885.01 2096.61 8.21 10.45 * 2.794 33.46 2.005.4 * 328.815 0.6 2235.02 166.64 769.3 4.5 INDI 729 INDI 729 PLS 1 Colorectal II 1638.46 1309.14 2926.31 * 0.835 17.41 * 2.794 39.21 680.9 * 328.815 0.69 1758.06 112.47 1045.34 6.96 INDI 731 INDI 731 PLS 1 Pancreas II * 166.572 1548.96 2239.58 15.11 27.42 387.15 26.61 1825.47 2061.47 0.94 2167.02 132.93 1110.67 7.2 INDI 732 INDI 732 PLS 1 Colorectal III 16503.63 1132.04 784.14 6.1 14.04 19.24 11.08 3.911.2 * 327.606 0.75 2272.7 * 15.64 765.63 3.97 INDI 734 INDI 734 PLS 1 Pancreas II 1815.12 1644.97 3661.32 20.33 33.97 63.00 23.5 * 76.03 3199 1.05 1472.51 84.9 935.4 2.47 INDI 735 INDI 735 PLS 1 Pancreas II 3539.18 1314 , 07 3658.57 6.41 8.74 67.04 26.09 2.813.4 2805 1.1 3345.63 99.23 1423.78 7.32 INDI 736 INDI 736 PLS 1 Lung III * 126.474 1735.72 3510 , 18 * 0.817 13.91 * 2.624 32.91 1.084.3 * 315.949 1.09 2122.26 99.23 213.92 3.55 INDI 737 INDI 737 PLS 1 Stomach I * 137.024 1175.19 2075.87 11, 59 29.75 * 2.782 8.28 816.79 * 3 25.874 0.9 2624.58 * 14.53 859.82 5.46 INDI 740 INDI 740 PLS 1 Pancreas II 2398.56 1525.09 1791.4 21.46 20.98 * 2.624 28.18 702.1 10111 0 , 9 1771.53 209.57 1152.24 4.94 INDI 741 INDI 741 PLS 1 Pancreas III 2079.72 3462.58 10114.38 22.44 41.85 286.51 58.51 4,661.1 4068 1.25 4089.88 248.77 775.14 8.49 INDI 742 INDI 742 PLS 1 Breast II 1319.48 1117.18 2513.85 * 0.817 6.23 * 2.624 34.82 882.9 * 315.949 0.82 1265.36 84, 9 707.23 5.68 INDI 743 INDI 743 PLS 1 Stomach II * 137.024 1011.47 1903.92 5.77 17.39 20.27 13.32 704.82 * 325.874 0.92 2277.1 * 14.53 702, 58 3.22 INDI 745 INDI 745 PLS 1 Pancreas II * 166.572 2584.69 10130.78 8.05 28.94 365.70 46.11 971.9 * 327.606 0.65 2458.79 * 15.64 * 78, 14 5.61 INDI 746 INDI 746 PLS 1 Colorectal III * 140.254 1993.46 2702.84 5.01 5.84 26.51 31.49 5.865.9 * 328.815 0.73 1874.53 157.18 865.95 9 , 81 INDI 747 INDI 747 PLS 1 Colorectal III * 140.254 1997.39 3290.63 6.32 7.30 * 2.794 29.57 1.100.2 * 328.815 1.1 1508.39 112.47 1045.34 7.66 INDI 749 INDI 749 PLS 1 Colorectal III * 1 40.254 949.34 3966.07 12.26 2.81 * 2.794 32.72 524.9 6072.08 0.76 944.51 237.15 1084.62 7.41 INDI 750 INDI 750 PLS 1 Colorectal III * 140.254 3696 , 18 4602.86 * 0.835 6.11 * 2.794 29.08 3327.08 3275.77 0.91 1820.73 125.04 2351.07 10.66 INDI 751 INDI 751 PLS 1 Colorectal III * 140.254 1958.04 3726 , 08 * 0.835 8.16 * 2.794 28.65 1.072.5 2235.86 1.27 1150.38 166.64 2492.23 8.67 INDI 752 INDI 752 PLS 1 Colorectal III * 140.254 1245.40 1674.04 * 0.835 7.98 * 2.794 33.18 1.696.0 * 328.815 0.61 874.78 192.57 1032.08 9.87 INDI 753 INDI 753 PLS 1 Esophagus III * 157.823 5758.10 2755.57 * 0.803 5.80 * 2.715 6.91 2.116.7 4696.88 1.33 1503.98 187.1 642.46 18.25 INDI 754 INDI 754 PLS 1 Breast III 1851.84 279.62 1233.68 * 0.803 17.39 * 2.715 9, 65 1127.11 * 312.793 0.44 686.39 115.42 261.84 2.97 INDI 755 INDI 755 PLS 1 Esophagus III * 157.823 4273.07 2215.89 * 0.803 14.22 * 2.715 8.02 3,376.9 2704.7 0.71 1732.96 115.42 347.67 3.99 INDI 756 INDI 756 PLS 1 Breast II 1426.09 1978.91 1233.68 * 0.803 20.27 * 2.715 10.68 1627.39 * 312.793 0.86 2266.71 88.71 711.31 10.85 INDI 757 INDI 757 PLS 1 Breast II * 157.823 452.68 542.25 4.88 7.11 16.67 8.18 2359.69 * 312.793 0, 63 1342.96 155.42 254.35 4.04 INDI 758 INDI 758 PLS 1 Colorectal II * 140.254 4386.52 2306.57 * 0.835 16.12 * 2.794 43.19 1.703.6 * 328.815 0.74 3047.96 98.38 1412.38 6.96 INDI 759 INDI 759 PLS 1 Breast III * 157.823 1270.26 2121.82 * 0.803 11.67 * 2.715 9.07 958.72 * 312.793 0.67 1233.71 88.71 381 68 4.3

INDI 760 INDI 760 PLS 1 Liver III ** 99634.823 3503.82 5268.16 5.9 14.04 * 2.715 10.41 2854.53 3886.20 0.43 1040.78 201.12 447.37 11, 58 INDI 761 INDI 761 PLS 1 Colorectal II * 140.254 2624.26 3805.01 * 0.835 7.12 * 2.794 30.6 3,251.4 * 328.815 0.77 1370.92 * 14.391 509.49 6.93 INDI 762 INDI 762 PLS 1 Esophagus II * 157.823 1428.68 1300.28 * 0.803 8.98 * 2.715 * 1.372 794.47 1969.27 0.65 730.12 136.9 574.51 11.68 INDI 763 INDI 763 PLS 1 Colorectal I 968.15 1348.93 3126.83 * 0.835 14.18 24.55 24.71 929.52 * 328.815 0.98 1045.07 * 14.391 346.94 6.93 INDI 764 INDI 764 PLS 1 Colorectal II * 140.254 2526 , 62 3301.39 7.67 7.95 * 2.794 36.15 2,285.5 * 328.815 0.64 1722.29 136.53 490.66 5.8 INDI 765 INDI 765 PLS 1 Colorectal I * 140.254 4628.73 5309 , 16 5.53 15.89 47.96 20.25 764.4 * 328.815 0.78 2171.71 112.47 * 74.45 7.96 INDI 766 INDI 766 PLS 1 Breast II * 157.823 894.34 1407, 93 5.39 5.86 * 2.715 11.04 884.27 * 312.793 0.69 720 136.9 269.26 4.45 INDI 767 INDI 767 PLS 1 Colorectal II 1238.34 1005.53 2396.09 * 0.835 13 38 * 2,794 31,08 20,381.1 * 328,815 0.69 1561.75 * 14.391 714.78 5.9 INDI 768 INDI 768 PLS 1 Colorectal II * 140.254 820.54 2225.1 * 0.835 5.49 * 2.794 20.25 1613.04 3918.16 1.23 1154.78 175.65 1283.76 13.28 INDI 769 INDI 769 PLS 1 Colorectal II * 140.254 430.20 3382.2 * 0.835 4.64 * 2.794 26.94 649.2 * 328,815 1.8 922.7 125.04 581.74 6.86 INDI 770 INDI 770 PLS 1 Colorectal II * 140.254 569.27 3108.07 * 0.835 3.10 21.62 23.83 1.311.9 4301.36 1.18 1137.2 175.65 1247.61 7.91 INDI 771 INDI 771 PLS 1 Liver II 22322.18 1383.22 2940.8 * 0.803 6.42 17.43 15.83 1.654.5 * 312.793 0.71 2230 , 37 187.1 1098.51 9.96 INDI 772 INDI 772 PLS 1 Colorectal II * 140.254 1523.63 2652.45 * 0.835 8.21 18.38 22 627.17 2887.77 1.27 649.53 184, 28 1557.48 7.91 INDI 773 INDI 773 PLS 1 Colorectal II * 140.254 2040.65 3791.39 * 0.835 6.12 * 2.794 33.58 1.730.2 * 328.815 0.83 3186.4 * 14.391 1850.09 5 , 67 INDI 774 INDI 774 PLS 1 Colorectal I 871.14 1539.48 3576.81 * 0.835 2.25 * 2.794 34.32 1.051.9 3790.07 1.11 418.31 112.47 1789.36 23.7 IND I 775 INDI 775 PLS 1 Liver II ** 99634.823 2947.09 4970.33 6.16 6.46 * 2.715 9.71 1,273.3 2887 0.6 277.81 171.97 1325.78 14.92 INDI 776 INDI 776 PLS 1 Esophagus II 4777.43 1116.63 855.56 11.29 3.89 19.70 12.76 5140.01 13059.34 1.29 1284.51 499.76 1118.54 10.69 INDI 777 INDI 777 PLS 1 Colorectal II * 140.254 1476.04 3821.36 * 0.835 5.93 * 2.794 34.26 1.413.3 2627.86 1.42 2117.52 200.57 908.83 9.57 INDI 778 INDI 778 PLS 1 Liver II 191825.44 899.77 985.68 * 0.803 10.77 * 2.715 * 1.372 1.690.7 7192.21 0.76 1584.68 300.55 1045.44 5.13 INDI 779 INDI 779 PLS 1 Colorectal II * 140.254 1468.11 5838.45 * 0.835 2.84 * 2.794 44.04 561.8 * 328.815 1.44 753.22 112.47 250.33 9.52 INDI 780 INDI 780 PLS 1 Colorectal II * 140.254 1317.10 2417.18 * 0.835 7.14 23.57 21.72 1.703.6 * 328.815 2.01 1588.46 215.81 957.67 7.29 INDI 781 INDI 781 PLS 1 Colorectal II * 140.254 836.67 2896.98 * 0.835 6, 52 * 2.784 23.89 555.6 2888 1.38 1370.92 200.57 865.95 11.11 INDI 782 INDI 782 PLS 1 Colorectal III 1486.77 3440.76 4697.44 12.26 15 , 36 * 2,794 40,61 27,188,4 13,401,61 1,21 2027,36 215,81 1929,6 7,58 INDI 783 INDI 783 PLS 1 Colorectal III * 140,254 2170,27 4864,89 * 0,835 13.99 * 2.794 27.58 3.018.3 * 328.815 0.61 1075.75 * 14.391 1210.95 3.44 INDI 784 INDI 784 PLS 1 Stomach I 1956.22 4296.56 517.31 11.59 29.98 19.01 13.86 900.5 3442 1.06 1819.95 262.26 1175.44 7.38 INDI 785 INDI 785 PLS 1 Colorectal I * 140.254 1101.65 722.01 * 0.835 2.16 * 2.794 33.12 2.490.9 * 328.815 0.62 1811.77 * 14.391 633.19 5.11 INDI 786 INDI 786 PLS 1 Liver I ** 100010.334 2209.04 2482.68 * 0.831 5.23 * 2.782 1,119.0 3442.41 1.55 2841.14 100.18 804.34 10.86 INDI 787 INDI 787 PLS 1 Breast II 1135.56 3375.30 5007.69 * 0.824 9.38 * 2.781 36.18 927.52 * 327.606 0.78 2804 * 15 , 64 779.33 4.44 INDI 788 INDI 788 PLS 1 Colorectal II * 140.254 1594.93 5027.41 * 0.835 11.43 * 2.794 31.61 20.037.4 * 328.815 0.91 1767 * 14.391 730.55 3, 42 INDI 789 INDI 789 PLS 1 Breast II * 166.572 1226.72 2547.86 * 0.824 27.15 * 2,781 23.65 517.31 * 327.606 1.3 2013.71 115.58 516.17 5.27 INDI 790 INDI 790 PLS 1 Breast II * 166,572 980.67 1330.92 6.1 13.01 36.93 21.81 804.06 * 327.606 0.74 1739.9 * 15.64 * 78.14 5.44 INDI 791 INDI 791 PLS 1 Breast III * 166.572 1667.55 1715.3 * 0.824 6.42 * 2.781 43.08 3139.31 * 327.606 0.86 2405.47 132.93 975.56 11.13 INDI 792 INDI 792 PLS 1 Stomach I 1425.93 113.86 2452.49 5.77 258.76 * 2.782 16.55 1047.90 * 325.874 1.13 2814.49 152.8 873.43 8.87 INDI 793 INDI 793 PLS 1 Breast II 6764.26 2403.91 2153.39 * 0.824 8.99 17.78 27.93 1.554.3 * 327.606 0.69 991.5 * 15.64 1726.98 4.54 INDI 794 INDI 794 PLS 1 Breast II * 166.572 2698.90 4228.37 * 0.824 14.39 * 2.781 36.06 554.05 * 327.606 0.85 413.18 * 15.64 695.76 4.8 INDI 795 INDI 795 PLS 1 Breast II 3621.85 2318, 31 650.02 * 0.824 14.47 * 2.781 20.34 1509.36 * 327.606 0.72 3619.19 161.07 * 78.14 4.26 INDI 797 INDI 797 PLS 1 Stomach II 5393.21 1775.05 1817.67 5.77 16.08 * 2.782 18.46 2.552.4 * 325.874 0.86 2115.52 152.8 746.99 8.94 INDI 798 INDI 798 PLS 1 Ovary II * 166.572 2917.91 1751.97 10.64 19, 97 * 2.781 23.48 645.9 3561.45 0.82 1476.9 285.18 911.88 5.92 INDI 799 INDI 799 PLS 1 Stomach II 1615.09 334.09 3274.4 * 0.831 8.68 * 2.782 11.35 * 77.625 * 325.874 0.73 2548 , 82 120.99 1054.63 2.93 INDI 800 INDI 800 PLS 1 Liver II 40 391.09 5928.20 1203.56 6.72 19.56 * 2.782 14.83 1.999.8 * 325.874 0.88 4426.4 120.99 269.88 7.65 INDI 801 INDI 801 PLS 1 Stomach I 3241.89 811.26 1295.77 * 0.816 25.42 17.01 14.86 1,137.4 * 319.819 0.58 2195.84 129.61 778 , 56 5.18 INDI 802 INDI 802 PLS 1 Pancreas II 1410.48 3008.40 2229.05 * 0.824 14.68 99.78 25.65 854.5 * 327.606 0.82 2592.65 105.47 667.1 7.61 INDI 803 INDI 803 PLS 1 Esophagus II 1443.75 1635.80 857.25 * 0.816 19.59 21.92 17.7 1,481.5 3880.89 0.55 1250.96 289.56 831.88 7.59 INDI 805 INDI 805 PLS 1 Colorectal II 1094.07 1879.28 1726.04 * 0.835 9.85 * 2.794 23.76 936.25 * 328.815 0.6 927.06 136.53 1058.51 6.79 INDI 806 INDI 806 PLS 1 Colorectal II * 140.254 864.88 2002.42 * 0.835 6.91 * 2.794 30.3 * 77.894 * 328.815 0.82 944.51 125.04 1018.75 4.25 INDI 808 INDI 808 PLS 1 Colorectal III * 14 0.254 929.25 677.98 * 0.835 10.95 * 2.794 28.02 * 77.894 * 328.815 0.54 * 33.46 136.53 991.83 5.23 INDI 809 INDI 809 PLS 1 Colorectal III * 140.254 609.98 2958.33 5.53 11.40 * 2.794 23.83 1977.85 * 328.815 1.26 1668.7 257.05 776.93 8.16 INDI 812 INDI 812 PLS 1 Ovary II 3550.69 1786.19 1969, 95 25.12 10.62 42.89 26.36 567.5 * 327.606 0.7 2718.31 * 15.64 637.99 2.5 INDI 814 INDI 814 PLS 1 Stomach I * 159.986 2161.28 1410.31 6.68 19.42 534.15 9.49 1093.75 2725.41 0.92 468.78 206.36 1539.19 8.06 INDI 815 INDI 815 PLS 1 Colorectal II * 140.254 2815.34 2254 * 0.835 8 , 17 * 2,794 33.81 19,767.1 * 328,815 0.67 922.7 147.18 581.74 6.87 INDI 816 INDI 816 PLS 1 Colorectal II 4141.88 784.22 2012.87 7.13 3.75 * 2,794 27,71 754,72 * 328,815 0,55 249,46 321,69 1148,62 4,8 INDI 817 INDI 817 PLS 1 Liver III 1637,93 3503,40 1063,93 6,31 31,20 * 2,689 13.14 1.750.3 * 319.819 0.52 2771.22 92.7 800.05 9.95 INDI 818 INDI 818 PLS 1 Ovary III * 166.572 2204.21 2893.84 337.91 122.41 25.83 40, 37 1,001.7 6091.44 1.33 844.35 161.0 7 5010.71 5.4 INDI 819 INDI 819 PLS 1 Breast III * 166.572 1065.80 841.27 * 0.824 59.52 18.14 31.81 1.724.2 3005.26 0.92 1744.23 * 15.64 * 78.14 3.01 INDI 821 INDI 821 PLS 1 Colorectal III * 140.254 751.89 2112.32 9.17 4.05 * 2.794 27.58 916.11 * 328.815 0.51 1005.68 166.64 451, 91 4.73 INDI 822 INDI 822 PLS 1 Colorectal II 1738.7 4043.05 4539.38 * 0.824 16.78 * 2,781 39 1,757.8 * 327,606 0.83 3652.08 * 15.64 492.2 4.95 INDI 823 INDI 823 PLS 1 Breast II * 166.572 2741.74 3363.01 * 0.824 13.82 * 2.781 29.17 2315.36 * 327.606 1.45 1983.16 * 15.64 709.94 7.54 INDI 824 INDI 824 PLS 1 Esophagus II 3292.47 1187.48 1548.12 * 0.816 10.07 78.67 19.26 946.4 * 319.819 0.54 6714.14 195.4 1138.49 5.39 INDI 825 INDI 825 PLS 1 Colorectal III * 140.254 1277.28 1892.74 * 0.835 7.85 * 2.794 30.42 1.938.7 * 328.815 0.84 1928.4 98.38 1032.08 5.42 INDI 826 INDI 826 PLS 1 Colorectal II * 140.254 1895.04 1414.66 * 0.835 3.25 * 2.794 27.83 2.232.9 * 328.815 0.72 1829.69 98.38 509.49 4.07 INDI 827 INDI 827 PLS 1 Colorectal III * 140.254 1400.6 1 2873 8.62 9.38 * 2.794 22.29 1.427.9 * 328.815 0.6 2036.36 * 14.391 1173.74 3.32 INDI 828 INDI 828 PLS 1 Breast II * 166.572 1155.71 1279.9 * 0.824 14.08 * 2.781 32.26 1945.32 * 327.606 0.81 2088.06 115.58 652.6 4.39 INDI 829 INDI 829 PLS 1 Colorectal III * 140.254 707.38 1757.26 * 0.835 12.75 * 2.794 37.51 946.34 * 328.815 0.67 342.02 161.97 674.58 5.14 INDI 830 INDI 830 PLS 0 Breast II 3119.96 1795.69 2283.9 * 0.824 21.08 * 2.781 27, 15 632.2 * 327.606 0.85 2525.62 * 15.64 364.94 4.75 INDI 831 INDI 831 PLS 1 Breast I 3669.49 3156.08 4878.94 8.62 13.38 * 2.781 36.79 1230.59 * 327.606 0.7 4330.46 * 15.64 1496.16 4.73 INDI 832 INDI 832 PLS 1 Breast II * 166.572 980.67 2487.92 * 0.824 6.67 * 2.781 24.82 832.8 * 327.606 0.73 1283.68 * 15.64 459.55 5.32 INDI 833 INDI 833 PLS 1 Breast I 3729.26 5175.06 4773.93 * 0.824 8.79 * 2,781 30.47 1,277.5 * 327,606 0.74 2193.4 * 15.64 813.19 8.16 INDI 835 INDI 835 PLS 1 Colorectal III * 140.254 1177.58 1303.39 * 0.835 8.40 * 2.794 19.26 1.229.9 * 328.815 0.56 1415.21 200.57 714.78 4.44 INDI 837 INDI 837 PLS 1 Colorectal II 1402.3 1594.93 4814.58 * 0.835 13.73 * 2.794 32.08 1768.43 * 328.815 0.77 2621.4 112.47 1331.22 6.83 INDI 838 INDI 838 PLS 1 Colorectal III * 140.254 1077.65 644.28 * 0.835 16.80 * 2.794 33.75 592.8 * 328.815 0.89 308.27 166.64 908.83 9.33 INDI 839 INDI 839 PLS 1 Breast II * 166.572 1748.22 2296.58 * 0.824 11.52 * 2.781 29.55 1988.55 * 327.606 0.87 2290.36 * 15.64 * 78.14 3.69 INDI 840 INDI 840 PLS 0 Mama II * 166.572 990.13 1889.36 * 0.824 3.72 * 2.781 32.26 869.01 * 327.606 0.76 2092.44 * 15.64 467.79 4.31 INDI 841 INDI 841 PLS 1 Colorectal III 847.42 3518, 21 5193.4 * 0.835 3.39 * 2.794 50.37 8.213.9 * 328.815 0.81 2827.31 * 14.391 1167.48 8.94 INDI 842 INDI 842 PLS 1 Breast I * 166.572 2285.03 1051.84 * 0.824 19.84 * 2.781 27.59 1.501.2 * 327.606 0.81 1253.64 * 15.64 * 78.14 3.88 INDI 843 INDI 843 PLS 1 Esophagus I * 159.986 894.44 1102.48 * 0.816 13 , 92 * 2,689 13.83 1827.31 * 319.819 0.49 1327.41 137.42 734.88 6.61 INDI 844 INDI 844 PLS 1 Stomach I 7963.52 1831.74 3280.95 * 0.816 7.70 * 2.689 21.59 2.281 3027 0.64 1967.42 * 13.517 678.86 5.38 INDI 846 INDI 846 PLS 1 Colorectal II * 168.118 2660.15 2800.57 * 0.845 13.42 * 2.915 9.3 39289 , 21 * 347,375 * 0.107 832.75 105.39 * 76.56 4.95 INDI 847 INDI 847 PLS 1 Liver III ** 100101.482 1223.67 1596.14 5.61 9.14 37.73 9.58 1,463.9 6835.48 * 0.107 2528.27 217.27 546.51 8 INDI 848 INDI 848 PLS 1 Colorectal II 1426.36 1095.47 1951.6 * 0.845 4.52 92.00 12.01 1309.54 * 347.375 1 , 4 1619.57 127.5 833.58 6.41

INDI 849 INDI 849 PLS 1 Colorectal II * 168.118 1086.94 1406.34 5.34 5.75 * 2.915 15.31 713.46 2203.03 * 0.107 1553.52 191.89 1038.51 11.25 INDI 850 INDI 850 PLS 1 Esophagus II * 168.118 2094.46 1435.32 6.97 109.91 * 2.915 7.71 778.4 12715.32 * 0.107 978.25 170.56 489.11 7.49 INDI 853 INDI 853 PLS 1 Esophagus II 1798.81 3036.10 1739.82 * 0.845 7.33 48.06 7.19 1,386.4 15471.84 * 0.107 1518.58 * 15.378 313.08 12.04 INDI 854 INDI 854 PLS 1 Colorectal II * 168,118 1817.34 2137.46 5.74 15.64 * 2,915 13.71 3780.79 2895.27 * 0.107 2299.89 162.85 281.14 14.67 INDI 855 INDI 855 PLS 1 Colorectal II 2830.21 4311.92 2509.68 * 0.845 24.30 * 2.915 10.51 1.126.0 2668.03 1.99 1070.42 146.25 646.28 9.33 INDI 856 INDI 856 PLS 1 Breast III 1101.09 597.86 2204, 78 * 0.82 3.84 23.05 14.63 1543.01 2786.53 0.81 798.44 156.31 532.3 11.13 INDI 857 INDI 857 PLS 1 Breast III * 132.09 758.27 1452.1 * 0.82 28.54 30.37 9.24 1.653.4 * 327.917 0.57 1309.03 169.09 305.85 4.57 INDI 858 INDI 858 PLS 1 Breast I 2548.88 1246.96 2824.49 7.12 11.99 * 2, 74 17.16 1118.21 * 327.917 1.02 1546.84 108.45 779.98 10.51 INDI 859 INDI 859 PLS 1 Esophagus II * 132.09 8167.48 1705.01 * 0.82 * 0.23 * 2 , 74 16.87 * 73.577 * 327.917 0.74 976.04 * 14.319 568.94 18.7 INDI 860 INDI 860 PLS 1 Breast I * 132.09 867.12 3842.41 * 0.82 4.22 * 2 , 74 22.09 1402.20 * 327.917 0.92 616.8 85.91 699.49 5.11 INDI 861 INDI 861 PLS 1 Liver II 1288.64 2903.49 2910.7 * 0.82 12.18 * 2.74 13.67 836.2 2087.27 1.15 495.98 85.91 298.95 12.74 INDI 862 INDI 862 PLS 1 Pancreas II 2062.31 2898.86 2517.87 * 0.824 16.98 * 2.781 22 , 09 768.4 2252 2.24 1223.61 173.16 355.91 7.39 INDI 864 INDI 864 PLS 1 Liver III ** 99798.215 1072.04 2828.49 17.78 12.31 * 2.74 12, 85 * 73.577 * 327.917 1.3 1052.62 85.91 507.58 7.84 INDI 865 INDI 865 PLS 1 Breast III * 168.118 1330.76 1154.88 * 0.845 3.96 * 2.915 * 1.372 683.78 * 347.375 * 0.107 1193.63 127.5 1136.85 6.75 INDI 867 INDI 867 PLS 1 Esophagus II 2154.08 4931.50 724.98 * 0.845 10.10 * 2.915 14.36 1.184.9 7798.77 * 0.107 1565 , 17 127,5 608,82 11,79 INDI 868 INDI 868 PLS 1 Fig o III 166472.07 1524.07 482.25 5.61 108.54 * 2.915 7.28 1.441.7 24959.33 * 0.107 1662.35 234.63 1546.04 15.47 INDI 870 INDI 870 PLS 1 Breast II 3796.42 1604.58 1818.22 * 0.82 18.34 * 2.74 11.29 474.0 * 327.917 1.36 230.61 108.45 412.53 5.7 INDI 872 INDI 872 PLS 1 Breast III * 132.09 5027.69 1978.72 * 0.82 4.43 * 2.74 18.57 1.910.4 * 327.917 1.15 1330.13 85.91 463.73 5.25 INDI 873 INDI 873 PLS 1 Breast III 2737.7 804.36 912.01 7.43 23.76 * 2.74 16.66 937.33 * 327.917 0.99 738.5 85.91 305.85 5.18 INDI 875 INDI 875 PLS 1 Breast II * 132.09 2527.34 2147.66 * 0.82 4.44 * 2.74 15.55 744.99 * 327.917 1.17 964.03 108.45 682 5.19 INDI 876 INDI 876 PLS 1 Breast II * 132.09 2475.01 2346.92 * 0.82 6.10 * 2.74 8.35 1552.16 * 327.917 1.17 798.44 85.91 1182.01 5.99 INDI 877 INDI 877 PLS 1 Breast I * 132.09 3467.97 2926.76 * 0.82 10.21 * 2.74 18.42 5.012.7 3238 1.36 2066.17 180.93 699.49 8.9 INDI 878 INDI 878 PLS 1 Breast III * 132.09 2203.30 3452.8 13.7 15.09 43.95 22.48 840.4 * 327.917 1.07 759.7 202.5 346.63 6.14 INDI 879 INDI 879 PLS 1 Breast II * 132.09 597.86 3261.69 * 0.82 16.14 * 2.74 15.85 2043.31 * 327.917 1.24 2693.85 85.91 2022.96 7.3 INDI 880 INDI 880 PLS 1 Breast II * 132.09 1207.22 3067.61 * 0.82 5.38 * 2.74 17 878.12 * 327.917 1.77 1586.13 108.45 785.67 6.74 INDI 881 INDI 881 PLS 1 Breast II * 132.09 1956.43 1650.45 * 0.82 10.83 * 2.74 18.19 663.5 4541.78 1.26 1304.81 108.45 1113.69 9.82 INDI 882 INDI 882 PLS 1 Breast II 1244.3 624.34 1607.61 5.23 4.18 * 2.74 19.14 591.36 * 327.917 1.26 898.42 264.59 791.34 4.28 INDI 883 INDI 883 PLS 1 Breast II * 132.09 1828.38 2555.13 * 0.82 11.75 17.28 19.83 728.6 * 327.917 1.81 1330.13 85.91 1150.58 5.61 INDI 884 INDI 884 PLS 1 Breast II * 132.09 2505.77 4884.25 72.6 5.56 * 2.74 16.07 * 73.577 * 327.917 0.75 1283.78 * 14.319 457.39 8.41 INDI 885 INDI 885 PLS 1 Breast II * 132.09 1828.38 1845.59 15.27 10.21 75.17 13.37 1005.68 * 327.917 1.16 1036.42 * 14.319 431.87 5.38 INDI 886 INDI 886 PLS 1 Breast II * 132.09 1589.97 1186.59 6.17 15.00 21.82 14.91 753.22 * 327.917 1.04 1455.95 85.91 127.67 5.19 INDI 887 I NDI 887 PLS 1 Breast III * 132.09 6858.12 2626.77 * 0.82 19.29 317.41 22.61 5528.10 * 327.917 0.86 1378.92 108.45 213.19 7.35 INDI 888 INDI 888 PLS 1 Pancreas II 2190.21 1394.16 2834.58 20.49 32.20 1004.95 32.96 2.339.8 2606 0.73 2437.66 84.9 1016.4 6.18 INDI 889 INDI 889 PLS 1 Colorectal II 3066.13 981.17 3648.19 * 0.819 13.60 * 2.688 24 6.637,5 4071 1.11 2399.04 237.68 3313.78 3.24 INDI 892 INDI 892 PLS 1 Stomach I * 130.232 2066 , 21 1801.63 8.02 17.97 25.82 14.37 881.49 * 1033.785 1.13 1715.64 * 13.973 869.48 8.11 INDI 893 INDI 893 PLS 1 Pancreas II 2004.23 1996.73 4636.63 5.4 32.82 38.76 52.23 * 76.03 2405.70 0.94 2071.3 123.81 1254.55 9.83 INDI 896 INDI 896 PLS 1 Lung 2776.32 1248, 49 2443.38 6.26 24.01 * 2.624 12.76 1.033.4 2606 0.78 2805.58 154.78 1740.23 5.24 INDI 897 INDI 897 PLS 1 Breast III 2776.32 1750.23 2410, 88 * 0.817 28.82 * 2.624 34.72 3.525.7 * 315.949 0.82 1402.01 99.23 575.14 5.85 INDI 898 INDI 898 PLS 1 Breast I 7017.03 1891.66 5198.39 7.87 6 , 58 * 2.624 38.7 2.019.7 * 315.949 1.33 1 440.75 123.81 1006.42 7.96 INDI 899 INDI 899 PLS 1 Breast I 7346.28 2842.50 2589.83 * 0.817 7.48 * 2.624 32.19 1741.28 * 315.949 1.05 2660.04 84.9 1094.86 6.07 INDI 902 INDI 902 PLS 1 Breast III 6680.42 1314.07 3425.1 7.73 39.20 27.46 28.57 890.0 10716 0.92 1948.01 164, 08 * 71.31 5.83 INDI 903 INDI 903 PLS 1 Breast I 6932.41 1270.35 3427.85 * 0.817 25.75 * 2.624 25.17 997.20 * 315.949 0.76 1710.65 112.06 666 , 37 5.39 INDI 905 INDI 905 PLS 1 Esophagus II * 166.572 3303.80 3662.77 * 0.824 8.90 * 2,781 28.42 1,246.2 2630.59 0.91 1489.79 * 15.64 337.6 16.59 INDI 907 INDI 907 PLS 1 Esophagus II * 166.572 3089.38 2098.97 * 0.824 6.16 * 2.781 18.15 677.1 * 327.606 0.8 974.25 * 15.64 578.25 4.04 INDI 908 INDI 908 PLS 1 Esophagus II * 166.572 4081.23 1723.44 5.01 6.05 * 2.781 19.73 2.556.7 4928.08 0.77 1839.57 * 15.64 * 78.14 6.9 INDI 909 INDI 909 PLS 1 Esophagus II * 166.572 6959.88 1256.44 * 0.824 16.24 19.97 31.28 1.035.4 2252.24 0.91 1223.61 132.93 833.26 15.0 INDI 911 INDI 911 PLS 1 Esophagus II * 166.572 2061.64 2381.37 7.49 2 4.57 * 2.781 30.28 4292.87 * 327.606 1.9 1796.2 * 15.64 866.33 3.31 INDI 913 INDI 913 PLS 1 Esophagus III 1768.45 1843.16 1021.38 * 0.824 15, 27 * 2.781 22.54 931.20 2630.59 1.46 1468.3 147.81 492.2 5.26 INDI 915 INDI 915 PLS 1 Breast II 1112.06 966.26 4595.75 5.23 17.26 * 2 , 74 8.5 836.2 * 327.917 0.73 1385.31 202.5 774.29 6.89 INDI 917 INDI 917 PLS 1 Liver III * 132.09 7732.06 2818.48 * 0.82 20.39 * 2.74 14.35 878.1 * 327.917 0.63 499.65 * 14.319 734.21 5.27 INDI 919 INDI 919 PLS 1 Breast II * 132.09 1094.45 2248.16 * 0.82 18, 39 27.94 18.8 * 73.577 * 327.917 0.74 849.19 85.91 734.21 8.95 INDI 920 INDI 920 PLS 1 Ovary III 2221.5 1757.35 1564.81 18.41 23.12 * 2.74 16.79 671.6 * 327.917 0.58 1689.67 * 14.319 690.76 5.24 INDI 921 INDI 921 PLS 1 Breast II * 132.09 3480.80 3644.99 6.8 32.01 * 2.74 16.91 1,267.9 4114.33 0.67 2239.35 126.63 312.72 5.38 INDI 922 INDI 922 PLS 1 Breast II 1200.09 2997.73 1994.41 * 0.82 15, 12 * 2.74 9.79 * 73.577 * 327.917 0.67 2310.48 * 14.319 213.19 7.49 INDI 923 INDI 923 PLS 1 Esophagus I 1738.4 2224.53 2265.92 * 0.82 44.46 * 2.74 23.61 736.8 * 327.917 0.85 2447.1 * 14.319 892.13 6.38 INDI 924 INDI 924 PLS 1 Breast III * 132, 09 1727.85 2628.76 * 0.82 12.76 * 2.74 12.74 687.8 * 327.917 0.74 1641.04 85.91 485.76 3.72 INDI 926 INDI 926 PLS 1 Breast II * 132.09 988.43 2416.2 * 0.82 3.79 * 2.74 16.24 671.6 * 327.917 0.61 707.82 * 14.319 227.91 4.71 INDI 927 INDI 927 PLS 1 Breast II 3311.13 6524.83 2183.11 * 0.82 16.92 * 2.74 9.65 1.971.9 * 327.917 0.66 2750.86 85.91 292.01 5.15 INDI 928 INDI 928 PLS 1 Stomach II 114 364.32 2475.01 3792.95 5.23 10.41 21.41 27.76 2,192.0 3680.07 1.01 234.12 212.46 1300.96 8.04 INDI 929 INDI 929 PLS 1 Stomach II 1333.1 1689.58 2778.46 9.94 30.66 * 2.74 14.16 * 73.577 * 327.917 0.79 755.84 * 14.319 292.01 7.88 INDI 930 INDI 930 PLS 1 Stomach II * 132.09 2505.77 2072.94 * 0.82 24.98 * 2.74 20.19 1.492.8 * 327.917 0.84 878.68 * 14.319 1033.78 15.55 INDI 931 INDI 931 PLS 1 Ovary III * 132.09 1005.10 2732.49 25.47 17.29 * 2.74 18.04 869.7 * 327.917 0.74 1814.81 85.91 1192.44 6.88 INDI 9 32 INDI 932 PLS 1 Ovary III 1944.2 2254.91 1888.62 48.28 22.05 * 2.74 17.68 1.783.5 2087.27 0.76 1566.46 85.91 708.2 3.38 INDI 933 INDI 933 PLS 1 Esophagus III * 132.09 1128.16 2025.81 * 0.82 10.28 * 2.74 13.37 886.5 * 327.917 0.74 * 27.606 180.93 802.66 5.11 INDI 935 INDI 935 PLS 1 Esophagus I 829.99 555.72 1833.86 * 0.82 4.60 * 2.74 13.27 945.8 * 327.917 0.84 1024.3 108.45 457.39 4.41 INDI 936 INDI 936 PLS 1 Ovary I * 132.09 1792.82 3009.19 * 0.82 13.22 * 2.74 18.61 2.244.8 * 327.917 0.86 3030.32 * 14.319 808.31 8, 23 INDI 937 INDI 937 PLS 1 Esophagus III * 132.09 1301.11 1675.77 25.63 4.35 26.32 9.79 * 73.577 * 327.917 0.86 751.98 169.09 587.08 5.99 LCR 588 LCR 588 PLS1 Normal NA 2003.87 957.85 2674.11 12.5 20.62 20.76 24.22 803.13 * 334.48 0.83 2207.1 127.59 772.37 10.08 CSF 589 CSF 589 PLS1 Normal NA 1477.71 955.15 2298.18 5.43 4.30 * 2.724 44.68 686.29 * 334.48 0.86 1100.17 132.13 581.32 26.01 CSF 590 LCR 590 PLS1 Normal NA * 152.33 1267.35 3851.64 8.93 15.40 * 2.724 26.89 * 71.073 * 334.48 0.74 988.31 108.1 3 755.99 4.85 CSF 591 CSF 591 PLS1 Normal NA * 152.33 395.10 3761.8 * 0.814 24.18 * 2.724 34.4 2040.86 * 334.48 0.69 2577.19 85.72 462.56 7.56 LCR 592 LCR 592 PLS1 Normal NA 2782.15 759.62 3411.16 * 0.814 10.49 * 2.724 24.47 432.26 * 334.48 1.26 * 27.566 210.9 961.21 8.06 LCR 593 LCR 593 PLS1 Normal NA * 152.33 828.82 4429.71 8.63 6.71 * 2.724 36.81 899.80 * 334.48 0.72 2506.05 197.6 979.4 7.79 LCR 594 LCR 594 PLS1 Normal NA * 152.33 1292.30 5105.37 * 0.814 * 0.233 * 2.724 19.85 467.51 * 334.48 0.82 1875.69 85.72 976.38 3, 71 CSF 595 CSR 595 PLS1 Normal NA * 152.33 680.45 3740.68 5.43 15.89 27.39 14.7 1302.73 * 334.48 0.94 1875.69 * 14.286 337.04 7, 9 CSF 596 CSF 596 PLS1 Colorectal I * 152.33 159.97 4318.06 6.12 4.59 33.23 21.45 2412.11 2431.46 1.31 832.59 183.67 477.91 8, 77 CSF 597 CSR 597 PLS1 Normal NA * 152.33 1971.39 1512.99 * 0.814 11.66 * 2.724 28.77 458.6 * 334.48 1.39 1401.74 97.39 918.3 5.71 LCR 599 LCR 599 PLS1 Normal NA 3164.21 1708.22 3519.12 * 0.814 12.39 * 2.724 20.66 1.732.4 * 334.48 0.84 2960.81 85.72 450.94 4.6 LCR 600 LCR 600 PLS1 Normal NA 1228.17 930.84 2616.6 * 0.814 15.25 * 2.724 22.86 579.46 * 334.48 0.87 1769.1 85, 72 602.69 4.93

CSF 601 CSF 601 PLS1 Normal NA * 152.33 221.14 1111.34 * 0.814 16.91 * 2.724 18.65 695.9 * 334.48 1.1 350.37 145.14 252.09 5.72 CSF 602 CSF 602 PLS1 Normal NA * 152.33 1209.28 2100.2 * 0.814 14.67 * 2.724 44.44 744.26 * 334.48 1.05 2193.28 97.39 1100.97 6.34 CSF 603 CSF 603 PLS1 Normal NA 1373.78 947.04 5027.65 * 0.814 12.46 27.99 31.7 470.47 * 334.48 1.02 1088.08 * 14.286 616.8 3.1 CSF 604 LCR 604 PLS1 Normal NA 3093.96 938.94 3232.35 * 0.814 11.72 * 2.724 23.91 506.29 * 334.48 0.76 370.06 97.39 581.32 8.71 LCR 605 LCR 605 PLS1 Normal NA * 152.33 278.96 2006.52 * 0.814 8.69 * 2.724 26.17 * 71.073 * 334.48 0.96 255.12 97.39 462.56 3.24 LCR 606 LCR 606 PLS1 Normal NA * 152.33 264.40 2360.76 8.63 8.40 * 2.724 21.17 * 71.073 * 334.48 1.05 711.77 183.67 306.65 5.16 LCR 607 LCR 607 PLS1 Normal NA * 152 , 33 581.31 2355.54 * 0.814 4.73 * 2.724 21.78 * 71.073 * 334.48 1.25 1197.78 190.72 366.56 7.59 LCR 608 LCR 608 PLS1 Normal NA * 152.33 3099.81 2951.57 14.54 14.32 * 2.724 22.43 635.57 3112.88 0.79 1255.43 247.88 383.09 11 , 15 CSF 610 CSF 610 PLS1 Normal NA * 152.33 604.67 2167.9 * 0.814 11.28 * 2.724 17.53 567.13 * 334.48 1.09 1764.68 145.14 139.51 5.31 CSF 611 CSF 611 PLS1 Normal NA * 152.33 615.08 3614.02 * 0.814 3.61 28.80 23.44 699.09 * 334.48 1.04 1267.85 136.56 893.5 5.04 CSF 612 CSF 612 PLS1 Normal NA * 152.33 675.208 5000.87 * 0.814 3.73 * 2.724 43.69 933.62 * 334.48 0.97 1589.86 168.99 630.8 16.92 CSF 613 CSF 613 PLS1 Normal NA * 152.33 2040.57 3032.85 * 0.814 14.72 * 2.724 19.5 443.95 * 334.48 0.74 952.84 168.99 2116.01 6.45 CSF 614 CSF 614 PLS1 Normal NA * 152.33 871.65 3434.85 5.43 14.30 21.96 20.37 974.54 * 334.48 1.45 2019.67 253.7 500.64 8.26 LCR 616 CSF 616 PLS1 Normal NA 1373.78 4161.04 4872.42 * 0.814 13.65 * 2.724 28.68 2136.51 * 334.48 0.84 5067.37 118.15 1121.29 8.72 CSF 617 CSF 617 PLS1 Normal NA * 152.33 481.06 3487.51 * 0.814 9.77 * 2.724 52.02 754.01 * 334.48 1.36 1096.13 210.9 4649.5 8.92 LCR 618 LCR 618 PLS1 Normal NA * 152.33 529.70 3492.78 9.22 9.58 * 2.724 29.41 1237.76 * 334.48 1.09 2299.71 145.14 75 9.27 7.14 CSF 619 CSF 619 PLS1 Normal NA 2954.5 427.79 1575.25 * 0.814 4.35 * 2.724 20.77 * 71.073 * 334.48 0.99 861.4 91.69 297.79 5.33 LCR 620 LCR 620 PLS1 Normal NA * 152.33 400.12 1788.15 14.54 4.61 * 2.724 64.02 548.73 * 334.48 1.23 851.78 183.67 795.13 7.17 CSF 621 CSF 621 PLS1 Normal NA 2477.69 1470.93 2214.79 * 0.814 12.71 * 2.724 23.07 591.84 3372.63 0.64 1992.51 * 14.286 836.84 8.83 CSF 622 CSF 622 PLS1 Normal NA * 152.33 990.35 2355.54 5.43 3.76 17.15 23.96 789.97 * 334.48 1.48 945 118.15 470.25 11.75 CSF 623 CSF 623 PLS1 Normal NA * 152.33 944.34 265.4 8.33 16.16 * 2.724 36.73 2241.09 * 334.48 0.79 1043.96 297.53 332.76 2.52 LCR 624 LCR 624 PLS1 Normal NA 1829.96 764.93 2128.84 * 0.814 10.63 * 2.724 26.84 1130.98 * 334.48 1.21 1294.84 * 14.286 685.84 4.83 LCR 625 LCR 625 PLS1 Normal NA * 152.33 1805.10 2389.45 * 0.814 15.36 * 2.724 29 441.0 * 334.48 1.13 1729.44 197.6 1277.01 10.23 LCR 626 LCR 626 PLS1 Normal NA * 152, 33 844.86 5570.59 * 0.814 7.73 * 2.724 18.72 1335.46 * 334.48 0.78 696.92 85.72 106 2.92 4.74 CSF 627 CSF 627 PLS1 Normal NA * 152.33 1039.28 3997.15 * 0.814 10.37 * 2.724 28.81 * 71.073 * 334.48 1.57 1624.54 108.13 752, 7 8.44 LCR 628 LCR 628 PLS1 Normal NA * 152.33 791.49 2175.71 * 0.814 4.59 * 2.724 21.45 776.85 * 334.48 1.42 1707.48 153.37 991.47 4.29 LCR 629 LCR 629 PLS1 Normal NA 1023.44 4194.81 7198.46 * 0.814 4.70 * 2.724 38.71 744.3 * 334.48 1.69 3730.94 85.72 985.44 9, 45 CSF 630 CSF 630 PLS1 Normal NA * 152.33 1645.78 4597.46 6.78 11.19 18.75 37.86 984.8 * 334.48 0.77 1210.09 136.56 899.72 4 62 LCR 631 LCR 631 PLS1 Normal NA * 152.33 1645.78 2089.79 9.79 16.27 * 2.724 41.59 876.26 * 334.48 0.77 1542.41 176.43 319.79 5 , 12 LCR 632 LCR 632 PLS1 Normal NA 4702.79 863.60 2269.51 * 0.814 4.11 * 2.724 32.43 1088.82 * 334.48 1.07 1880.16 108.13 630.8 6.19 CSF 633 CSF 633 PLS1 Normal NA * 152.33 2086.80 2512.11 * 0.814 9.07 17.15 23.28 692.69 * 334.48 0.82 6476.03 85.72 1132.84 4.66 CSF 634 LCR 634 PLS1 Normal NA 4144.23 882.39 3817.28 * 0.814 15.41 23.37 27.7 667.18 * 334.48 0.78 1134.56 140 , 9 545.13 9.88 LCR 635 LCR 635 PLS1 Normal NA * 152.33 * 12.797 2925.37 * 0.814 13.50 * 2.724 17.15 461.59 * 334.48 1 * 27.566 * 14.286 * 23.506 5.86 LCR 636 CSF 636 PLS1 Normal NA * 152.33 1583.54 5212.7 * 0.814 5.92 16.75 26.99 1.357.4 * 334.48 0.78 3577.49 153.37 3234.51 7.68 CSF 637 CSF 637 PLS1 Normal NA * 152.33 565.78 1163.12 * 0.814 9.87 * 2.724 37.28 518.35 * 334.48 1.03 462.68 161.31 852.7 14.82 CSF 638 CSF 638 PLS1 Normal NA 1359.07 1143.21 927.57 8.63 21.25 * 2.724 21.67 1040.03 2431.46 0.72 1326.12 204.33 1876.1 7.71 CSF 639 LCR 639 PLS1 Normal NA * 152.33 1259.04 3687.88 * 0.814 2.45 * 2.724 20.71 715.16 * 334.48 1.73 769.85 97.39 1285.32 11.19 LCR 640 LCR 640 PLS1 Normal NA * 152.33 478.51 3156.18 * 0.814 20.98 * 2.724 36.37 2040.86 * 334.48 1.18 1450.47 85.72 552.43 7.6 LCR 641 LCR 641 PLS1 Normal NA * 152.33 * 12.797 4076.61 17.44 14.61 * 2.724 26.56 503.29 5011.79 1.29 * 27.566 419.04 * 23.506 4.9 LCR 642 LCR 642 PLS1 Normal NA 2116.26 733, 15 5489.72 * 0.814 3.67 * 2.724 19.5 836.22 * 334.48 1.23 19 02.53 85.72 729.53 7.18 CSF 643 CSF 643 PLS1 Normal NA * 152.33 1066.55 1990.91 * 0.814 16.61 * 2.724 20.08 1152.19 * 334.48 0.94 2855 , 99 145.14 1098.06 5.86 LCR 644 LCR 644 PLS1 Normal NA 947.91 2133.11 3915.11 * 0.814 11.80 * 2.724 39.01 783.41 * 334.48 0.97 1942.92 102.86 1876.1 9.32 LCR 645 LCR 645 PLS1 Normal NA * 152.33 1994.43 2016.92 * 0.814 17.24 * 2.724 20.08 524.40 * 334.48 0.93 1924.95 153.37 188.2 5.08 LCR 646 LCR 646 PLS1 Normal NA 3076.45 1702.53 5376.64 * 0.814 4.30 * 2.724 25.08 * 71.073 * 334.48 1.12 962.67 127.59 1307.4 7.75 LCR 647 LCR 647 PLS1 Normal NA 2386.2 638.55 3872.79 * 0.814 8.06 * 2.724 34.15 * 71.073 * 334.48 1.12 493.79 97.39 1434.79 9.58 LCR 648 LCR 648 PLS1 Normal NA 1560.48 638.55 3077.44 * 0.814 3.96 * 2.724 20.31 579.46 * 334.48 0.96 847.93 118.15 470.25 4.01 LCR 649 CSF 649 PLS1 Normal NA * 152.33 240.29 2303.39 * 0.814 4.38 * 2.724 18.9 1273.78 * 334.48 1.13 2087.86 145.14 949.01 8.63 CSF 650 CSF 650 PLS1 Normal NA * 152.33 834.16 1126.87 10.35 11.72 38.46 11.26 1659.79 491 0.34 1.8 1512.36 241.98 746.1 5.78 CSF 652 CSF 652 PLS1 Normal NA * 152.33 607.27 2402.49 * 0.814 5.70 * 2.724 16.56 * 71.073 * 334, 48 0.71 505.98 * 14.286 315.43 7.34 CSF 653 CSF 653 PLS1 Normal NA * 152.33 1061.09 4365.89 8.03 10.73 * 2.724 49.21 * 71.073 * 334.48 0 , 76 1128.48 153.37 519.33 11.11 CSF 654 CSF 654 PLS1 Normal NA * 152.33 586.50 2402.49 * 0.814 18.28 * 2.724 26.41 545.68 * 334.48 1, 39 2033.27 * 14.286 742.8 11.99 LCR 655 LCR 655 PLS1 Normal NA * 152.33 680.45 2836.32 * 0.814 26.89 * 2.724 35.37 1138.04 * 334.48 1.09 1092 , 1 85.72 651.62 9.28 LCR 658 LCR 658 PLS1 Normal NA * 152.33 1611.81 4397.8 * 0.814 4.08 * 2.724 29.27 500.29 * 334.48 1.07 1882, 39 * 14.286 493.1 7.78 LCR 659 LCR 659 PLS1 Normal NA 2019.85 860.92 1590.82 * 0.814 13.83 * 2.724 18.47 * 71.073 * 334.48 1.1 192.03 168.99 454.82 3.6 LCR 660 LCR 660 PLS1 Normal NA * 152.33 1668.46 2162.69 * 0.814 9.97 * 2.724 20.08 1064.37 * 334.48 1.34 1760.27 * 14.286 973, 35 6.14 LCR 661 LCR 661 PLS1 Normal NA * 152.33 2272.57 2538.23 * 0.814 14.01 * 2.724 2 4.98 789.97 * 334.48 0.95 1080.03 85.72 415.46 6.08 CSF 662 CSF 662 PLS1 Normal NA * 152.33 1546.87 2533.01 * 0.814 13.62 * 2.724 35 , 13 482.35 * 334.48 1.02 1607.18 * 14.286 588.47 19.55 LCR 663 LCR 663 PLS1 Normal NA 1359.07 1896.69 3192.94 * 0.814 6.18 * 2.724 14.12 449 , 8 * 334.48 0.87 2416.51 85.72 1321.14 4.54 CSF 665 CSF 665 PLS1 Normal NA 3145.72 2972.63 4722.06 * 0.808 13.57 * 2.749 26 466.98 * 324.366 0.96 685.06 * 14.148 399.63 5.82 CSF 666 CSF 666 PLS1 Normal NA 6246.67 1565.05 358.48 * 0.808 40.56 18.15 64.8 1925.96 * 324.366 1.32 2228 , 36 159.22 2056.72 4.86 CSF 667 CSF 667 PLS1 Normal NA * 149.188 5596.48 1854.72 6.13 9.40 * 2.749 27.09 719.23 * 324.366 1.04 4171.77 150, 55 230.38 6.36 LCR 668 LCR 668 PLS1 Normal NA * 149.188 739.01 4756.67 * 0.808 20.33 * 2.749 21.66 975.49 * 324.366 1.2 1762.48 141.53 429.86 7 , 22 LCR 670 LCR 670 PLS1 Normal NA * 149.188 2975.72 2481.11 * 0.808 13.53 * 2.749 29.93 520.28 * 324.366 1.37 1847.11 202.26 761.98 7.07 LCR 671 LCR 671 PLS1 Normal NA 1208.28 1015.45 538.64 * 0.808 22.10 * 2.749 28.29 1878.27 * 324.366 1.48 1887.66 159.22 414.85 3.13 CSF 672 CSF 672 PLS1 Normal NA * 149.188 4575.64 2898.04 * 0.808 16.46 31.04 14.17 1582.53 * 324.366 1.7 229.11 111.64 409.8 9.63 CSF 673 CSF 673 PLS1 Normal NA * 149.188 2137.94 2704.97 6.13 5.83 24.27 28.68 1048.20 * 324.366 1.45 2804.92 219.91 596.54 7.17 LCR 674 LCR 674 PLS1 Normal NA * 149.188 1044.50 2325.54 * 0.808 7.84 * 2.749 21.36 1037, 74 * 324.366 1.3 970.39 183.53 378.96 4.27 LCR 675 LCR 675 PLS1 Normal NA * 149.188 2050.31 3965.69 * 0.808 3.68 * 2.749 36.99 883.71 * 324.366 1, 07 409.52 111.64 1158.43 4.56 CSF 676 CSF 676 PLS1 Normal NA 2386.17 2609.78 2739.02 * 0.808 11.11 * 2.749 79.33 2123.34 * 324.366 1.51 869.92 * 14,148 843.18 10.76 CSF 677 CSF 677 PLS1 Normal NA 1471.25 1767.82 2085.6 * 0.808 15.65 * 2.749 18.14 1104.35 * 324.366 1.23 1623.01 154.93 217.48 4 , 6 LCR 678 LCR 678 PLS1 Normal NA * 149.188 1161.11 3011.77 * 0.808 7.05 * 2.749 60.85 1189.76 * 324.366 1.18 681.51 * 14,148 325.15 6.42 LCR 679 LCR 679 PLS1 Norm al NA * 147.076 3318.90 324.45 * 0.792 18.40 * 2.747 24.9 523.16 * 323.745 1.09 2648.89 163.47 696.28 5.48 LCR 680 LCR 680 PLS1 Normal NA 4859.55 767.56 4920.51 5.26 6.52 18.02 40.3 1.023.3 * 323.745 1.25 989.83 106.34 720.08 10.49 LCR 681 LCR 681 PLS1 Normal NA 3001.45 1313, 65 1849.1 25.51 8.30 * 2.749 24.6 2416.55 * 324.366 1.55 1916.14 183.53 478.44 3.77 LCR 682 LCR 682 PLS1 Normal NA 2243.27 1199.15 2761, 72 * 0.808 29.30 * 2.749 18.76 1027.31 * 324.366 1.07 799.42 111.64 429.86 4.82 LCR 683 LCR 683 PLS1 Normal NA * 149.188 1651.35 1677.77 * 0.808 13, 15 * 2.749 19.14 * 76.613 * 324.366 1.2 1423.36 132.09 217.48 4.22 LCR 684 LCR 684 PLS1 Normal NA 1491.49 2174.26 2158.92 * 0.808 14.28 * 2.749 48.63 * 76.613 * 324.366 0.92 402.65 88.14 255.36 12.1 LCR 685 LCR 685 PLS1 Normal NA 1451 4062.23 2827.02 * 0.808 23.13 41.45 71.49 6030.66 * 324.366 1, 16 2076.27 141.53 302.62 4.24 LCR 686 LCR 686 PLS1 Normal NA * 149.188 1131.90 3228.16 * 0.808 9.99 * 2.749 35.28 502.38 * 324.366 1.98 1380.85 88 , 14 204.27 7.56

LCR 687 LCR 687 PLS1 Normal NA * 149.188 2753.94 1492.83 * 0.808 9.00 20.27 25.07 1578.71 * 324.366 1.34 1135.61 100.38 357.83 5.82 LCR 688 LCR 688 PLS1 Normal NA * 149.188 1702.07 1436.88 * 0.808 20.92 36.83 40.07 1.044.7 * 324.366 1.49 1273.55 127.19 444.65 4.43 LCR 689 LCR 689 PLS1 Normal NA * 149.188 1015.45 4097.65 4.99 6.59 * 2.749 35.12 568.69 * 324.366 1.66 871.73 * 14,148 291.09 4.75 LCR 690 LCR 690 PLS1 Normal NA * 149.188 1416.87 3905 , 49 5.56 30.18 27.54 42.7 1093.78 * 324.366 1.74 2272.5 111.64 681.28 9.97 LCR 691 LCR 691 PLS1 Normal NA * 149.188 1624.54 2512.25 * 0.808 6.90 20.51 22.87 887.08 * 324.366 1.69 999.85 * 14,148 622.45 5.71 CSF 692 CSF 692 PLS1 Normal NA * 149.188 2877.02 2365.11 * 0.808 18.37 17.68 50.59 5826.48 * 324.366 1.68 2513.06 * 14.148 1304.97 12.44 LCR 693 LCR 693 PLS1 Normal NA 2960.28 1108.56 2032.05 5.56 25.50 * 2.749 49.65 735 , 34 * 324.366 1.36 2327.4 100.38 347.07 4.25 CSF 694 CSF 694 PLS1 Normal NA 2182.09 4258.17 1504.02 * 0.808 7.79 22.86 20.11 712.80 * 324.366 1.5 22 30.45 191.17 384.17 5.02 LCR 697 LCR 697 PLS1 Normal NA 2816.36 727.59 1431.29 * 0.808 6.03 * 2.749 22.47 599.4 * 324.366 1.24 724.18 159 , 22 816.48 13.56 LCR 698 LCR 698 PLS1 Normal NA 1793.24 2599.57 2408.55 5.26 70.21 38.24 63.3 2371.10 2637.28 0.62 1202.17 127, 42 1077.26 9.46 CSF 699 CSF 699 PLS1 Normal NA 4207.44 2473.54 3534.35 * 0.792 15.22 19.17 28.47 723.72 2320.19 0.82 2096.1 113.68 973 , 17 5.52 LCR 700 LCR 700 PLS1 Normal NA 1642.89 1036.32 5352.12 9.44 13.01 27.98 47.52 1450.51 10293.21 0.96 1728.49 283.74 1015, 27 35.81 LCR 701 LCR 701 PLS1 Normal NA 3629.18 632.39 2871.58 6.98 20.12 * 2.747 28.34 539.39 2637.28 1.2 1247.73 204.18 333.81 9 , 05 LCR 702 LCR 702 PLS1 Normal NA 3928.82 1677.21 2322.78 5.95 10.11 * 2.747 67.58 1649.97 * 323.745 1.13 1615.84 376.13 3995.43 5.9 LCR 703 CSF 703 PLS1 Normal NA 2090.42 3012.79 1453.66 * 0.808 7.17 19.57 35.79 568.69 * 324.366 1.6 1567.78 159.22 4459.79 9 CSF 704 LCR 704 PLS1 Normal NA 11636.84 1098.52 4027.82 11.58 66.85 * 2.747 48.63 2.616.3 366 1 2.13 993.86 421.52 708.21 9.29 LCR 705 LCR 705 PLS1 Normal NA 5830.13 2151.03 1416.34 * 0.792 13.48 32.93 25.13 1.038.1 2213 0.56 2010.87 213.49 344.18 6.09 LCR 706 LCR 706 PLS1 Normal NA * 147.076 3638.83 3212.96 * 0.792 17.88 31.78 19.98 1089.97 * 323.745 1.38 4334.73 204.18 864.86 9.29 LCR 707 LCR 707 PLS1 Normal NA 1431.34 2453.08 2143.59 4.92 49.27 * 2.747 22.09 1585.60 * 323.745 0.73 2485.07 184.61 461.12 5.59 LCR 709 LCR 709 PLS1 Normal NA * 149.188 2222.74 1750.75 * 0.808 13.69 * 2.749 36.76 1795.46 * 324.366 2.41 2597.27 209.44 2156.5 3.1 LCR 710 CSF 710 PLS1 Normal NA * 149.188 2041.26 2291.63 5.85 7.85 * 2.749 34.49 958.3 * 324.366 1.79 754.55 183.53 1131.61 9.28 CSF 711 CSF 711 PLS1 Normal NA 1978.16 1180.13 1466.48 * 0.792 20.99 * 2.747 41.28 1344.94 * 323.745 0.76 945.56 98.61 2977.3 6.77 LCR 712 LCR 712 PLS1 Normal NA * 149.188 1594 , 78 2591.56 * 0.808 12.72 * 2.749 24.79 1822.97 * 324.366 0.75 1977.44 * 14,148 1014.6 5.82 LCR 713 LCR 713 PLS1 Normal NA * 149.188 1621.56 5114.97 * 0.808 22.37 44.90 17 3.706.2 * 324.366 1.24 1526.57 88.14 793.31 6.24 LCR 714 LCR 714 PLS1 Normal NA 4289.43 2466.10 4126.36 * 0.808 20.29 * 2.749 33.61 917.47 * 324.366 1.16 2754.32 100.38 902.99 8.68 LCR 715 LCR 715 PLS1 Normal NA 4574.88 1074.14 3512.51 6.64 32.43 * 2.747 44.11 972.02 2213, 19 0.91 757.05 213.49 1444.48 14.98 LCR 716 LCR 716 PLS1 Normal NA * 147.076 1254.04 2658.48 8.73 31.89 * 2.747 41.4 885.32 3256.64 1, 1 1864.37 356.97 1144.66 6.28 CSF 717 CSF 717 PLS1 Normal NA 1858.22 1380.88 2467.58 8.73 23.37 40.51 52.74 1788.39 * 323.745 1.15 1899 , 71 174.27 528.89 8.12 CSF 718 CSF 718 PLS1 Normal NA 9343.19 1476.04 2009.51 * 0.808 14.91 * 2.749 23.65 777.59 * 324.366 2.07 3126.48 191, 17 1971.17 4.82 LCR 719 LCR 719 PLS1 Normal NA * 149.188 1743.90 2634.08 * 0.808 13.15 * 2.749 22.52 593.24 * 324.366 1.06 2592.94 * 14.148 1204.78 4, 18 CSF 720 CSF 720 PLS1 Normal NA 986.02 3285.53 445.64 * 0.808 28.09 * 2.749 34.17 2475.33 * 324.366 1.11 1307.9 132.09 1269.79 4.94 CSF 721 CSF 721 PLS1 Normal NA * 149,188 733 , 30 2549.07 33.07 8.25 17.20 40.07 1302.07 * 324.366 1.17 1070.25 * 14.148 487.91 6.04 LCR 722 LCR 722 PLS1 Normal NA * 149.188 1681.18 3276.62 * 0.808 8.40 * 2.749 50.12 709.60 * 324.366 1.77 296.9 * 14.148 1042.6 5.04 LCR 723 LCR 723 PLS1 Normal NA * 149.188 1812.74 1972.9 * 0.808 6.04 20, 04 35.4 1076.20 * 324.366 1.46 1650.75 * 14.148 1276.21 7.58 LCR 724 LCR 724 PLS1 Normal NA 2280.41 2208.83 2408.55 6.86 24.73 19.96 19, 07 1298.13 * 309.784 0.52 3370.01 95.83 1808.57 5.44 CSF 725 CSF 725 PLS1 Normal NA 4069.65 523.86 2175.85 * 0.808 24.22 * 2.749 53.44 2492.18 * 324.366 1.59 1552.06 191.17 1339.78 7.57 CSF 726 CSF 726 PLS1 Normal NA 1664.26 902.34 1203.14 * 0.792 6.14 * 2.747 30.94 1434.77 * 323.745 0, 93 2291.3 194.57 344.18 4.3 CSF 727 CSF 727 PLS1 Normal NA 1978.16 998.63 1171.62 * 0.792 22.10 * 2.747 18.97 1387.76 * 323.745 0.57 1142.45 113.68 107.3 4.95 LCR 728 LCR 728 PLS1 Normal NA * 147.076 3620.65 2039.53 * 0.792 25.04 * 2.747 31.44 * 72.617 2213.19 0.93 * 26.698 208.87 399.32 17.58 LCR 729 LCR 729 PLS1 Normal NA 4476.46 1177.40 792.49 4.92 43.15 17.64 23.33 2384.11 * 323.745 0.77 1888.65 98.61 1559.28 6.54 LCR 730 LCR 730 PLS1 Normal NA * 149.188 827.77 4169.43 * 0.808 4.90 * 2.749 29.81 * 76.613 * 324.366 1.11 1536.37 * 14,148 827.96 3.73 LCR 731 LCR 731 PLS1 Normal NA 1934.43 1087.68 1416, 34 * 0.792 6.50 * 2.747 28.81 * 72.617 * 323.745 0.66 837.87 98.61 1107.77 4.24 LCR 732 LCR 732 PLS1 Normal NA 1504.93 754.46 1582.75 * 0.792 44, 46 * 2.747 31.11 1187.68 * 323.745 0.72 1504.4 140.17 727.95 4.81 CSF 733 CSF 733 PLS1 Normal NA 7269.66 911.25 1896.91 5.99 12.37 25, 67 21.41 1567.27 2005.08 1.08 2264.08 159.22 986.29 4.81 LCR 734 LCR 734 PLS1 Normal NA 3724.69 2211.72 2245.16 7.68 7.81 * 2.747 26 , 71 1310.09 3660.51 1.65 1030.27 194.57 842.62 5.99 LCR 736 LCR 736 PLS1 Normal NA * 149.188 3316.62 1894.09 * 0.808 13.31 22.39 29.55 2151 , 87 * 324.366 1.5 1544.21 246.4 877.02 3.92 CSF 737 CSF 737 PLS1 Normal NA 1632.22 2070.36 3539.81 5.77 23.47 16.87 82.53 1067.66 3052.15 1.44 921.51 106.34 184.86 6.03 LCR 738 LCR 738 PLS1 Normal NA * 149.188 3378.86 2707.81 * 0.808 15.25 * 2.749 26.37 2500.62 * 324.366 1.33 2800.51 100.38 429.86 8.64 LCR 739 LCR 739 PLS1 Normal NA * 149.188 1603.71 1784.45 * 0.808 11.84 * 2.749 24.37 1305.73 * 324.366 1.25 3112.91 167.59 384.17 3.33 LCR 740 LCR 740 PLS1 Normal NA * 149.188 1743 , 90 2475.45 8.02 17.48 25.44 38.62 1409.10 * 324.366 1.3 2658.1 111.64 * 21.339 5.5 LCR 741 LCR 741 PLS1 Normal NA * 149.188 1731.94 736.6 * 0.808 13.77 * 2.749 57.65 1027.31 * 324.366 1.07 1668.62 167.59 722.09 15 LCR 742 LCR 742 PLS1 Normal NA 1430.76 6005.58 2418.85 6.13 17.30 32.20 19.3 614.90 3640.15 2.93 6565.33 226.72 4475.33 8.36 CSF 743 CSF 743 PLS1 Normal NA * 149.188 1375.53 6590.95 * 0.808 13.49 25.91 49.45 1791.53 2181.48 1.58 1928.37 183.53 267.48 13.22 CSF 745 CSF 745 PLS1 Normal NA 1132.13 5798.06 1725.78 5.95 25.76 31.40 22.9 744 , 44 2637.28 1.03 16244.26 194.57 3957.22 7.51 CSF 746 CSF 746 PLS1 Normal NA 2478.16 3920.45 2963.42 * 0.808 5.65 * 2.749 32.89 2196.88 * 324.366 1.06 22 51.46 111.64 1080.62 4.55 CSF 747 CSF 747 PLS1 Normal NA * 147.076 1458.63 3174.93 * 0.792 48.18 * 2.747 55.59 4709.15 * 323.745 0.65 3952.85 113, 68 339.01 8.49 CSF 749 CSF 749 PLS1 Normal NA 2263.67 1520.51 3359.33 6.28 36.89 19.09 25.82 1062.18 2005.08 1.15 1742.43 233.4 2091.07 5.77 LCR 750 LCR 750 PLS1 Normal NA 931.71 691.87 2920.23 * 0.792 16.39 * 2.747 30.82 2956.53 * 323.745 0.71 229.73 113.68 1236.67 7 , 27 LCR 751 LCR 751 PLS1 Normal NA 3063.8 2558.47 3621.8 5.95 14.78 * 2.747 31.61 1049.13 2320.19 0.89 2494.38 272.25 923.2 10.86 LCR 752 LCR 752 PLS1 Normal NA 1337.48 886.38 3997.55 4.92 9.26 * 2.747 34.28 964.73 2845.75 1.05 721.83 222.52 296.56 9.2 LCR 753 LCR 753 PLS1 Normal NA 3688.83 3663.09 2715.05 * 0.792 8.32 * 2.747 23.47 716.84 * 323.745 0.67 1952.93 163.47 743.6 4.94 LCR 754 LCR 754 PLS1 Normal NA 1610.91 1813.08 2515.92 7.85 14.98 * 2.747 20.18 648.75 * 323.745 1.09 1331.14 248.24 339.01 3.64 LCR 755 LCR 755 PLS1 Normal NA 5218, 29 462.35 2243.61 * 0.808 6.98 * 2.749 20.84 890.44 * 3 24.366 1.77 685.06 327.82 846.97 5.76 CSF 757 CSF 757 PLS1 Normal NA * 149.188 3229.62 2384.91 * 0.808 7.25 54.07 47.06 1949.90 * 324.366 2.11 378.68 100.38 1011.08 14.75 CSF 758 CSF 758 PLS1 Normal NA * 149.188 605.50 1666.55 * 0.808 25.31 52.47 17.06 2627.88 * 324.366 1.32 1133.74 159.22 664.69 4.45 CSF 759 CSF 759 PLS1 Normal NA 1934.43 532.72 4475.32 * 0.792 24.67 19.17 40.26 2414.54 * 323.745 1.7 1398.39 113.68 647.75 6.98 CSF 760 CSF 760 PLS1 Normal NA 2913.08 1629.33 1395.25 * 0.792 11.08 * 2.747 25.31 * 72.617 5030.98 0.79 2222.84 194.57 923.2 6.73 CSF 761 CSF 761 PLS1 Normal NA 1275.39 2853.31 1487.6 * 0.792 2.44 * 2.777 21.75 1490.00 6536.67 0.8 2569.06 174.27 622.99 11.15 CSF 762 CSF 762 PLS1 Normal NA * 149,188 2201.52 4344.74 * 0.808 12.99 * 2.749 46.57 538.33 * 324.366 1.74 2924.54 150.55 854.52 9.41 LCR 763 LCR 763 PLS1 Normal NA * 149.188 819.15 1750.75 * 0.808 19.18 * 2.749 25.54 * 76.613 * 324.366 1.26 3315.54 150.55 1175.07 4.06 LCR 764 LCR 764 PLS1 Normal NA * 149.188 2017.13 4083.3 * 0 80 8 4.64 17.68 27.27 544.37 * 324.366 2.55 2167.68 88.14 1191.61 26.06 LCR 765 LCR 765 PLS1 Normal NA * 149.188 2341.19 2314.23 * 0.808 4.43 * 2.749 21.87 2.378.9 * 324.366 1.37 1849.13 122.16 1314.5 7.11 CSF 766 CSF 766 PLS1 Normal NA 2855.4 2523.29 2575.06 * 0.792 16.64 * 2.747 29.59 1045 , 44 * 323.745 0.62 861.68 194.57 165.1 3.64 CSF 768 CSF 768 PLS1 Normal NA 4921.76 1904.19 4317.5 * 0.792 23.35 24.54 36.67 5365.09 * 323.745 0 , 71 837.87 174.27 493.17 11.68 CSF 769 CSF 769 PLS1 Normal NA 1101.74 * 13.395 2403.18 9.08 18.29 * 2.747 29.46 1256.25 * 323.745 0.79 * 26.698 231.31 * 21.526 6.92 LCR 770 LCR 770 PLS1 Normal NA 3239.02 1177.40 2333.5 7.33 9.34 * 2.747 25.77 2092.63 * 323.745 1 1564.25 174.27 465.75 4.74 CSF 771 CSF 771 PLS1 Normal NA 1198.17 1854.71 3805.23 * 0.808 27.12 42.60 58.99 1261.97 * 324.366 2.05 3169.51 * 14.148 * 21.339 21.73 CSF 772 CRL 772 PLS1 Normal NA 1248.71 870.90 2438.66 * 0.808 21.28 * 2.749 25.31 1682.45 * 324.366 1.55 801.22 * 14.148 267.48 8.36 CRL 773 CRL 773 PLS1 Normal NA * 14 9.188 2338.15 2136.36 * 0.808 5.40 21.92 17.63 461.14 * 324.366 1.49 1552.06 141.53 1426.81 8.61

LCR 774 LCR 774 PLS1 Normal NA 7072.51 3031.34 1585.24 * 0.808 28.34 * 2.749 22.87 * 76.613 * 324.366 1.18 3324.72 100.38 183.77 7.28 LCR 775 LCR 775 PLS1 Normal NA * 149.188 1207.94 2898.04 15.03 19.41 36.83 34.25 1041.23 6975.85 2.61 2190.66 400.85 419.88 6.26 LCR 776 LCR 776 PLS1 Normal NA * 149.188 1196.22 1922.23 * 0.808 18.57 * 2.749 24.22 1933.93 * 324.366 1.23 2467.97 88.14 714 4.37 LCR 777 LCR 777 PLS1 Normal NA 2603.55 875.75 2411 , 23 5.26 13.97 16.87 55.48 7377.64 2845.75 0.86 302.79 239.88 349.32 13.59 CRL 778 CRL 778 PLS1 Normal NA 1358.26 343.70 2360, 29 7.33 54.87 27.21 18.61 2054.62 4255.21 1.14 735.51 213.49 759.14 6.7 CSF 779 CSF 779 PLS1 Normal NA 2580.82 2874.06 1943.64 5.26 72.03 42.03 21.36 1438.70 3459.38 0.76 1368.91 309.41 680.24 4.26 LCR 780 LCR 780 PLS1 Normal NA 2490.17 1403.05 2774.38 8 , 03 25.20 20.33 29.03 1426.92 1997.01 0.91 1463.98 309.41 318.04 4.72 CSF 781 CSF 781 PLS1 Normal NA 7604.41 1711.08 3534.35 4, 92 31.44 23.77 36.27 779.22 * 323.745 1.49 1917.42 98.61 93 0.4 10.83 CSF 782 CSF 782 PLS1 Normal NA * 149.188 5713.11 538.64 * 0.808 10.95 47.66 22.96 1083.22 * 324.366 1.26 1706.45 150.55 891.9 5 , 78 LCR 783 LCR 783 PLS1 Normal NA * 149.188 905.48 2122.25 7 7.68 28.94 27 * 76.613 * 324.366 1.32 1571.72 226.72 1014.6 4.31 LCR 784 LCR 784 PLS1 Normal NA * 149.188 2047.29 1353.05 * 0.808 12.67 * 2.749 19.74 5.714.9 * 324.366 0.85 1353.91 88.14 587.81 6.99 LCR 785 LCR 785 PLS1 Normal NA * 149.188 1681, 18 1604.86 * 0.808 19.87 * 2.749 19.68 2530.19 * 324.366 1.98 1832.95 * 14,148 846.97 4.85 LCR 786 LCR 786 PLS1 Normal NA * 149.188 1544.25 2915.1 * 0.808 18.47 * 2.749 36.91 2921.34 * 324.366 1.04 1700.47 * 14.148 547.83 6.29 CSF 812 CSF 812 PLS1 Pancreas II 1110.21 1700.18 5671.74 5.46 8.28 829 , 35 11.29 5,550.7 6146.18 1.01 3291.27 281.53 607.1 5.61 CSF 814 CSF 814 PLS1 Pancreas II 1695.51 2356.29 3139.22 4.83 34.86 25, 57 68.72 2.620.2 * 313.835 1.15 3205.65 162.22 254.37 9.26 NL 918 NL PLS 918 Normal NA * 126.474 3001.22 7204.71 * 0.817 36.52 * 2.624 51.7 1.693 .7 * 315.94 9 0.88 1717.8 105.8 562.54 3.14 NL 919 NL PLS 919 Normal NA 867.44 2604.27 6107.1 * 0.817 36.17 * 2.624 30.99 1587.54 2904.45 0.78 1468 , 98 181.6 213.92 2.64 NL 920 NL PLS 920 Normal NA * 140.254 1828.03 9739.63 * 0.835 9.03 * 2.794 19.26 1362.41 * 328.815 0.6 2330.16 112.47 * 74.45 2.78 NL 921 NL PLS 921 Normal NA * 140.254 1511.74 4974.1 * 0.835 21.19 * 2.794 37.45 1244.08 * 328.815 0.61 1664.24 112.47 * 74.45 2.58 NL 922 NL PLS 922 Normal NA * 140.254 1847.75 1295.64 * 0.835 6.91 * 2.794 28.59 2.132.4 * 328.815 0.63 1977.85 98.38 * 74.45 2.31 NL 923 NL PLS 923 Normal NA * 140.254 1476.04 2910.31 10.7 37.38 * 2.794 34.48 2,482.6 * 328.815 0.54 1879.02 171.2 471.48 5.34 NL 924 NL PLS 924 Normal NA * 140.254 528.47 5535.97 * 0.835 5.51 21.29 31.61 1946.49 * 328.815 0.57 1588.46 125.04 275.97 5.49 NL 925 NL PLS 925 Normal NA * 140.254 812.48 5128.61 * 0.835 13.47 * 2.794 47.09 1.818.4 * 328.815 0.62 2420.97 136.53 223.36 5.05 NL 930 NL PLS 930 Normal NA * 132.958 1507.42 4137, 04 * 0.824 21.88 54.56 23.1 1428.04 * 327 , 347 0.57 1089.27 136.7 372.71 4.45 NL 931 NL PLS 931 Normal NA * 132.958 1327.46 1647.12 * 0.824 30.68 * 2.717 22.07 2249.87 * 327.347 1.06 2969.01 * 15.284 739.46 3.11 NL 932 NL PLS 932 Normal NA * 132.958 1081.60 3238.93 * 0.824 20.31 * 2.717 25.45 1870.61 * 327.347 0.55 1363.58 181.13 591.45 6.11 NL 938 NL PLS 938 Normal NA 1820.88 1281.05 1395.62 * 0.797 24.71 * 2.636 12.94 * 71.888 * 320.985 0.67 1766.41 93.06 1027.02 3, 04 NL 939 NL PLS 939 Normal NA 2796.31 1146.13 215.18 * 0.797 20.75 * 2.636 7.92 1.542.0 * 320.985 0.65 764.72 141.28 350.47 3.74 NL 940 NL PLS 940 Normal NA 1820.88 2588.22 3476.35 * 0.797 8.77 * 2.636 16.63 1734.11 * 320.985 0.51 2083.46 101.92 1032.47 0.36 NL 941 NL PLS 941 Normal NA * 131.746 666.79 2387.31 * 0.797 4.67 * 2.636 14.75 1890.62 * 320.985 0.61 1585.67 * 13.953 1908.72 4.83 NL 942 NL PLS 942 Normal NA 2520.63 271.12 930.22 10.48 14.53 24.73 * 1.372 774.99 5600.29 0.66 1209.79 367.15 * 75.36 2.27 NL 943 NL PLS 943 Normal NA * 131.746 2281.85 3175, 39 * 0.797 7.27 * 2.636 11.5 10 78.59 * 320.985 0.61 2388.42 * 13.953 * 75.36 6.96 NL 944 NL PLS 944 Normal NA * 132.958 2619.80 4199.71 6.13 45.70 * 2.717 33.59 2.731.4 2043 0.73 3285.13 204.69 1240.67 5.2 NL 945 NL PLS 945 Normal NA * 132.958 680.96 2011.04 * 0.824 8.47 * 2.717 18.01 2543.45 * 327.347 0.88 3651, 78 167.9 942.01 6.16 NL 946 NL PLS 946 Normal NA * 132.958 1512.16 4113.2 * 0.824 17.67 * 2.717 35.46 1,276.7 * 327.347 0.58 3048.96 167.9 703.63 3.92 NL 947 NL PLS 947 Normal NA * 132.958 1735.05 1777.13 11.89 20.65 19.41 35.65 2826.23 * 327.347 0.58 1903.26 210.11 652.22 7.16 NL 948 NL PLS 948 Normal NA * 132.958 2039.04 3296.72 * 0.824 11.58 * 2.717 26.41 1,165.2 * 327.347 0.52 1862.61 * 15.284 344.42 3.23 NL 949 NL PLS 949 Normal NA * 132,958 1982.00 2169.04 * 0.824 16.77 * 2.717 21.95 944.1 * 327.347 0.61 1275.94 91.71 372.71 3.05 NL 950 NL PLS 950 Normal NA 1186.44 1336 , 92 2727.96 * 0.824 21.38 * 2.717 22.62 1.520.2 * 327.347 0.55 1696.32 136.7 159.19 2.73 NL 951 NL PLS 951 Normal NA * 132.958 1341.66 1951.53 * 0.824 4.56 * 2.717 2 0.95 1257.98 * 327.347 0.6 2021.09 153.27 511.8 4.76 NL 952 NL PLS 952 Normal NA * 132.958 2105.60 2511.86 * 0.824 40.50 * 2.717 19.99 670, 68 * 327.347 0.5 3001.91 117.07 519.98 6.4 NL 964 NL PLS 964 Normal NA * 157.823 762.50 507.22 * 0.803 12.17 * 2.715 13.55 754.11 * 312.793 0.71 1201 , 22 226.7 843.87 2.84 NL 965 NL PLS 965 Normal NA 1329.61 840.27 1618.33 * 0.803 18.89 * 2.715 8.18 3641.94 * 312.793 0.71 911.4 * 14.785 510.48 2.6 NL 1160 NL PLSA 1160 Normal NA 1156 2703.73 2305.41 * 0.82 3.75 * 2.74 15.72 2019.48 * 327.917 0.71 1475.33 * 14.319 513.79 2.54 NL 1163 NL PLSA 1163 Normal NA * 132.09 1834.31 3107.95 * 0.82 11.15 * 2.74 22.96 1.722.9 * 327.917 0.68 1121.94 * 14.319 220.58 2.8 NL 1164 NL PLSA 1164 Normal NA * 132.09 498.26 2014.03 * 0.82 16.50 * 2.74 17.8 1303.51 * 327.917 0.67 1559.92 149.48 373.27 5 , 3 NL 1165 NL PLSA 1165 Normal NA * 132.09 1292.54 3363.21 5.55 8.53 * 2.74 18.42 1192.70 * 327.917 0.79 1077 126.63 305.85 4.87 NL 1166 NL PLSA 1166 Normal NA * 147,076 1057,92 2931,05 * 0,792 11,67 * 2,747 19.12 582.00 * 323.745 0.54 2015.34 98.61 1275.35 6.13 NL 1167 NL PLSA 1167 Normal NA 2198.77 1325.59 1363.62 * 0.792 10.11 16.87 20.92 906.85 * 323.745 0.46 2350.95 152.14 423.47 3.25 NL 1168 NL PLSA 1168 Normal NA 2740.52 5080.71 1535.16 * 0.792 17.83 * 2.747 21.46 1,180.1 * 323.745 0 , 35 2145.72 127.42 1239.91 3.86 NL 1169 NL PLSA 1169 Normal NA 1696.38 870.45 1566.88 6.29 13.43 * 2.747 19.63 549.2 * 323.745 0.35 1566 , 4 140.17 484.09 3.2 NL 1170 NL PLSA 1170 Normal NA 911.97 2989.92 2036.87 * 0.792 11.28 16.87 26.89 2079.94 * 323.745 0.58 2114.12 194 , 57 1447.55 5.01 NL 1171 NL PLSA 1171 Normal NA * 147.076 1439.15 1847.89 * 0.792 20.38 16.87 54.87 786.21 * 323.745 0.75 1060.73 152.14 409, 04 3.19 NL 1172 NL PLSA 1172 Normal NA * 147.076 4443.66 1813.35 6.64 8.61 * 2.747 25.09 1.233.3 * 323.745 1.05 * 26.698 127.42 930.4 0.89 NL 1173 NL PLSA 1173 Normal NA * 147.076 1531.17 3090.82 * 0.792 4.24 * 2.747 17.03 878.16 * 323.745 0.82 1026.22 127.42 418.68 4.09 NL 1174 NL PLSA 1174 Normal NA * 147,076 1708.26 1 500.81 * 0.792 14.19 * 2.747 18.14 1180.10 * 323.745 0.57 1672.01 152.14 484.09 3.76 NL 1175 NL PLSA 1175 Normal NA * 147.076 1036.32 3202.09 * 0.792 10.18 * 2.747 25.27 1.747.5 * 323.745 0.43 1419.5 127.42 812.62 2.33 NL 1176 NL PLSA 1176 Normal NA * 147.076 2365.57 1079.83 * 0.792 5.87 * 2.747 23.37 1521.74 * 323.745 0.59 1339.52 140.17 743.6 5.76 NL 1177 NL PLSA 1177 Normal NA * 147.076 865.14 839.38 * 0.792 8.14 * 2.747 25.13 1356, 59 * 323.745 0.53 1502.27 184.61 1313.61 5.84 NL 1178 NL PLSA 1178 Normal NA * 147.076 609.25 2804.06 * 0.792 11.80 * 2.747 21.27 1.650.0 * 323.745 0, 49 1466.11 140.17 951.87 2.77 NL 1179 NL PLSA 1179 Normal NA 2490.17 2467.69 2111.56 * 0.792 29.15 * 2.747 28.68 1164.99 * 323.745 0.5 1506.53 213.49 502.19 4.16 NL 1180 NL PLSA 1180 Normal NA * 147.076 568.31 4160.08 * 0.792 6.71 * 2.747 30.48 1290.81 * 323.745 0.72 1986.32 174.27 905, 12 4.6 NL 1181 NL PLSA 1181 Normal NA * 147.076 1196.52 1778.84 * 0.792 4.72 * 2.747 32.65 5.319.1 * 323.745 0.45 1615.84 * 14.348 307.37 3.71 NL 1182 NL P LSA 1182 Normal NA * 147,076 1620.89 2143.59 * 0.792 16.92 * 2.747 20.48 993.94 * 323.745 0.95 798.35 140.17 1435.28 2.5 NL 1183 NL PLSA 1183 Normal NA * 147.076 1756.35 1962.27 4.92 5.72 * 2.747 27.55 1796.59 * 323.745 0.53 2410.89 174.27 474.95 3.96 NL 1184 NL PLSA 1184 Normal NA 2108.4 1624, 93 2087.48 * 0.797 8.86 * 2.636 16.28 1288.80 * 320.985 0.6 2671.94 148.44 458.54 2.67 NL 1185 NL PLSA 1185 Normal NA 1049.45 1921.46 1246.47 * 0.797 18.78 * 2.636 15.17 1139.44 * 320.985 0.78 1574.04 118.51 2455.19 4.34 NL 1186 NL PLSA 1186 Normal NA 2542.11 720.83 1280.39 * 0.777 13, 36 * 2.636 11.2 1377.90 * 320.985 0.77 2192.72 83.72 885.23 5.01 NL 1187 NL PLSA 1187 Normal NA 938.03 653.27 2419.08 * 0.797 2.70 * 2.636 11 , 9 1062.12 * 320.985 0.67 1388.52 93.06 491.56 2.35 NL 1188 NL PLSA 1188 Normal NA * 131.746 693.81 368.76 8.65 3.63 * 2.636 6.95 1323, 14 * 320.985 0.72 8995.68 175.42 1122.87 2.91 NL 1189 NL PLSA 1189 Normal NA 1187.44 1442.91 2692.32 * 0.777 17.57 * 2.636 14.05 2876.84 * 320.985 0.7 3698.47 133.92 59 1.62 3.32 NL 1190 NL PLSA 1190 Normal NA 6007.15 1031.42 3866.16 * 0.777 13.87 * 2.636 11.2 1.444.8 * 320.985 0.72 1983.34 * 13.953 * 75.36 2 , 86 NL 1191 NL PLSA 1191 Normal NA 1083.51 1496.85 2550.98 * 0.797 14.70 * 2.636 15.6 * 71.888 * 320.985 0.56 1731.35 118.51 350.47 3.54 NL 1192 NL PLSA 1192 Normal NA 943.52 666.79 1884.11 * 0.797 7.55 * 2.636 10.57 529.46 * 320.985 0.64 1898.17 162.23 458.54 6.56 NL 1193 NL PLSA 1193 Normal NA 1551.35 1409.19 791.31 * 0.797 9.04 * 2.636 8.9 824.15 * 320.985 0.62 1072.11 162.23 * 75.36 3.72 NL 1194 NL PLSA 1194 Normal NA 4548.56 1692.34 1066.68 * 0.797 12.97 * 2.636 13.7 3454.35 * 320.985 0.63 1183.93 * 13.953 775.11 3.48 NL 1195 NL PLSA 1195 Normal NA 2218.22 2035.99 2442, 97 6.19 27.55 * 2.636 13.86 3719.63 * 320.985 0.77 3042.25 83.72 244.16 5.04 NL 1196 NL PLSA 1196 Normal NA 862.07 1817.02 3314.54 * 0.777 5, 38 * 2,636 16.77 * 71.888 * 320.985 0.62 1842.48 93.06 723.4 6.75 NL 1197 NL PLSA 1197 Normal NA 1926.54 504.57 3064.56 8.87 13.19 * 2.636 11 .9 2321.80 * 320.985 0.7 2 1434.8 110.38 * 75.36 3.93

NL 1198 NL PLSA 1198 Normal NA 1939.86 673.54 1863.22 * 0.797 14.48 * 2.636 9.88 * 71.888 * 320.985 0.59 1745.95 * 13.953 1158.97 2.93 NL 1199 NL PLSA 1199 Normal NA 830.05 1631.68 1560.13 * 0.797 5.99 * 2.636 7.72 553.40 * 320.985 0.75 986.38 93.06 * 75.36 2.26 NL 1200 NL PLSA 1200 Normal NA * 131.746 2002.31 1947.04 * 0.797 5.10 * 2.636 9.62 2037.41 * 320.985 0.97 2552.3 101.92 775.11 2.71 NL 1201 NL PLSA 1201 Normal NA 1906.61 2362.66 1893 , 62 * 0.797 4.67 * 2.636 22.39 498.67 * 320.985 0.73 1854.2 * 13.953 627.72 2.96 NL 1202 NL PLSA 1202 Normal NA 1187.44 1031.42 1829.11 * 0.777 5 , 98 * 2,636 * 1,372 1178.52 * 320.985 0.65 1221.29 83.72 458.54 3.61 NL 1203 NL PLSA 1203 Normal NA 3746.81 254.19 1431.93 5.97 27.42 16, 07 12.33 741.72 * 320.985 0.59 1218.41 110.38 723.4 2.45 NL 1204 NL PLSA 1204 Normal NA 2259.79 1742.89 1776.26 * 0.797 20.13 * 2.636 14.48 1372.12 * 320.985 0.64 2972.72 83.72 591.62 2.27 NL 1205 NL PLSA 1205 Normal NA * 131.746 1496.85 1472.04 * 0.797 6.71 * 2.636 9.17 1.661.5 2488 0 , 6 977.82 148, 44 379.01 3.32 NL 1206 NL PLSA 1206 Normal NA 1293.86 599.21 2991.82 * 0.797 24.10 * 2.636 10.69 1288.80 * 320.985 0.5 2433.04 133.92 458.54 3.22 NL 1207 NL PLSA 1207 Normal NA * 131.746 741.10 2623.48 4.89 19.88 * 2.636 15.63 948.29 * 320.985 0.46 1112.2 148.44 310.33 4.27 NL 1208 NL PLSA 1208 Normal NA * 131.746 909.92 1538.03 * 0.777 16.77 * 2.636 12.98 648.67 * 320.985 0.55 1524.66 83.72 255.81 0.94 NL 1209 NL PLSA 1209 Normal NA 2442.28 734.35 1298.3 * 0.797 14.38 * 2.636 13.44 811.15 * 320.985 0.61 1452.18 * 13.953 244.16 3.07 NL 1210 NL PLSA 1210 Normal NA * 131.746 1847, 34 3912.24 * 0.797 6.55 * 2.636 13.34 1,100.6 * 320.985 0.68 1915.78 * 13.953 676.47 5.36 NL 1211 NL PLSA 1211 Normal NA 1729.76 1065.16 3232.16 * 0.797 36.32 17.59 10.45 762.16 * 320.985 0.89 2039.26 * 13.953 * 75.36 3.83 NL 1212 NL PLSA 1212 Normal NA 2238.98 855.91 1874.61 * 0.797 5.44 * 2.636 7.17 570.27 * 320.985 0.47 1813.2 * 13.953 * 75.36 3.5 NL 1213 NL PLSA 1213 Normal NA * 131.746 795.13 2070.08 * 0.797 10.52 * 2.636 14.11 832 , 0 * 32 0.985 0.65 1186.8 83.72 1107.21 5.04 NL 1214 NL PLSA 1214 Normal NA 889 2177.45 1495.82 * 0.797 17.30 * 2.636 15.54 2312.08 * 320.985 0.56 1839.55 * 13.953 743.02 5.66 NL 1215 NL PLSA 1215 Normal NA * 131.746 430.18 500.32 * 0.797 6.69 * 2.636 * 1.372 706.22 2283.48 0.73 1072.11 200.36 350.47 3 , 8 NL 1216 NL PLSA 1216 Normal NA * 131.746 687.06 1619.3 * 0.797 2.78 * 2.636 12.61 2.187 * 320.985 0.58 1492.75 148.44 641.85 4.56 NL 1217 NL PLSA 1217 Normal NA * 131.746 1968.62 3179.59 * 0.797 10.02 * 2.636 12.67 921.6 * 320.985 0.6 1959.83 * 13.953 648.85 3.69 NL 1218 NL PLSA 1218 Normal NA 1032, 52 1813.65 511.47 * 0.797 33.06 * 2.636 14.36 2221.81 * 320.985 0.53 1783.95 93.06 1496.7 3.51 NL 1219 NL PLSA 1219 Normal NA 3385.48 1088.78 4256.2 5.1 7.50 * 2.636 13.44 764.7 * 320.985 1.36 3723.11 * 13.953 2444.93 3.96 NL 1220 NL PLSA 1220 Normal NA 2855.32 1142.76 2023.78 * 0.797 4.77 * 2.636 12.81 980.54 * 320.985 0.48 1865.92 * 13.953 * 75.36 5.81 NL 1221 NL PLSA 1221 Normal NA * 131.746 1267.56 967.97 * 0.777 12.75 * 2, 636 10.37 916.26 * 320.985 0.65 2936.5 * 13.953 330.73 3.45 NL 1222 NL PLSA 1222 Normal NA * 133.814 239.05 834.98 5.77 13.81 * 2.745 10.39 655 , 04 2573.12 0.74 1362.87 243.51 548.95 3.06 NL 1223 NL PLSA 1223 Normal NA * 133.814 1485.55 2541.95 * 0.83 5.04 * 2.745 18.88 1028.88 * 328.934 0.71 1668.16 84.68 873.8 3.95 NL 1224 NL PLSA 1224 Normal NA * 133.814 2008.83 956.88 * 0.83 27.53 * 2.745 18.03 811.69 * 328.934 0 , 65 987.14 150.94 463.08 6.1 NL 1225 NL PLSA 1225 Normal NA * 133.814 518.99 346.47 6.84 7.90 * 2.745 10.33 587.67 2124.39 0.63 864 , 15 218.53 406.53 2.12 NL 1226 NL PLSA 1226 Normal NA * 131.746 1267.56 2058.49 5.32 20.63 * 2.636 15.6 769.86 * 320.985 0.49 1510.15 * 13.953 662.74 6.68 NL 1227 NL PLSA 1227 Normal NA * 131.746 2002.31 2814.64 * 0.777 11.43 * 2.636 15.66 1918.54 * 320.985 0.85 618.02 141.28 289.19 4, 9 NL 1228 NL PLSA 1228 Normal NA * 131.746 1742.89 1096.37 * 0.797 5.17 * 2.636 15.02 1477.04 * 320.985 0.68 1376.96 83.72 523.43 2.2 NL 1229 NL PLSA 1229 Normal NA * 131,746 1274,31 1470,21 5.1 7.05 * 2.636 * 1.372 913.60 * 320.985 0.71 1457.97 148.44 1054.11 2.59 NL 1230 NL PLSA 1230 Normal NA * 131.746 1409.19 4055.66 4.89 5, 80 * 2,636 15.6 1,268.9 * 320,985 0.73 1684.67 235 406.44 4.9 NL 1231 NL PLSA 1231 Normal NA * 131.746 1840.60 2186.61 * 0.797 9.22 * 2.636 10.29 1195 , 35 * 320.985 0.69 1000.65 93.06 491.56 4.52 NL 1232 NL PLSA 1232 Normal NA * 131.746 805.26 2716.7 * 0.797 7.71 * 2.636 10.29 2062.63 * 320.985 0 , 64 1731.35 * 13.953 775.11 5.46 NL 1233 NL PLSA 1233 Normal NA 960.04 423.41 1314.44 * 0.797 8.95 * 2.636 * 1.372 1530.11 * 320.985 0.58 1608.93 118 , 51 1401.44 2.86 NL 1234 NL PLSA 1234 Normal NA * 131.746 3062.75 262.53 * 0.797 11.00 * 2.636 14.75 932.2 * 320.985 0.57 1195.42 * 13.953 569.38 4 , 3 NL 1235 NL PLSA 1235 Normal NA 1246.26 1479.99 2907.08 * 0.797 12.14 * 2.636 14.29 2.184.4 * 320.985 0.54 1469.56 83.72 867.41 3.68 NL 1236 NL PLSA 1236 Normal NA * 131.746 896.42 2044.98 * 0.777 4.19 * 2.636 9.44 1.172.9 * 320.985 0.48 1014.93 110.38 340.67 2.44 NL 1237 NL PLSA 1237 Normal NA *1 31.746 494.43 1634.14 * 0.797 3.45 * 2.636 11.08 929.57 * 320.985 0.65 1842.48 101.92 * 75.36 3.9 NL 1238 NL PLSA 1238 Normal NA 6559.32 1381, 41 4319.23 * 0.83 25.35 17.59 23.91 1303.10 * 328.934 0.71 2038.23 98.45 296.89 3.46 NL 1239 NL PLSA 1239 Normal NA * 133.814 455.65 2856.7 * 0.83 19.86 * 2.745 15.46 1222.02 * 328.934 0.65 1374.4 110.68 620.4 3.59 NL 1240 NL PLSA 1240 Normal NA 1617.53 1251.50 1922.53 * 0.83 15.87 * 2.745 15.8 * 74.812 * 328.934 0.83 1293.85 110.68 878.24 3.1 NL 1241 NL PLSA 1241 Normal NA * 133.814 819.04 4202.93 * 0.83 43.72 * 2.745 21.26 1569.50 * 328.934 0.69 2302.78 * 14,113 689.1 4.33 NL 1242 NL PLSA 1242 Normal NA 4580.53 493.63 769.42 * 0.83 8.91 * 2.745 17.46 551 , 85 * 328,934 0.68 1270.91 121.83 674.59 2.82 NL 1243 NL PLSA 1243 Normal NA * 133.814 1345.65 2487.95 * 0.83 9.94 * 2.745 11.15 541.67 * 328.934 0.58 1612.53 167.92 693.92 3.04 NL 1244 NL PLSA 1244 Normal NA * 133.814 4050.72 4492.93 * 0.83 13.24 * 2.745 17.22 * 74.812 * 328.934 0.95 1907, 18 * 14,113 774.1 3.04 NL 1245 NL PLS A 1245 Normal NA * 133.814 2136.94 1456.73 * 0.83 5.81 * 2.745 17.11 825.2 * 328.934 0.73 1437.96 84.68 559.34 3.5 NL 1246 NL PLSA 1246 Normal NA * 133.814 137.04 109.44 6.84 10.15 * 2.745 8.9 670.74 2349.40 0.64 418.28 198.16 185.72 4.25 NL 1247 NL PLSA 1247 Normal NA * 133.814 1514, 87 4386.63 6.04 22.80 * 2.745 15.93 1037.28 * 328.934 0.76 1709.26 84.68 1103.62 2.44 NL 1248 NL PLSA 1248 Normal NA * 133.814 1287.19 3297.8 * 0.83 5.83 * 2.745 22.04 750.0 * 328.934 0.83 1583.31 * 14.113 1012.2 5.03 NL 1249 NL PLSA 1249 Normal NA * 131.746 1523.82 1549.07 * 0.777 3, 80 * 2,636 9.79 452.02 * 320.985 0.85 1106.47 * 13.953 613.41 5.13 NL 1250 NL PLSA 1250 Normal NA 1608.13 1692.34 1161.37 10.25 11.68 * 2.636 19 , 47 2106.94 * 320.985 0.63 2927.45 126.34 627.72 3.75 NL 1251 NL PLSA 1251 Normal NA 4565.36 3140.14 937.07 7.74 20.79 16.07 12.19 782.72 * 320.985 0.58 2942.54 141.28 330.73 2.93 NL 1252 NL PLSA 1252 Normal NA * 131.746 1274.31 2176.86 5.21 5.92 * 2.636 9.92 1139.44 * 320.985 0.59 1527.56 155.41 546.66 2.6 NL 1253 NL PLSA 1253 Normal NA * 131.746 663.41 653.72 * 0.777 10.44 * 2.636 * 1.372 579.96 3208.81 0.76 795.86 235 569.38 2.64 NL 1254 NL PLSA 1254 Normal NA 2301 , 56 866.04 704.74 5.1 17.80 * 2.636 12.54 1328.89 * 320.985 0.56 1140.87 93.06 * 75.36 3.88 NL 1255 NL PLSA 1255 Normal NA 798.94 1131, 04 NA 20.25 5.11 18.60 NA * 71.628 * 322.249 0.67 1024.11 83.39 534.89 3.89 NL 1256 NL PLSA 1256 Normal NA * 133,157 320.93 NA * 0.803 8.10 * 2.713 NA 442.27 * 322.249 0.62 1553.5 87.56 523.64 8.2 NL 1257 NL PLSA 1257 Normal NA * 133.157 2931.23 NA 11.54 9.95 * 2.713 NA 521.24 * 322.249 0 , 62 1172.89 * 13.899 267.83 1.94 NL 1258 NL PLSA 1258 Normal NA * 133,157 3199.54 NA * 0.803 6.16 21.26 NA * 71.628 * 322.249 0.52 1052.11 91.61 756, 76 4.55 NL 1259 NL PLSA 1259 Normal NA * 133.1557 1527.60 NA * 0.803 8.54 * 2.713 NA 1276.52 * 322.249 0.58 1002.16 99.4 398.26 8.74 NL 1260 NL PLSA 1260 Normal NA * 133,157 1313.48 NA * 0.803 4.51 * 2.713 NA 899.38 * 322.249 0.67 1445.25 * 13.899 640.06 6.27 NL 1291 NL PLSA 1291 Normal NA * 127.9 73 1148.45 669.1 * 0.805 28.41 * 2.369 16.47 781.59 * 322.135 0.89 757.07 164.29 869.3 4.17 NL 1292 NL PLSA 1292 Normal NA * 127.973 1091.92 1624 , 24 * 0.805 16.31 * 2.369 15.95 893.91 * 322.135 0.61 1710.47 121.5 414.21 3.46 NL 1293 NL PLSA 1293 Normal NA * 127.973 807.71 1173.66 10.81 3.84 * 2.369 16.47 1091.44 * 322.135 0.64 1391.7 237.61 212.09 4.93 NL 1294 NL PLSA 1294 Normal NA 939.65 1555.73 1993.82 7.16 27.18 * 2.369 17.37 * 75.427 * 322.135 0.75 1476.01 224.92 119.92 6.7 NL 1295 NL PLSA 1295 Normal NA * 133.814 819.04 999.57 * 0.83 6.91 * 2.745 10.33 808 , 99 * 328,934 0,59 1091,47 150,94 1168,48 2,6 NL 1296 NL PLSA 1296 Normal NA * 133,814 2242,20 1085,1 * 0,83 3,52 * 2,745 17.88 * 74,812 * 328,934 0, 64 557.19 104.72 371.26 2.97 NL 1297 NL PLSA 1297 Normal NA * 133.814 1102.54 395.42 * 0.83 4.33 * 2.745 14.58 * 74.812 * 328.934 0.59 989.95 * 14,113 296.89 3.88 NL 1298 NL PLSA 1298 Normal NA * 133.814 1635.58 2718.22 * 0.83 7.53 * 2.745 17.99 * 74.812 * 328.934 0.68 1374.4 141.81 829 4.51 NL 1299 NL PLSA 1299 No extension NA * 133.814 1121.94 2242.48 5.77 9.13 * 2.745 14.21 950.98 * 328.934 0.61 1208 159.62 986.77 3.61 NL 1300 NL PLSA 1300 Normal NA * 133.814 569, 81 1962.18 10.07 12.10 23.67 18.48 782.11 * 328.934 0.58 1111.3 183.56 698.72 2.62 NL 1301 NL PLSA 1301 Normal NA * 133.814 1769.59 2858, 71 * 0.83 15.10 * 2.745 17.73 * 74.812 * 328.934 0.6 797.6 110.68 635.33 0.25 NL 1302 NL PLSA 1302 Normal NA 3161.55 925.10 1137.7 * 0.83 5.83 * 2.745 16.01 665.50 * 328.934 0.67 880.85 167.92 645.22 1.74 NL 1303 NL PLSA 1303 Normal NA 925.88 557.09 951.06 * 0.83 19, 36 * 2.745 16.55 739.38 * 328.934 0.68 3049.46 98.45 334.79 2.33 NL 1304 NL PLSA 1304 Normal NA * 133.814 364.20 1517.68 * 0.83 23.27 * 2.745 10, 86 * 74,812 * 328,934 0.69 2122.06 183.56 527.94 4.75 NL 1305 NL PLSA 1305 Normal NA * 133.814 6012.14 1999.88 * 0.83 20.48 32.36 19.91 1721, 15 * 328.934 0.62 2164.09 98.45 277.3 2.87 NL 1306 NL PLSA 1306 Normal NA * 133.814 430.37 1661.49 6.3 6.11 * 2.745 12.25 647.2 * 328.934 0 6 1,732.79 132.15 468.61 3.69

NL 1307 NL PLSA 1307 Normal NA * 133.814 2674.36 2282.33 6.3 5.57 * 2.745 14.03 458.8 * 328.934 0.64 2152.07 110.68 615.39 3.65 NL 1308 NL PLSA 1308 Normal NA * 133.814 1329.41 2354.13 * 0.83 9.86 * 2.745 15.93 1.545.5 * 328.934 0.68 3512.15 84.68 * 20.74 2.43 NL 1309 NL PLSA 1309 Normal NA * 133,814 1257.99 1553.1 * 0.83 6.78 18.70 15.03 508.79 * 328.934 0.65 1618.37 98.45 283.88 3.5 NL 1310 NL PLSA 1310 Normal NA * 133.814 3570.40 982.1 5.24 4.28 * 2.745 15.72 * 74.812 2721.62 1 2549.09 211.92 5090.66 3.06 NL 1311 NL PLSA 1311 Normal NA * 133.814 1661.71 3374, 57 4.98 4.46 * 2.745 11.43 1482.72 * 328.934 0.79 3844.34 141.81 579.94 2.63 NL 1312 NL PLSA 1312 Normal NA * 133.814 1576.83 1689.12 5.24 7.68 * 2.745 20.32 * 74.812 * 328.934 0.57 1513.39 110.68 659.96 3.58 NL 1313 NL PLSA 1313 Normal NA * 133.814 2992.19 2101.18 * 0.83 13.65 * 2.745 10.92 471.22 * 328.934 1.11 1472.74 198.16 574.82 4.13 NL 1314 NL PLSA 1314 Normal NA * 133.814 411.44 1600.37 5.24 9.87 * 2.745 18.48 928.90 * 328.934 0.79 * 28.66 159.62 33 4.79 2.5 NL 1315 NL PLSA 1315 Normal NA * 133.814 1423.69 1833.4 * 0.83 18.63 * 2.745 14.98 * 74.812 * 328.934 0.57 864.15 98.45 564.52 6 , 98 NL 1316 NL PLSA 1316 Normal NA * 133.814 9079.93 1663.46 * 0.83 7.78 * 2.745 12.46 639.40 * 328.934 0.58 2748.64 121.83 517.33 * 0.004 NL 1317 NL PLSA 1317 Normal NA * 133,814 480.96 2206.63 5.77 3.90 * 2.745 15.5 582.53 * 328.934 0.71 1316.82 84.68 640.29 1.93 NL 1318 NL PLSA 1318 Normal NA 1380 , 96 1063.76 2300.28 * 0.83 10.35 * 2.745 9.7 686.49 * 328.934 0.58 1600.84 * 14,113 590.15 2.37 NL 1319 NL PLSA 1319 Normal NA * 133.814 1012, 11 1574.76 * 0.83 24.78 * 2.745 15.29 556.95 * 328.934 0.65 1709.26 159.62 689.1 4.29 NL 1320 NL PLSA 1320 Normal NA * 133.814 684.56 642, 66 * 0.83 6.29 * 2.745 10.08 702.29 * 328.934 0.73 1762.24 171.94 559.34 4.62 NL 1321 NL PLSA 1321 Normal NA * 133.814 1782.68 1215.76 * 0 , 83 4.27 * 2.745 16.38 731.41 * 328.934 0.7 1898.28 121.83 429.46 5.03 NL 1322 NL PLSA 1322 Normal NA * 133.814 909.01 2051.51 * 0.83 8 , 55 * 2.745 14.49 * 74.812 * 328.934 0.58 9 03.15 110.68 429.46 5.18 NL 1323 NL PLSA 1323 Normal NA * 133.814 960.52 1184.51 5.77 17.50 17.59 14.62 697.0 * 328.934 0.67 736.94 183.56 703 , 51 4.28 NL 1324 NL PLSA 1324 Normal NA * 133.814 774.14 1446.91 * 0.83 10.30 * 2.745 9.83 * 74.812 * 328.934 0.86 1562.89 183.56 640.29 4, 17 NL 1325 NL PLSA 1325 Normal NA * 133.814 1021.79 440.77 * 0.83 6.88 * 2.745 6.8 * 74.812 3894 0.72 864.15 211.92 322.33 4.08 NL 1326 NL PLSA 1326 Normal NA * 133,814 1989.13 2234.51 7.24 10.85 * 2.745 20.05 1.690.6 * 328.934 0.62 981.52 190.98 708.29 7.38 NL 1327 NL PLSA 1327 Normal NA * 133.814 1426.95 3025.62 5.24 9.30 * 2.745 14.4 * 74.812 * 328.934 0.64 2116.06 218.53 463.08 5.41 NL 1328 NL PLSA 1328 Normal NA 868.85 1123.64 2814,03 * 0.83 13.05 * 2.72 23.08 904.2 * 334.715 0.96 891.87 * 14.229 340.04 6.47 NL 1329 NL PLSA 1329 Normal NA 1105.39 1595.10 1527 , 06 * 0.83 6.39 * 2.72 17.62 490.87 * 334.715 0.84 1540.42 * 14.229 264.56 3.01 NL 1330 NL PLSA 1330 Normal NA * 139.248 543.66 2457.33 * 0 , 83 19.68 * 2.72 11.02 1.325.6 * 334.715 0.69 1254.56 * 14.229 * 76.98 2.74 NL 1331 NL PLSA 1331 Normal NA 1961.52 1055.16 2847.87 * 0.83 7.45 * 2.72 9.87 1876.89 * 334.715 1.07 653.06 136.77 * 76.98 4.96 NL 1332 NL PLSA 1332 Normal NA 1387.15 802.46 2911.65 * 0.83 21.31 * 2.72 19.33 1308.48 * 334.715 1.06 1436.53 127.8 450.18 5.25 NL 1333 NL PLSA 1333 Normal NA * 139.248 995.35 1757.05 * 0.83 17.84 * 2.72 14.53 1487.04 * 334.715 0, 64 1824.74 * 14.229 365.71 2.7 NL 1334 NL PLSA 1334 Normal NA * 139.248 5597.97 2431.68 * 0.83 5.04 51.33 13.28 1434.80 * 334.715 0.94 1466, 17 97.32 666.65 4.78 NL 1335 NL PLSA 1335 Normal NA * 139.248 1183.68 3344.72 * 0.83 19.72 * 2.72 17.79 1589.56 * 334.715 0.85 1528.03 * 14.229 * 76.98 4.93 NL 1336 NL PLSA 1336 Normal NA * 139.248 1832.90 2768.28 * 0.83 8.37 * 2.72 20.05 1.007.2 * 334.715 0.75 2311.82 108.22 495.47 4.87 NL 1337 NL PLSA 1337 Normal NA * 139.248 3185.75 2203.57 * 0.83 8.30 * 2.72 20.12 * 74.234 * 334.715 1.02 1397.08 85.37 581, 18 5.93 NL 1338 NL PLSA 1338 Normal NA 1488.89 1940.61 2676.93 * 0.83 11.19 * 2.72 23 , 79 818.91 * 334.715 0.77 2013.74 108.22 1431.37 5.91 NL 1339 NL PLSA 1339 Normal NA 1521.06 288.06 2129.13 * 0.83 18.85 * 2.72 14 , 81 966.3 * 334.715 0.82 1915.26 91.5 * 76.98 2.97 NL 1340 NL PLSA 1340 Normal NA 2494.9 1115.08 2849.86 * 0.83 16.76 * 2.72 14.15 598.55 * 334.715 0.8 2815.16 * 14.229 560.29 4.14 NL 1341 NL PLSA 1341 Normal NA * 139.248 978.28 1273.69 * 0.83 5.46 * 2.72 11, 28 * 74,234 * 334,715 0.68 976.67 108.22 * 76.98 2.31 NL 1342 NL PLSA 1342 Normal NA 2913.77 751.61 2565.99 8.12 9.13 39.14 12.92 1023 , 56 * 334.715 0.82 998.51 97.32 414.89 2.75 NL 1343 NL PLSA 1343 Normal NA 953.69 1824.17 2504.71 * 0.83 2.89 * 2.72 16.43 1619 , 08 * 334,715 0.83 1607.44 * 14.229 * 76.98 2.92 NL 1344 NL PLSA 1344 Normal NA 953.69 521.29 3181.8 * 0.83 15.56 * 2.72 16.95 1771 , 11 * 334,715 0.69 1520.6 85.37 632.06 3.85 NL 1345 NL PLSA 1345 Normal NA 1540.44 1873.62 3322.56 * 0.83 22.90 * 2.72 17.58 1.317.0 * 334.715 0.63 2322.08 * 14.229 402.84 2.9 NL 1346 NL PLSA 1346 Normal NA * 139.248 1545.97 3658.21 * 0.83 16.98 * 2, 72 20.21 6472.69 * 334.715 0.77 3169.72 * 14.229 * 76.98 3.16 NL 1347 NL PLSA 1347 Normal NA * 139.248 2532.95 1071.63 * 0.83 9.19 * 2.72 16.25 1699.25 * 334.715 0.97 1722.08 190.43 779.92 6.3 NL 1348 NL PLSA 1348 Normal NA 1093.52 859.06 2635.29 * 0.83 9.29 * 2.72 14.07 760.98 * 334.715 0.83 1550.34 118.32 326.89 2.25 NL 1349 NL PLSA 1349 Normal NA 908.19 873.23 2376.5 * 0.83 33.20 * 2.72 15.33 952.8 * 334.715 0.77 1890.08 * 14.229 285.98 3.3 NL 1350 NL PLSA 1350 Normal NA 2612.85 1206.58 1793.9 5.78 10.60 * 2.72 14, 4 1440.59 * 334.715 0.99 1054.4 97.32 738.33 6.95 NL 1351 NL PLSA 1351 Normal NA 3262.18 462.69 1606.14 * 0.83 6.48 * 2.72 9, 75 1789.16 * 334.715 0.72 1525.55 102.88 489.91 3.96 NL 1352 NL PLSA 1352 Normal NA 874.44 1476.70 3268.21 * 0.83 9.86 22.56 11.38 * 74.234 * 334.715 0.85 2204.35 85.37 908.03 5.66 NL 1353 NL PLSA 1353 Normal NA 1751.06 610.91 2907.67 * 0.83 12.41 * 2.72 11.59 2282 , 90 * 334,715 1.5 1384.76 97.32 738.33 6.6 NL 1354 NL PLSA 1354 Normal NA * 139.248 819.43 1185.84 * 0.83 9.85 * 2, 72 14.57 1,111.7 * 334.715 0.88 1654.72 85.37 * 76.98 3.98 NL 1355 NL PLSA 1355 Normal NA 2349.46 1551.75 2152.62 * 0.83 10.64 * 2 , 72 18.13 575.79 * 334.715 0.89 1849.84 85.37 271.78 6.35 NL 1356 NL PLSA 1356 Normal NA * 139.248 2391.56 1774.5 * 0.83 3.54 * 2, 72 19 * 74.234 * 334.715 0.82 508.8 145.32 606.85 5.54 NL 1357 NL PLSA 1357 Normal NA 1081.68 2098.26 1891.05 * 0.83 5.62 * 2.72 15, 56 923.07 * 334.715 0.74 597.74 145.32 306.73 4.3 NL 1358 NL PLSA 1358 Normal NA 3930.57 802.46 1519.36 13.49 10.55 * 2.72 18.61 1161.81 * 334.715 0.71 1565.22 * 14.229 249.86 4.81 NL 1359 NL PLSA 1359 Normal NA 2135.62 2107.04 1275.6 5.32 7.50 * 2.72 12.56 888, 2 * 334,715 0.75 1446.41 108.22 352.97 8.08 NL 1360 NL PLSA 1360 Normal NA 1201.53 1035.21 3831.1 * 0.83 11.56 * 2.72 16.73 782, 0 * 334,715 0.76 1463.7 * 14.229 916.58 3.26 NL 1361 NL PLSA 1361 Normal NA * 139.248 1212.31 2867.79 * 0.83 16.44 * 2.72 16.55 535.6 * 334.715 0.54 1654.72 85.37 983.75 5.26 NL 1362 NL PLSA 1362 Normal NA 5162.77 1763.16 2062.63 * 0.83 27.60 16 , 61 17.58 1,181.4 * 334,715 0.66 850.77 97.32 234.78 4.85 NL 1363 NL PLSA 1363 Normal NA 1579.4 1882.35 2117.38 * 0.83 16.99 * 2.72 18, 9 1741.10 * 334.715 0.66 2003.62 85.37 829.51 4.95 NL 1364 NL PLSA 1364 Normal NA 1954.63 1006.73 2595.68 * 0.83 8.62 31.74 14.28 1139.50 * 334.715 0.67 633.82 85.37 * 76.98 3.52 NL 1365 NL PLSA 1365 Normal NA * 139.248 3233.53 1394.38 5.55 4.44 * 2.72 13.51 824 , 21 * 334,715 0.6 1667.18 * 14.229 420.86 4.67 NL 1366 NL PLSA 1366 Normal NA * 139.248 1737.04 1963.1 * 0.83 19.38 20.87 14.69 1018.08 * 334.715 0.65 1789.63 97.32 365.71 5.92 NL 1367 NL PLSA 1367 Normal NA 1906.62 1252.42 1782.26 * 0.83 26.55 17.47 13.68 1240.43 * 334.715 0, 67 1940.47 97.32 299.88 5.43 NL 1368 NL PLSA 1368 Normal NA 1592.44 983.97 1525.14 * 0.83 5.56 * 2.72 14.97 560.68 * 334.715 1, 05 1059.26 * 14.229 484.32 5.82 NL 1369 NL PLSA 1369 Normal NA 841.02 822.26 3449.63 * 0.83 5.14 * 2.72 17.55 2.609.7 * 334.715 0.92 3159, 05 85.37 326.89 4.92 NL 1370 NL PLSA 1370 Normal NA 3604.41 734.69 1848.28 * 0.83 10, 15 16.32 12.97 1568.96 * 334.715 1.04 1318.35 91.5 414.89 5.7 NL 1371 NL PLSA 1371 Normal NA 1081.68 1049.46 2333.2 * 0.83 9.66 20.30 13.72 1411.68 * 334.715 1.02 1779.61 136.77 326.89 2.13 NL 1372 NL PLSA 1372 Normal NA * 139.248 2344.52 1413.58 * 0.83 6.16 * 2 , 72 15.13 535.62 * 334.715 1.24 2388.9 * 14.229 * 76.98 3.92 NL 1373 NL PLSA 1373 Normal NA 2135.62 1641.40 2768.28 * 0.83 22.72 18, 03 16.06 1498.70 * 334.715 1.02 2173.74 85.37 784.49 3.36 NL 1374 NL PLSA 1374 Normal NA * 139.248 1664.57 1434.71 * 0.83 27.80 * 2.72 8.77 2282.90 * 334.715 0.75 1707.09 97.32 285.98 3.13 NL 1375 NL PLSA 1375 Normal NA * 139.248 1574.86 1577.19 * 0.83 9.41 * 2.72 11 , 84 724.4 * 334.715 0.72 2386.33 * 14.229 340.04 4.14 NL 1376 NL PLSA 1376 Normal NA * 139.248 1069.41 1758.99 * 0.83 6.76 * 2.72 9.15 1059.23 * 334,715 0.7 1466.17 118.32 890.84 5.13 NL 1377 NL PLSA 1377 Normal NA * 139.248 1470.94 2031.38 5.78 9.40 * 2.72 18 988.07 * 334.715 0.92 1822.23 168.99 * 76.98 5.64 NL 1378 NL PLSA 1378 Normal NA * 139.248 329.46 1098.24 * 0.83 13.21 * 2.72 11.18 636.7 * 334.715 1.44 2092.33 145.32 426.79 4.65 NL 1379 NL PLSA 1379 Normal NA * 139.248 368.21 1285.16 * 0 , 83 18.30 * 2.72 17.3 * 74.234 * 334.715 0.73 703.62 153.51 426.79 4.96 NL 1380 NL PLSA 1380 Normal NA 2687.28 1243.82 1557.9 * 0.83 4 , 25 * 2.72 18.07 1251.73 * 334.715 0.73 2453.34 * 14.229 384.47 4.07 NL 1381 NL PLSA 1381 Normal NA * 139.248 630.56 2595.68 * 0.83 9.20 * 2.72 18.41 1735.11 * 334.715 1.02 1127.46 118.32 * 76.98 6.17 NL 1382 NL PLSA 1382 Normal NA * 141.842 1354.68 1462.18 * 0.827 7.00 20, 14 13.13 2375.10 * 336.559 0.76 1802.53 106.2 681.87 4.67 NL 1383 NL PLSA 1383 Normal NA * 141.842 1781.21 3942.71 * 0.827 20.09 * 2.767 13.68 3652 , 22 * 336.559 0.59 1797.58 * 14.082 519.24 4.13 NL 1384 NL PLSA 1384 Normal NA 2701.01 989.11 3066.97 * 0.827 11.95 * 2.767 12.77 1918.02 * 336.559 0 , 54 3225.07 * 14.082 719.26 5.5 NL 1385 NL PLSA 1385 Normal NA * 141.842 1315.11 1179.22 7.61 13.00 * 2.767 13.21 514.80 * 336.559 1.44 1056.32 152.56 * 78.12 7.24

NL 1386 NL PLSA 1386 Normal NA * 141.842 1621.46 1170.05 * 0.827 7.92 * 2.767 12.59 * 72.188 * 336.559 0.6 2257.39 * 14.082 673.46 3.13 NL 1387 NL PLSA 1387 Normal NA * 141.842 2355.94 1724.22 * 0.827 18.70 * 2.767 * 1.372 468.53 * 336.559 0.72 2188.76 * 14.082 418.33 3.15 NL 1388 NL PLSA 1388 Normal NA * 141.842 2840.06 2142, 21 * 0.827 4.02 * 2.767 11.09 636.57 * 336.559 0.75 1270.44 97 * 78.12 3.35 NL 1389 NL PLSA 1389 Normal NA * 141.842 766.59 1585.67 5.18 4, 12 * 2,767 11.04 674.56 * 336.559 0.61 1175.76 97 413.31 5.34 NL 1390 NL PLSA 1390 Normal NA * 141.842 1575.92 2775.88 4.96 4.43 * 2.767 16.8 1532.57 * 336.559 0.98 1189.91 106.2 377.56 4.54 NL 1391 NL PLSA 1391 Normal NA 1245.95 2073.56 2549.1 * 0.827 23.46 17.87 13.72 796.57 * 336.559 0.72 1872.07 * 14.082 533.07 4.87 NL 1392 NL PLSA 1392 Normal NA * 141.842 1428.28 2338.6 * 0.827 21.67 * 2.767 14.68 928.1 * 336.559 0.54 2797.11 106.2 755.96 2.39 NL 1393 NL PLSA 1393 Normal NA * 141.842 1964.45 4091.8 5.18 102.98 * 2.767 19.07 1355.83 * 336.559 0.76 1095.97 97 677.67 7.93 NL 1394 NL PLSA 1394 Normal NA * 141.842 1264.30 2758.82 * 0.827 3.61 30.99 11.82 1418.46 * 336.559 0.65 2697.91 87.1 1004.81 2.82 NL 1395 NL PLSA 1395 Normal NA * 141.842 1309.46 1877.98 * 0.827 3.82 * 2.767 15.1 847.56 * 336.559 0.65 1426.05 * 14.082 698.58 3.61 NL 1396 NL PLSA 1396 Normal NA * 141.842 1269.94 2760.72 * 0.827 25.65 * 2.767 10.24 609.7 * 336.559 0.62 745.7 87.1 569.31 3.56 NL 1397 NL PLSA 1397 Normal NA * 139.248 1120.79 3011.51 * 0.83 10.12 * 2.72 13.59 * 74.234 * 334.715 0.89 1443.94 97.32 * 76.98 4.66 NL 1398 NL PLSA 1398 Normal NA 1165, 24 644.60 660.56 * 0.83 16.40 * 2.72 16.43 1816.30 * 334.715 0.73 1983.39 91.5 * 76.98 5.23 NL 1399 NL PLSA 1399 Normal NA * 139.248 949.85 2015.76 * 0.83 12.36 * 2.72 12.56 969.04 * 334.715 0.79 1644.76 91.5 847.23 5.64 NL 1400 NL PLSA 1400 Normal NA 2093, 49 554.85 2433.65 * 0.83 5.61 * 2.72 10.21 3256.08 * 334.715 0.8 1980.86 * 14.229 671.53 4.9 NL 1401 NL PLSA 1401 Normal NA 2225.14 818.77 1632.28 * 0.797 10.77 25.24 12.26 2286.21 * 320.985 0.61 2039.26 141.28 406.44 2.91 NL 1402 NL PLSA 1402 Normal NA * 131.746 1355.24 4256.2 5.97 5.32 * 2.636 15.72 881.81 * 320.985 0.81 1708 141.28 598.93 5.47 NL 1403 NL PLSA 1403 Normal NA * 131.746 896.42 3160.7 * 0.777 8.24 * 2.636 8.81 * 71.888 * 320.985 0.94 1851.27 110.38 1567.13 3.65 NL 1404 NL PLSA 1404 Normal NA 2204.41 1901.25 1576.73 * 0.797 6.80 * 2.636 12.19 759.60 * 320.985 0.75 1397.19 110.38 424.19 3.99 NL 1405 NL PLSA 1405 Normal NA 1112.13 1038.17 1273.24 6.41 4.92 * 2.636 15.05 1.899.9 * 320.985 0.61 1948.08 141.28 475.2 3.49 NL 1406 NL PLSA 1406 Normal NA 1665.44 1001.05 1150.79 4.89 7.97 * 2.636 15.08 520.0 * 320.985 0.84 1371.18 148.44 926.1 3.74 NL 1407 NL PLSA 1407 Normal NA * 131.746 768 , 12 418.39 * 0.797 5.06 * 2.636 12.15 * 71.888 * 320.985 0.91 2615.06 141.28 538.98 2.13 NL 1408 NL PLSA 1408 Normal NA 938.03 1786.69 1787.56 * 0.797 12.15 * 2.636 * 1.372 638.76 * 320.985 0.72 1789.8 * 13.953 762.36 4.82 NL 1409 NL PLSA 1409 Normal NA 808.88 1230.46 1262.52 * 0.797 21.93 * 2,636 14.48 698.66 * 320.985 0.54 1658.43 101.92 849.38 3.88 NL 1410 NL PLSA 1410 Normal NA * 131.746 3173.78 3853.02 9.1 16.62 * 2.636 14.05 991.3 * 320.985 0, 69 1504.35 * 13.953 982.86 5.63 NL 1411 NL PLSA 1411 Normal NA 1526.3 1024.67 3510.62 * 0.777 13.95 * 2.636 9.7 * 71.888 * 320.985 0.67 2237.1 83, 72 920.32 4.07 NL 1412 NL PLSA 1412 Normal NA * 131.746 531.62 1399.24 5.32 1.94 * 2.636 * 1.372 * 71.888 3312.07 0.5 969.27 259.31 768.75 3 , 01 NL 1413 NL PLSA 1413 Normal NA 982.19 1146.13 2835.13 7.07 9.30 * 2.636 13.08 1.619.4 * 320.985 0.54 2065.77 110.38 * 75.36 4.01 NL 1414 NL PLSA 1414 Normal NA * 131.746 1597.97 1479.35 * 0.777 3.65 * 2.636 6.89 503.38 * 320.985 0.72 1244.3 * 13.953 * 75.36 2.65 NL 1425 NL PLSA 1425 Normal NA 1886.13 904.42 1726.04 * 0.83 11.54 * 2.72 17.41 1,229.1 * 334.715 0.8 2001.09 97.32 365.71 4.17 NL 1426 NL PLSA 1426 Normal NA * 139.248 465.48 1617.73 * 0.83 7.02 * 2.72 16.66 * 74.234 * 334.715 2.54 1360.14 145.32 920.84 3.82 NL 1427 NL PLSA 1427 Normal NA 4179.94 437 , 63 2859.82 * 0.83 22.75 * 2.72 20.5 5 1,246.1 * 334,715 0.7 1694.61 123.13 * 76.98 6.98 NL 1428 NL PLSA 1428 Normal NA 863.26 898.75 957.85 5.32 8.72 * 2.72 14, 89 1059.23 * 334.715 1.15 1814.7 234.83 249.86 4.39 NL 1429 NL PLSA 1429 Normal NA 835.49 310.13 1555.97 * 0.83 8.62 * 2.72 21, 18 1.001.7 * 334.715 0.81 2217.12 * 14.229 967.14 5.18 NL 1430 NL PLSA 1430 Normal NA * 139.248 1635.61 2730.52 * 0.83 4.28 * 2.72 21.83 955.49 * 334.715 1.17 1293.8 118.32 * 76.98 6.4 NL 1431 NL PLSA 1431 Normal NA 1717.69 622.13 1982.6 9.56 13.11 * 2.72 17.16 1.302.8 * 334.715 0, 83 1587.57 * 14.229 1008.43 7.8 NL 1432 NL PLSA 1432 Normal NA 1865.71 757.26 1838.56 * 0.83 11.66 * 2.72 13.51 456.4 * 334.715 0.9 1779.61 108.22 581.18 3.98 NL 1433 NL PLSA 1433 Normal NA 982.41 1046.61 2621.43 * 0.83 10.51 * 2.72 16.4 1122.82 * 334.715 0.86 1852.36 113.35 438.55 2.81 NL 1434 NL PLSA 1434 Normal NA * 139.248 884.57 1229.73 * 0.83 10.32 * 2.72 13.11 1153.44 * 334.715 0.71 1764 , 59 * 14.229 719.52 5.06 NL 1435 NL PLSA 1435 Normal NA 1129.23 667.09 1803.6 * 0.83 8.09 * 2.72 15 , 99 818.9 2132 0.7 1639.78 118.32 450.18 6.47 NL 1436 NL PLSA 1436 Normal NA 1105.39 647.41 345.43 9.32 3.74 * 2.72 14.15 1175.79 * 334.715 0.86 1764.59 252.14 352.97 2.67 NL 1437 NL PLSA 1437 Normal NA 930.87 2145.07 984.36 * 0.83 10.34 * 2.72 10.27 * 74.234 * 334.715 0.83 429.47 113.35 825.06 2.67 NL 1438 NL PLSA 1438 Normal NA * 139.248 459.91 2354.85 * 0.83 31.21 * 2.72 17.23 1040.00 * 334.715 0.91 2097.4 * 14.229 * 76.98 2.72 NL 1439 NL PLSA 1439 Normal NA 1858.91 881.73 2090 * 0.83 30.39 * 2.72 17.93 1504.53 * 334.715 0.72 1193.38 * 14.229 313.51 3.71 NL 1440 NL PLSA 1440 Normal NA 1592.44 700.87 1425.1 * 0.83 2.08 * 2.72 11.07 1.472.5 * 334.715 0.66 1017.94 85.37 455.95 3.98 NL 1441 NL PLSA 1441 Normal NA * 139.248 1563.30 528.24 * 0.83 5.59 * 2.72 14.65 734.9 * 334.715 0.6 1293.8 * 14.229 340.04 4.86 NL 1442 NL PLSA 1442 Normal NA * 139.248 354.36 367.46 * 0.83 5.83 * 2.72 9.63 750.5 * 334.715 0.6 1729.57 91.5 * 76.98 2.74 NL 1443 NL PLSA 1443 Normal NA * 133.814 1410.68 1801.74 * 0.83 10.80 * 2.745 15.72 1.1 81.8 * 328.934 0.55 1874.55 91.79 296.89 6.94 NL 1444 NL PLSA 1444 Normal NA * 133.814 1316.41 1639.79 4.98 20.02 * 2.745 13.46 516.35 * 328.934 0.66 1134 211.92 579.94 2.31 NL 1445 NL PLSA 1445 Normal NA * 133.814 1466.01 1900.73 * 0.83 4.34 20.37 10.2 587.7 * 328.934 0.61 1131.16 98.45 93.07 2.39 NL 1482 NL PLSA 1482 Normal NA 931.28 908.79 1815 * 0.807 11.78 * 2.707 10.21 661.8 * 327.937 1.19 1656.38 104.41 1061.43 5.11 NL 1483 NL PLSA 1483 Normal NA * 130.362 888.35 1728.04 * 0.807 5.72 21.45 8.3 541.2 * 327.937 1.02 1514.21 * 14.112 1032.37 5, 16 NL 1484 NL PLSA 1484 Normal NA * 130.362 1553.45 2739.11 * 0.807 14.91 * 2.707 7.68 1167.30 * 327.937 1.27 1460.13 104.41 1077.91 3.95 NL 1485 NL PLSA 1485 Normal NA * 130.362 694.37 1824.89 * 0.807 9.79 18.54 7.8 952.20 * 327.937 0.82 1127.81 98.12 1421.29 10.91 NL 1486 NL PLSA 1486 Normal NA 951 , 37 684.44 931.99 * 0.807 3.47 * 2.707 10.21 461.8 * 327.937 0.99 1227.94 175.8 669.98 6.03 NL 1487 NL PLSA 1487 Normal NA * 130.362 622.76 2390.93 * 0.807 5.98 * 2.707 8.6 7 753.81 * 327.937 1.27 1004.97 91.56 764.88 7.39 NL 1488 NL PLSA 1488 Normal NA * 130.362 341.72 1231.01 * 0.807 7.82 * 2.707 7.5 * 74.728 * 327.937 0.85 1034.13 * 14,112 960.56 5.12 NL 1489 NL PLSA 1489 Normal NA 1648.48 729.24 2051.08 * 0.807 12.98 * 2.707 * 1.371 * 74.728 * 327.937 1.27 1508.2 121 , 93 1126.82 5.75 NL 1490 NL PLSA 1490 Normal NA * 130.362 1184.17 2932.31 * 0.807 3.01 * 2.707 * 1.371 * 74.728 * 327.937 1.08 877.21 * 14.112 873.55 4.23 NL 1491 NL PLSA 1491 Normal NA * 130.362 1178.90 1274.78 * 0.807 12.46 * 2.707 8.83 1001.54 * 327.937 1.68 1616.92 91.56 1174.99 4.96 NL 1492 NL PLSA 1492 Normal NA * 130.362 829.86 3090.03 * 0.807 27.76 * 2.707 11.49 600.82 * 327.937 0.82 1157.19 * 14.112 736.87 10.63 NL 1493 NL PLSA 1493 Normal NA * 130.362 598, 27 1212.31 * 0.807 45.52 * 2.707 * 1.371 522.6 * 327.937 1.09 675.68 104.41 1198.82 7.74 NL 1494 NL PLSA 1494 Normal NA 1372.89 1055.88 2169.06 * 0.807 20.34 * 2.707 8.41 461.83 * 327.937 1.47 1022.46 * 14,112 640.59 9.38 NL 1495 NL PLSA 1495 Normal NA * 130.362 101 6.94 3211.15 * 0.807 24.60 21.86 8.46 889.6 * 327.937 1.33 1629.05 116.28 2195.92 9.3 NL 1496 NL PLSA 1496 Normal NA * 130.362 2385.89 2567 , 48 * 0.807 8.62 34.09 11.07 516.5 * 327.937 1.1 1562.46 147.98 1376.33 5.04 NL 1497 NL PLSA 1497 Normal NA 2271.2 1071.50 1834.78 * 0.807 5.54 * 2.707 13.18 941.7 * 327.937 0.97 718.71 * 14,112 960.56 8.67 NL 1498 NL PLSA 1498 Normal NA * 130.362 527.89 1227.89 8.27 16.10 * 2.707 8.03 727.25 * 327.937 1 1042.89 116.28 969.1 6.02 NL 1499 NL PLSA 1499 Normal NA 1478.19 1348.71 3360.61 * 0.807 27.87 17.71 * 1.371 * 74.728 * 327.937 1.1 1790.71 104.41 1284.79 13.01 NL 1500 NL PLSA 1500 Normal NA * 130.362 2309.18 1448.67 * 0.807 5.96 * 2.707 11.42 * 74.728 * 327.937 0.72 2235.9 84.67 1167.01 8.06 NL 1501 NL PLSA 1501 Normal NA 1103.54 1569.75 3397.27 * 0.807 3.64 * 2.707 10.55 2.470.1 * 327.937 0.67 1849.1 * 14,112 951, 98 4.09 NL 1502 NL PLSA 1502 Normal NA * 130.362 736.74 2577.93 * 0.807 13.67 * 2.707 * 1.371 566.13 * 327.937 1.3 1367.42 91.56 1596.41 8.96 NL 1503 NL PLSA 1503 No extension NA * 130.362 1221.12 981.85 * 0.807 11.68 * 2.707 8.78 455.84 * 327.937 0.77 1394.28 * 14,112 891.19 5.1 NL 1504 NL PLSA 1504 Normal NA 801.89 2471 , 44 1602.83 * 0.807 43.63 * 2.707 9.78 632.7 * 327.937 0.82 504.14 * 14,112 947.69 5.76 NL 1505 NL PLSA 1505 Normal NA 3519.77 1006.58 2480.66 6.1 21.20 32.48 7.86 2.164.1 * 327.937 0.65 1732.56 104.41 951.98 2.72 NL 1506 NL PLSA 1506 Normal NA 2149.22 1184.17 2619.81 5, 57 10.75 30.86 15.76 1.564.8 * 327.937 0.38 1769.26 * 14.112 1138.93 5.88 NL 1507 NL PLSA 1507 Normal NA * 130.362 908.79 3222.04 * 0.807 10.69 * 2.707 * 1.371 3157.36 * 327.937 0.72 1784.58 110.45 1122.77 4.4 NL 1508 NL PLSA 1508 Normal NA * 130.362 376.36 1589.9 * 0.807 6.92 * 2.707 12.18 * 74.728 * 327,937 0.73 842.52 192.89 1019.83 5.47 NL 1509 NL PLSA 1509 Normal NA * 130.362 1189.44 1932.49 * 0.807 11.64 * 2.707 16.64 * 74.728 * 327.937 0.58 1514 , 21 * 14,112 869.12 7.17 NL 1510 NL PLSA 1510 Normal NA 2687.24 1100.20 1184.33 * 0.807 8.45 * 2.707 11.19 * 74.728 * 327.937 0.78 1322.77 * 14.112 1476, 8 11.86

NL 1511 NL PLSA 1511 Normal NA 1083.1 867.95 2581.41 * 0.807 15.39 * 2.707 9.41 * 74.728 * 327.937 1.17 1641.19 104.41 1057.29 2.63 NL 1512 NL PLSA 1512 Normal NA 1648.48 797.00 1894.31 * 0.807 12.05 * 2.707 * 1.371 1008.63 * 327.937 0.87 1104.34 104.41 1261.55 2.41 NL 1513 NL PLSA 1513 Normal NA * 130.362 1042 , 88 2421.93 * 0.807 33.85 30.05 15.92 * 74.728 * 327.937 0.77 1616.92 91.56 899.96 3.68 NL 1514 NL PLSA 1514 Normal NA * 130.362 608.06 307.85 * 0.807 5.03 * 2.707 * 1.371 * 74.728 * 327.937 0.79 575.52 91.56 679.68 2.35 NL 1515 NL PLSA 1515 Normal NA 2619.19 1629.67 1688.87 8.82 17.52 23, 51 8.3 2134.81 * 327.937 0.68 1445.13 * 14,112 899.96 6.58 NL 1516 NL PLSA 1516 Normal NA * 130.362 741.74 1606.07 * 0.807 7.80 * 2.707 8.93 * 74.728 * 327.937 0.82 1133.68 91.56 1077.91 6.51 NL 1517 NL PLSA 1517 Normal NA * 130.362 1783.26 2247.16 * 0.807 17.20 * 2.707 12.82 566.13 * 327.937 0.83 1034, 13 * 14,112 1073.79 14.63 NL 1518 NL PLSA 1518 Normal NA 1022.07 944.69 1445.48 5.3 13.42 23.51 9.27 * 74.728 * 327.937 0.82 1 698.99 184.47 746.25 15.43 NL 1519 NL PLSA 1519 Normal NA * 130.362 776.86 1202.97 * 0.807 13.63 * 2.707 * 1.371 461.83 * 327.937 0.92 761.82 91.56 828.89 6.16 NL 1520 NL PLSA 1520 Normal NA * 130.362 1921.65 3463.45 * 0.807 10.12 21.45 12.35 1270.81 * 327.937 0.97 1604.8 104.41 1118.72 6 , 65 NL 1521 NL PLSA 1521 Normal NA * 130,362 949.83 777.05 * 0.807 5.21 * 2.707 * 1.371 * 74.728 * 327.937 0.72 727.33 192.89 801.68 5.06 NL 1522 NL PLSA 1522 Normal NA * 130.362 496.67 1719.87 * 0.807 11.76 * 2.707 12.79 * 74.728 * 327.937 1.05 923.56 91.56 873.55 6.24 NL 1523 NL PLSA 1523 Normal NA 1383.38 603 , 16 2836.3 * 0.807 3.97 * 2.707 * 1.371 747.15 * 327.937 0.93 1729.5 116.28 1044.86 3.74 NL 1524 NL PLSA 1524 Normal NA * 130.362 1079.32 1641.71 * 0.807 4.86 * 2.707 13.09 600.82 * 327.937 0.82 952.59 253.64 864.68 4.97 NL 1525 NL PLSA 1525 Normal NA * 130.362 3691.29 6723.23 * 0.807 28.43 52 , 42 14.02 578.7 * 327.937 0.79 2175.98 147.98 951.98 10.21 NL 1526 NL PLSA 1526 Normal NA * 130.362 719.26 1997.49 5.43 13.70 * 2.707 * 1 , 371 * 74.728 * 327.937 0.78 1436.15 116.28 1167.01 4.83 NL 1527 NL PLSA 1527 Normal NA * 130.362 300.66 1285.76 * 0.807 18.32 18.54 * 1.371 1.300.7 * 327.937 0.93 1328.72 104.41 846.85 3.62 NL 1528 NL PLSA 1528 Normal NA * 130.362 1300.70 1033.54 5.04 31.66 * 2.707 10.87 547.4 * 327.937 0.81 504.14 * 14,112 750.92 9.93 NL 1529 NL PLSA 1529 Normal NA 1268.5 771.83 1930.82 * 0.807 16.33 16.87 13.12 1,498.8 * 327.937 0.71 1406.23 104 , 41 727.45 12.83 NL 1530 NL PLSA 1530 Normal NA 6938.81 898.56 1630.36 * 0.807 12.26 18.96 8.3 486.0 * 327.937 0.77 1424.17 166.86 1077 , 91 5.26 NL 1531 NL PLSA 1531 Normal NA 3566.84 518.27 762.34 * 0.807 20.55 * 2.707 * 1.371 844.9 * 327.937 0.69 1319.8 91.56 533.61 2.1 NL 1532 NL PLSA 1532 Normal NA * 130.362 822.27 1369.26 * 0.807 5.73 * 2.707 10.47 566.1 * 327.937 0.68 756.07 * 14,112 590.5 7.13 NL 1533 NL PLSA 1533 Normal NA * 147.7 581.90 1053.57 6.68 8.61 26.00 11.76 1.216.0 2035 1.49 815.91 295.1 1078.2 6.69 NL 1534 NL PLSA 1534 Normal NA 1417.02 1913.34 1148.02 * 0.83 19.41 22.89 26.33 880.9 * 339.24 1.28 1710.98 * 15.72 1296.18 16.21 NL 1535 NL PLSA 1535 Normal NA 1982.85 1003.81 2028.19 5.96 42, 78 16.32 13.15 1177.15 * 339.24 1.31 2549.02 * 15.72 1201.35 4.74 NL 1536 NL PLSA 1536 Normal NA * 147.7 1937.20 1859.46 * 0, 83 20.67 * 2.72 29.36 1.245.2 * 339.24 1.85 2061.47 * 15.72 1144.57 14.73 NL 1537 NL PLSA 1537 Normal NA * 147.7 2080.31 3009, 22 * 0.83 8.52 24.46 14.66 1274.46 * 339.24 1.33 2127.2 * 15.72 975.31 4.65 NL 1538 NL PLSA 1538 Normal NA 1126.57 1758.17 3950.71 5.22 19.88 19.67 11.9 2006.17 * 339.24 1.89 1530.47 94.3 1303.95 10.69 NL 1539 NL PLSA 1539 Normal NA * 147.7 7286, 22 4002.7 15.12 19.04 22.89 28.45 1085.45 * 339.24 1.6 3851.78 * 15.72 1193.3 8.03 NL 1540 NL PLSA 1540 Normal NA 1928.4 1590 , 92 2778.71 * 0.83 9.40 * 2.72 25.24 504.11 * 339.24 1.95 1200.57 94.3 1391.99 9.41 NL 1541 NL PLSA 1541 Normal NA 1337, 36 961.74 1231.27 * 0.83 12.77 24.46 8.13 740.8 2035 2.67 299.3 230.18 1692.47 17 NL 1542 NL PLSA 1542 Normal NA * 147.7 1572, 99 2677.48 7.65 22.61 * 2.72 2 0.6 1,362.7 * 339.24 1.81 2931.84 94.3 1217.36 13.16 NL 1543 NL PLSA 1543 Normal NA 1417.02 1033.85 2603.63 5.22 23.55 19.67 14.21 1.143 , 3 * 339.24 1.42 1181.17 94.3 601.16 6.45 NL 1544 NL PLSA 1544 Normal NA 4262.92 829.37 1132.9 * 0.83 16.05 27.52 8.67 639.60 * 339.24 1.57 1142.44 94.3 1119.87 5.52 NL 1545 NL PLSA 1545 Normal NA 2366.93 1063.88 1371.53 6.68 5.07 47.22 23.17 1560.89 * 339.24 1.46 2046.34 220.67 568.65 6.85 NL 1546 NL PLSA 1546 Normal NA * 147.7 1501.24 2440.77 6.68 14.39 * 2.72 16 , 37 956.48 * 339.24 1.45 2289.84 * 15.72 1241.23 9.79 NL 1547 NL PLSA 1547 Normal NA 2366.93 1057.87 1863.29 10.49 45.05 18.05 9.05 2047.23 * 339.24 2.18 2376.74 94.3 684.53 7.19 NL 1548 NL PLSA 1548 Normal NA 3702.41 1273.79 2825.51 * 0.83 9.89 * 2 , 72 12.35 763.94 * 339.24 1.44 2592.95 189.11 1082.4 5.87 NL 1549 NL PLSA 1549 Normal NA * 147.7 1123.90 3596.37 * 0.83 24, 54 53.75 13.91 1461.45 * 339.24 1.69 2167.74 94.3 601.16 4.65 NL 1550 NL PLSA 1550 Normal NA 16406.21 642.36 4074.79 6.2 16, 34 * 2.72 19.55 86 2.03 * 339.24 1.39 2681.08 * 15.72 568.65 12.49 NL 1551 NL PLSA 1551 Normal NA 2174.27 1519.18 3264.45 * 0.83 23.70 22.89 14 , 88 1530.99 * 339.24 1.94 1666.33 * 15.72 1189.28 5.47 NL 1552 NL PLSA 1552 Normal NA 4917.37 2110.12 1947.6 6.68 7.46 16.32 11, 48 667.05 2035.45 1.93 1186.02 210.71 1296.18 6.32 NL 1553 NL PLSA 1553 Normal NA 1819.84 1471.34 2359.54 5.22 16.66 * 2.72 13, 99 * 77.35 * 339.24 1.5 1676.25 * 15.72 881.42 8.26 NL 1554 NL PLSA 1554 Normal NA 3716.7 1501.24 2421.42 * 0.83 15.33 * 2 , 72 13.44 685.41 * 339.24 1.5 1701.05 * 15.72 1361.64 8.26 NL 1555 NL PLSA 1555 Normal NA * 147.7 829.37 2016.67 5.22 28, 42 * 2.72 11.43 612.26 * 339.24 1.38 1278.31 94.3 724.65 4.42 NL 1556 NL PLSA 1556 Normal NA 4888.12 1339.68 1080 * 0.83 8, 19 * 2.72 18.72 956.48 * 339.24 1.7 1644.04 * 15.72 1099.12 4.87 NL 1557 NL PLSA 1557 Normal NA 1074.27 1572.99 1148.02 15.12 9.02 49.85 22.12 759.30 * 339.24 1.86 607.08 * 15.72 1241.23 9.64 NL 1558 NL PLSA 1558 Normal NA * 147.7 2050.50 2553.17 * 0.83 12.18 * 2.72 14.51 956.48 * 339.24 1.58 2502.6 * 15.72 1221.36 10.06 NL 1559 NL PLSA 1559 Normal NA * 147.7 1782.05 2043.56 * 0.83 30.15 29.02 15.12 526.5 * 339.24 0.73 1530.47 164.42 1909.62 6.04 NL 1560 NL PLSA 1560 Normal NA * 147.7 1782.05 1424.7 5.22 16.77 * 2.72 21 , 2 947.00 * 339.24 1.33 1770.66 * 15.72 1241.23 8.05 NL 1561 NL PLSA 1561 Normal NA 1205.32 1644.70 243.59 * 0.83 8.04 * 2 , 72 8.67 937.5 * 339.24 1.5 1950.7 * 15.72 679.45 6.76 NL 1562 NL PLSA 1562 Normal NA * 147.7 1123.90 2553.17 8.6 15, 49 19.67 12.17 791.85 4316.53 1.7 2109.48 405.13 568.65 14.35 NL 1563 NL PLSA 1563 Normal NA 4089.77 3089.44 2825.51 * 0.83 32, 67 * 2.72 24.69 589.57 * 339.24 1.38 1715.95 * 15.72 1448.17 5.98 NL 1564 NL PLSA 1564 Normal NA * 147.7 943.71 1197.2 * 0 , 83 11.40 20.49 13.91 1.496.2 * 339.24 1.37 2021.13 * 15.72 524.05 4.89 NL 1565 NL PLSA 1565 Normal NA 1738.72 955.73 1634.03 * 0.83 21.71 * 2.72 12.17 985.00 * 339.24 1.46 995.39 164.42 783.15 11.48 NL 1566 NL PLSA 1566 Normal NA * 147.7 835.39 1897.76 5.71 6.95 26.00 7.99 539.94 2452.42 1.6 5 1483.73 322.68 1809.18 3.71 NL 1567 NL PLSA 1567 Normal NA * 147.7 702.75 1415.2 * 0.83 5.58 * 2.72 13.75 * 77.35 * 339 , 24 1.5 1108.6 134.68 876.84 8.62 NL 1568 NL PLSA 1568 Normal NA 2339.33 1135.90 1371.53 7.17 18.08 45.88 14.36 1.620.9 13499 1 , 61 1012.21 116.54 1292.29 4.58 NL 1569 NL PLSA 1569 Normal NA 1996.48 1495.26 1405.7 9.08 18.59 33.42 11.53 2067.80 2850.45 1, 92 1244.26 280.31 940 8.3 NL 1570 NL PLSA 1570 Normal NA 3247.53 1267.80 1493.12 * 0.83 8.86 * 2.72 17.22 876.14 * 339.24 1.91 1895 , 53 * 15.72 1528.97 4.31 NL 1571 NL PLSA 1571 Normal NA 3545.52 1207.87 2743.65 * 0.83 25.36 24.46 15.85 1362.70 * 339.24 1, 38 2213.45 * 15.72 1027.29 5.33 NL 1572 NL PLSA 1572 Normal NA * 147.7 684.64 1932.26 6.93 7.94 * 2.72 22.52 1090.26 * 339, 24 1.27 1082.05 * 15.72 802.25 8.08 NL 1573 NL PLSA 1573 Normal NA 2242.94 672.57 299.47 * 0.83 40.36 24.46 11.67 777.88 * 339.24 1.34 1740.8 * 15.72 1078.2 4.61 NL 1574 NL PLSA 1574 Normal NA * 147.7 787.19 2155.09 * 0.83 6.76 37.68 12.7 713 , 03 * 339.24 1.43 863.63 * 1 5.72 417.22 8.15 NL 1575 NL PLSA 1575 Normal NA * 147.7 1201.87 1748.54 7.41 42.12 26.00 12.95 3906.71 * 339.24 1.48 1224.83 * 15.72 512.65 5.16 NL 1576 NL PLSA 1576 Normal NA * 147.7 1075.89 2162.79 * 0.83 4.82 * 2.72 12.39 909.14 2035.45 1.56 1651 , 47 * 15.72 1065.57 8.27 NL 1577 NL PLSA 1577 Normal NA * 147.7 1225.85 2972.03 6.68 25.06 * 2.72 18.38 1157.78 * 339.24 2 , 03 2198.2 * 15.72 1140.47 4.16 NL 1578 NL PLSA 1578 Normal NA 6338.55 1399.54 2827.46 * 0.83 24.89 * 2.72 19.17 1466.40 * 339 , 24 1.71 2500.03 * 15.72 1514.41 6.02 NL 1579 NL PLSA 1579 Normal NA 1792.77 654.45 898.98 11.89 24.23 * 2.72 11.24 1481.28 * 339.24 1.53 1419.94 94.3 714.71 9.86 NL 1580 NL PLSA 1580 Normal NA 1443.65 853.45 2332.49 10.02 12.25 * 2.72 20.03 1177, 15 * 339.24 3.74 1990.92 248.05 1433.28 3.45 NL 1581 NL PLSA 1581 Normal NA 1901.22 6030.73 2593.92 8.13 10.49 * 2.72 14.51 1.873 , 4 * 339.24 1.65 2494.88 94.3 1403.3 9.93 NL 1582 NL PLSA 1582 Normal NA * 147.7 149.31 1704.62 7.65 37.38 37.68 14.25 1630.91 * 339.24 1.66 1380.78 * 15.72 1444.45 17.21 NL 1583 NL PLSA 1583 Normal NA * 147.7 1216.86 2936.82 9.55 14.56 36.27 15.09 539.94 * 339.24 1.83 1920.59 * 15.72 535.34 14.94 NL 1584 NL PLSA 1584 Normal NA 8542.93 1063.88 2263 6.68 15.33 18.02 13.56 504.11 * 339.24 1.92 1444.45 134, 68 899.61 5.26 NL 1585 NL PLSA 1585 Normal NA * 147.7 1090.89 2749.49 * 0.83 15.73 16.32 21.37 1284.24 * 339.24 1.8 1473.9 * 15.72 1433.28 5.82 NL 1586 NL PLSA 1586 Normal NA 2811.58 793.22 2070.46 * 0.83 11.47 * 2.72 20.08 495.19 * 339.24 1.71 1611.88 * 15.72 332.39 6.82 NL 1587 NL PLSA 1587 Normal NA 1537.11 1159.90 2677.48 * 0.83 13.50 34.85 18.94 2726.78 * 339.24 1 , 75 1562.5 * 15.72 931.09 9.59 NL 1588 NL PLSA 1588 Normal NA * 147.7 1489.28 1668.36 8.36 7.11 * 2.72 9.88 2641.96 * 339 , 24 1.76 1843 * 15.72 1319.44 10.57 NL 1589 NL PLSA 1589 Normal NA * 147.7 472.85 2720.28 7.65 24.14 * 2.72 11.14 * 77.35 3604.40 1.65 858.86 264.68 913.16 11.84

NL 1590 NL PLSA 1590 Normal NA 4961.28 859.47 4542.25 * 0.83 18.68 * 2.72 12.65 * 77.35 * 339.24 1.41 1424.84 94.3 684.53 6.48 NL 1591 NL PLSA 1591 Normal NA 5196.03 1243.83 816.19 * 0.83 6.09 * 2.72 11.67 740.76 * 339.24 2.02 1845.5 134.68 1052 , 87 7.28 NL 1592 NL PLSA 1592 Normal NA 2839.56 1626.77 1263.46 * 0.83 25.03 * 2.72 16.5 1.357.8 * 339.24 1.46 1995.95 164, 42 1010.09 6.53 NL 1593 NL PLSA 1593 Normal NA * 147.7 290.30 2147.39 6.2 15.07 * 2.72 10.48 1042.28 * 339.24 1.72 110.11 264.68 658.95 6.81 NL 1594 NL PLSA 1594 Normal NA 1337.36 1357.64 3109.19 7.65 38.31 * 2.72 16.69 * 77.35 * 339.24 1.67 2655 , 12 * 15.72 4998.47 7.45 NL 1595 NL PLSA 1595 Normal NA 1537.11 1345.67 2584.21 * 0.83 34.17 21.30 19.87 1254.94 * 339.24 1, 9 1968.29 * 15.72 1209.37 9.41 NL 1596 NL PLSA 1596 Normal NA * 147.7 612.14 1895.85 5.22 21.02 26.00 14.98 504.11 * 339.24 2.34 916.24 94.3 1148.66 7.83 NL 1597 NL PLSA 1597 Normal NA * 147.7 600.04 1769.56 5.22 20.80 22.89 9.36 815.18 * 339, 24 2.23 1021.83 210.71 1384.43 13.97 NL 159 8 NL PLSA 1598 Normal NA 1982.85 1832.79 2454.32 * 0.83 8.02 * 2.72 11.14 * 77.35 * 339.24 1.95 2051.38 94.3 1069.79 4 , 84 NL 1599 NL PLSA 1599 Normal NA 1792.77 1024.84 2051.24 * 0.83 11.32 * 2.72 9.42 * 77.35 * 339.24 1.83 1639.09 * 15.72 1197.33 7.46 NL 1600 NL PLSA 1600 Normal NA * 147.7 1441.43 2901.64 * 0.83 31.11 19.67 22.66 3801.32 * 339.24 1.33 1402.8 280 , 31 585 7.48 NL 1601 NL PLSA 1601 Normal NA 3120.47 1531.14 3066.04 5.22 6.38 30.50 18.32 2139.96 * 339.24 1.14 1520.62 335.64 1035.85 6.2 NL 1602 NL PLSA 1602 Normal NA * 147.7 1291.77 2062.77 5.22 22.80 16.32 13.36 1186.85 2035.45 2.04 1351.45 164.42 601.16 10.25 NL 1603 NL PLSA 1603 Normal NA 2297.98 1847.71 1670.27 6.2 31.69 * 2.72 19.55 1148.11 * 339.24 1.64 2231.25 94, 3 1260.97 3.81 NL 1604 NL PLSA 1604 Normal NA 3205.14 1453.39 4686.33 * 0.83 61.79 * 2.72 19.65 5460.88 * 339.24 1.56 1785.61 94.3 1425.81 8.67 NL 1605 NL PLSA 1605 Normal NA 2160.55 1423.48 2309.32 * 0.83 8.19 25.23 12.7 1167.46 * 339.24 1.35 508, 04 * 15.72 1311.71 6.9 NL 1606 N L PLSA 1606 Normal NA * 147.7 1261.81 1531.17 6.2 20.96 16.32 9.54 1148.11 * 339.24 1.89 1496.02 134.68 1010.09 8.69 NL 1607 NL PLSA 1607 Normal NA 1231.65 1859.64 1717.98 15.58 12.13 * 2.72 12.52 1186.85 * 339.24 1.67 2041.29 264.68 1418.32 5.91 NL 1608 NL PLSA 1608 Normal NA 3176.89 2062.43 232.42 * 0.83 6.69 * 2.72 22.8 843.26 * 339.24 1.5 1745.78 94.3 704.71 9.57 NL 1609 NL PLSA 1609 Normal NA 4610.45 1970.19 1694.63 5.94 11.26 18.38 14.98 * 72.188 * 336.559 0.69 2860.01 87.1 731.57 9.39 NL 1610 NL PLSA 1610 Normal NA 2056.28 2350.17 1794.56 * 0.827 27.83 20.39 18.55 509.62 * 336.559 0.9 2398 * 14.082 398.13 16.75 NL 1611 NL PLSA 1611 Normal NA * 141.842 2119.57 1905.82 * 0.827 12.20 * 2.767 14.87 * 72.188 * 336.559 0.6 2588.8 101.68 1073.27 5.36 NL 1612 NL PLSA 1612 Normal NA 1108.62 1844.12 2990, 65 * 0.827 11.46 * 2.767 12.59 604.34 * 336.559 0.58 2902.03 * 14.082 706.88 4.56 NL 1613 NL PLSA 1613 Normal NA 1343.57 2825.52 2032.24 * 0.827 5, 06 * 2.767 16.2 * 72.188 * 336.559 0.74 2387.73 87.1 447.99 7.3 N L 1614 NL PLSA 1614 Normal NA * 141.842 822.02 703.14 * 0.827 9.88 24.87 10.18 762.92 * 336.559 0.96 1249.07 184.57 881.65 6.43 NL 1615 NL PLSA 1615 Normal NA * 141.842 667.22 1414.33 * 0.827 33.64 16.86 14.64 847.56 * 336.559 0.69 1862.12 130.87 739.73 9.48 NL 1616 NL PLSA 1616 Normal NA * 141.842 3832.12 475.84 5.39 17.15 70.35 26.86 445.71 * 336.559 1.38 2398 * 14.082 1273.34 71.16 NL 1617 NL PLSA 1617 Normal NA * 141.842 617.74 2332.97 6.16 19.22 * 2.767 18.41 1603.13 2678.49 0.76 1409.19 * 14.082 1083.94 14.83 NL 1618 NL PLSA 1618 Normal NA 3361.09 1904.25 1118.72 5.83 9.10 * 2.767 24.65 * 72.188 * 336.559 0.89 2640.69 * 14.082 979.16 8.45 NL 1619 NL PLSA 1619 Normal NA * 141.842 1884.20 2181.41 * 0.827 7.58 * 2.767 15.67 * 72.188 * 336.559 0.83 2833.78 106.2 1192.04 10.27 NL 1620 NL PLSA 1620 Normal NA 1607.48 922.13 1408.81 * 0.827 7.11 * 2.767 16.2 609.7 * 336.559 0, 94 1921.93 * 14.082 1030.22 6.48 NL 1621 NL PLSA 1621 Normal NA 1736.84 2407.95 1922.54 * 0.827 24.03 * 2.767 11.3 1.551.8 * 336.559 0.94 174 8.12 * 14.082 1108.68 10.14 NL 1622 NL PLSA 1622 Normal NA * 141.842 1467.98 3316.1 * 0.827 14.50 * 2.767 9.42 729.55 * 336.559 0.85 1455 87.1 780, 08 11.99 NL 1623 NL PLSA 1623 Normal NA 2083.45 888.71 2194.49 * 0.827 7.17 * 2.767 19.3 * 72.188 * 336.559 0.72 2387.73 * 14.082 819.68 18.68 NL 1624 NL PLSA 1624 Normal NA * 141.842 722.36 2103.03 * 0.827 7.01 * 2.767 17.93 583.0 * 336.559 0.71 1979.47 * 14.082 1378.92 15.06 NL 1625 NL PLSA 1625 Normal NA 1505.68 913.77 1957.84 * 0.827 13.77 24.87 9.42 620.41 * 336.559 0.64 1306.16 130.87 279.15 7.37 NL 1626 NL PLSA 1626 Normal NA 940.86 601.28 1737.17 * 0.827 13.31 * 2.767 10.59 1281.40 * 336.559 0.78 500.42 196.33 639.43 13.88 NL 1627 NL PLSA 1627 Normal NA * 141.842 399.85 2927.8 * 0.827 18.72 19.89 12.68 572.43 * 336.559 0.66 783.83 87.1 560.33 10.69 NL 1628 NL PLSA 1628 Normal NA 1185.79 822.02 2181.41 * 0.827 7.51 * 2.767 10.42 527.80 * 336.559 0.78 1033.07 * 14.082 1478.49 5.84 NL 1629 NL PLSA 1629 Normal NA * 141.842 2151.22 418.85 * 0.827 36.68 * 2.767 12.77 2 065.95 * 336.559 0.87 3522.81 97 1519.52 10.33 NL 1630 NL PLSA 1630 Normal NA * 141.842 1151.67 2342.35 * 0.827 23.49 * 2.767 14.45 631.18 * 336.559 0, 78 1684.08 * 14.082 1044.64 11.14 NL 1631 NL PLSA 1631 Normal NA * 141.842 1106.73 1998.74 * 0.827 17.64 * 2.767 15.13 768.5 * 336.559 0.95 1149.88 * 14.082 1015, 73 8.6 NL 1632 NL PLSA 1632 Normal NA * 141.842 950.02 596.91 * 0.827 12.78 * 2.767 10.98 847.56 2288.36 1.21 1265.69 165.88 452.86 11.52 NL 1633 NL PLSA 1633 Normal NA 2521.83 944.44 1425.37 * 0.827 8.87 * 2.767 12.21 824.83 * 336.559 0.72 1844.71 * 14.082 858.6 3.98 NL 1634 NL PLSA 1634 Normal NA * 141.842 2901.18 2082.53 4.96 12.65 * 2.767 16.9 561.87 * 336.559 0.91 1450.17 * 14.082 1212.55 26.22 NL 1635 NL PLSA 1635 Normal NA * 141.842 736 , 18 1713.12 * 0.827 5.69 * 2.767 16.2 * 72.188 * 336.559 0.74 1074.96 178.49 372.35 4.3 NL 1636 NL PLSA 1636 Normal NA 5396.23 2703.47 2662.33 * 0.827 11.41 * 2.767 16.8 696.5 * 336.559 1.27 2219.23 87.1 1041.05 6.82 NL 1637 NL PLSA 1637 Normal NA * 141.842 3070.22 1772, 34 * 0.827 16.71 * 2.767 13.97 1468.96 * 336.559 1.56 2408.26 * 14.082 723.37 20.57 NL 1638 NL PLSA 1638 Normal NA 1325.14 546.54 1517.44 * 0.827 4.22 * 2,767 14.09 * 72.188 * 336.559 0.87 1738.25 165.88 1293.37 9.55 NL 1639 NL PLSA 1639 Normal NA * 141.842 560.21 346.99 * 0.827 11.90 * 2.767 12.77 * 72.188 * 336.559 0.82 1916.94 106.2 569.31 4.82 NL 1640 NL PLSA 1640 Normal NA 1221.8 705.80 1939.25 * 0.827 9.89 * 2.767 17.87 1.015.8 * 336.559 0.75 1684 , 08 97 438.18 9.68 NL 1641 NL PLSA 1641 Normal NA * 141.842 1467.98 1794.56 * 0.827 4.44 * 2.767 9.68 609.69 * 336.559 1.04 2873.13 114.85 1933, 66 7.62 NL 1642 NL PLSA 1642 Normal NA 1543.68 3462.09 1894.69 * 0.827 11.41 * 2.767 18.81 535.62 * 336.559 1.23 3380.2 * 14.082 1273.34 7.53 NL 1643 NL PLSA 1643 Normal NA * 141.842 1337.71 3176 * 0.827 17.11 * 2.767 17.18 633.87 * 336.559 0.77878.44 * 14.082 324.21 10.91 NL 1644 NL PLSA 1644 Normal NA 1185.79 2110.94 1920.68 * 0.827 7.33 * 2.767 12.77 922.3 * 336.559 1.14 2014.59 * 14.082 1122.72 6.83 NL 1645 NL PLSA 1645 Nor poorly NA * 141,842 1496.36 2518.96 * 0.827 20.57 * 2.767 12.49 699.2 * 336.559 0.92 2077.49 * 14.082 1157.57 3.65 NL 1646 NL PLSA 1646 Normal NA 1138.17 716 , 84 1824.21 * 0.827 13.95 * 2.767 12.77 * 72.188 * 336.559 0.74 3065.64 * 14.082 956.98 9.03 NL 1647 NL PLSA 1647 Normal NA * 141.842 1490.68 1364.67 * 0.827 24.57 * 2.767 12.45 1250.63 * 336.559 0.76 1595.88 * 14.082 505.29 6.55 NL 1648 NL PLSA 1648 Normal NA 1021.07 634.22 2707.71 5.07 5.93 * 2.767 16.9 535.6 * 336.559 0.92 1829.81 * 14.082 613.48 14.31 NL 1649 NL PLSA 1649 Normal NA * 141.842 4173.99 2370.48 7.16 6.59 * 2.767 19.95 2.800 , 2 * 336.559 0.8 2234.49 * 14.082 486.46 7.73 NL 1650 NL PLSA 1650 Normal NA * 141.842 1388.63 1652.13 * 0.827 22.34 * 2.767 11.72 * 72.188 * 336.559 0.72 1681.62 * 14.082 1073.27 6.28 NL 1651 NL PLSA 1651 Normal NA * 141.842 694.77 3950.54 * 0.827 9.34 * 2.767 15.57 853.27 * 336.559 0.96 1583.68 123.05 1044.64 7.22 NL 1652 NL PLSA 1652 Normal NA * 141.842 2729.61 3264.22 * 0.827 18.58 19.13 12.72 1500.70 * 336.559 0.63 3230.41 127 1 051.83 7.81 NL 1653 NL PLSA 1653 Normal NA 1915.01 1550.33 884.32 7.16 10.18 22.88 13.04 439.3 2824 0.74 2506.07 282.42 457.71 6 NL 1654 NL PLSA 1654 Normal NA * 141.842 1575.92 1967.13 * 0.827 46.13 * 2.767 11.82 1162.22 * 336.559 0.61 2007.06 * 14.082 739.73 12.73 NL 1655 NL PLSA 1655 Normal NA 16512.36 1329.23 2136.61 6.27 7.21 * 2.767 14.13 1072.12 2678.49 2.04 977.53 234.28 2252.69 6.5 NL 1656 NL PLSA 1656 Normal NA 2056.28 810.92 2346.1 11.06 14.21 * 2.767 11.3 483.86 * 336.559 0.97 2072.45 172.26 * 78.12 8.14 NL 1657 NL PLSA 1657 Normal NA 906, 95 1168.54 2088.12 * 0.827 7.05 23.38 15.71 2735.51 * 336.559 0.72 940.71 * 14.082 927.12 10.66 NL 1658 NL PLSA 1658 Normal NA * 141.842 2402.17 1679 , 84 * 0.827 16.69 * 2.767 12.59 2.575.2 * 336.559 0.58 1664.43 * 14.082 755.96 6.69 NL 1659 NL PLSA 1659 Normal NA 2090.26 271.16 3638.68 * 0.827 31 , 95 * 2,767 13.93 1,213.9 * 336.559 0.88 2770.96 * 14.082 551.3 8.52 NL 1660 NL PLSA 1660 Normal NA * 141.842 672.73 4017.17 * 0.827 19.05 * 2.767 21.33 3959 , 21 * 336,559 0.92 1327.65 87.1 1588.07 7.27 NL 1661 NL PLSA 1661 Normal NA * 141.842 565.68 464.82 6.05 6.10 * 2.767 10.76 468.53 2092.79 0.64 1349.18 218.56 919.6 8.34 NL 1662 NL PLSA 1662 Normal NA 2686.57 997.49 1333.43 * 0.827 36.92 * 2.767 15.02 836.18 * 336.559 1.17 2362.11 * 14.082 635.13 5.95 NL 1663 NL PLSA 1663 Normal NA * 141.842 913.77 3000.18 * 0.827 4.87 * 2.767 12.06 2219.45 * 336.559 0.48 1882.03 * 14.082 747.86 7, 12 NL 1664 NL PLSA 1664 Normal NA 1620.32 656.22 2662.33 5.18 6.87 * 2.767 21.5 593.67 * 336.559 1.52 2077.49 * 14.082 630.83 10.84 NL 1665 NL PLSA 1665 Normal NA * 141.842 4926.21 2028.52 7.61 6.97 * 2.767 21.6 2.785.8 * 336.559 0.55 2356.98 * 14.082 457.71 4.87 NL 1666 NL PLSA 1666 Normal NA * 141.842 1402.79 1570.91 * 0.827 36.17 * 2.767 14.05 * 72.188 * 336.559 0.8 1882.03 * 14.082 1073.27 5.25 NL 1667 NL PLSA 1667 Normal NA * 141.842 716.84 3198.99 * 0.827 9.73 * 2.767 17.25 847.56 * 336.559 1.01 1735.78 106.2 1058.99 4.46 NL 1668 NL PLSA 1668 Normal NA * 141.842 2266.47 3160.67 * 0.827 23.15 * 2 , 7 67 13.72 2089.68 * 336.559 0.38 3493.15 114.85 723.37 7.5

NL 1669 NL PLSA 1669 Normal NA 1736.84 1502.03 992.32 6.71 9.77 19.39 10.76 682.76 2678.49 0.66 2578.44 286.95 505.29 8.77 NL 1701 NL PLSA 1701 Normal NA 2254.19 3781.59 4612.17 8.62 24.92 21.20 18.09 2024.99 * 332.479 1.48 * 30.173 118.75 1088.63 7.33 NL 1702 NL PLSA 1702 Normal NA 1600.12 3193.60 3203.93 * 0.833 15.02 * 2.733 27.85 650.36 * 332.479 1.18 3115.57 118.75 923.5 10.33 NL 1703 NL PLSA 1703 Normal NA 5305 , 31 1094.80 2425.33 6.66 9.23 18.42 17.16 982.59 1994.88 1.21 1554.55 185.29 391.29 15.56 NL 1704 NL PLSA 1704 Normal NA 4500.37 1449 , 40 2362.3 * 0.833 2.24 * 2.733 17.75 1222.04 * 332.479 1.37 2231.3 240.18 659.24 4.99 NL 1705 NL PLSA 1705 Normal NA 1200.48 2016.95 3508.48 5 , 25 31.65 * 2.733 23.37 675.33 * 332.479 1.22 1001.09 176.07 765.99 8.98 NL 1706 NL PLSA 1706 Normal NA 1121.29 1105.81 2181.29 * 0.833 10, 93 * 2.733 11.15 545.35 * 332.479 1.4 725.4 102.9 939.86 4.78 NL 1707 NL PLSA 1707 Normal NA * 131.507 947.33 2407.06 * 0.833 6.81 * 2.733 7, 52 * 77,963 * 332,479 1.22 1087.89 155.91 695.39 6.81 NL 1708 NL PLSA 1708 Normal NA * 131.507 1807.34 3172.69 * 0.833 8.68 * 2.733 15.49 697.26 * 332.479 1.16 2439.35 155.91 644 5.39 NL 1709 NL PLSA 1709 Normal NA * 131.507 2193.35 1944.45 7.31 21.94 33.05 16.13 949.94 * 332.479 0.95 1937.92 194.06 811.95 4.43 NL 1710 NL PLSA 1710 Normal NA 3918.26 615.38 1484.88 * 0.833 6.37 * 2.733 13.78 694.13 * 332.479 1.05 510.7 247.07 851.5 5.36 NL 1711 NL PLSA 1711 Normal NA * 131.507 1674.69 921.29 6.02 3.12 * 2.733 11.37 600.72 * 332.479 1.42 1691.19 83.33 336.46 4.81 NL 1712 NL PLSA 1712 Normal NA * 131.507 1460.72 1865.86 * 0.833 8.91 * 2.733 12.17 1128.02 * 332.479 1.48 1703.55 118.75 794.81 9.82 NL 1713 NL PLSA 1713 Normal NA 1843.17 1506.12 2875.07 * 0.833 19.08 * 2.733 12.17 * 77.963 * 332.479 1.73 1256.55 118.75 993.78 5.51 NL 1714 NL PLSA 1714 Normal NA 946.05 5556.32 3301.44 * 0.833 5.21 * 2.733 10.61 1208.54 * 332.479 0.55 2288.42 83.33 1073.01 9.71 NL 1715 NL PLSA 1715 Normal NA * 131.507 1149.96 5708.31 * 0.833 13.41 22.76 14.48 878.62 2589.23 0, 84 2001.83 144.71 369.66 8.83 NL 1716 NL PLSA 1716 Normal NA * 131.507 3887.84 2332.53 * 0.833 8.29 * 2.733 13.03 1378.95 * 332.479 1.3 1138.48 83 , 33 659.24 3.41 NL 1717 NL PLSA 1717 Normal NA 1439.48 1824.72 4623.3 * 0.833 22.64 * 2.733 14.69 824.02 * 332.479 1.13 1617.45 102.9 419.56 3 , 28 NL 1718 NL PLSA 1718 Normal NA * 131,507 834.21 3031.03 * 0.833 4.82 * 2.733 8.25 478.36 * 332.479 0.93 1480.23 185.29 355.02 6.35 NL 1719 NL PLSA 1719 Normal NA * 131.507 1580.18 2945.18 * 0.833 10.75 * 2.733 14.41 * 77.963 * 332.479 1.09 1544.46 102.9 789.07 4.49 NL 1720 NL PLSA 1720 Normal NA 5127, 7 1012.60 3070.66 * 0.833 4.40 * 2.733 21.64 1141.38 * 332.479 1.35 2800.46 102.9 871.07 4.5 NL 1721 NL PLSA 1721 Normal NA * 131.507 1497.60 3025 , 87 * 0.833 12.74 19.61 17.4 930.4 * 332.479 1.26 1895.57 102.9 713.24 8.14 NL 1722 NL PLSA 1722 Normal NA * 131.507 1720.71 3151.89 * 0.833 9, 22 * 2.733 9.95 * 77.963 * 332.479 1.27 2354.74 118.75 340.2 5.49 NL 1723 NL PLSA 1723 Normal NA 2043.16 649.19 2350.72 5 12.83 * 2.733 24.63 814 , 43 * 332.479 0.91 1510.26 83.33 546.78 6.47 NL 1724 NL PLSA 1724 Normal NA 1130.08 561.15 1067.99 5.5 4.88 * 2.733 14.79 2.091.7 1995 1.03 329.06 233.1 * 92.89 4.77 NL 1725 NL PLSA 1725 Normal NA 171255.75 23665.88 1241.58 * 0.833 10.72 * 2.733 14.27 * 77.963 * 332.479 0.89 1486 , 22 102.9 1251.26 4.86 NL 1726 NL PLSA 1726 Normal NA * 131.507 1191.50 593.19 * 0.833 13.49 * 2.733 11.71 1437.69 * 332.479 0.98 1392.92 176.07 956.14 8.18 NL 1727 NL PLSA 1727 Normal NA * 131.507 2102.02 3765.81 * 0.833 4.67 * 2.733 16.93 * 77.963 * 332.479 1.5 2687.98 132.45 969.63 5.03 NL 1728 NL PLSA 1728 Normal NA 2985.13 1577.32 4604.75 * 0.833 16.27 25.48 46.21 2.817.6 * 332.479 1.04 1034.14 83.33 873.85 17.75 NL 1729 NL PLSA 1729 Normal NA 998.52 1738.00 4434.83 * 0.833 8.27 22.76 18.84 3191.78 * 332.479 1.16 1013.92 125.81 1078.22 7.05 NL 1730 NL PLSA 1730 Normal NA * 131.507 901 , 37 1461.68 * 0.833 3.62 * 2.733 8.53 551.5 * 332.479 0.93 1195.28 118.75 362.37 5.5 NL 1731 NL PLSA 1731 Normal NA 841.32 2571.42 2852, 9 * 0.833 6.7 9 * 2.733 18.71 885.08 * 332.479 1.57 3063.71 144.71 575.53 7.75 NL 1732 NL PLSA 1732 Normal NA * 131.507 1166.56 1557.91 5.63 13.46 18.82 9.45 * 77.963 * 332.479 1.58 505.99 218.3 553.21 4.74 NL 1733 NL PLSA 1733 Normal NA * 131.507 1319.81 1845.09 8.62 6.24 * 2.733 7.77 484, 42 * 332,479 1.34 1732.47 155.91 376.91 4.79 NL 1734 NL PLSA 1734 Normal NA * 131.507 1194.28 2522.01 * 0.833 24.11 21.20 11.15 * 77.963 * 332.479 3, 36 2135.37 210.52 419.56 3.06 NL 1735 NL PLSA 1735 Normal NA * 131.507 4262.03 3724.78 * 0.833 6.60 18.82 20.14 788.92 * 332.479 1.58 1605.23 * 13.888 945.29 7.99 NL 1736 NL PLSA 1736 Normal NA 3263.06 1188.73 1713.28 5.25 7.74 * 2.733 10.61 1098.04 * 332.479 0.8 1993.28 194.06 559 , 62 4.47 NL 1737 NL PLSA 1737 Normal NA 3282.36 925.67 3824.82 * 0.833 5.82 * 2.733 21.83 1114.68 * 332.479 1.65 2923.27 132.45 1129.96 3, 99 NL 1738 NL PLSA 1738 Normal NA * 131,507 1125,10 2461.93 * 0.833 7.22 18.82 7.26 551.5 * 332.479 0.56 1460.28 83.33 904.29 6.09 NL 1739 NL PLSA 1739 Normal NA 858.76 1600.18 2 311.07 * 0.833 12.84 * 2.733 10.75 490.49 * 332.479 1.31 2290.63 * 13.888 1320.33 3.24 NL 1740 NL PLSA 1740 Normal NA * 131.507 1543.10 1776.63 * 0.833 6 , 08 20,01 9,55 551,5 * 332,479 0,52 1769,81 102,9 789,07 7,85 NL 1741 NL PLSA 1741 Normal NA 806,46 3098,65 3231,74 * 0,833 6,71 * 2.733 12.21 619.29 * 332.479 1.05 164.43 83.33 647.06 9.79 NL 1742 NL PLSA 1742 Normal NA 884.93 1089.30 3304.93 * 0.833 9.24 * 2.733 14.34 1.388.3 * 332.479 1.28 1980.47 194.06 713.24 4.97 NL 1743 NL PLSA 1743 Normal NA * 131.507 2128.50 2656.3 * 0.833 8.18 * 2.733 10.89 * 77.963 * 332.479 1 , 16 1842.93 102.9 412.55 4.52 NL 1744 NL PLSA 1744 Normal NA * 131.507 3089.48 1786.16 * 0.833 3.42 * 2.733 16.56 1698.13 * 332.479 0.98 1931.55 102.9 1030.99 10.42 NL 1745 NL PLSA 1745 Normal NA 2635.41 2084.39 3138.04 * 0.833 12.43 * 2.733 19.3 493.52 * 332.479 0.81 2470.69 * 13.888 845, 89 6.85 NL 1746 NL PLSA 1746 Normal NA 3311.33 2562.42 642.83 6.27 5.66 * 2.733 29.15 4011.70 2654.31 0.92 1202.9 260.35 1007.12 13 59 NL 1747 NL PLSA 1747 Norm al NA 1626.99 2867.14 2545.43 * 0.833 12.67 * 2.733 11.07 644.14 1994.88 1.13 2284.02 176.07 * 92.89 4.27 NL 1748 NL PLSA 1748 Normal NA * 131.507 449.31 1918.74 * 0.833 5.64 * 2.733 9.65 631.70 * 332.479 2.07 1633.77 132.45 1474.2 0.91 NL 1749 NL PLSA 1749 Normal NA * 131.507 2087.33 2996.64 * 0.833 14.80 * 2.733 11.33 681.59 * 332.479 1.43 2205.04 102.9 546.78 2.38 NL 1750 NL PLSA 1750 Normal NA 1332.98 1726.47 1947.66 * 0.833 13.69 * 2.733 11.76 533.12 * 332.479 1.09 1765.65 166.31 294.42 5.35 NL 1751 NL PLSA 1751 Normal NA * 131.507 3963.03 3462.68 * 0.833 6.22 * 2.733 11.76 * 77.963 * 332.479 1.25 1687.07 118.75 628.65 4.66 NL 1752 NL PLSA 1752 Normal NA * 131.507 1056.36 2900.69 * 0.833 3.97 * 2.733 9.8 * 77.963 * 332.479 0.92 3051.95 150.42 328.94 7.21 NL 1753 NL PLSA 1753 Normal NA * 131.507 952.75 826.09 6.79 9.64 20.41 10 2047.17 1994.88 1, 19 2341.44 285.25 556.42 5.87 NL 1754 NL PLSA 1754 Normal NA * 131.507 1089.30 2004.09 5.76 12.50 * 2.733 14.1 * 77.963 * 332.479 0.82 1542.44 102 , 9 823.31 7.06 NL 175 5 NL PLSA 1755 Normal NA 911.11 1144.43 1626.71 * 0.833 5.51 * 2.733 9.9 650.36 * 332.479 1.14 2642.38 155.91 384.12 5.81 NL 1756 NL PLSA 1756 Normal NA * 131.507 2755.11 3020.71 * 0.833 19.97 * 2.733 13.22 1385.84 * 332.479 1.28 2018.95 118.75 1036.27 4.87 NL 1757 NL PLSA 1757 Normal NA 4157.45 1714.95 1562.59 6.79 13.93 18.82 18.87 982.6 * 332.479 1.01 953.79 194.06 569.18 6.82 NL 1758 NL PLSA 1758 Normal NA 3563.93 7351, 58 1415.43 12.63 11.02 * 2.733 10.43 * 77.963 * 332.479 1.05 2027.52 132.45 1109.35 4.07 NL 1759 NL PLSA 1759 Normal NA * 131.507 2311.76 2657.98 * 0.833 21.22 * 2.733 13.22 613.10 * 332.479 1.34 2488.64 83.33 806.25 7.23 NL 1760 NL PLSA 1760 Normal NA * 131.507 1050.88 329.34 * 0.833 4.72 * 2.733 9.5 * 77.963 * 332.479 0.84 1071.15 155.91 575.53 4.87 NL 1761 NL PLSA 1761 Normal NA * 131.507 6055.07 2382.18 * 0.833 10.00 * 2.733 18.48 709, 8 * 332.479 0.72 2535.9 83.33 591.3 7.35 NL 1762 NL PLSA 1762 Normal NA * 131.507 2514.48 3696.29 * 0.833 4.00 * 2.733 11.67 846.45 * 332.479 2, 4 2042,54 118, 75 656.2 6.34 NL 1763 NL PLSA 1763 Normal NA 1430.59 2897.50 2832.46 * 0.833 12.05 * 2.733 12.09 * 77.963 * 332.479 1.29 3106.13 218.3 546.78 5 , 48 NL 1764 NL PLSA 1764 Normal NA * 131,507 1305,80 1917.13 * 0.833 12.12 * 2.733 19.19 687.9 * 332.479 0.76 1954.91 83.33 514.28 3.23 NL 1765 NL PLSA 1765 Normal NA * 131,507 2586.43 1944.45 * 0.833 5.16 * 2.733 15.42 989.14 * 332.479 1.38 1013.92 102.9 597.58 6.7 NL 1766 NL PLSA 1766 Normal NA * 131.507 1111 , 32 1529.89 * 0.833 11.22 19.61 10.98 644.1 * 332.479 0.84 722 194.06 376.91 4.95 NL 1767 NL PLSA 1767 Normal NA * 131.507 2478.58 3503.19 * 0.833 12.72 * 2.733 13.11 * 77.963 * 332.479 1.87 2385.84 83.33 467.67 5.04 NL 1768 NL PLSA 1768 Normal NA * 131.507 1015.33 1929.98 5 7.24 * 2.733 7 , 77 594.55 * 332.479 1.07 2094.24 273.05 309.91 4.21 NL 1769 NL PLSA 1769 Normal NA * 131.507 1100.31 1765.52 * 0.833 16.34 * 2.733 14.27 472.31 * 332.479 0.97 1984.74 102.9 725.06 3.45 NL 1770 NL PLSA 1770 Normal NA * 131.507 933.79 1196.35 * 0.833 4.64 * 2.733 13.63 827.22 * 332.479 1.45 3382.6 83.33 * 92.89 2.87 NL 1771 NL PLSA 1771 Normal NA * 131.507 1155.49 2686.66 * 0.833 3.29 * 2.733 13.18 490.49 2654.31 1.2 1609.3 194.06 487.82 2.84 NL 1772 NL PLSA 1772 Normal NA 1033.55 3071.14 4414.59 * 0.833 9.08 * 2.733 10.33 594.55 * 332.479 1.08 2018.95 83 , 33 585.01 4.37 NL 1773 NL PLSA 1773 Normal NA 806.46 1361.93 3136.31 * 0.833 3.22 * 2.733 11.11 606.91 * 332.479 1.23 1254.63 185.29 540, 33 18.02 NL 1774 NL PLSA 1774 Normal NA 806.46 3950.49 3062.04 * 0.833 11.33 * 2.733 19.09 2605.46 * 332.479 0.97 2920.94 102.9 440.38 6.32 NL 1775 NL PLSA 1775 Normal NA * 131,507 1067.33 3089.64 * 0.833 5.73 * 2.733 12.61 606.91 * 332.479 1.16 1597.1 118.75 713.24 4.69 NL 1776 NL PLSA 1776 Normal NA * 131.507 3004.00 2493.61 * 0.833 11.21 * 2.733 14.48 722.42 * 332.479 1.17 206.97 * 13.888 760.19 6.39 NL 1777 NL PLSA 1777 Normal NA * 127.973 1065, 09 1289.06 * 0.805 7.75 * 2.369 16.3 504.26 * 322.135 1.22 1339.65 * 13.379 494.31 4.27 NL 1778 NL PLSA 1778 Normal NA 1677.86 1218.76 1829.08 5 36 6.52 * 2.369 40 , 82 881.77 * 322.135 1.56 897.66 164.29 389.1 5.3

NL 1779 NL PLSA 1779 Normal NA * 127.973 1311.20 2334.61 * 0.805 11.93 * 2.369 26.76 511.71 * 322.135 1.73 1966.52 90.59 1082.51 4.28 NL 1780 NL PLSA 1780 Normal NA * 127,973 1739,28 2994,38 * 0,805 11,07 * 2,369 7,72 519,17 * 322,135 1,03 1053,57 90,59 752,15 2,7 NL 1781 NL PLSA 1781 Normal NA * 127,973 3299 , 13 3669.87 * 0.805 7.53 * 2.369 21.67 694.98 * 322.135 1.29 2296.13 90.59 869.3 1.92 NL 1782 NL PLSA 1782 Normal NA * 127.973 1102.67 3111.73 * 0.805 8.59 * 2.369 17.13 * 75.427 * 322.135 1.23 876.65 * 13.379 785.31 5.04 NL 1783 NL PLSA 1783 Normal NA 1776.7 716.28 1117.89 * 0.805 19.30 * 2.369 30.2 602.32 * 322.135 1.09 2231.03 * 13.379 532.64 2.09 NL 1784 NL PLSA 1784 Normal NA * 127.973 836.61 1377.58 * 0.805 13.01 * 2.369 20.97 2276.33 2667.75 0.97 1449.7 237.61 232.78 6.1 NL 1785 NL PLSA 1785 Normal NA * 127.973 1385.01 207.28 * 0.805 11.25 * 2.369 20.57 1502.97 * 322.135 1.88 887, 14 282.92 3788.93 2.33 NL 1786 NL PLSA 1786 Normal NA 1062.41 1876.69 3097.79 * 0.805 10.03 * 2.369 28.04 910.1 * 322.135 1.26 1 241.96 90.59 791.88 2.3 NL 1787 NL PLSA 1787 Normal NA * 127.973 679.94 1144.82 8.98 13.50 * 2.369 27.11 * 75.427 * 322.135 2.39 539.26 121, 5 389.1 2.71 NL 1788 NL PLSA 1788 Normal NA * 127.973 1633.38 2677.94 * 0.805 8.81 * 2.369 23.31 * 75.427 * 322.135 3.44 929.28 90.59 1527.44 13, 84 NL 1789 NL PLSA 1789 Normal NA * 127,973 979.57 2901.16 * 0.805 7.09 * 2.369 20.03 519.17 * 322.135 3.82 1633.58 90.59 613.7 3.46 NL 1790 NL PLSA 1790 Normal NA * 127,973 2701,77 2428,51 * 0,805 15,37 * 2,369 24,27 1158,58 * 322,135 1.27 1421.59 181.5 691.08 5.01 NL 1791 NL PLSA 1791 Normal NA 1382, 01 1237.75 2703.56 5.72 33.07 * 2.369 21.61 549.20 * 322.135 1.41 1130.93 * 13.379 804.97 3.35 NL 1792 NL PLSA 1792 Normal NA * 127.973 2397.71 3647 , 62 * 0.805 9.54 * 2.369 22.77 749.92 * 322.135 0.67 2018.11 * 13.379 824.45 3.18 NL 1793 NL PLSA 1793 Normal NA * 127.973 6327.48 1612.66 21.22 14 , 45 * 2,369 21,42 1494,19 * 322,135 1,97 717,68 312,94 1264,92 4,48 NL 1794 NL PLSA 1794 Normal NA * 127,973 374,88 2948,74 * 0,805 13,46 * 2,369 23 , 48 583.26 * 322.135 1.58 527.66 121.5 * 24.873 4.21 NL 1795 NL PLSA 1795 Normal NA 1995.98 6034.70 2297.06 * 0.768 17.23 * 2.652 27.6 * 73.544 * 302.743 1 , 54 1828.15 96.04 893.18 4.04 NL 1796 NL PLSA 1796 Normal NA * 123.336 1429.10 1865.65 * 0.768 14.85 * 2.652 26.41 809.97 * 302.743 0.95 1749.86 111.67 2278.07 2.13 NL 1797 NL PLSA 1797 Normal NA * 123.336 1378.68 2362.5 9.97 2.52 * 2.652 16.58 * 73.544 * 302.743 0.85 1413.47 87.79 744, 12 4.46 NL 1798 NL PLSA 1798 Normal NA 1256.98 2027.41 2523.74 * 0.768 19.93 * 2.652 30.07 * 73.544 * 302.743 1.36 492.6 96.04 998.65 4.91 NL 1799 NL PLSA 1799 Normal NA * 123.336 562.52 3601.42 6.54 35.04 19.88 21.63 2138.40 * 302.743 1.05 464.97 119.13 871.49 3.92 NL 1800 NL PLSA 1800 Normal NA * 123.336 1683.29 2922.26 * 0.768 12.71 22.62 22.3 833.44 * 302.743 1.05 1108.97 147.17 1222.83 22.91 NL 1801 NL PLSA 1801 Normal NA 2794 , 77 3388.13 4176.8 * 0.768 13.29 * 2.652 21.9 3169.21 * 302.743 1.39 1787.93 103.98 2132.97 2.23 NL 1802 NL PLSA 1802 Normal NA 4100.92 647, 15 2028.8 4 * 0.768 10.66 * 2.652 31.25 * 73.544 * 302.743 1.59 1214.4 115.43 315.21 3.87 NL 1803 NL PLSA 1803 Normal NA * 123.336 1683.29 1995.42 * 0.768 11.98 * 2.652 26.96 545.66 * 302.743 0.8 3514.25 96.04 744.12 1.95 NL 1804 NL PLSA 1804 Normal NA 740.02 2538.98 2473.05 * 0.768 17.69 23.17 29.29 3320.75 * 302.743 1.15 2263.78 * 13.922 1039.58 3.04 NL 1805 NL PLSA 1805 Normal NA 1687.72 2713.88 460.12 * 0.768 39.76 * 2.652 22.57 1788.23 * 302.743 1.87 1159.65 100.04 1197.35 5.27 NL 1806 NL PLSA 1806 Normal NA * 123.336 1490.91 2465.54 * 0.768 4.10 * 2.652 19.9 * 73.544 * 302.743 1.06 1559.63 147.17 755.7 3.66 NL 1807 NL PLSA 1807 Normal NA * 123.336 910.21 1688.16 * 0.768 1.63 * 2.652 19.9 * 73.544 * 302.743 1.06 1256.16 119.13 293.02 2.92 NL 1808 NL PLSA 1808 Normal NA 1041.01 1112.37 1595.9 * 0.768 15.09 * 2.652 16.21 1058.20 * 302.743 0.79 765.97 96.04 347.36 1.84 NL 1809 NL PLSA 1809 Normal NA * 123.336 1019.13 2019.55 * 0.768 15.15 16.60 18.97 483.65 * 302.743 1.21 2213.61 87.79 525.16 2.17 NL 1810 NL PLSA 1810 Norm al NA * 123.336 4581.44 2325.09 * 0.768 * 0.22 * 2.652 19.9 * 73.544 * 302.743 1.37 1848.31 * 13.922 418.17 5.33 NL 1811 NL PLSA 1811 Normal NA * 123.336 2128, 83 2019.55 * 0.768 15.13 17.14 17.9 2.302.4 * 302.743 0.93 993.68 * 13.922 914.66 3.77 NL 1812 NL PLSA 1812 Normal NA * 123.336 2429.91 883.5 * 0.768 4.18 * 2.652 16.67 1461.91 * 302.743 0.83 1204.94 * 13.922 529.6 3.8 NL 1813 NL PLSA 1813 Normal NA * 123.336 557.26 2159.03 * 0.768 7.55 * 2.652 19.59 506 , 38 * 302,743 0.8 1485.3 * 13.922 456.51 2.95 NL 1814 NL PLSA 1814 Normal NA * 123.336 1353.53 2326.96 6.8 2.43 17.14 18.81 * 73.544 * 302.743 1.16 1108.97 96.04 577.56 5.07 NL 1815 NL PLSA 1815 Normal NA * 123.336 1139.90 1828.63 * 0.768 3.41 * 2.652 21.07 450.10 * 302.743 1.27 1256.16 * 13.922 493.6 2.73 NL 1816 NL PLSA 1816 Normal NA * 123.336 583.62 1991.7 * 0.768 3.56 * 2.652 13.73 * 73.544 * 302.743 0.94 1775.89 * 13.922 1617.24 2.21 NL 1817 NL PLSA 1817 Normal NA 936.15 1041.01 1708.47 * 0.768 16.85 * 2.652 24.63 655.8 * 302.743 0.98 2430.44 * 13.922 712.89 2.89 NL 1818 NL PLSA 1818 Normal NA * 123.336 1206.18 2580.14 * 0.768 30.77 * 2.652 23.09 728.00 * 302.743 0.85 2184.44 * 13.922 900.36 8.68 NL 1819 NL PLSA 1819 Normal NA 1679.96 2424.03 2270.91 7.85 34.47 * 2.652 19.67 673.05 * 302.743 0.775758 * 13.922 293.02 4.04 NL 1820 NL PLSA 1820 Normal NA * 123.336 392 , 76 1,774.99 * 0.768 2.94 * 2.652 14.82 * 73.544 * 302.743 1.15 1049.27 126.39 293.02 1.8 NL 1821 NL PLSA 1821 Normal NA * 123.336 866.88 2270.91 * 0.768 9.58 * 2.652 23.09 * 73.544 * 302.743 1.08 1616.72 * 13.922 860.57 7.39 NL 1822 NL PLSA 1822 Normal NA * 123.336 165.53 1965.73 * 0.768 12.87 * 2.652 15 , 82 638.70 * 302.743 1.01 1959.91 * 13.922 275.93 3.01 NL 1823 NL PLSA 1823 Normal NA * 123.336 2059.24 1743.57 * 0.768 7.91 * 2.652 23.53 1145.77 * 302,743 1.24 1415.4 140.4 555.97 4.66 NL 1824 NL PLSA 1824 Normal NA * 123.336 557.26 1806.43 * 0.768 8.06 * 2.652 26.41 2257.67 * 302.743 0.94 898 , 18 * 13.922 484.44 3.15 NL 1825 NL PLSA 1825 Normal NA * 123.336 1356.33 1776.84 * 0.768 15.28 * 2.652 26.36 * 73.544 * 302.7 43 0.62 811 * 13.922 336.78 1.89 NL 1826 NL PLSA 1826 Normal NA * 123.336 2785.35 2446.79 * 0.768 12.54 * 2.652 20.93 * 73.544 * 302.743 0.91 869.02 179, 24 615.63 1.42 NL 1827 NL PLSA 1827 Normal NA 833.57 1328.42 1978.71 * 0.768 6.18 * 2.652 20.2 809.97 * 302.743 1.13 1612.78 133.47 502.7 3.59 NL 1828 NL PLSA 1828 Normal NA * 123.336 1468.41 3246.66 * 0.768 13.05 * 2.652 23.59 * 73.544 * 302.743 1.41 1506.76 96.04 681.1 5.08 NL 1829 NL PLSA 1829 Normal NA * 123.336 1062.93 2351.27 * 0.768 8.83 * 2.652 22.96 * 73.544 * 302.743 0.72 773.15 * 13.922 628.11 3.91 NL 1830 NL PLSA 1830 Normal NA * 123.336 4932 , 87 5983.29 * 0.768 3.09 * 2.652 29.34 870.32 * 302.743 1.3 2424.07 * 13.922 1266.9 6.31 NL 1831 NL PLSA 1831 Normal NA 5676.12 1502.17 1651.24 * 0.768 4.00 * 2.652 27.12 1.333.2 6363 1.73 1146.48 * 13.922 1413.6 7.42 NL 1832 NL PLSA 1832 Normal NA * 123.336 753.84 2435.54 * 0.768 1.76 * 2.652 16.11 * 73.544 * 302.743 0.7 1103.35 107.86 827.45 4.1 NL 1833 NL PLSA 1833 Normal NA * 123.336 1082.14 2971.68 * 0.768 5.10 * 2.6 52 26.19 452.31 * 302.743 1.06 1015.88 103.98 1662.33 5.4 NL 1834 NL PLSA 1834 Normal NA * 123.336 764.56 2099.47 * 0.768 21.56 * 2.652 20.2 * 73.544 * 302.743 1.11 273.08 * 13.922 935.94 2.36 NL 1835 NL PLSA 1835 Normal NA * 123.336 1054.71 2647.93 * 0.768 21.88 * 2.652 22.1 1044.25 * 302.743 0.98 1917.16 * 13.922 437.51 8.65 NL 1836 NL PLSA 1836 Normal NA * 123.336 1065.67 1135.73 * 0.768 9.39 * 2.652 21.97 * 73.544 * 302.743 1.28 1090.27 166.74 1039.58 9.38 NL 1837 NL PLSA 1837 Normal NA * 123.336 1785.92 3909.28 * 0.768 3.20 * 2.652 29.63 1604.04 * 302.743 1.35 2629.65 147.17 1187.75 7.28 NL 1838 NL PLSA 1838 Normal NA * 123.336 416.08 659.75 * 0.768 11.98 * 2.652 15.63 1.002.7 * 302.743 0.73 901.83 147.17 511.73 3.73 NL 1839 NL PLSA 1839 Normal NA * 123.336 578.34 2627.21 * 0.768 6.12 * 2.652 26.41 * 73.544 * 302.743 1.01 1880.66 * 13.922 1229.16 5.73 NL 1840 NL PLSA 1840 Normal NA * 123.336 7531.68 1987, 99 * 0.768 8.19 * 2.652 21.83 643.58 * 302.743 0.72 1371.01 * 13.922 511.73 4.29 NL 1841 NL PLSA 1841 Normal NA 1 112.1 509.97 1762.05 * 0.768 7.70 * 2.652 24.93 * 73.544 * 302.743 1.47 1199.26 * 13.922 1878.24 5.17 NL 1842 NL PLSA 1842 Normal NA * 123.336 1538.82 1802 , 73 * 0.768 10.21 * 2.652 24.93 1183.05 * 302.743 0.99 1951.75 * 13.922 357.81 4.61 NL 1843 NL PLSA 1843 Normal NA * 123.336 1200.65 2071.57 * 0.768 4, 85 * 2,652 21,9 465,68 * 302,743 1,78 1715,92 * 13,922 594,6 4,06 NL 1844 NL PLSA 1844 Normal NA 1147,99 1322,84 2356,89 * 0,768 13,83 * 2,652 22, 5 529.39 * 302.743 1.81 1524.35 111.67 573.27 2.14 NL 1845 NL PLSA 1845 Normal NA * 123.336 1929.33 1117.34 * 0.768 20.50 * 2.652 24.27 576.23 * 302.743 0 , 82 1723.89 * 13.922 1341.05 3.6 NL 1846 NL PLSA 1846 Normal NA * 123.336 1558.58 2918.46 7.06 17.58 * 2.652 27.28 538.67 * 302.743 1.15 1321.07 * 13.922 827.45 3.91 NL 1847 NL PLSA 1847 Normal NA * 123.336 5194.49 2700.75 * 0.768 3.14 * 2.652 20.93 * 73.544 * 302.743 1.07 2118.06 87.79 564.64 2.11 NL 1848 NL PLSA 1848 Normal NA * 123.336 610.05 1610.65 * 0.768 8.84 * 2.652 11.2 * 73.544 * 302.743 0.82 1378.71 133.47 805.07 4.7 1 NL 1849 NL PLSA 1849 Normal NA * 123.336 1723.14 1815.68 * 0.768 21.38 * 2.652 25.28 445.7 * 302.743 0.88 2377.45 * 13.922 293.02 7.55 NL 1850 NL PLSA 1850 Normal NA * 123.336 1197.88 3780.3 * 0.768 19.88 * 2.652 24.69 862.38 * 302.743 1.55 2105.66 * 13.922 812.56 5.46 NL 1851 NL PLSA 1851 Normal NA * 123.336 1519, 08 2893.79 * 0.768 6.95 * 2.652 21.76 538.67 * 302.743 1.35 2385.91 * 13.922 623.96 4.94 NL 1852 NL PLSA 1852 Normal NA * 123.336 3916.14 3334.98 * 0.768 25.71 17.69 27.55 648.46 * 302.743 0.97 1169.06 96.04 673.06 3.92 NL 1853 NL PLSA 1853 Normal NA * 123.336 1142.66 1649.39 * 0.768 6.30 * 2.652 18 , 16 * 73,544 * 302,743 0,75 321,77 111,67 357,81 3,86 NL 1854 NL PLSA 1854 Normal NA * 123,336 2184,08 1492,69 * 0,768 2,42 * 2,652 16,39 501,81 * 302.743 0.79 664.26 119.13 988.31 7.17 NL 1855 NL PLSA 1855 Normal NA 1198.61 1197.88 2267.18 * 0.768 6.10 * 2.652 24.87 * 73.544 * 302.743 1.42 1025 , 14 103.98 1139.23 3.94 NL 1856 NL PLSA 1856 Normal NA * 123.336 775.30 1891.58 * 0.768 9.18 * 2.652 22.03 604.85 * 302.7 43 0.86 1430.89 122.78 555.97 3.74 NL 1857 NL PLSA 1857 Normal NA * 123.336 3187.83 3770.56 * 0.768 8.94 * 2.652 28.89 668.11 * 302.743 1.43 2328 , 9 * 13.922 790.01 4.44

NL 1858 NL PLSA 1858 Normal NA * 123.336 641.84 1041.9 * 0.768 1.74 * 2.652 19.52 * 73.544 * 302.743 1.19 1019.58 153.81 1194.16 4.63 NL 1859 NL PLSA 1859 Normal NA * 123.336 4264.45 3093.61 * 0.768 6.79 * 2.652 25.62 851.83 * 302.743 1.22 3004.4 * 13.922 782.44 4.56 NL 1860 NL PLSA 1860 Normal NA * 123.336 1010.93 1448.49 * 0.768 15.99 * 2.652 19.75 * 73.544 * 302.743 1.32 1481.41 87.79 484.44 6.62 NL 1861 NL PLSA 1861 Normal NA * 123.336 2701.98 2612.14 * 0.768 18 , 34 * 2,652 17.99 626.55 * 302.743 1.33 1317.24 * 13.922 1298 3.24 NL 1862 NL PLSA 1862 Normal NA * 123.336 853.37 2550.05 * 0.768 7.69 * 2.652 19.29 * 73.544 * 302.743 1 , 84 1727.89 * 13.922 856.91 4.76 NL 1863 NL PLSA 1863 Normal NA * 123.336 2773.43 2657.36 * 0.768 6.76 * 2.652 22.7 * 73.544 * 302.743 1.4 1664.22 * 13.922 1848.79 4.75 NL 1864 NL PLSA 1864 Normal NA * 123.336 1401.07 2293.32 * 0.768 8.46 * 2.652 22.57 461.21 * 302.743 2.42 2242.85 * 13.922 1119.59 1.83 NL 1865 NL PLSA 1865 Normal NA * 123.336 1906.32 4206.45 * 0.768 11.92 * 2.652 26.85 1276.17 * 3 02.743 0.84 2432.57 * 13.922 656.86 5.76 NL 1866 NL PLSA 1866 Normal NA * 123.336 1683.29 2151.58 * 0.768 4.93 * 2.652 25.39 768.7 * 302.743 1.09 852, 66 * 13.922 525.16 4.32 NL 1867 NL PLSA 1867 Normal NA * 123.336 802.16 1069.51 * 0.768 31.17 * 2.652 13.73 * 73.544 2320.11 1.45 1086.53 173.04 1690, 26 4.34 NL 1868 NL PLSA 1868 Normal NA * 123.336 1112.37 1606.96 * 0.768 21.21 * 2.652 25.1 638.70 * 302.743 1.27 1206.83 * 13.922 677.08 2.87 NL 1869 NL PLSA 1869 Normal NA * 123.336 583.62 1708.47 * 0.768 15.13 * 2.652 17.65 1264.43 * 302.743 1.04 1838.23 119.13 154.46 4.21 NL 1870 NL PLSA 1870 Normal NA * 123.336 312.88 685.72 * 0.768 8.57 * 2.652 24.09 * 73.544 * 302.743 2.04 1225.77 * 13.922 5070.86 1.99 PANC 669 PANC 669 PLS 1 Pancreas II * 144.477 1265.44 2977.95 * 0.803 16.61 204.20 21.28 2.313.2 * 324.823 0.99 1199.41 261.85 1604.13 8.32 PANC 670 PANC 670 PLS 1 Pancreas II 4033.39 1075.54 1036.63 6, 78 4.87 26.48 14.02 595.93 1948.94 0.54 1075.47 288.93 724.15 1.44 PANC 671 PANC 671 PLS 1 Pancreas II 921.94 111 6.19 2452.33 6.78 51.07 145.76 24.96 10.179.9 5013 0.57 2252.97 261.85 1543.96 6.45 PANC 672 PANC 672 PLS 1 Pancreas II 4045.8 899, 69 4707.2 * 0.803 27.84 259.77 20.14 698.7 * 324.823 0.43 3490.58 * 16.814 490.7 5.36 PANC 673 PANC 673 PLS 1 Pancreas II 2416.71 1099.25 2337, 59 * 0.803 8.36 25.75 41.66 * 75.547 1949 0.79 2014.92 178.35 771.16 5.21 PANC 674 PANC 674 PLS 1 Pancreas II 1264.58 1279.03 3967.26 17.35 21, 25 139.00 20.56 9.295.3 1949 0.81 1668.78 231.87 1045.15 3.65 PANC 675 PANC 675 PLS 1 Pancreas II 4841.94 1065.38 1711.44 12.16 23.80 730 , 04 12.4 12,380.4 4087 0.63 1900.93 288.93 582.5 11.55 PANC 676 PANC 676 PLS 1 Pancreas II * 144.477 1472.88 2396.02 * 0.803 7.16 112.68 41, 94 2.563.1 * 324.823 0.95 1823.1 156.71 597.24 2.83 PANC 677 PANC 677 PLS 1 Pancreas II 1656.32 2012.63 3536.6 25.28 30.80 316.30 30.53 4.840.3 * 324.823 0.78 2677.24 131.71 400.16 5.47 PANC 678 PANC 678 PLS 1 Pancreas II 7999.16 1913.31 3584.05 * 0.803 15.65 * 2.727 20.46 1423.81 * 324.823 1.03 4563.1 178.35 328.93 5.86 PANC 679 PANC 679 PLS 1 Pancreas II * 144.477 256.14 3666.7 8.25 12.20 54.25 25.15 628.80 * 324.823 0.68 1717.69 131.71 997.62 3.56 PANC 680 PANC 680 PLS 1 Pancreas II 2810.21 1408.21 4248.42 7.6 13.14 53.21 25.15 1,278.5 1949 1.04 2090.23 409.12 589.88 2.16 PANC 757 PANC 757 PLS 1 Pancreas II * 141.604 1066.71 6808.96 7.5 4.61 156.20 20.19 1793.79 * 319.113 0.71 587.4 106.95 * 75.2 2.37 PANC 758 PANC 758 PLS 1 Pancreas II 1606.51 1466.86 7084.71 5.1 13.13 490.69 24.21 68801.67 * 319.113 0.72 1700.86 261.66 494.82 3.21 PANC 759 PANC 759 PLS 1 Pancreas II * 141.604 558.97 6696.45 7.5 8.97 101.63 38.41 5464.39 * 319.113 0.67 2616.87 * 15.991 2334.43 20.51 PANC 760 PANC 760 PLS 1 Pancreas II * 141.604 662.45 1502.95 * 0.822 9.68 * 2.733 19.14 3601.31 4397.67 0.59 1059.45 175.46 * 75.2 14.64 PANC 761 PANC 761 PLS 1 Pancreas II 1947, 39 716.98 3729.66 19.2 22.12 72.17 25.6 1207.91 * 319.113 0.53 917.15 202.87 1239.35 2.3 PANC 762 PANC 762 PLS 1 Pancreas II * 141.604 940.89 4611.54 9.53 25.78 2159.22 2 7.34 15.443.4 * 319.113 2.29 2278.14 189.54 1295.6 4.38 PANC 764 PANC 764 PLS 1 Pancreas II 8545.31 968.23 5044.91 9.19 23.10 211.05 30 , 76 551.5 * 319.113 0.61 1134.7 356.92 513.2 4.83 PANC 765 PANC 765 PLS 1 Pancreas II * 141.604 1524.50 5539.94 13.22 26.01 983.42 58.65 12,251.4 2325.42 0.79 2998.98 160.48 2433.31 23.56 PANC 766 PANC 766 PLS 1 Pancreas I 4450.92 9672.24 1343.62 19.36 14.49 33.77 34.43 2,070.4 * 319,113 0.73 1574.35 144.35 1752.13 1.6 PANC 767 PANC 767 PLS 1 Pancreas II * 141.604 1823.98 3773.44 * 0.822 20.63 40.07 28.25 568.2 * 319,113 0.61 1946.2 126.71 555.16 * 0.004 PANC 768 PANC 768 PLS 1 Pancreas II 1580.51 1510.77 2940.24 * 0.822 19.92 * 2.733 41.7 1876.38 * 319.1113 1, 28 1607.83 126.71 555.16 2.53 PANC 769 PANC 769 PLS 1 Pancreas II * 141.604 1867.98 7543.33 * 0.822 25.92 40.54 20.66 729.09 * 319.11 13 1.28 1602, 67 106.95 292.57 0.32 PANCA 1001 PANCA 1001 PLS 1 Pancreas II 1422.46 1202.66 379.12 17.8 25.25 114.95 31.77 938.11 5391.04 1.34 1026.89 281 , 23 482.7 3.41 PANCA 1004 PA NCA 1004 PLS 1 Pancreas II * 138.33 1225.77 1558.16 10.11 34.12 396.56 22.39 6.795.8 6270 1.96 3256.06 175.17 585.88 3.69 PANCA 1005 PANCA 1005 PLS 1 Pancreas II 3164.66 1441.48 2658.85 31.04 43.98 51.62 65.96 2460.96 2349.29 1.94 3240.21 236.37 688.73 3.32 PANCA 1006 PANCA 1006 PLS 1 Pancreas II 10155.54 1090.99 2430.93 21.14 45.76 955.34 109.23 1.876.0 4778 2.23 2370.01 240.17 1255.94 15.71 PANCA 1008 PANCA 1008 PLS 1 Pancreas II 1213.43 1641.85 744.84 7.77 23.61 20.51 40.28 1.172.4 * 341.473 1.94 2940.45 145.52 791.91 4.54 PANCA 1009 PANCA 1009 PLS 1 Pancreas II 1259.86 11995.13 1345.03 29.03 48.99 2168.59 40.2 8.246.0 6270 1.58 1821.18 342.09 520.5 2.23 PANCA 1010 PANCA 1010 PLS 1 Pancreas I 2103.29 3559.62 6904.68 * 0.83 38.70 17.82 6.86 748.40 * 322.664 0.67 3023.24 207.33 1182.14 6.62 PANCA 1011 PANCA 1011 PLS 1 Pancreas II 4402, 97 3186.20 7115.7 22.24 33.59 367.04 33.77 2.277.0 4087 0.69 4862.17 242.65 1139.01 6.24 PANCA 1012 PANCA 1012 PLS 1 Pancreas II 5490.65 1289 , 30 3426.09 7.1 2 37.21 106.32 51.19 1382.40 2981.99 1.18 2640.97 148.9 1536.41 5.85 PANCA 1013 PANCA 1013 PLS 1 Pancreas III 1826.89 185.81 859.87 10, 17 16.13 166.49 18.97 482.6 4385 0.95 1838.07 298.47 808.2 5.4 PANCA 1014 PANCA 1014 PLS 1 Pancreas II 2355.36 1904.69 8629.35 13.15 28 , 59 * 2,764 68 7,747.4 1917 1.16 3320.56 207.33 4003.08 7.36 PANCA 1015 PANCA 1015 PLS 1 Pancreas III 1024.76 1170.81 3496 6.24 21.11 214.54 20, 41 825.73 3309.35 1.39 2016.53 183.93 402 7.43 PANCA 1016 PANCA 1016 PLS 1 Pancreas I 3041.93 456.21 728.85 5.79 71.94 29.08 39.24 4032 , 96 3469.20 0.87 1972.28 214.72 175.77 4.83 PANCA 1018 PANCA 1018 PLS 1 Pancreas II 4337.27 2021.41 8390.89 86.09 29.67 349.93 44.53 3,361 , 7 2982 1.42 3004.16 347.74 1177.38 16.28 PANCA 1020 PANCA 1020 PLS 1 Pancreas II 6472.92 891.69 3105.43 24.27 42.98 44.18 28.18 1.740.5 8087 1.22 932.52 387.76 306.81 7.42 PANCA 1022 PANCA 1022 PLS 1 Pancreas II * 139.05 403.52 11256.12 7.56 15.82 57.32 25.61 1,207.8 6454 1.07 2085.47 280.74 * 28.11 3.78 PANCA 102 4 PANCA 1024 PLS 1 Pancreas II 5101.61 1636.36 2058.01 * 0.83 21.82 * 2.764 31.54 1299.31 * 322.664 0.65 1485.9 167.07 745.7 2.91 PANCA 1025 PANCA 1025 PLS 1 Pancreas III 4600.96 1387.24 6916.79 126.45 38.60 453.17 34.19 3.435.4 2643 0.83 1671.68 167.07 680.9 5.7 PANCA 1028 PANCA 1028 PLS 1 Pancreas III 3307.88 1575.85 4580.97 34.07 11.47 75.09 34.26 1.027.7 * 322.664 0.86 2630.34 191.95 460.36 13.08 PANCA 1030 PANCA 1030 PLS 1 Pancreas II 4337.27 2826.24 5500.47 23.46 51.12 42.31 34.14 * 77.788 4237 0.82 2477.09 268.47 474.46 6.97 PANCA 1031 PANCA 1031 PLS 1 Pancreas II 1348.2 2105.45 5647.59 11.45 20.86 885.17 45.58 5127.96 2814.01 0.78 1624.48 214.72 857.85 7.8 PANCA 1034 PANCA 1034 PLS 1 Pancreas II 2385.3 970.23 1722.52 17.78 28.33 56.17 23.44 2598.10 6721.89 0.79 1963.14 542.98 797 3.65 PANCA 1036 PANCA 1036 PLS 1 Pancreas II 11113.1 1877 , 89 3900.6 9.31 23.35 * 2.764 36.32 1207.82 2814.01 0.73 2556.57 228.99 984.72 12.35 PANCA 1037 PANCA 1037 PLS 1 Pancreas II 5780.98 2452, 76 3607.54 1 5.25 13.51 120.38 35.58 1.014.3 2468 0.77 4050.67 268.47 626.58 7.07 PANCA 1039 PANCA 1039 PLS 1 Pancreas II 4717.07 1423.92 5093.25 * 0.833 23, 83 221.40 44.83 1635.70 * 338.379 1.29 2348.86 183.93 421.51 7.54 PANCA 1040 PANCA 1040 PLS 1 Pancreas II 19555.82 1362.87 3834.85 23.06 44.60 37.84 26.43 1,759.7 7096 1.42 294.57 315.49 594.38 7.11 PANCA 1042 PANCA 1042 PLS 1 Pancreas II 1073.98 5523.72 1883.91 * 0.833 16.25 20.95 13.27 722.6 * 338.379 1.1 1729.96 125.85 1042.83 5.46 PANCA 1043 PANCA 1043 PLS 1 Pancreas II 5626.99 1251.50 4933.72 6.68 6.39 171.84 53 , 55 3382.77 * 338.379 0.94 931.07 167.07 858.91 5.52 PANCA 1044 PANCA 1044 PLS 1 Pancreas II 3817.28 3561.93 10869.84 11.45 43.82 57.32 54.06 2.527 , 4 2814 0.92 1793.03 242.65 1510.79 11.92 PANCA 1047 PANCA 1047 PLS 1 Pancreas II * 154.335 468.37 3500.61 9.52 23.30 68.31 10.64 1471.33 2106 , 36 1.52 1384.82 439.72 * 76.52 6.13 PANCA 1048 PANCA 1048 PLS 1 Pancreas III 3073.04 1632.19 9040.37 32.68 26.96 117.51 43.89 2492.13 * 338.379 1.15 1254, 2 167.07 613.35 6.71 PANCA 1050 PANCA 1050 PLS 1 Pancreas II 2415.29 1682.48 4638.21 * 0.833 10.87 123.90 27.8 1.067.9 2106 1.2 1532.99 148, 9 884.91 9.24 PANCA 1051 PANCA 1051 PLS 1 Pancreas II 4667.25 1704.03 2466.36 14.41 22.44 149.40 51.76 1.688.0 * 338.379 1.69 2149.58 183.93 979.57 6.19 PANCA 1052 PANCA 1052 PLS 1 Pancreas II 1542.64 3584.80 4120.61 7.34 37.46 * 2.767 52.89 774.06 * 338.379 0.85 951.6 183.93 1578, 7 11.71 PANCA 1053 PANCA 1053 PLS 1 Pancreas II * 154.335 3078.29 4583.04 8.29 23.47 1487.88 102.66 1.944.8 2406 2.38 1312.15 112.8 1215 17.43 PANCA 1054 PANCA 1054 PLS 1 Pancreas II 7639.81 2114.12 8444.11 6.01 49.08 225.22 64.56 1212.37 7222.30 1.33 3251.23 183.93 1167.84 17.21 PANCA 1055 PANCA 1055 PLS 1 Pancreas II * 154.335 423.92 2020.28 * 0.833 18.13 19.52 10.07 519.13 * 338.379 0.761.66 149.75 321.43 6.22 PANCA 1056 PANCA 1056 PLS 1 Pancreas II 5575.8 8527.97 2036.6 43.71 12.45 76.70 62.53 1.702.3 39518 1.92 2185.61 679.56 6019.65 7.88 PANCA 1057 PANCA 1057 PLS 1 Pancreas as II 4700.45 1150.84 3735.93 * 0.833 30.49 642.95 26.06 1522.40 * 338.379 1.03 1786.57 106.53 306.81 1.79 PANCA 1058 PANCA 1058 PLS 1 Pancreas II 2611.59 1862.17 7180.44 23.06 20.03 75.09 35.6 4572.63 * 338.379 0.99 1631.92 128.97 728.27 7.26 PANCA 1059 PANCA 1059 PLS 1 Pancreas II 1683 , 86 1571.14 3036.07 11.88 12.07 511.14 29.88 1855.86 * 338.379 1.19 1174.77 199.75 488.38 8.27 PANCA 1061 PANCA 1061 PLS 1 Pancreas II 4353, 68 1456.24 2023.54 13.99 13.95 497.22 56.03 6.197.9 3782 1.47 3295.57 249.28 1704.97 15.06

PANCA 1063 PANCA 1063 PLS 1 Pancreas II * 154.335 1222.75 7088.54 74.34 14.19 317.41 19.74 2308.63 * 338.379 1 1567.65 105.91 743.8 3.03 PANCA 1149 PANCA 1149 PLS 1 Pancreas II * 147,076 2488,16 1635,68 * 0,792 14,18 44,30 37,92 1,553,6 * 323,745 1,49 1347,91 174,27 178,36 6,72 PANCA 1152 PANCA 1152 PLS 1 Pancreas I 2066 , 02 1193.78 4173.87 11.22 62.61 33.69 44.33 1.016.0 * 323.745 0.58 2031 98.61 493.17 1.69 PANCA 1153 PANCA 1153 PLS 1 Pancreas II 1863.94 1060 , 85 1833.86 5.55 4.29 189.15 21.96 811.15 * 327.917 0.83 336.71 * 14.319 271 4.31 PANCA 1155 PANCA 1155 PLS 1 Pancreas II * 132.09 2784.67 5110 , 97 40.09 22.23 * 2.74 40.01 9.260.1 * 327.917 0.67 1512.09 * 14.319 1300.96 4.55 PANCA 1156 PANCA 1156 PLS 1 Pancreas II 4189.16 2413.63 2630, 75 * 0.82 12.91 226.44 33.01 2459.62 * 327.917 0.79 1134.25 * 14.319 1202.86 20.98 PAP 938 PAP 938 PLS 1 Ovary III * 146.583 3760.06 3170.67 519 , 78 23.25 29.69 17.59 945.6 5138 1.07 2397.23 179.17 1628.42 19.49 PAP 939 PAP 939 PLS 1 Ovary III 3078.34 5130.59 2235, 8 269.12 53.37 20.94 10.94 1.793.9 14386 1.4 1830.06 162.25 2199.96 6.57 PAP 940 PAP 940 PLS 1 Ovary III * 146.583 3428.83 1502.85 178, 88 338.84 26.60 16.17 444.8 21 501 0.8 1152.8 143.09 1272.89 11.31 PAP 941 PAP 941 PLS 1 Ovary III * 146.583 603.16 1135.67 58.75 14, 90 19.02 7.26 789.7 3590 0.91 604.03 307.18 921.49 6.69 PAP 944 PAP 944 PLS 1 Ovary III 40359.14 2629.64 2648.15 491.71 40.54 144 , 76 18.43 3,381.1 * 328.486 0.89 381.98 143.09 618.01 13.56 PAP 945 PAP 945 PLS 1 Ovary III 4997.93 2914.26 1462.84 372.01 218.90 17.73 14 , 26 479.8 3765 0.74 1658.68 120.42 1704.01 8.53 PAP 946 PAP 946 PLS 1 Ovary III * 146.583 2112.97 2433.61 618.68 47.54 558.22 25.01 3.302 , 6 1971 0.97 316.47 245.96 367.73 16.34 PAP 947 PAP 947 PLS 1 Ovary III * 146.583 315.76 1657.41 9.21 12.01 17.73 21.92 653.7 1971 0, 95 1113.68 208.62 654.58 16.76 PAP 949 PAP 949 PLS 1 Ovary III * 146.583 4315.56 1718.57 4.86 24.56 29.69 15.36 1.679.9 1971 1.37 3604, 65 * 15.177 4570.53 7.47 PAP 950 PAP 950 PLS 1 Ovary III 150 0.16 1460.74 3882.74 3329.74 248.67 * 2.74 7.91 1,489.8 91223 1.78 1400.14 179.17 1622.98 19.34 PAP 951 PAP 951 PLS 1 Ovary III 1921 , 24 1622.12 1072.46 37.57 28.80 * 2.74 13.77 * 73.577 * 327.917 0.82 1130.15 85.91 631.94 17.69 PAP 953 PAP 953 PLS 1 Ovary III * 146.583 813, 98 726.57 24.63 12.98 42.39 10.68 691.0 2882 1.04 376.16 278.24 1767.97 5.79 PAP 954 PAP 954 PLS 1 Ovary III * 146.583 1204.94 2568, 03 ** 600.004 68.67 33.97 9 * 73.835 13465 1.14 * 29.079 143.09 711.79 11.93 PAP 955 PAP 955 PLS 1 Ovary III 1398.1 996.49 1737.7 177.69 8, 19 43.58 15.79 451.7 * 328.486 1.03 1144.09 143.09 1183.73 18.48 PAP 956 PAP 956 PLS 1 Ovary III 2357.46 2405.94 880.92 59.52 15.94 * 2.74 10.05 * 73.835 4798 0.86 1144.09 91.06 1922.22 6.85 PAP 957 PAP 957 PLS 1 Ovary III * 146.583 1353.25 1890.97 7.76 18.48 * 2, 74 16.76 2.340.5 * 328.486 1.22 2231.68 91.06 1468.16 22.17 PAP 959 PAP 959 PLS 1 Ovary III 3649.47 1647.11 2218.39 425.72 29.31 * 2, 74 36.02 736.3 * 328.486 1.27 559.71 120.42 1080.03 21.61 PAP 961 PA P 961 PLS 1 Ovary III 1603.12 1914.09 979.82 253.84 52.20 98.83 23.63 5.926.2 * 328.486 0.82 887.03 * 15.177 1308 13.89 PAP 962 PAP 962 PLS 1 Ovary III 37732.35 679.58 2505.63 16.22 9.68 17.09 10.12 1,233.1 * 328.486 1.09 1391.19 91.06 2320.13 8.36 PAP 974 PAP 974 PLS 1 Ovary I 1418 , 44 3162.93 1856.45 37.69 22.06 * 2.74 15.89 1.029.2 * 328.486 0.99 1905.02 120.42 976.29 16.37 PAPA 1330 PAPA 1330 PLS 1 Ovary III 1222 , 17 1361.24 417.68 470.04 201.12 * 2.74 * 1.35 * 73.577 2324 0.95 1123.99 * 14.319 1060.55 69.52 PAPA 1331 PAPA 1331 PLS 1 Ovary III * 146.583 4061.22 957.01 433.88 386.71 * 2.74 * 1.36 * 73.835 4112 0.69 976.07 * 15.177 3158.64 6.7 POP 1332 POP 1332 PLS 1 Ovary I * 146.583 1377.09 1285.84 5.58 4.20 * 2.74 13.4 759.1 * 328.486 0.74 887.03 179.17 1153.54 14.17 POPE 1333 POPE 1333 PLS 1 Ovary III * 146.583 1780.26 1200.29 523 , 47 316.48 113.11 14.77 472.73 8513.73 0.68 997.42 162.25 406.23 3.45 POPE 1334 POPE 1334 PLS 1 Ovary III * 146.583 5717.79 2098.69 318.93 22 , 10 * 2.74 19.24 465.7 23186 2.71 1848.75 143.09 1045.82 16.3 POPE 1335 POPE 1335 PLS 1 Ovary III 951.61 2854.67 1396.19 11.05 10.23 * 2.74 14 805.0 * 328.486 0 , 63 2311.74 * 15.177 2042.34 7.78 POPE 1336 POPE 1336 PLS 1 Ovary I * 146.583 3670.67 4076.83 14.37 7.23 * 2.74 17.16 * 73.835 35886 1.64 1530.84 187 , 01 1924.8 14.01 PAPA 1339 PAPA 1339 PLS 1 Ovary I 1500.16 2125.25 1278.24 2176.6 37.35 ** 2081.912 11.96 1.073.6 4798 5.97 1656.38 307 , 18 927.99 8.54 PAPA 1342 PAPA 1342 PLS 1 Ovary III 7212.72 1883.62 1221.2 379.12 63.58 72.26 14.82 * 73.835 2521 0.83 1128.87 132.32 868 , 95 10.75 PAPA 1343 PAPA 1343 PLS 1 Ovary III 1377.81 1853.17 1196.49 176.09 ** 139.475 54.16 13.57 511.7 4456 1.28 541.67 91.06 908.44 12.57 PAPA 1344 PAPA 1344 PLS 1 Ovary III 2130.42 1110.66 774.26 265.09 14.96 35.19 14.51 1.706.1 * 328.486 0.94 726.53 257.18 414.63 14 , 42 PAPA 1345 PAPA 1345 PLS 1 Ovary III * 146,583 1046.15 310.04 56.83 88.65 * 2.74 * 1.36 * 73.835 7133 1.13 1035.99 120.42 2271.32 11.82 PAPA 1346 PAPA 1346 PLS 1 Ovary III * 146.583 3530.52 831.42 918.76 ** 139.475 151.45 11.01 * 73.835 7052 0.81 186.75 91.06 632.72 2.83 PAPA 1347 PAPA 1347 PLS 1 Ovary I * 146.583 1193.13 1459.03 535.16 14.97 1275.52 11.21 3,180.5 2792 0.81 * 29.079 179.17 1302.17 4.97 POPE 1348 POPE 1348 PLS 1 Ovary III 3350.8 1629.00 728.48 549.33 147.53 27.84 8.43 * 73.835 5475 0.62 1413.58 120.42 4212.49 6.16 POPE 1349 POPE 1349 PLS 1 Ovary III * 146.583 2586.04 1297.25 2235.26 * * 139.475 87.17 9.54 646.3 15756 1.74 1567.2 143.09 1073.84 6.07 POPE 1350 POPE 1350 PLS 1 Ovary II 2346.58 1904.94 1014.04 7.39 9.85 * 2.74 12.56 1558.46 * 328.486 1.16 1214 * 15.177 1540.65 5.47 PAPA 1353 PAPA 1353 PLS 1 Ovary I * 146.583 1484.70 2096.76 24.82 10.30 42.39 14 , 92 914.00 * 328.486 3.05 1113.68 221.8 875.57 8.51 POPE 1354 POPE 1354 PLS 1 Ovary I 1337.33 1607.90 852.37 5.58 9.80 * 2.74 12 , 32 1,179.5 * 328,486 1.74 1702.42 234.2 573.21 19.76 POPE 1355 POPE 1355 PLS 1 Ovary III * 146.583 1592.84 1044.45 30.48 8.48 * 2.74 8, 26 * 73.835 3590 0.73 499.81 91.06 6 50.95 8.67 PAPA 1356 PAPA 1356 PLS 1 Ovary II * 146.583 5267.95 1445.69 1469.45 23.74 62.26 16.53 * 73.835 50659.05 1.24 2219.6 143.09 1033, 29 6.69 PAPA 1357 PAPA 1357 PLS 1 Ovary III * 146.583 3546.43 1493.32 1428.31 ** 139.475 37.90 13.78 * 73.835 3060 1.01 1750.98 208.62 715.32 12.84

* Protein concentration below the detection limit of the assay; value adjusted as a lower limit of detection ** Protein concentration above the limit of detection of the assay; value set as upper limit of detection NA: Not available G-CSF GDF15 HE4 HGF IL-6 IL-8 Kallikrein-6 Leptin Mesothelin Midkine Myeloperoxidase NSE OPG OPN PAR Prolactin sEGFR sFas SHBG (pg / ml) (ng / ml) (pg / ml) (pg / ml) (pg / ml) (pg / ml) (pg / ml) (pg / ml) (ng / ml) (pg / ml) (ng / ml) (ng / ml) (ng / ml) (pg / ml) (pg / ml) (pg / ml) (pg / ml) (pg / ml) (nM) * 43.82 0.53 * 642.952 377.26 * 1.886 * 1.373 5938 , 28 75826.61 14.29 315.23 14.22 12.04 0.47 56516.58 8852.96 11606.6 3284.17 * 34.132 55.06 * 43.82 2.39 5779.11 659.68 21.28 29.82 3409.18 211751.33 32.57 260.56 23.88 23.25 0.34 61001.39 20782.58 14374.99 1911.81 * 34.132 72.92 * 43.82 0, 5 * 642,952 329.07 * 1.886 35.06 3338.6 2683.07 15.09 491.81 12.02 12.84 0.37 88896.24 7534.43 38375 1743.94 * 34.132 173.78 152.24 0.19 7819.17 266.66 15.3 15.89 3162.89 41859.78 16.52 230.45 6.49 22.79 * 0.029 42549.61 4722.42 12072.51 1059.24 * 34.132 29 , 47 * 43.82 0.3 * 642.952 370.88 * 1.886 * 1.373 4442.46 100119.47 8.81 238.47 13.33 27.2 * 0.029 24274.11 6945.9 23718.17 17 36.92 * 34.132 78.07 * 43.82 1.09 * 642.952 269.99 * 1.886 9.47 4845.57 6804.38 16.88 276.87 10.6 22.32 0.57 66920.22 9321 , 12 16602.78 1871.12 * 34.132 251.78 422.75 0.28 9276.09 246.56 * 1.886 250.72 5123.27 25346.38 12.82 331.33 12.08 21.66 * 0.029 53,572.27 12,378.98 22077.74 2704.94 5389.05 61.54 * 43.82 0.42 4701.46 312.81 * 1.886 * 1.373 5369.64 127 507.81 21.4 216.77 8.42 15.58 * 0.029 73727.23 4793.64 14294.39 1203.16 * 34.132 207.62 * 43.82 0.14 6559.67 226.23 13.61 10.53 3060.7 28575.13 7.42 172.98 16.65 11.1 * 0.029 41313.18 3057.66 34149.07 1683.93 * 34.132 144.11 * 43.82 0.14 * 642.952 205.65 * 1.886 9.47 5617.59 13222.45 15 , 21 191.06 9.54 11.81 * 0.029 55873.69 3804.41 32295.94 1949.59 * 34.132 236.94 * 43.82 0.44 * 642.952 358.09 * 1.886 10.88 5754.98 126964.73 32.38 295.14 15.44 10.87 0.33 49915.81 10755.72 26147.85 1899.59 * 34.132 125.95 * 43.82 0.33 4701.46 408.95 * 1.886 * 1.373 6281.47 50112.25 14.09 1357.7 13.14 46.86 0.39 44818.58 2307.6 10917.49 1853.87 * 34.132 91.35 463.34 0.38 * 642.952 325 , 83 * 1,886 9.29 4969.81 44504.42 17.77 274.39 76.86 45.03 * 0.029 19598.92 6016.05 22781.25 1639.15 * 34.132 28.49 * 43.82 2.08 * 642,952 351,67 * 1,886 * 1,373 4394,61 102,187.16 1.49 241.12 18.95 24.98 0.4 38244.16 6233.89 143071.71 429.59 * 34.132 85.86 * 43.82 0.58 * 642.952 212.54 * 1.886 10.7 3668.5 4783.86 23.33 338.12 10.07 22.69 0.74 32019.25 16444.34 17990.85 2585.99 * 34.132 84, 74

* 43.82 0.2 11146.85 299.72 * 1.886 133.5 4744.28 21580.74 9.73 261.83 23.49 24.17 * 0.029 41694.21 6470.56 26446.49 1801.28 3705.77 232.86 * 43.82 0.25 * 642.952 233.03 * 1.886 10.53 7945.8 15779.86 4.37 213.99 18.76 21.47 * 0.029 34248.73 3892.01 31045 .8 1399.04 * 34.132 33.17 138.48 0.33 * 642.952 279.95 * 1.886 25.01 6394.6 24543.56 9.42 230.45 11.39 14.07 * 0.029 31969.37 9923 , 12 19489.58 2137.26 * 34.132 71.03 162.34 0.89 * 642.952 415.25 * 1.886 33.18 7117.39 10706.52 9.7 284.24 15.08 8.88 * 0.029 81075.21 8126.61 19542.04 622.59 270.66 120.16 185.28 0.16 * 642.952 279.95 * 1.886 21.34 2884.76 125608.48 14.92 235.81 15.14 13.41 * 0.029 65374.68 8312.38 16854.58 2317.39 * 34.132 63.93 * 43.82 0.31 * 642.952 421.54 * 1.886 * 1.373 4064.76 59629.28 16.34 108.22 11.89 20.03 * 0.029 54862.15 5528.15 38032.32 2011.12 * 34.132 51.98 * 43.82 0.37 5779.11 198.72 * 1.886 22.44 3766.32 3295.05 24.68 826.44 7 , 65 10.38 * 0.029 92181.23 11174.03 11496.66 1680.93 * 34.132 52.25 * 43.82 0.66 * 642.952 520.74 * 1.886 28.71 505 8.88 162648.18 27.83 310.56 12.12 8.1 0.52 73545.84 9734.67 55189.55 2002.9 * 34.132 76.49 * 43.82 0.45 4701.46 325, 83 * 1,886 11.24 4936.57 34589.3 27.28 227.74 14.32 15.15 * 0.029 28230.8 10907.68 10418.15 2143.5 * 34.132 87.36 * 43.82 0.32 * 642,952 370.88 * 1,886 13.73 6533.35 39817.83 21.49 176.06 13.2 11.81 * 0.029 75268.97 5834.97 11262.65 1349.87 * 34.132 32.8 * 43.82 0 , 39 * 642,952 345.23 * 1,886 24.27 4031.23 7490.35 8.06 364.67 11.75 14.07 * 0.029 62916.67 13787.56 19043.38 1674.95 * 34.132 79.42 * 43.82 0.27 * 642.952 226.23 20.75 29.82 4121.72 46338.18 13.17 222.29 8.55 7.56 * 0.029 49218.3 7682.14 28394.16 921.04 * 34.132 51.29 * 43.82 0.16 6559.67 273.31 14.74 29.27 4365.46 8941.34 12.98 202.68 7.22 15.89 * 0.029 45101.04 1624.8 11551 , 65 809.86 613.4 49.15 * 43.82 0.36 * 642.952 293.15 * 1.886 * 1.373 3358.03 47939.92 15.27 130.28 9.87 15.37 * 0.029 28843.86 6726.19 14025.42 1193.72 * 34.132 137.22 * 43.82 0.18 4701.46 393.15 * 1.886 * 1.373 2273.05 12683.08 9.7 166.76 14.19 17.45 * 0.029 90811 52 853 5.74 9565.22 2261.61 * 34.132 71.54 * 43.82 0.25 8310.74 184.77 11.32 9.82 1704.23 47847.13 9.82 169.89 9.38 13, 18 * 0.029 39975.19 1740.08 114088.44 557.4 * 34.132 65.05 * 43.82 0.12 * 642.952 293.15 * 1.886 12.66 4402.57 98257.84 12.13 768.37 13 , 14 10.62 * 0.029 48239.82 9396.2 26394.56 3071.16 * 34.132 48.33 * 43.82 0.57 * 642.952 266.66 * 1.886 34.31 3753.73 2683.07 11.8 199.8 9.67 14.51 * 0.029 45571.29 7497.54 87118.19 1241.95 * 34.132 204.66 * 43.82 0.33 * 642.952 345.23 * 1.886 38.07 3554.16 33825, 55 9.94 185.13 11.42 18.66 0.4 36012.6 5097.22 7306.13 851.75 323.21 35.16 * 43.82 0.41 * 642.952 184.77 * 1.886 * 1.373 2954.79 10955.28 14.08 191.06 6.32 16.62 * 0.029 43875.22 4972.04 6349.47 1509 * 34.132 47.95 * 43.82 0.28 * 642.952 188.27 * 1.886 8 , 76 4777.04 82194.51 8.95 162.02 22.63 16.73 * 0.029 70688.39 6964.23 16775.1 2187.27 * 34.132 62.63 * 43.82 0.48 15138.84 246 , 56 18.6 11.24 8181.02 26027.24 60.05 153.97 16.98 9.64 * 0.029 25538.59 9471.33 22651.03 1611.39 * 34.132 25.71 * 43.82 0 .3 * 642,952 587.65 * 1.886 34.12 5816.8 41525.72 17.05 150.69 50.31 17.45 * 0.029 35279.63 13555.14 14065.79 1221.11 513.19 44.54 * 43.82 1.11 * 642.952 * 27.259 * 1,886 8.59 3936.31 3346.97 26.23 133.78 3.64 20.71 0.74 26113.26 4936.32 3370.16 868.77 * 34.132 84.83 * 43.82 0.2 * 642,952 259.98 * 1.886 10.88 2961.81 7659.66 11.82 248.98 18.17 13.52 * 0.029 20488.45 7534.43 11990.44 1775.09 * 34.132 81.6 * 43, 82 0.4 * 642.952 325.83 * 1.886 9.47 3693.5 123867.04 12.85 230.45 12.45 11.1 0.59 38099.33 6598.28 9607.38 2480.88 * 34.132 53 , 03 * 43.82 1.69 5246.17 665.64 18.05 25.93 4129.51 54014.62 15.36 290.32 57.75 15.15 0.77 62096.25 11402.75 11524, 16 1338.34 * 34.132 69.22 134.98 0.2 4701.46 377.26 * 1.886 13.02 2716.3 61581.96 17.29 273.14 17.61 17.95 * 0.029 35132.71 2510.57 34122.93 2016 , 26 * 34,132 40.62 * 43.82 0.38 4142.95 249.92 * 1.886 * 1.373 4137.3 50933.86 11.98 157.21 35.84 25.79 0.3 29656.95 9847, 71 14079.25 2197.71 * 34.132 88.23 258.89 0.18 * 642.952 358.09 * 1.886 22.25 4711.6 10211.5 23.68 271.9 81.99 23.8 * 0.029 53 572.27 10 793.69 12645.33 2209.21 * 34.132 65.22 162.34 0.2 5246.17 312.81 * 1.886 85.72 4302.1 45168.67 10.37 462.09 216.86 63 , 82 * 0.029 31169.07 7719.11 10418.15 2335.34 2526.2 34.09 * 43.82 0.39 * 642.952 289.86 * 1.886 14.81 3029.99 35304.97 9.41 160, 43 11.46 12.73 * 0.029 47586.16 3874.48 20432.58 1615.35 210.15 19.22 * 43.82 0.6 * 642.952 358.09 14.74 * 1.373 5369.64 11563.45 27.39 2052.28 63.2 10.02 * 0.029 46697.26 5600.23 31071.82 1250.49 * 34.132 39.96 * 43.82 2.34 7320.94 279.95 * 1.886 21.7 3713 , 54 4865.49 16.92 291.53 30.2 30.55 0.3 117552.35 9659.37 27173.57 547.33 * 34.132 187.09 * 43.82 0.15 * 642.952 * 27.259 * 1.886 * 1.373 3703.52 12931.85 9.58 144.04 16.13 10.14 * 0.029 46228.54 4811.46 13134.11 1473.82 * 34.132 146.75 182.06 0.18 * 642.952 296.44 * 1.886 * 1.373 2541.57 21502.86 14.03 163.61 20.43 12.96 * 0.029 39543.61 9546.51 21438.63 2729.94 * 34.132 58.1 * 12.46 0.33 * 676.132 166.86 11.32 13.41 5280.53 13131.04 16.33 1211.47 2.89 7.66 0.28 30422.97 7065.34 17390.24 1997.64 2005.11 22.39 1 00.72 1.15 * 676.132 * 27.292 * 0.628 9.37 8376.85 98628.68 46.14 643.05 9.63 7.85 0.72 45896.1 14155.68 9941.77 3495.56 2218, 49 27.98 * 12.46 0.29 * 676.132 * 27.292 * 0.628 103.93 4720.47 14025.05 12.46 959.06 12.32 11.19 0.44 49894.78 7552.21 14213.81 772.68 1563.27 46.49 379.95 0.3 * 676.132 261.74 * 0.628 * 1.37 3615.49 39054.37 10.55 326.7 25.72 6.29 0.31 20095.65 5656.69 15193.88 1757.99 1147.97 14.81 * 12.46 0.26 * 676.132 220.14 3.98 * 1.37 5387.22 61763.77 17.8 254.25 11.83 8.4 0.31 29714.84 3199.86 16791.76 3027.7 921.34 8.97 86.4 0.85 * 676.132 166.86 6.08 9.68 6303.18 21774.89 51.5 695.16 19 8.31 0.43 58400.91 11915.85 28607.49 2610.65 1049.34 17.98 * 12.46 1.43 * 676.132 301.57 39.67 298.93 4040.62 7262.69 44.06 1160.92 77.96 8.95 0.76 130703.62 23744.51 7469.56 2192.58 1185.35 28.13 142 1.02 * 676.132 208.67 * 0.628 14.67 4842.78 1289.02 30.27 3588.75 22.19 3.66 0.71 40861.7 14232.08 30373.03 2718.47 1306.16 69.16 * 12.46 0.31 * 676.132 275.19 * 0.628 11.85 6022.49 18569.89 15.04 310.79 7.74 8.95 0.37 73 136.49 5617.4 12934.3 986.02 1147.97 1.98 144.68 0.72 * 676.132 191.09 * 0.628 11.53 2277.61 17 493.33 16.93 485.74 7.2 8.86 0.56 49537.58 12663.61 14719.95 2258.64 782.1 60.36 * 12.46 0.33 * 676.132 * 27.292 * 0.628 9.68 4483.64 3610.32 23 , 48 352.36 18.64 9.66 0.33 17 798.77 10743.62 10 502.05 3226.17 1517.64 41.46 142 2.32 * 676.132 317.1 4.83 29.28 5868.89 1482.38 29 , 23 565.31 30.83 7.28 0.56 98302.54 10897.55 11380.85 1204.47 511.51 30.78 264.09 0.11 * 676.132 * 27.292 * 0.628 * 1.37 3559, 36 8114.82 15.32 618.96 11.85 6.69 0.18 23204.43 11647.11 10351.45 4428.47 679.94 31.22 152.67 0.57 * 676.132 * 27.292 * 0.628 8 , 45 5174.1 24941.05 20.37 459.87 11.33 9.48 0.82 52451.16 8891.01 143808.05 1729.06 591.29 38.98 * 12.46 0.63 * 676.132 * 27.292 * 0.628 * 1.37 3437.46 3019.28 10.77 266.05 17.35 7.08 0.29 54786.62 5105.85 8000.78 1602.8 983.15 105.49 * 12.46 0 , 34 * 676,132 288.46 * 0.628 9.68 5095.31 3296.59 11.37 1121.56 184.9 28.21 0.2 21297.99 2151.52 71518.03 2689.93 420.42 9, 79 * 12.46 0.23 * 676 , 132 * 27,292 * 0.628 * 1.37 3741.24 4678.89 9.88 248.24 78.59 9.13 0.26 14033.22 6401.42 5870.77 3448.62 1185.35 25.32 92 , 17 1.09 * 676.132 * 27.292 4.83 21.24 6539.67 1904.1 17.33 4062.06 5.74 7.37 0.32 79350.56 2029.51 7680.31 1067.31 1306.16 17.19 * 12.46 0.54 * 676.132 * 27.292 7.52 26.14 4268.26 256659.28 32.15 442.19 6.25 5.47 0.25 36729.01 10820.59 65090 1144.09 987, 89 16.2 * 12.46 0.48 * 676.132 * 27.292 * 0.628 * 1.37 3824.19 19580.31 5.22 299.94 11.31 7.08 0.4 27811.88 6870.29 6303, 25 2087.66 921.34 32.76 * 12.46 0.87 * 676.132 * 27.292 * 0.628 11.53 2911.39 13293.79 29.77 459.87 58.99 8.22 0.45 44294.35 11781.5 7358.38 4557.81 1767.3 33.38 293.09 0.43 * 676.132 * 27.292 * 0.628 * 1.37 4557.93 65548.31 19 271.85 8.27 6.49 0.24 38,466.75 6792.22 15614.76 2650.27 921.34 53.4 * 12.46 0.46 * 676.132 * 27.292 * 0.628 * 1.37 9447.81 10801.08 10.82 337.09 4.15 4.37 0.23 43938.48 2838.59 10049.71 3167.01 1082.3 2.41 144.68 0.88 * 676.132 * 27.292 * 0.628 * 1.37 6862.98 16799.36 39.64 391 , 5 6.52 2.91 0.35 98 451.77 8852.3 69629.93 2684.97 660.33 45.31 * 12.46 0.57 * 676.132 * 27.292 * 0.628 8.75 1490.62 19252.61 15.44 446.64 8.09 7.47 0 , 31 55481.52 13123.17 8033.85 1951.51 226.88 28.8 * 10.48 1.09 * 623.86 207.92 * 0.50 * 4.627 11985.87 9828.36 61.08 683.58 20.81 11.34 0.78 94143.47 20959 124290.8 2605.02 * 33.693 57.45 * 10.48 0.4 * 623.86 * 26.389 * 0.50 * 4.627 4083.65 8130.26 13.93 520.27 10.38 8.24 0.58 15126.05 11226.84 111039.59 2591.28 * 33.693 172.9 * 10.48 0.69 * 623.86 * 26.389 3.48 * 4.627 4286.73 7954.92 39.83 973.43 9.78 4.65 0.6 54396.06 4074.68 31431.72 1121.94 * 33.693 78.68 * 10.48 1.54 * 623.86 171 * 0.506 * 4.627 9186.58 4876.59 47.96 355.59 11.61 4.65 0.58 95148.43 7002.44 34669.21 2420.07 * 33.693 77.9 * 10.48 11.04 * 623.86 213.93 * 0.50 * 4.627 4610.42 * 417.822 8.9 933.6 12.98 9.52 0.33 47742.37 4402.56 88157.48 1370.84 * 33.693 160.47 67.47 0.54 * 623.86 * 26.389 * 0.506 * 4.627 4617.38 21340.78 31.89 253.23 8.37 5.81 0.56 46925.4 3729.42 25094.46 2133.31 * 33.693 134.3 44.18 0 , 82 * 623.86 260.9 * 0.506 * 4.627 5350.87 29425.44 9.53 1224.06 8.3 5.81 0.31 52169.84 3427.6 103128.9 706.8 * 33.693 29.58 * 10.48 0.65 * 623, 86 344.65 20.16 * 4.627 2963.48 4967.45 16.38 255.52 14.16 3.39 0.45 56903.26 6808.03 9826.33 372.91 * 33.693 33.91 * 10, 48 0.92 * 623.86 183.48 * 0.50 37.01 7270.6 13172.35 41.6 285.06 11.08 9.83 0.41 81996.86 6121.73 7011.77 998.49 * 33.693 51.85 * 10.48 0.69 * 623.86 * 26.389 * 0.50 * 4.627 7907.51 30859.71 27.68 1282.65 15 8.24 0.36 40274.02 5527.94 103644.32 1193 , 81 * 33.693 41.3 31.43 1.38 * 623.86 201.87 63.29 36.16 3810.58 37779.38 31.65 697.01 15.28 10.44 1.1 129972.05 8114.77 23792.78 1275.78 * 33.693 64.98 * 10.48 0.55 * 623.86 * 26.389 6.05 * 4.627 4225.52 7407 11.3 383.51 14.35 13.05 0, 54 53901.53 10931.33 51067.92 4592.95 * 33.693 89.13 * 10.48 1.28 * 623.86 * 26.389 * 0.50 35.3 7113.55 67875.46 31.32 504.07 8, 98 6.18 0.84 38612.18 20302.66 17860.94 3038.19 * 33.693 111.78 * 10.48 0.5 * 623.86 * 26.389 * 0.50 * 4.627 3883.34 9275.43 24.18 548.4 8.75 8.24 0.72 28352.05 7785.79 71838.75 1884.02 * 33.693 108.35 * 10.48 0.87 * 623.86 * 26.389 6.86 * 4.627 7416.71 4084.45 25.85 316.27 7.16 5.43 0.4 77518.74 8004.95 12939.3 505.27 * 33.693 63.9 * 10.48 0.8 * 623.86 164.7 * 0.50 37.01 2954.2 18437.81 20.7 924.51 9.59 6, 89 0.4 23806.57 18419.59 16367.87 2864.19 * 33.693 168.89 259.42 0.16 * 623.86 177.26 17.36 * 4.627 2574 5231.4 11.4 493.9 10.73 10.29 0.5 64573.21 6635.72 242232.89 467.09 * 33.693 29.77 * 10.48 0.61 * 623.86 249.33 * 0.50 * 4.627 7323.24 26884.12 28.55 1122.93 13.54 7.58 0.35 93883.3 8246.77 44551.91 2635.6 * 33.693 27.3

* 10.48 0.3 * 623.86 * 26.389 * 0.506 * 4.627 6304.77 37988.4 24.18 576.28 10.98 5.81 0.48 49302.98 11751.72 51884.88 1753.22 * 33.693 57.33 * 10.48 0.82 * 623.86 213.93 4.36 * 4.627 3100.35 44372.05 27.85 639.11 17.4 6.18 0.41 13144.47 15752.13 133030 , 34 2240.92 * 33.693 165.43 * 10.48 0.52 * 623.86 213.93 4.36 * 4.627 5958.45 4496.51 44.13 548.4 12.42 5.99 0.47 43038.92 8732.7 64057.18 1134.95 * 33.693 107.84 * 10.48 0.77 * 623.86 * 26.389 * 0.50 * 4.627 3803.98 * 417.822 15.1 225.36 9.33 7, 24 0.63 81561.95 7632.78 31598.41 962.98 * 33.693 148.82 * 10.48 1.18 * 623.86 * 26.389 * 0.506 * 4.627 4708.15 26311 12.98 298.51 10, 13 5.05 0.69 49657.3 1928.34 15932.33 1086.96 * 33.693 52.9 * 10.48 0.5 * 623.86 * 26.389 * 0.506 * 4.627 4523.67 77952.5 18.61 248.62 12.11 6.18 0.35 42967.33 5233.56 25606.03 850.43 * 33.693 20.85 109.3 3.57 * 623.86 164.7 15.57 30.47 6170, 79 18783.84 40.59 381.38 10.63 10.14 0.9 80223.13 17414.05 23655.26 2366.03 * 33.693 144.7 * 10.48 0.46 6825.96 225.86 23 , 6 41.23 5227.54 24534.74 48.91 377.1 13 , 3 6.71 0.24 21882.99 11614.54 16945.85 324.07 * 33.693 47.08 * 10.48 0.32 * 623.86 * 26.389 * 0.506 * 4.627 4324.26 10950.13 14, 54 389.9 13.17 11.63 0.22 36503.67 9556.5 7324.07 3052.4 * 33.693 155.81 * 10.48 2.48 * 623.86 328.27 * 0.506 * 4.627 8316, 63 27834.77 71.55 8858.12 78.86 4.25 2.95 143572.21 11500.37 22378.27 1885.45 * 33.693 197.82 * 10.48 0.64 * 623.86 * 26.389 * 0.50 * 4.627 5471.43 2974.47 21.55 534.37 9.87 7.58 0.53 32825.27 13364.47 16349.76 3473.62 * 33.693 155.01 * 10.48 0.87 * 623 , 86 * 26,389 * 0.506 * 4.627 4834.69 24182.36 17.91 481.65 13.26 5.81 0.55 56550.18 5359.51 30045.03 1224.1 * 33.693 122.6 128.77 0.88 * 623.86 164.7 * 0.50 * 4.627 6079.48 79963.44 32.24 617.64 7.39 4.25 0.54 82722.47 19866.36 24581.65 1862.57 * 33.693 64.45 37.93 0.3 * 623.86 * 26.389 * 0.506 * 4.627 3542.66 16112.38 16.56 489.82 6.6 6.18 0.34 32379.48 8865.74 16999.88 1499.98 * 33.693 130, 83 * 10.48 0.64 4279.87 371.61 * 0.506 48.21 4368.73 17183.81 15.49 746.5 16.11 10.14 0.72 71145.12 9065.7 48079.45 1022 72 * 33,693 6 4.91 50.23 0.69 * 623.86 213.93 3.48 34.87 6957.75 14350.9 14.2 776.66 10.1 7.06 0.41 99530.71 4816.13 15421 , 86 804.9 * 33.693 66.02 * 10.48 0.59 * 623.86 * 26.389 * 0.506 * 4.627 5913.27 24347.27 27.46 218.29 7.32 3.83 0.42 51321, 06 3069.19 12297.44 498.49 * 33.693 79.53 * 10.48 0.7 4805.65 207.92 30.26 53.41 4003.2 3630.05 14.19 495.94 9.36 7 , 41 0.41 79825.71 4279.29 34272.1 678.16 * 33.693 94.57 172.5 0.31 * 639.193 615.77 11.64 25.97 4503.76 18849.83 9.22 170, 59 74.4 * 0.751 0.29 107720.37 2655.7 22601.29 300.29 913.7 53.05 1302.05 0.55 * 639.193 736.31 34.47 123.63 1923.32 5824.79 9.58 373.68 203.03 85.49 0.34 162072.21 13234.47 34424.79 296.55 1221.9 39.68 612.64 1.4 * 639.193 1134.94 118.09 32.92 1579.14 29116.77 17.51 140.82 49.19 * 0.751 0.46 270318.19 2667.4 6260.26 888.88 1244.59 44.22 190.53 0.77 5936.13 427.97 59, 08 20.57 5194.58 4975.92 15.83 182.48 22.31 * 0.751 0.31 92854.43 1086.59 11195.83 646.07 1117.02 62.8 61.85 0.68 * 623.86 289.36 * 0.506 * 4.627 5737.43 233461.04 26.38 253.23 8.98 6.89 0.67 38901.81 6079.11 8712.12 2892.26 * 33.693 82.84 * 10.48 0.33 * 623.86 * 26.389 * 0.50 * 4.627 4458.06 12707.1 15.86 215 , 93 6.76 8.57 0.43 38467.26 3628.51 10 355.65 515.46 * 33.693 82.6 119.13 0.47 * 623.86 * 26.389 4.36 204.54 3478.1 98473 , 34 15.44 298.51 6.26 8.57 0.37 29453.87 3548 27944.67 1523.31 4834.29 36.41 50.23 0.75 * 623.86 158.34 * 0.506 32, 69 5019.24 96112.28 14.03 385.64 8.3 12.2 0.47 42931.54 10005.05 61562.84 2519.78 * 33.693 58.39 * 10.48 0.82 * 623.86 * 26.389 * 0.506 * 4.627 5347.23 24860.31 12.62 234.71 11.92 9.83 0.75 72929.7 8114.77 113474.51 1586.79 * 33.693 183.95 * 10.48 0, 6 * 623.86 * 26.389 * 0.506 * 4.627 4855.88 29650.74 23.46 408.96 31.03 8.41 0.89 42645.06 17652.91 7324.07 3137.92 * 33.693 100.39 56 , 11 0.44 * 623.86 * 26.389 * 0.50 * 4.627 4499.47 16433.34 26.26 266.95 9.33 9.21 0.51 40129.84 8114.77 15275.53 2975.24 * 33.693 172.33 * 10.48 0.34 4279.87 189.65 6.86 * 4.627 5703.95 19068.42 24.01 253.23 13.11 9.83 0.27 65777.93 3749.63 27522, 66 828.23 * 33.693 63.56 * 10.48 0, 34 * 623.86 * 26.389 * 0.506 * 4.627 4551.38 9479.74 28.7 253.23 4.54 5.43 0.3 22206.47 4857.7 18538.26 2080.63 * 33.693 50.47 * 10.48 0.57 * 623.86 * 26.389 * 0.506 * 4.627 6117.47 15720.25 24.58 372.82 12.92 9.21 0.66 39335.75 6335.35 34437.61 1343.94 * 33.693 42 , 07 * 10.48 0.14 * 623.86 * 26.389 * 0.506 * 4.627 3738.19 29260.84 20.18 230.04 4.47 3.61 0.23 31932.63 1500.99 106868.96 1438 , 54 * 33.693 115.15 * 37.3 0.2 * 639.193 379.19 * 0.68 11.67 7479.37 15997.47 20.42 1087.03 251.35 95.34 0.58 67901.16 1420.03 11701.29 426.83 1998.71 90.01 * 37.3 0.35 * 639.193 1048.55 20.39 * 1.36 4721.27 96591.86 16.93 4740.93 49.3 21 , 01 0.17 103022.16 1684.45 15233.98 627.67 1823.73 68.03 654.84 0.31 * 639.193 353.81 11.12 8.46 3585.36 21289.59 16.69 376 , 27 40.87 55.29 0.13 151,104.83 1908.44 12424.87 2617.8 1162.73 81.82 296.08 0.54 5204.4 366.59 21.91 17.28 4652.57 151228.49 26 , 19 521.75 22.11 81.55 0.29 45701.48 2737.73 9810.14 502.99 2259.09 34.2 640.83 0.22 * 639.193 227.4 14.23 13.94 5663 , 4 11017.24 13.66 519.59 55, 25 34.31 0.17 57472.29 1885.9 28787.01 * 39.613 2461.58 62.38 135.25 0.13 * 639.193 353.81 5.3 21.66 3516.24 28532.96 9.75 606.3 33.41 48.77 0.15 40074.25 2434.48 145833.88 533.26 1814.95 25.59 * 37.3 0.29 * 639.193 211.5 * 0.68 13.94 6946 , 78 56938.49 27.17 438.19 112.02 113.16 0.31 33980.17 2055.64 10401.77 1169.25 2483.06 51.8 * 37.3 0.39 * 639.193 327.65 20.9 18.38 4542.38 349514.53 11.36 538.83 38.39 105.97 0.22 45455.96 887.56 12487.64 994.4 1298.89 57.89 514.73 0, 33 * 639.193 1501.07 4.22 11.67 2431.37 81582.87 9.57 7390.04 82.5 42.76 0.2 78099.88 10116.98 53068.01 488.61 2029.21 47, 63 422.12 0.49 * 639.193 327.65 14.23 9.35 5497.06 49944.32 8.26 302.14 16.92 85.49 0.35 52 751.35 * 132.654 14160.15 1389.75 913.7 48.96 322.03 0.28 4438.36 397.76 18.35 15.62 1055.98 36908.42 7.8 633.67 91.41 119.49 0.24 218461.28 16036, 24 44,467.35 1596.69 1691.64 100.91 246.14 1.13 * 639.193 541.47 16.3 9.93 6338.79 39396.55 12.96 394.16 36.95 100.37 0, 31 43736.19 1289.79 106934.95 1148.13 2129.18 27.65 119.79 0.27 * 639.193 403.88 4.76 18.93 2843.96 12246.83 24.88 1658.25 ** 20 176.97 0.32 93090.11 3487.67 13145.53 426.25 2272.05 86.46 623.95 0.21 4438.36 250.34 22.42 8.16 10801.86 14228.67 18.61 901.06 27.22 71.14 0.19 61302.59 2796.49 54812, 57 1585.72 2289.32 87.35 479.3 0.52 * 639.193 * 26.646 * 0.68 8.16 6833.77 6473.06 18.19 373.68 41.91 10.26 0.3 74100, 63 2411.32 12299.26 503.59 1744.57 87.35 * 41.51 1.26 * 1970.97 192.36 * 16.134 61.37 2761.3 2156.08 20.19 631.76 13.78 5.98 0.64 92865.73 7088.54 48963.5 1944.54 464.13 249.03 * 41.51 0.26 * 1970.97 * 27.42 * 16.134 11.01 7007.89 39228.3 58.06 679.66 20.96 9.55 0.21 24017.12 3053.87 27504.43 4662.78 * 33.371 212.85 476.33 0.68 * 639.193 584.35 41.67 28.39 4173.06 24094.97 15.51 158.22 276.48 7.77 0.37 73365.45 2702.54 12973.43 775.54 2198.53 21.35 363.34 1.17 22122.73 1360.07 164, 22 15.06 4572.12 6737.34 11.33 236.49 41.91 * 0.751 0.86 255477.37 3998.12 27997.96 726.77 2401.38 39.32 394.49 0.54 * 639.193 508.47 82.31 14.5 1879.15 7408.66 9.68 259.41 123.99 * 0.751 0.58 71351.98 4330.2 25363.17 743.16 1180.96 207.34 890.3 1.39 * 639.193 1156.25 225.56 35.04 3956.49 29138.73 16 , 68 205.04 140.63 * 0.751 0.81 163373.59 3439.47 28690.22 2410.89 421.21 10.83 * 10.48 1 * 623.86 392.89 * 0.506 * 4.627 5969.76 5231.4 28.1 2109.5 12.14 5.81 0.72 65104.56 2754.34 36139.02 668.66 * 33.693 107.28 * 10.48 1.21 * 623.86 255.13 10 , 01 * 4,627 4855.88 17245.16 17.62 2577.56 15 6.89 0.67 80259.27 7545.49 7365.53 1226.74 * 33.693 30.63 * 10.48 0.79 * 623.86 * 26.389 * 0.506 * 4.627 4617.38 6544.7 20.27 230.04 9.91 5.05 0.92 57609.44 4402.56 9786.93 1414.1 * 33.693 110.38 369.61 0.92 * 639.193 594 , 89 158.36 39.28 3184.17 * 412.805 23.43 278.17 179.24 * 0.751 0.43 178099.97 4927.55 12644.48 968.46 744.03 84.52 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85 4118.84 46.86 319.44 9.97 24.89 0.42 94192.71 2123.98 54431.53 374.33 1112.44 20.55 1032.78 2.76 4438.36 391.61 161 , 41 56.44 2361.63 * 412.805 31.55 896.3 25.53 38.47 0.49 171112.65 13767.43 25267.96 888.88 292.42 125.34 * 37.3 1.44 5936.13 300.56 24.95 35.04 4343.87 3992.51 19.56 831.4 29 6.52 46.34 0.31 46998.79 3379.33 18247.19 * 39.613 2611.66 10.92 840.05 0.36 * 639.193 366.59 42.9 23.28 938.34 2610.65 8 , 7 357.9 11.22 44.7 0.15 50855.53 4852.42 26780.27 795.52 904.36 75.52 * 37.3 3.11 * 639.193 286.61 7.44 17.28 9048, 68 * 412.805 30.97 519.59 21.28 26.46 0.55 120875.92 10484.37 28078.33 250.02 1673.97 69.94 538.08 0.41 * 639.193 250.34 11.64 * 1.36 3582.47 31674.86 22.63 226.28 9.21 40.87 0.19 49943.56 2878.99 12613.12 941.3 3443.59 19.66 783.8 3.13 5204 , 4 746.04 36.96 34.51 2200.25 23 988.92 26.49 1580.77 14.68 59.97 0.54 103430.8 17964.81 28464.56 991.59 2560.27 91.4 908.68 0.38 * 639.193 300.56 62 12.81 1080.6 11056.21 10.86 493.31 21.92 39.54 0.35 42400.5 9561.93 22758.34 1024.03 1289, 85 59.28 289.53 0.31 5204.4 286.61 14.75 12.24 6250.32 * 412.805 35.93 428.63 22.34 211.81 0.23 32605.53 1033.79 155178, 45 2694.76 1518.66 17.26 663.21 1.06 * 639.193 227.4 24.7 15.62 2423 * 412.805 23.94 987.48 16.64 74.44 0.34 52681.08 9117.78 19818.4 1232.4 1393.44 63.16 * 37.3 0.67 5574.06 366.59 16.81 12.81 1733.09 * 412.805 30.16 1095.56 18.44 166.96 0.36 52470.3 7405.53 5495.18 1053.14 2440.09 29.12 1593.5 0.89 * 639.193 211.5 44.38 23.28 5249.34 * 412.805 44.09 553.52 25.84 71.14 0.28 155312.13 1409.12 42454.64 479.67 763.06 56 , 69 434.28 0.6 7984.19 327.65 7.44 20.02 4131.97 6990.01 22.4 296.25 28.52 82.14 0.27 30044.54 3596.39 44930.22 464.85 1629.72 54.22 * 37.3 1.51 4438.36 726.53 40.18 20.02 1937.13 93343.55 17.67 158.22 145.58 * 0.751 0.59 139799.19 4441 , 54 3813.28 609.39 843.42 108.44

176.14 1.59 * 639.193 265.11 25.45 11.09 3648.84 * 412.805 10.34 201.38 56.8 * 0.751 0.31 148437.73 3863.69 11480.39 1795.57 672, 22 12.03 282.94 0.68 * 639.193 300.56 15.78 17.83 6209.33 * 412.805 20.17 197.68 272.7 * 0.751 0.3 64732.45 2644 77516.61 985.27 4182, 36 32.64 341.22 0.81 15556.43 803.58 211.68 47.73 2383.93 13249.4 14.91 502.16 227.01 * 0.751 0.44 211068.5 6017.83 84328.83 431 , 47 233.82 52.95 446.37 0.99 * 639.193 508.47 54.45 31.05 1810.21 10860.15 11.31 302.14 143.36 * 0.751 0.61 66097.67 1773, 67 6771.85 745.13 * 33.985 82.61 * 37.3 0.46 * 639.193 530.56 7.44 18.38 4400.03 * 412.805 21.98 140.82 107.46 * 0.751 0.27 95537 , 81 2550.65 16538.87 1658.95 516.19 25.59 204.7 0.65 10484.95 1091.98 83.27 520.65 5319.46 3413.42 16.42 190.17 197.57 8.63 0.62 250 139.1 5787.65 16 321.48 411.26 * 33.985 22.74 * 37.3 0.76 * 639.193 1987.48 10.07 143.91 3892.4 23 109.92 8.83 212.26 353.54 * 0.751 0.57 87875.17 8051.21 37248.1 479.07 1038.92 10.64 127.57 0.74 * 639.193 474.63 22.93 12.24 1959.23 * 412.805 9.86 272 14.9 11.19 0.24 17 3004.05 4640.25 11543.54 694.23 1176.41 32.6 440.33 11.55 * 639.193 942.08 14.23 30.79 979.39 8149, 77 10.93 445.29 232.81 24.89 0.12 74653.09 5634.76 455208.32 443.7 2995.24 174.76 256.28 0.6 4438.36 463.14 16.3 66 , 75 3576.71 2847.7 19.77 319.44 106.36 49.04 0.44 70586.3 8051.21 7709.69 296.02 2782.52 12.66 115.86 1.66 * 639.193 1057 , 27 68.8 68.34 2339.33 * 412.805 18.47 262.59 108.73 * 0.751 0.45 174527.49 6688.21 16569.93 367.04 1660.71 14.35 * 37.3 0 , 82 * 639.193 1117.81 72.91 30.79 4913.29 4680.16 8.26 488.85 587.26 14.4 0.3 93798.4 6481.1 11732.82 597.47 876.29 19.82 190.53 1.06 * 639.193 1544.75 94.77 58.29 3753 * 412.805 9.39 249.73 38.1 20.15 0.65 110 986.43 4802.4 5318.41 1056.7 461.46 9.98 850.68 0.74 * 639.193 1004.59 47.83 74.44 1741.35 * 412.805 27.07 302.14 234.65 26.46 0.35 68697.39 8024.74 48869.3 748 , 43 791.52 11.71 261.19 4.34 * 701.526 * 26.487 19.68 40.82 468.46 2015.92 16.18 314.59 6.84 18.83 0.4 133796.95 7903, 11 226 451.28 * 39.516 2418.57 144.14 1231.6 1, 15 * 701.526 856.85 193.8 60.82 1901.1 14621.68 12.86 557.13 496.86 74.36 0.29 85706.64 13082.18 66871.31 293.12 2054.75 42, 17 392.17 1.52 9054.24 887.06 30.22 31.17 6356.89 4081.59 13.5 336.98 74.11 11.28 0.42 213140.84 6718.69 14240.47 886 , 16 1885.36 14.37 981.84 0.85 * 701.526 614.45 18.48 61.42 1913.39 2625.35 10.66 330.46 86.81 12.93 0.48 107281.37 7165 , 32 14,137.81 714.58 875.27 127.08 * 35.8 1 * 701.526 484.16 21.46 58.41 1950.36 1536.03 7.54 241.21 61.44 * 0.692 0.32 58152.02 10372.4 14514.1 700.36 497.37 50.42 274.21 0.8 * 701.526 1238.45 52.74 187.94 1979.21 2625.35 22.69 194.2 172.43 * 0.692 0.5 149389.02 5579.59 17512.81 889.15 3275.59 48.94 * 35.8 0.73 * 701.526 627.22 38.78 288.71 3870.61 1257.47 15.38 187.57 286 10.42 0.4 104904.23 10290.48 7156.63 1484.2 1456.09 54.94 153.2 4.52 5324.01 994.52 272.65 86.5 2542.78 3789 , 09 12.77 321.24 160.55 * 0.692 0.81 108256.13 8112.48 21564.19 861.58 3495.25 65 380.51 0.51 * 701.526 1227.02 26.16 90.84 1622 , 7 4223.05 7.39 347.3 40.12 29.27 0.42 153701.25 12168.71 16339.33 1402.63 2129.14 54.77 514.53 0.75 * 701.526 1329.28 56.04 39.94 7016.5 3789.09 35.61 96.93 41.05 * 0.692 0.4 231776.63 4085.32 26183.2 1838.09 1035.76 18.75 * 35.8 24.29 4209.16 1485.9 40.48 47.33 1270.75 2015.92 17.34 512 , 41 53.17 7.7 0.6 245532.25 15966.15 16475.51 300.79 1179.9 50.9 * 35.8 0.5 * 701.526 2533.54 37.08 84.65 1399.21 1536.03 11.64 219.81 480.64 * 0.692 0.44 237704.11 8160.84 19582.82 839.38 2608.88 82.11 418.99 0.91 * 701.526 470.83 43.85 29 , 44 5832.27 1536.03 12.68 134 16.35 * 0.692 0.47 126493.58 2612.39 6587.43 798.27 587.63 28.04 * 35.8 1.45 * 701.526 575.88 97.88 27.42 1512.16 2230.49 12.44 197.48 104.76 * 0.692 0.61 131530.6 6147.37 4749.65 877.95 1291.17 63.7 * 35.8 0, 67 * 701.526 1249.86 45.52 183.37 1484.75 5741.06 5.51 159.96 166.8 * 0.692 0.43 132895.87 5359.8 24118.94 440.35 382.62 23.07 392.17 0.64 * 701.526 276.42 6.71 12.47 1604.84 6082.22 14.23 180.84 161.04 10.42 0.53 107431 4564.4 34539.5 510.15 953, 44 192.62 * 35.8 0.48 * 701.526 575.88 * 0 , 621 * 1398 1210.18 8576.17 5.76 203.97 41.08 10.59 0.25 48644.38 5516.73 9739.32 713.16 558.9 128.7 126.85 0.5 * 701.526 1563.21 15.16 788 1516.09 266387.95 13.05 165.29 617.51 8.08 0.32 62302.62 10651.26 13829.64 1367.17 735.48 10.09 * 35.8 2 , 37 8533.8 1834.58 216.13 658.17 4197.62 6731.6 19.73 422.13 326.19 * 0.692 0.96 177684.75 11391.78 17716.66 814.36 1109.88 10 , 05 * 35.8 1.24 4209.16 1519.11 38.78 481.12 3357.18 44627.42 15.47 284.55 309.78 15.13 0.37 73247.24 12019.66 37859, 83 812.9 612.26 8.85 320.46 0.53 * 701.526 1452.57 33.66 117.14 3552.87 74270.45 14.06 292.87 48.97 12.61 0.61 103654.11 11986 , 56 17750.63 1592.29 1567.48 24.68 849.71 1.3 * 701.526 470.83 63.7 30.59 1197 2809.39 25.07 180.84 35.48 * 0.692 0.6 108180 , 97 1596.67 9878.64 812.9 899.95 154.78 485.75 0.48 * 701.526 1628.99 8.64 81.26 4803.75 39671.95 11.5 200.74 87.05 29 , 52 0.71 43707.89 7935.3 30420.37 726.72 669.75 16.98 287.04 1.85 * 701.526 170.8 11.78 13.02 1882.69 2015.92 14.02 681 , 16 66.06 21.21 0.58 5596.69 10897.7 2 883.71 864.55 231.28 36.78 340.83 1.01 * 701.526 1385.54 18.48 228.87 5674 4890.92 27.02 134 308.39 8.08 0.44 98277.49 6099.92 4786.1 936.51 998.71 13.24 384.41 1.19 * 701.526 396.38 33.95 15.79 1648.57 10446.2 18.35 347.3 33.79 30.01 0.37 125584.08 13632.39 17648.72 958.49 756.03 71.03 * 35.8 2.17 * 701.526 232.14 46.64 26.56 1539.66 5856.14 30.93 395, 85 15.17 45.73 0.49 72758.31 10831.96 11041.49 980.58 608.15 94.89 415.19 0.83 * 701.526 290.92 47.75 24.84 1841.9 3713, 77 16.28 471.58 23.37 41.83 0.4 103178.43 12583.36 10521.65 315.65 891.72 62.78 784.47 1.21 * 701.526 333.74 25.58 20, 29 5 246.49 1536.03 31.47 235.18 23.79 37.8 0.63 88033.63 2240.64 9826.41 506.18 1146.95 40.89 532.28 2.11 * 701.526 802, 02 22.05 13.57 1610.79 102316.18 7.22 870.13 67.16 21.49 0.65 164375.62 6941.77 158673.33 639.93 2096.08 39.61 * 35.8 1.71 * 701.526 269.12 11.78 * 1398 1496.49 3637.49 34.35 271.86 8.45 19.68 0.42 71693.89 5265.79 25777.02 1247.51 1906.01 70 , 83 549.85 1.54 * 701.526 261.79 16.67 13.57 4119.48 9 707.86 17.83 424.48 13.85 45.2 0.48 48275.97 7005.59 26386.32 992.82 887.61 48.77 234.55 2.71 * 701.526 170.8 26.74 12, 47 5,805.35 2015.92 22.15 280.35 13.35 43.14 0.53 71360.8 3747.56 29707.51 686.91 563 30.62 220.89 0.34 * 701.526 1169.61 38 , 78 135.56 3613.99 1257.47 15.61 267.58 47.99 13.25 0.28 128241.82 9327.23 3404.34 2477.44 317.13 16.28 * 35.8 2.18 * 701.526 3649.58 92.08 173.59 2133.07 2230.49 18.35 513.49 205.18 10.25 0.66 95212.93 10519.97 2784.23 1600.81 1641.76 11.14 407 , 56 0.62 * 701.526 575.88 12.4 315.29 1817.52 39840.69 16.63 120.18 154.25 14.52 0.31 67889.33 8096.36 23611.45 348.88 480.96 43.21 220.89 0.85 * 701.526 4561.36 17.88 3294.37 1590.97 5326.48 5.56 221.37 187.69 16.2 0.62 231385.91 5093.73 22850.18 915, 39 821.81 6.34 437.84 1.05 6949.84 1519.11 42.73 129.18 3543.49 8116.08 21.83 261.82 299.6 8.82 0.45 111939.65 9783 , 57 4310.73 574.59 1015.18 3.91 137.57 3.54 4770.23 1732.27 22.64 45.25 3301.76 2718.34 29.53 490.52 124.12 * 0.692 0 , 45 233869.79 18630.8 31541.67 870.5 1583.98 126.33 467.49 3.92 * 701.526 677.89 51.91 43.77 1027.99 * 155.532 25.67 321.24 51.02 14.2 0.66 150412.28 10897.72 4931 , 7 731.73 521.97 61.17 115.86 1.19 * 701.526 1283.99 196.29 47.03 1600.88 1257.47 8.77 470.46 108.78 7.7 0.31 215830 , 28 19947.76 34744.36 541.49 871.16 12.39 574.18 0.82 * 701.526 1053.4 22.64 31.46 2355.28 1786.06 19.29 488.31 78.92 8 , 45 0.35 171445,6 16845.51 19837 1169.13 813.58 15.76 567.26 1.03 4490.68 6293.23 177.81 51.8 2671.65 82003.24 6.04 351, 13 187.81 16.05 1.22 67889.33 7053.49 35188.45 980.58 961.67 3.06 * 35.8 1.05 * 701.526 1123.36 49.42 27.13 4276.15 12780 , 9 17.07 205.58 28.6 14.83 0.66 72453.48 2537.74 9215.87 1070.82 439.97 9.05 868.08 0.58 4770.23 1076.8 334.47 34, 08 3419.78 4361.64 10.71 763.54 217.97 8.82 0.27 162987.94 4642 6943.53 513.46 1019.29 46.6 * 35.8 0.96 * 701.526 * 26.487 * 0.621 * 1398 1901.1 2432.91 10.91 177.44 97.63 9.9 0.6 46497.38 3472.71 30929.88 544.18 1089.29 80.96 740.06 1.29 * 701.526 3064, 92 432.48 85.27 1529.83 4081.59 15.44 451 , 17 295.97 9.9 1.91 225 208.72 6766.45 31 201.73 1044.42 2191.14 14.55 * 35.8 0.45 * 701.526 2395.46 23.82 329.01 5132.14 7639.44 8.76 295.62 511.7 65.8 0.33 122150.42 16676.15 65275.14 623.28 439.97 19.06 256.81 1.01 * 701.526 1100.12 5.4 44.66 2151.94 933.19 3.54 244.2 78.56 15.74 0.46 74229.54 10208.61 19853.95 1202.58 358.05 65.47 299.7 0.79 * 701.526 1158.08 * 0.621 100.8 4466.72 1786.06 6.7 575.82 74.87 14.52 0.42 68542.01 14770.91 24541.87 1407.6 464.56 57.91 274.21 1.36 * 701.526 4834.41 88.64 31.17 3118.63 933.19 9.98 311.91 371.03 34.1 1.2 192469.35 25554.03 7828.92 1140.62 751.92 10.29 137.57 0.56 * 701.526 416.9 62.06 20.29 4424.45 19030.25 16.58 168.8 25.84 * 0.692 0.42 125295.59 2537.74 36933.73 658 , 07 604.05 59.27 220.89 1.19 * 701.526 4388.88 72.89 55.4 1274.55 39314.53 14.59 264.7 225.53 8.08 0.58 138276.24 17252 , 46 17818.57 1271.73 542.49 13.57 388.3 1.74 6949.84 3485.51 45.25 59.61 2724.46 13376.65 10.54 242.71 426.6 13.57 0.4 115 229.46 17 269.43 70 166.04 4 680.56 764.25 6.11 162, 32 1.01 * 677.872 1073.29 19.6 27.75 1881.62 1671.07 24.51 353.09 135.06 10.02 0.89 141474.47 15103.6 24310.36 1925.53 1465, 06 63.12 791.42 0.8 * 677.872 1370.31 59.66 36.78 3681.66 50454.43 13.72 208.89 106.46 13.75 0.49 113599.51 8025.5 5460, 23 1914.63 1389.86 98.12 599.67 1.35 * 677.872 2791.4 158.6 40.52 4398.05 1203.32 16.8 1036.73 81.77 33.68 0.6 47352, 5 16658.35 28119.65 1809.55 2593.39 103.92 * 12.832 0.53 * 677.872 1145.05 57.07 17.48 5953.79 33511.75 9.02 545 237.6 53.31 0.53 38363 , 58 17242.57 8142.3 1884.38 1338.16 59.36 * 12.832 0.42 * 677.872 1176.63 10.72 97.24 3132.74 2622 18.38 330.19 228.83 36.6 0 , 33 141823.16 18881.61 26538.92 1696.57 1333.46 94.56 164.82 3.54 5007.58 1945.82 21.8 44.27 2171.88 3238.37 13.57 510.18 83.36 10.99 0.95 174463.68 17944.45 19827.78 1538.95 2659.24 61.56 390.06 1.94 6827.94 2293.11 77.3 60.54 3924.67 35103, 19 16.74 697.29 128.78 43.56 0.44 154494.31 10375.52 11317.09 1375.51 1450.96 38.48 437.1 1.73 * 677.872 490.36 73.44 23.79 7016 .18 3485.89 38.29 168.09 45.11 8.83 0.6 121616 8082.58 25612.59 1988.6 2010.31 95.85 563.37 1.15 * 677.872 509.8 77.3 41.59 4696.66 6002, 73 15.83 182.24 29.83 12.55 0.6 130896.25 8959.42 13194.81 311.87 1300.56 40.98 213.72 0.24 * 677.872 747.76 4.42 354, 14 2364.84 21240.89 7.3 356.3 129.75 24.73 0.26 129 753.02 10 279.64 20 443.63 1458.23 1164.25 53.72 1677.11 0.79 4540.43 475 , 64 76.87 5229.77 2719.17 3834.86 15.5 251.54 18.17 11.63 0.47 23382.13 8272.96 15369.54 617.1 2010.31 68.53

964.36 1.1 * 677.872 460.79 33.2 64.92 2598.68 1203.32 13.51 439.11 19.95 13.46 1.01 104 105.43 15 705.5 14 594.41 706.55 995.05 116.58 218.5 2.59 * 677.872 1184.5 109.37 57.81 5880.42 9053.98 55.87 766.38 123.35 19.27 1.63 227530.55 12916.1 9208.61 3277.87 4944.26 55.44 220.88 1.18 * 677.872 1662.45 34.51 24.58 4393.03 3485.89 15.47 348.26 48.96 9.35 0.81 95406.71 12260.09 11037.49 1133.17 1653.06 73.35 569.45 0.35 5469.4 654.45 36.91 14.08 2501.58 4766.94 14.31 943.13 41, 17 38.64 0.41 81173.81 9896.39 48680.38 1326.06 1004.45 153.5 372.72 2.41 11152.32 2880.25 88.87 44.27 2171.88 39875.45 12 , 65 510.18 356.6 20.04 1.73 177877.65 10663.33 18133.06 1515.17 3327.44 59.14 553.22 2 * 677.872 1278.09 19.6 34.91 4310.38 90882.57 16.65 277.88 517.43 55.36 1.53 133005.71 9973.01 4342.07 1330.76 2922.69 65.18 1032.39 0.71 * 677.872 257.05 35.38 10, 67 6938.82 4766.94 15.26 186.82 17.42 16.04 0.37 70836.71 2147.03 17035.98 3571.88 1126.65 59.19 493.67 2.09 * 677.872 357.88 137 , 85 24.58 2213.82 9238.41 16, 32 335.17 30.06 4.37 0.49 146349.34 8635.05 24961.28 1477.19 1004.45 103.19 919.8 1.18 * 677.872 280.37 22.47 18.8 2817.59 907 , 29 3.85 168.09 30.69 22.55 0.43 27952.26 2844.19 210008.73 933.53 473.17 74.6 1330.79 0.37 * 677.872 336.29 25.32 15 , 12 2753.44 7008.31 7.99 126.56 31.72 46.24 0.18 43923.09 11605.16 127365.37 3538.51 637.76 49.67 134.28 0.7 * 677.872 257 , 05 5.78 46.16 2411.78 * 121.197 14.97 589.62 48.07 17.95 0.35 109926.15 15783.22 141532.6 2725.55 1718.87 134.12 390.06 0.97 * 677.872 257.05 8.93 10.93 9678 10858.22 9.75 325.17 32.19 35.88 0.9 18959.37 3139.41 162287.31 1108.79 1230.06 57.32 1244, 54 0.28 4067.23 245.15 23.13 20.37 8222.35 1772.94 20.72 590.84 20.47 63.53 0.25 43057.35 3194.87 124856.93 1581.81 1272 , 36 71.03 1300.98 0.56 * 677.872 208.27 * 0.661 16.7 1115.55 2695.56 6.78 456.04 6.14 39.74 0.32 32799.55 5414.81 170929, 23 1502.11 637.76 47.62 177.26 0.51 * 677.872 * 27.062 7.59 20.9 3292.54 907.29 18.74 398.13 23.78 22.31 0.52 93750.82 4291.01 34173.93 1105.31 1389.86 5 2.04 37.93 0.22 * 623.86 * 26.389 3.03 * 4.627 4955.12 34125.72 45.93 1094.74 16.42 3.83 0.23 47067.54 7024.08 114547.01 866.54 * 33.693 33.13 * 10.48 0.96 * 623.86 * 26.389 * 0.506 * 4.627 3523.26 30978.84 13.04 381.38 7.49 3.39 0.44 32899.46 1797 , 07 88 385.17 428.33 * 33.693 18.26 * 10.48 2.31 4279.87 350.08 40.52 * 4.627 3870.08 3863.4 50.47 1539.09 30.37 6.54 0 , 6 71145.12 5485.78 25333.34 1151.9 * 33.693 23.15 * 10.48 0.59 * 623.86 213.93 * 0.50 * 4.627 3823.78 18350.5 16.27 672.03 13 , 23 5.43 0.36 56055.85 4033.89 12901.69 1814.13 * 33.693 49.96 * 10.48 1.16 5830.35 1034.05 38.62 * 4.627 6034 19930.32 7.39 355.59 36.9 5.62 0.34 182230 9043.46 71788.39 654.46 * 33.693 34.47 * 10.48 2.09 * 623.86 371.61 14.85 360.15 4732.68 6474.14 37.45 1175.44 153.39 4.65 0.62 50896.49 4361.43 16222.89 454.85 * 33.693 15.89 * 10.48 0.19 * 623.86 189.65 * 0.50 * 4.627 3097.23 33583.46 13.44 196.85 13.64 5.99 0.21 23249.66 2327.78 12,107.61 2723.17 * 33.693 65.65 * 10.48 0.45 * 623 , 86 * 26.389 32.86 * 4.627 4810.01 23150.49 21.59 38 5.64 * 0.082 5.43 0.34 71787.1 3347.59 21475.25 1625.66 * 33.693 29.98 222.29 1.3 * 623.86 317.26 3.48 58.52 3549.13 * 417.822 34.99 307.41 15.4 6.36 0.61 47280.71 4054.28 16801.64 752.54 * 33.693 84.82 88.97 1.54 * 623.86 237.65 13.02 51, 02 2068.76 * 417.822 14.35 471.39 13.05 5.43 0.44 89556.92 4774.59 15494.94 834.39 * 33.693 83.89 * 10.48 0.36 * 623.86 * 26.389 * 0.50 * 4.627 4110.57 34901.37 16.22 276.03 15.28 5.05 0.4 87243.88 6015.22 31264.95 2649.39 * 33.693 30.27 * 10.48 0.71 3743.16 * 26.389 9.24 * 4.627 4117.31 5231.4 14.08 469.33 16.02 11.63 0.61 47209.66 4733.09 176588.1 243.06 * 33.693 40.97 * 10.48 0.47 * 623.86 177.26 * 0.50 38.29 5652.0 4295.01 18.7 871.44 8.98 5.43 0.28 139089.28 5107.94 11400.82 1463.06 * 33.693 30.13 * 41.51 0.41 * 1970.97 271.86 * 16.134 10.5 4063.76 31317.73 20.92 648.8 17.71 12.39 0.21 37354.89 15634.7 15750.27 1589, 73 * 33,371 132,87 * 10,48 0,6 * 623,86 * 26,389 * 0,506 * 4,627 5180.63 * 417,822 23.19 285.06 7.98 5.81 0.38 47635.86 1648.31 9232 , 35 1318.47 * 33.693 65.65 83, 39 0.44 * 673.809 335.34 * 0.579 * 1.378 7592.38 32742.96 46.24 245.85 11.27 10.16 0.42 49477.59 11209 22518.63 2013.66 2817.56 182.88 138, 8 0.86 4684.39 705.09 14.66 41.7 4718.7 102631.79 14.52 477.81 17.15 10.9 0.63 38156.54 8501.41 176095.2 1105.4 3315.93 46.66 146.86 0.78 * 673.809 197.43 3.48 14.44 8620.34 14050.95 62.1 528.1 8.77 6.52 0.4 81181.92 7432.06 82912.07 5583.96 2956.23 74.63 34.3 1.18 * 673.809 168.62 4.29 * 1.387 5777.24 16696.46 20.31 619.23 7.21 9.78 0.49 70411.2 11443 , 07 25141.44 1442.7 1310.13 30.49 * 41.51 0.48 * 1970.97 * 27.42 * 16.134 11.01 5725.86 1042.25 38.38 654.44 14.01 3 , 52 0.34 40080.29 9273.62 13417.19 3469.64 * 33.371 216.88 * 41.51 0.89 12729.83 * 27.42 * 16.134 20.32 6925.57 5343.35 29.75 457, 8 4.93 4.81 0.76 55882.59 7646.94 13875.55 2620.2 * 33.371 130.37 * 41.51 1.02 * 1970.97 210.5 * 16.134 12.05 6639.79 16692 , 44 25.92 1259.74 4.4 6.35 0.99 88883.49 5492.9 56940.58 1591.6 * 33.371 113.57 * 10.51 0.75 * 673.809 211.24 * 0.579 8, 83 7883.35 11,107.11 42.39 739.52 9.13 5, 84 0.57 43372.42 9750.07 20817.24 1650.88 2254.34 37.41 142.85 1.06 * 673.809 346.78 8.91 15.56 8147.06 14931.68 30.03 2343.09 11 , 18 7.39 0.59 51850.88 17966.3 39697.89 1926.57 2014.94 138.6 * 41.51 0.3 * 1970.97 183.18 * 16.134 8.97 3296.98 2604, 15 17.83 825.1 5.61 8.09 0.17 45319.51 14790.99 14806.45 4811.68 * 33.371 209.54 * 41.51 0.79 * 1970.97 263.27 * 16.134 12.57 7,603.07 8365.7 44.49 526.02 9.04 5.6 0.33 107094.97 11063.31 27833.24 2176.65 * 33.371 35.39 * 41.51 0.57 * 1970.97 210 , 5 * 16,134 21.96 4868.23 5817.21 23.46 2435.35 12.77 5.79 0.32 134757.67 13015.21 25734.47 3185.81 * 33.371 133.39 426.06 0.58 * 673.809 659.39 * 0.579 12.2 8620.34 14882.23 31.67 1992.24 57.38 12.66 0.42 59864.7 20768.38 177045.84 1748.14 1925.18 104.46 * 41 , 51 0.55 * 1970.97 * 27.42 * 16.134 12.57 5891.88 35541.83 19.4 365.59 ** 20.02 6.89 0.39 20869.67 7732.75 12570.03 4628.35 * 33.371 192.17 * 41.51 0.38 189497.53 * 27.42 * 16.134 8.97 5999.98 30868.86 17.6 287.23 4.23 5.4 0.34 71831.86 7217.5 8608 3787.94 * 33,371 229.79 * 41.51 0.39 * 1970.97 427.67 * 16.134 20.59 3339.9 128365.2 16.69 1232.23 5.72 10.93 0.39 59923.23 31498.93 24095.57 6270 , 12 * 33,371 94.63 367.94 1.51 7338.12 312.05 14.66 16.12 10385.81 6189.73 48.38 569.06 8.49 11.97 0.65 110489.92 20947 , 7 12,580.08 924.05 3426.18 63.44 * 41.51 0.46 * 1970.97 210.5 * 16.134 10.24 5392.9 7047.89 15.26 806.75 6.91 12, 67 0.33 34 301.58 3141.65 11531.56 1563.53 * 33.371 53.35 * 41.51 0.73 * 1970.97 271.86 * 16.134 36.96 6838.43 7047.89 68 2288.8 5.89 7.75 0.51 59634.92 5406.35 19728.38 2248.87 709.86 33.29 230.44 0.6 * 673.809 197.43 * 0.579 * 1.378 4834.44 5270.01 17.05 445 , 66 6.88 11.26 0.48 37473.71 16067.05 24962.43 2584.08 1814.04 101.24 297.48 0.19 * 673.809 * 26.852 * 0.579 * 1.378 6845.31 13373.82 7 , 32 307.43 8.74 8.21 0.24 30413.09 11706.59 25177.23 1906.44 1208.28 101.64 * 10.51 0.23 * 673.809 * 26.852 * 0.579 * 1.378 6905.35 8562.63 15.12 179.6 6.63 7.8 0.26 14019.98 5709.69 16518.44 * 122.771 1046.12 29.58 39.7 0.3 * 673.809 * 26.852 * 0.579 * 1.378 5117.58 23991.45 18, 47 597.29 7.38 6.96 0.27 46861.05 4826.57 309232.56 1974.7 1589.27 70.96 193.44 0.22 * 673.809 * 26.852 * 0.579 * 1.378 5295.02 150049, 23 27.28 314.58 9.22 10.9 0.33 28776.12 9371.94 13655.05 2967.14 2484.09 21.25 354.76 0.39 * 673.809 * 26.852 3.48 * 1.388 4591 , 39 11,807.3 21.53 1606.85 14.52 8.62 0.29 36200.4 6052.87 64472.98 1075.56 2117.93 49.05 185.86 0.5 * 673.809 380.33 * 0.579 * 1.388 7650.15 9215.7 14.39 1973.25 12.2 7.8 0.87 57445.01 6339.38 10901.93 1270.91 2604.71 44.62 * 41.51 0.6 * 1970, 97 237.16 * 16.134 12.57 6956.41 33687.14 48.66 2771.91 17.71 9.07 0.71 65265.99 12252.35 7043.64 4740.38 * 33.371 46.2 273.2 0, 41 * 673.809 * 26.852 10.38 11.08 5916.92 2964.53 17.81 556.85 10.87 8.01 0.37 58583.33 10829 5461.15 1873.99 1716.13 40.26 146.86 0 , 39 * 673.809 237.87 * 0.579 * 1.378 10702.48 121636,02 37.53 365.77 5.91 6.07 0.5 54079.31 8182.98 8999.19 2803.3 4527.15 20.53 * 41.51 0.37 44647.45 330.75 * 16.134 28.38 3757.49 4845.96 26.82 1153.23 11.78 4.81 0.24 33001.28 5881.98 11307.93 404, 1,235.7 5 80.49 134.74 0.9 4366.79 * 26.852 9.65 11.64 11397.12 66691.96 18.2 788.51 10.56 10.16 0.48 90502.19 5338.73 109253, 53 1655.42 1952.85 31.27 * 10.51 0.35 * 673.809 * 26.852 * 0.579 * 1.378 7888.69 31141.8 43.24 275.88 3.8 7.8 0.48 43228.54 6024 , 25 14793.9 2403.18 3289.93 61.8 87.89 0.26 * 673.809 * 26.852 * 0.579 * 1.378 4633.68 17154.04 16.31 185.13 10.59 7.17 0.48 18152 , 88 6253.38 6982.91 2176.98 1603.43 89.66 230.44 1.96 * 673.809 380.33 7.41 36.33 6725.95 38084.61 17.17 658.62 27.41 12.32 0.66 126178.96 13704.08 8261.05 2060.56 2624.75 40.8 * 10.51 0.57 * 673.809 183.25 * 0.579 * 1.378 11573.01 14832.67 38.33 316.95 12 , 4 8.21 0.49 46527.42 6942.48 15640.43 826.37 1862.76 48.23 * 41.51 0.68 48497.26 245.92 * 16.134 25.01 10242.21 10348.4 35.39 835.53 4.85 13.79 0.37 44646.37 16814 20826.81 1175.62 * 33.371 308.32 * 41.51 1.16 64060.26 164.52 * 16.134 7.97 4858, 9 10 687.21 41.43 419.12 2.71 6.89 0.66 75661.48 5752.36 195281.36 2404.03 * 33.371 163.68 158.78 0.48 * 673.809 197.43 * 0.579 * 1.388 4744.33 253 33.02 13.16 243.3 8.88 9.01 0.32 22942.84 5338.73 16630.11 904.67 978.95 56.55 * 41.51 1.19 * 1970.97 347.22 * 16.134 12.3 8957.32 24833.51 35.13 1752.2 7.43 6.71 0.52 67897.59 8332.81 11345.23 3257.49 * 33.371 80.59 182.05 0.92 * 673.809 211.24 * 0.579 13.32 7936.82 129836.1 38.06 769.76 14.58 6.52 0.62 46527.42 12939.05 15321.34 2385.53 2416.79 41.11 * 39 , 77 0.79 * 706.944 193.8 * 21.183 14.19 3244.5 1937.47 13.44 488.5 13.75 11.4 0.66 38152.23 12878.49 15699.41 2409.98 * 32.555 224.77 197.21 0.86 * 673.809 211.24 3.48 12.76 7899.37 30767.59 47.37 511.48 11.92 9.01 0.39 50332.42 9517.31 81518.88 1524, 56 2537.78 53.81 * 41.51 0.13 * 1970.97 * 27.42 * 16.134 * 1.329 3983.37 3930.06 13.13 157.01 * 0.028 4.81 0.17 26120.63 2569 , 9 95 354.83 3488.26 * 33.371 22.96 50.06 0.28 6196.87 237.87 12.54 11.08 5556.7 9282.69 34.93 546.63 8.88 9.21 0 , 34 21612.54 11033.56 11726.03 1757.3 2069.94 63.16 354.76 1.07 6196.87 358.08 28.75 35.77 8371.02 20950.24 72.66 1322.19 17.7 20.15 0.71 76157.04 13468.52 37817.71 1184.1 3 1288.39 46.36 189.66 0.19 * 673.809 * 26.852 * 0.579 * 1.378 7750.43 11225.74 7.97 370.33 6.71 9.59 0.33 15575.14 9226.65 26552, 97 2019.9 1918.26 65.79 * 41.51 1.12 37400.42 263.27 * 16.134 11.01 6925.57 15855.78 51.07 764.27 3.65 5.21 0.46 97559 , 13 9956.34 174800.24 4262.62 * 33.371 145.92 307.75 0.82 * 673.809 183.25 3.48 9.39 7803.47 11284.75 22.9 482.05 16.58 7, 8 0.45 24464.66 11267.5 29027.29 1024.68 1758.17 51.98 113.97 1.49 * 673.809 323.77 7.41 8.27 5091.17 7725.34 57.46 347, 4 12.91 15.79 0.51 59247.63 6110.14 6726.72 1481.31 696.47 38.51

* 41.51 0.84 * 1970.97 271.86 * 16.134 11.79 6143.32 36705.35 29.56 457.8 10.84 9.71 0.77 102353.17 5492.9 54606.79 * 40.28 * 33.371 55.02 575.51 0.61 * 677.872 291.81 34.08 22.48 1184.92 94044.67 8.18 372.09 15.72 37.83 0.21 45221.98 11816 , 93 12,101.36 792.28 1164.25 149.46 374.89 0.97 * 677.872 257.05 47.99 17.22 960.1 7580.4 11.3 507.54 41 73.58 0.23 60710, 34 9016.7 61329.49 1992.25 1328.76 89.73 41.68 0.41 * 677.872 336.29 8.04 9.35 6175.56 28979.55 8.51 354.7 416.51 25, 91 0.19 58165.53 2220.12 145180.53 1957.05 600.15 155.08 322.05 1.39 * 677.872 363.21 35.82 16.7 1694.88 50062.65 9.36 418, 9 33.19 31.75 0.43 89655.83 9284.17 15197.42 1248.63 1042.05 123.91 1271.05 1.23 * 677.872 825.46 80.52 54.54 2630.42 50871, 43 15.6 757.93 28.33 42.4 0.5 47099.57 9666.67 26607.58 2257.29 901.03 48.45 927.25 0.36 * 677.872 1117.26 6.23 10.4 2121 , 28 44,749.49 10.03 6839.92 45.07 23.41 0.3 131661.29 5979.55 27706.95 1476.01 854.03 97.47 702.54 0.35 * 677.872 557.54 6 , 69 10.93 3427.63 302227.36 22.17 2429.41 18, 46 15.2 0.28 71637.14 2881.04 25509.72 3199.08 1215.96 102.43 859.8 0.73 * 677.872 336.29 33.64 29.6 1401.82 9450.59 23, 04 664.75 28.86 29.33 0.31 102 133.74 7038.09 18 115.93 1403.83 1512.06 119.13 848.47 2.12 * 677.872 314.29 11.62 12.5 2282, 48 35088.45 20.2 558.84 13.16 28.43 0.47 128804.26 10375.52 66008.51 2501.56 2315.92 88.87 573.49 1.1 * 677.872 529.03 718, 27 106.31 5395.53 12003.67 18.62 253.47 47.35 21.81 0.76 146045.08 4627.28 30083.78 1830.04 1690.67 56.55 625.65 1.7 * 677.872 1016.74 19.6 28.81 5707.59 74514.51 17.13 1494.2 48.69 20.56 0.29 126823.42 4067.27 40494.05 1542.51 1032.65 33.83 439.21 1.23 * 677.872 182.49 75.59 27.75 4845.2 5141.25 21.48 518.02 11.1 18.48 0.66 20381.42 12163.71 24995.55 870.67 1403.96 64.13 370.54 0.68 * 677.872 460.79 11.62 14.34 2723.74 5321.32 5.97 1000.58 15.01 14.91 1 145 184.98 2222.5 9208.61 1055, 52 1493.26 68.65 * 12.832 0.43 * 677.872 721.41 4.42 586.99 3221.86 2391.42 5.76 274.2 165.2 6.06 0.25 97994.21 11431.99 5423.32 1557.99 703.59 22.88 54.0 6 0.3 * 677.872 846.72 7.36 320.47 3515.89 3043.23 20.14 261.11 192.51 28.54 0.23 71560.82 3620.97 13125.52 1543.7 1164, 25 13.61 583.58 0.83 * 677.872 3966.92 47.99 71.53 1677.84 907.29 13.08 330.19 188.9 12.25 0.64 120 743.17 10548.18 13073, 54 1952.19 1314.66 13.97 47.94 0.57 * 677.872 1790.51 16.95 46.97 3722.74 45113.94 13.04 137.58 70.83 8.3 0.44 98750, 26 7 455.34 11037.49 1676.2 1032.65 11.45 159.81 0.22 4540.43 480.56 8.49 174.58 1778.28 4055.19 19.3 223.6 147.6 12 , 25 0.25 116153.42 11085.89 17190.36 1187.88 1460.36 160.54 * 12.832 0.5 * 677.872 942.98 31.02 105.17 5804.62 6634.03 13.01 221, 53 216.64 10.67 0.41 125324.58 8349.15 10406.81 1534.19 1460.36 24.46 244.47 0.46 * 677.872 604.2 333.88 23.26 6531.12 8898, 37 13.31 272.35 20.47 8.13 0.31 67164.85 5283.29 19314.44 1210.05 1286.46 70.53 162.32 0.26 * 677.872 * 27.062 * 0.661 * 1.383 1903, 24 5321.32 11.88 335.17 8.12 8.83 0.51 8494.63 3435.51 8320.69 1707.36 901.03 122.08 71.77 0.1 * 677.872 * 27.062 * 0.661 * 1.383 6181.06 32742.37 13.17 168 , 09 11.66 13.16 0.15 27652.29 2366.52 20631.79 1686.98 656.57 29.36 * 12.832 0.19 * 677.872 * 27.062 * 0.661 19.85 1714.08 8517.37 27.08 285.15 13.13 6.84 0.18 23692.33 8558.78 8818.72 1418.01 971.54 157.42 109.72 0.5 4996.3 * 26.852 5.09 129.43 6431.73 2344.1 53.68 340.44 14.61 11.26 0.48 45668.73 6368.05 11216.97 857.84 2172.61 30.74 * 10.51 0.39 * 673.809 * 26.852 6.65 15.56 13057.97 10439.47 65.18 764.11 22.51 10.71 0.99 40388.19 8240.84 * 134.38 1197.09 1966.67 28.12 * 11.11 1.55 * 648.383 489.82 43.63 50.81 6878.13 17264.21 18.6 913.61 11.79 15.33 0.72 68644.7 14885 39705.59 1693.48 949.53 144.41 50, 71 1.97 * 648.383 445.99 * 1.463 29.74 6760.47 10237.03 29.22 600.16 1.86 8.01 1.25 65389.19 4601.65 26089.22 3150.2 4867.08 30.47 * 11.11 0.41 8302.85 329.09 12.4 19.09 5801.01 5660.55 27.47 638.06 8.79 17.32 0.58 85943.37 7440.15 10237.56 1263.82 2699.27 20.51 * 10.51 0.22 * 673.809 * 26.852 * 0.579 * 1.378 3635.95 27109.85 11.67 196.05 7.54 7.8 0.29 27429.1 4372, 9 26,428.07 1294.21 1230.19 251.12 * 10, 51 0.25 * 673.809 * 26.852 * 0.579 * 1.378 2256.43 4972.54 29.71 517.73 2.61 4.88 0.27 21947.58 4174.93 202711.77 2131.24 993.92 103.17 * 11.11 0.54 4448.53 319.59 29.53 23.75 6589.75 8991.29 16.69 585.74 5.26 18.78 0.72 70066.36 7907.38 20213.46 3071, 51 2265.05 48.56 287.13 0.51 * 673.809 323.77 17.44 14.72 5926.28 2258.61 42.89 1057.35 16.44 6.07 0.46 55596.09 9837.4 40674 , 62 1332.2 1230.19 46.06 50.71 1.05 4448.53 221.35 * 1.463 22.98 4557.79 14867.2 12.92 483.58 5.72 8.01 0.59 84672 , 1 6741.09 12973.43 1812.48 806.66 40.09 * 11.11 0.55 * 648.383 245.78 25.07 23.75 6424.94 2605.26 29.39 481 2.66 8, 34 0.6 63093.34 10099.03 41481.91 2509.4 687.67 26.61 * 11.11 3.1 * 648.383 1625.07 59.75 154.09 6634.53 5586.98 25.84 973 , 74 22.29 13.91 0.92 135702.62 28660.6 20247.58 1966.48 1607.99 92.78 * 39.77 0.58 4519.74 218.86 * 21.183 10.64 2935.8 5905.87 17.45 458.19 22.28 8.08 0.33 52736.95 7252.37 66082.73 2686.95 * 32.555 24.38 397.23 0.26 * 673.809 168.62 * 0.579 * 1.378 8398, 54 30045.07 14.78 417.29 9.97 14 , 34 0.48 37571.37 10682.97 9384.62 4818.63 1164.32 31.84 * 39.77 0.44 4889.12 260.3 * 21.183 12.65 5719.17 12321.47 16.91 473.44 10.49 7.5 0.44 34458.25 11941.06 23484.54 1620.3 * 32.555 83.88 101.11 0.54 * 673.809 * 26.852 * 0.579 * 1.378 6543.81 116476.07 8 , 52 288.12 5.14 6.52 0.54 31979.94 4656.26 113595.28 2139.11 2267.93 25.82 * 39.77 0.55 * 706.944 * 28.096 * 21.183 15.23 6927, 68 2083.03 19.37 843.78 7.61 4.14 0.3 29324.15 9192.18 200229.11 1970.34 * 32.555 154.88 * 39.77 0.51 * 706.944 * 28.096 * 21.183 * 1.362 3524 9370.27 15.49 798.24 8.86 5.72 0.25 34855.93 3136.07 34518.65 1235.14 * 32.555 40.65 166.61 0.57 * 673.809 * 26.852 * 0.579 * 1.388 5056.05 1791.68 44.28 589.26 8.32 9.97 0.47 41835.13 10070.42 6662.4 2851.13 1134.91 32.41 * 11.11 1.86 4448.53 445.99 20.15 26.77 5205.74 21042.99 22.87 1319.89 5.88 13.91 0.55 52195.25 13891.35 20519.79 2449.55 896.37 45.8 324, 71 0.5 * 673.809 * 26.852 * 0.579 * 1.378 6195.83 18113.85 48.55 333.44 6.32 7.39 0.38 30616.25 12116.93 14793.9 2004.3 1149.62 63, 48 * 11 , 11 1.27 25498.96 398.93 72.99 32.66 6243.95 19163.52 24.96 1959.28 2.86 28.07 0.64 145241.7 8746.74 35840.47 716.24 824.72 30.24 * 10.51 2.53 12509.86 455.02 19.48 348.33 7280.96 70368.66 26.79 490.5 19.22 14.67 0.76 334292.69 4033 , 72 10625.1 * 122.771 3626.17 49.35 * 39.77 1.06 * 706.944 * 28.096 * 21.183 * 1.392 4396.16 4124.45 30.15 329.65 9.19 6.32 0.51 23472 , 77 5417.48 13897.12 880.88 * 32.555 89.08 * 11.11 0.56 * 648.383 194.55 * 1.483 41.2 4462.11 3889.23 32.58 438.99 1.8 * 0.579 0, 58 50619.15 14646.32 14429.05 4460.86 1251.54 107.05 * 11.11 1.09 * 648.383 208.29 29.53 26.77 7688.44 28537.19 30.23 470.63 4 7.67 0.52 104077.49 6199 121685.68 1446.41 2070.64 39.15 * 39.77 0.52 * 706.944 268.55 * 21.183 * 1.392 7314.44 7786.39 22.43 376.08 13.32 8.93 0.3 40043.65 4225.35 83331.4 1137.44 * 32.555 26.82 * 10.51 1.08 * 673.809 391.29 6.27 16.12 6114.89 33121.35 19.45 1125.13 6.55 11.97 0.52 43851.69 5224.76 11334.75 2781.9 1331.84 13.89 * 10.51 0.48 * 673.809 * 26.852 * 0.579 * 1.378 6865, 3 81024,65 23.31 275 , 88 11.52 6.96 0.38 17853.93 4174.93 25499.22 * 122.771 665.2 93.11 * 10.51 0.34 4366.79 224.7 8.17 * 1.388 8343.55 35409 , 39 18.57 314.58 11.94 7.8 0.33 50237.47 5053.97 77635.99 956.03 1610.5 40.84 146.86 0.23 * 673.809 * 26.852 * 0.579 8.83 8509.08 144069.02 19.28 285.69 12.76 6.96 0.26 43564.19 5652.56 20562.8 3340.15 1475.51 52.46 * 39.77 0.53 * 706.944 168, 58 * 21.183 * 1.362 4722.91 88533.6 23.93 1034.47 18.98 7.5 0.53 42052.7 8954.79 127624.89 1524.6 * 32.555 12.78 * 11.11 0.51 6479.5 257.31 * 1.463 22.22 7968.96 2309.19 60.32 204.42 * 0.027 10.2 0.41 91195.1 6373.08 273320.27 1416.71 1575.39 54.09 * 39.77 0.36 * 706.944 235.48 * 21.183 * 1.362 6334.26 15789.55 19.62 847.25 14.62 6.02 0.31 95654.03 3495.71 26078.97 1509.34 * 32.555 55.35 142.4 1 * 648.383 194.55 * 1.463 20.66 6323.2 18958.77 43.16 398.26 2.87 8.99 0.97 83503.49 4238.32 159191.71 1568.57 1427.32 105.16 * 39.77 0.4 * 706.944 235.48 * 21.183 * 1.362 4028.08 3803.47 29.29 553.09 13.15 7.65 0.31 17126.38 8974.55 43334 , 84 1287.36 * 32.555 52.0 4 * 39.77 0.46 * 706.944 218.86 * 21.183 * 1.362 4185.39 26940 23.44 373.7 11.51 4.47 0.42 71676.32 3950.21 16168.35 1083.04 * 32.555 30.04 * 10.51 0.32 * 673.809 * 26.852 * 0.579 * 1.378 7158.13 25657.79 24.04 447.82 6.32 6.52 0.5 18980.21 2382.32 106431.66 3102, 35 1222.89 49.72 * 39.77 0.3 * 706.944 227.18 * 21.183 13.16 5787.86 13847.17 30.13 599.14 17.41 8.08 0.47 68044.25 2957.68 52297 , 48 2661.99 * 32.555 14.08 166.61 0.44 * 673.809 288.11 * 0.579 9.96 10453.67 67592.48 33.32 283.24 8.83 7.39 0.65 34276.1 4203.19 11726.03 1089.76 2322.19 34.71 74.22 0.34 * 673.809 * 26.852 11.11 * 1.378 10652.46 17649.02 16.83 235.61 6.27 6.52 0, 38 27845.33 3808.14 22662.92 1181.25 1973.57 65.01 130.64 1.05 * 673.809 183.25 * 0.579 * 1.378 18532.26 88353.39 57.79 544.58 9.58 7 , 39 1.14 54695.5 8009.47 22139.52 2021.46 4047.82 40.99 * 39.77 0.49 * 706.944 210.52 * 21.183 * 1.392 6935.98 11043.15 14.07 553, 09 11.17 6.02 0.37 23843.16 5436.33 98375.69 2407.41 * 32.555 60.53 * 11.11 0.65 8302.85 221.35 34.99 31.21 6796.6 3889 , 23 27.11 417.45 4.17 24.4 0.86 33701.34 3895.04 62019.57 765.86 2676.7 55.52 * 41.51 4.73 * 1970.97 173.9 * 16.134 47.53 2111.95 38824.8 24.91 1072.51 39.66 13.24 0.44 146063.82 15971.86 95731.71 2110.56 * 33.371 112.78 39.7 0.97 * 673.809 300.17 * 0.579 13.32 16762.71 23898.04 35.17 268.46 14.35 7.39 0.9 24787.01 5196.28 7741.75 2199.12 2544.48 82.05 185.86 1.42 * 673.809 380.33 71.45 36.33 10948.21 264146,54 48.51 482.05 18.47 5.13 0.67 87405.41 11794.48 77518.58 1319.03 2450.46 13.12 * 39.77 0.52 * 706.944 168.58 * 21.183 8.66 7174.55 66986.84 46.68 378.46 13.32 6.92 0.32 55751.66 3640.57 19783.49 917.3 * 32.555 43 , 82 * 39.77 0.34 5257.04 252.04 * 21.183 12.91 5358.71 6214.79 18.88 557.15 11.12 6.32 0.25 53991.89 6385.98 9812.05 1095.21 * 32.555 38.94 * 39.77 0.39 * 706.944 325.93 * 21.183 8.66 5849.21 25116.12 26.9 5032.66 18.44 4.78 0.52 79210.38 6061 , 49 104146.99 2170.92 * 32.555 61.08 * 39.77 0.43 * 706.944 177.01 * 21.183 * 1.392 7417.01 27742.62 17.63 800.01 15.53 7.5 0.36 45844.33 2341 , 65 38983,61 21 42.03 * 32.555 21.84 * 11.11 0.45 * 648.383 289.73 12.4 20.66 4363.03 3578.49 16.45 277.74 4.37 10.49 0.36 71030.39 5774.14 10331.87 2872.47 1460.42 18.65 185.86 0.95 * 673.809 224.7 * 0.579 23.96 5073.6 10746.24 32.43 356.62 12.26 6.07 0 , 6 63525.42 13880.84 58735.18 1388.02 1186.32 48.5 * 13.55 0.29 * 679.575 * 27.296 * 0.555 * 1.377 8061.81 27516.51 15.54 373.01 8.05 * 0.714 0.23 29580.6 3844.35 25668.35 5268.7 * 34.349 25.05 114.31 0.31 12334 350.67 41.98 17.92 3925.93 47147.95 22.07 2242.63 7, 34 11.1 0.51 86 231.11 10 263.62 113 147.65 2867.71 268.54 138.4

* 13.55 0.96 * 679.575 * 27.296 * 0.555 * 1.377 5072.16 16491 57.41 698.92 15.88 11.33 0.62 47099.39 34783.33 11306.39 2526.03 * 34.349 51, 71 114.31 0.49 * 679.575 * 27.296 * 0.555 * 1.377 11036.18 21107.24 39.06 369.36 4.77 7.14 0.32 31126.01 9839.53 21721.44 2455.56 * 34.349 15.81 * 13.55 1.66 * 679.575 183.58 4.85 12.94 9536.24 1290.73 40.27 1094.89 8.72 * 0.714 0.61 219771.47 20611.31 48308.27 2065.94 * 34.349 196.39 * 39.77 0.38 * 706.944 185.41 * 21.183 9.15 6390.1 3469.68 10.22 408.81 14.4 7.79 0.28 43483.53 6712 , 05 121317.81 1844.09 * 32.555 43.57 69.54 0.61 * 673.809 183.25 * 0.579 * 1.378 7519.16 29380.3 22.9 297.82 11.75 7.39 0.55 63144.54 6597.61 8978.82 1778.71 1904.4 37.37 * 39.77 0.44 5257.04 185.41 * 21.183 12.65 6795.5 26376.2 29.07 ** 10790.977 17.12 7, 21 0.19 67761.09 5229.41 18061.26 397.25 * 32.555 17.93 * 41.51 0.5 * 1970.97 * 27.42 * 16.134 8.72 3079.75 1463.62 15.59 470, 44 4.6 7.06 0.36 40201.61 8161.48 59073.63 3741.95 * 33.371 99.5 * 13.55 1.42 * 679.575 * 27.296 * 0.555 * 1.377 4519.3 11016.38 75.49 619.29 3.83 * 0.714 0.42 56858,02 11892.04 131651.03 3418.28 * 34.349 88.05 * 13.55 0.26 * 679.575 * 27.296 * 0.555 * 1.377 4387.64 2487 , 5 17.71 681.52 6.19 * 0.714 0.24 54371.5 7398.13 15807.41 3829.29 * 34.349 85.65 248.45 0.66 * 673.809 346.78 * 0.579 12.76 5977.9 55,900.01 13.39 345.08 10.79 9.4 0.57 32531.94 9488.23 172623.36 2819.78 1883.6 23.79 * 41.51 0.57 * 1970.97 228.34 * 16.134 8.97 6792.44 8017.82 23.88 482.98 13.39 10.17 0.62 91615.95 9828.42 122866.75 2961.91 * 33.371 124.49 199.1 0.36 10297.81 235.48 * 21.183 13.16 7374.19 29833.68 17.25 2042.89 15.08 14.3 0.46 29686.41 10447.79 59053.61 1346.84 234.43 20.62 * 10.51 0 , 59 * 673.809 * 26.852 * 0.579 * 1.378 12110.02 15275.06 20.89 240.75 7.04 4.63 0.46 41064.18 5224.76 36807.59 1493.22 858.16 29.75 297 , 48 0.91 * 673.809 650.14 26.14 28.73 6277.23 8023.06 11.39 460.74 27.82 6.07 0.35 141274.57 11238.25 91141.48 993.77 1186 , 32 56.73 * 39.77 0.25 * 706.944 * 28.096 * 21.183 * 1.362 3800.93 1630.07 12.24 314.42 13.15 4.14 0.22 44735.62 4576.3 1611 2.08 1379.07 * 32.555 48.27 * 10.51 2.82 6196.87 1091.06 25.48 48.23 4283.06 8313.69 24.89 745.21 7.32 7.17 0, 66 52799.33 2243.9 3948.19 2023.03 3153.03 36.54 * 39.77 0.47 * 706.944 202.17 * 21.183 11.64 5295.9 4124.45 10.21 559.18 8, 32 13.52 0.48 27581.52 14379.41 71390.22 1536.36 * 32.555 42.81 * 39.77 1.01 * 706.944 177.01 * 21.183 * 1,362 4392.73 2877.81 42.48 436 , 03 10.1 6.62 0.51 65869.03 6443.41 7525.53 537.21 * 32.555 100.6 139.28 0.17 * 679.575 293.76 14.61 * 1.377 5803.62 10775.74 21.73 230.82 12.83 * 0.714 0.1 51206.54 5073.14 221335.34 5695.87 * 34.349 23.64 92.38 0.34 * 679.575 * 27.296 23.76 41.36 7822.28 20057.67 13.32 436.62 18.45 * 0.714 0.16 32297.92 4979.18 17964.37 2900.6 * 34.349 12.43 155.31 0.84 * 679.575 * 27.296 * 0.555 55.55 8781 , 54 1961.69 36.21 938.76 6.92 7.41 0.54 31743.02 7705.19 13036.78 2071.3 1743.23 106.16 54.02 0.68 * 679.575 276.19 * 0.555 17.36 10723.77 6960.12 114.49 2302.33 15.34 * 0.714 0.28 128677.1 6380.3 17798.87 6794.86 * 34.349 130.66 178.22 0.28 * 673.809 * 26.852 * 0 , 579 * 1.388 5348.73 15372.3 32.6 432.63 6.99 6.74 0.33 32231.09 13350.8 26499.45 3211.59 1751.17 73.1 * 39.77 0.46 * 706,944 * 28,096 * 21,183 * 1,362 6136.62 12860.04 17.3 368.92 6.81 5.41 0.33 24802.34 3459.58 40229.62 1231.75 * 32.555 60.57 208.41 0.28 * 673.809 * 26.852 * 0.579 * 1.378 8157.92 8737.57 19.74 426.08 7.88 7.8 0.43 28363.39 8588.33 8033.28 1169.76 1461.22 66.92 * 39, 77 0.55 * 706.944 185.41 * 21.183 9.64 3713.25 1109.73 44.83 873.1 15.48 5.72 0.35 29324.15 15478.63 37718.83 1883.14 * 32.555 84 , 03 182.05 0.23 * 673.809 * 26.852 * 0.579 * 1.378 7592.38 49830.1 49.99 324.05 8.66 5.37 0.25 48812.28 5909.8 7237.41 1752.72 1446 , 91 32.54 * 10.51 0.32 * 673.809 * 26.852 * 0.579 * 1.378 8448.19 151531.77 12.69 258.48 13.9 5.13 0.37 17067.33 6368.05 8074.78 2040.22 2477.37 25.26 122.37 0.43 * 673.809 * 26.852 * 0.579 13.6 6412.33 37790.2 17.42 426.08 12.91 6.52 0.29 44426.18 4174.93 19688 , 13 3363.92 1553.83 18.78 * 10.51 0.27 * 673.809 * 26.852 * 0.579 * 1.378 6870.3 64832.75 13.14 176.82 * 0.027 7.8 0.45 2338 0.75 4429.52 9263.2 3605.02 781.65 14.79 87.89 1.1 6196.87 312.05 5.09 23.96 5888.87 28927.1 10.93 900.77 14, 72 6.52 0.32 61432.24 7720.57 21832.22 1764.94 2069.94 29.75 96.75 0.34 * 673.809 * 26.852 * 0.579 * 1.378 14159.62 10315.13 18.14 356, 62 6.99 4.88 0.32 41690.7 5452.78 190405.08 1780.24 688.67 26.51 223.15 0.54 * 673.809 * 26.852 * 0.579 * 1.378 5034.15 3105.97 30, 38 507.3 18.18 6.52 0.34 36102.16 6425.42 108721.88 1602.57 1468.37 17.55 * 10.51 0.49 * 673.809 * 26.852 * 0.579 9.39 10916.53 8241, 67 33.34 404.03 10.68 5.61 0.37 17793.91 4883.39 40887.85 1035.96 1814.04 36.86 237.68 0.7 * 673.809 275.88 * 0.579 10.8 6296.45 60749 16.29 1700.52 6.66 7.39 0.39 43276.5 10099.56 24586.23 1560.49 1281.13 20.57 396.53 0.7 * 677.872 420.51 41, 91 29.34 3283.1 61345.07 16.12 803.78 22.4 15.2 0.56 113287.65 4833.16 9208.61 1055.52 910.44 45.13 1008.57 1.86 * 677.872 450.81 7.59 23 3309.07 1203.32 20.84 795.55 24.96 23.04 0.93 219428.97 10433.06 8712.18 2663.15 966.84 44.38 * 12.832 1 , 56 4067.23 208.27 4.42 13.29 2340.31 21352.3 18.24 404.12 12.9 17.68 0.39 180 120.42 13051.3 8 605.54 1605.67 1850.48 110.02 506.08 1.06 7714.91 357 , 88 99.56 63 4564.48 2976.11 47.51 1163.44 13.22 19.53 0.53 152615.64 10586.56 17876.07 901.49 1408.66 44.87 253.8 2, 74 5007.58 245.15 55.35 51.83 3422.87 2055.15 17.09 294.1 12.9 9.68 0.37 209550.22 17320.51 65189.64 610.43 957.44 34.6 306.37 1.19 6379.16 257.05 10.72 17.22 2472.34 39255.56 14.89 433.39 14.95 33.04 0.51 58827.93 5377.22 6902.68 425.76 2583 , 98 48.97 688.85 0.57 * 677.872 440.78 7.14 * 1.383 3667.18 37919.2 19.86 3661.28 25.36 23.29 0.24 53117.4 6659.43 18578.36 1328 , 41 1685.97 87.53 1687.2 1.61 * 677.872 * 27.062 10.72 * 1.383 1491.77 30245.78 14.82 378.31 15.11 72.62 0.6 63718.35 9475.36 9102.39 2428.54 571.93 28.62 239.79 3.36 4540.43 450.81 22.69 24.06 5052 7081.53 26.39 364.24 25.16 66.61 0.97 140431 , 26 9437.11 44774.49 1324.88 1634.26 57.74 370.54 1.8 * 621.156 249.29 * 19 8.46 3795.46 2282.59 12.94 229.74 52.18 35, 51 0.36 61545.07 7094.94 27834.62 1 727.76 1773.51 106.73 611.69 5.61 * 677.872 352.52 105.53 31.46 1219.77 2546.87 20.17 441.96 16.17 7.95 0.5 93256.07 19702.81 28171.26 2165.36 642.46 42.4 2213.65 0.89 * 677.872 291.81 157.97 36.78 7407.5 7580.4 22.37 427.62 20.51 21.31 1.05 64763.77 3955.55 18184.45 1173.9 821.12 23.12 110.58 2.27 * 677.872 * 27.062 6.69 12.5 2739.72 6557.43 10.86 395.12 51 , 94 16.87 0.52 145 286.02 8349.15 14094.02 1347.23 1352.26 33.6 * 12.832 0.43 * 677.872 233.06 37.13 20.9 7625.21 100895.84 48, 54 233.79 14.56 9.68 0.4 78897.39 9265.05 16091.75 1173.9 2903.87 39.91 1139.31 0.77 * 677.872 182.49 14.51 15.65 5856, 02 3109.27 13.84 573.73 18.95 14.34 0.55 225891.95 8520.65 26350.14 1410.92 661.27 43.2 402.98 0.72 4540.43 389.56 13 , 62 18.53 6461.18 72079.58 28.48 706.13 31.26 42.89 0.57 115164.64 3658.1 15868.32 1561.56 807.02 49.67 405.13 0.64 * 677.872 * 27.062 10.28 12.5 3287.82 4814.85 11.35 453.24 13.99 42.79 0.39 93009,04 3955.55 19519.79 1754.19 2330.03 21.28 105 , 2.81 * 677.872 485.46 34.95 22.48 3053.4 1 4828.44 20.79 447.62 55.73 21.06 0.54 355673.3 11585.92 13056.22 1572.28 1338.16 52.6 * 12.832 1.64 * 677.872 1008.61 188.58 31 , 19 1527.51 2838.4 17.42 308.12 373.77 21.06 0.57 134744.82 3528.2 9491.44 1981.32 435.54 37.72 335.82 1.42 * 675.007 1411.8 103.14 5289.6 1450.94 3883.51 11.93 331.51 125.38 8.11 0.58 129437.47 12412.11 28991.65 1813.63 1394.42 7.03 * 11.424 0.24 * 675.007 259.89 5.23 31.57 1648.92 13667.77 18.16 255.52 27.7 10.71 0.34 86032.94 9614.49 43987.64 2353.61 2813.64 90.58 137.88 0.58 * 675.007 347.35 9.42 15.84 2128.55 68240.48 31.17 191.62 35.44 7.96 0.63 213519.49 8439.49 17978.8 2677.89 4303.45 101.15 279.11 0.85 * 675.007 421.8 4.78 311.14 3178.67 3035.15 17.87 386.55 47.08 8.11 0.43 106894.01 9415.92 23566, 91 2170.7 1825.65 18.68 * 11.424 0.72 * 675.007 259.89 * 0.647 16.5 7120.06 10211.18 31.13 267.78 22.08 6.52 0.78 99282.9 6709 , 94 45731.62 819.53 2214.78 50.07 * 11.424 0.43 * 675.007 1075.83 42.32 62.91 7258.98 838.79 62.07 321.74 426.83 24.92 0, 45 189078,41 7416,5 2882 3.77 1803.51 1606.4 72.7 556.34 0.77 7990.97 524.4 6.12 41.98 1937.37 11712.49 10.84 1130.85 51.6 23.67 0, 36 169951 10386.8 79257 1448.54 3309.89 133.24 52.48 0.16 * 675.007 384.99 7.45 390.48 5192.72 30023.2 11.67 276.82 59.69 6.81 0.26 49434.03 2781.64 47571.49 1935.26 4054.67 16.52 * 11.424 1.93 6391.86 742.8 188.13 54.56 1007.76 40957.46 32.62 198.55 26.38 4.21 0.74 148122.28 12314.24 16336.52 1311.02 4663.37 67.72 361.92 1.15 * 675.007 914.1 92.09 38.17 3596.92 3883.51 21.03 257.07 167.19 27.71 0.94 107682.51 8199.44 6184.87 933.93 1478.54 69.71 253.87 0.99 * 675.007 164.51 11.81 16.95 1609 15755.76 23.73 155.17 10.59 5 0.42 129908.5 4789.92 14354.79 3215.91 1139.19 63.63 204.22 0.25 5140.13 286.77 7.45 12.12 7211.51 48232.55 11.87 263.21 59.39 40.47 0.2 62267.01 3416.01 13852.83 1299.79 1184.57 19.76 225.02 0.2 * 675.007 232.32 4.33 * 1.379 3053.47 61388.3 7.54 158.97 11.7 14.09 0.25 56479.07 4417.73 30671.65 2408.77 1801.41 71.1 1506.96 0, 51 * 675.007 607.79 8.77 20.53 4877.02 4798.64 10.84 429.8 21.57 27.49 0.32 211582.2 8918.48 26195.27 * 40.87 1854.71 47.42 69.11 5.2 107.882.98 1993.32 103.53 117.88 4332.52 3580.85 32.47 495 141.17 21.82 0.93 247870.19 24426.74 72053 2430.6 3434.12 68.1 292.87 0.65 * 675.007 493.35 18.65 336 , 61 8165.96 32808.35 14.54 266.26 65.23 29.03 0.34 133829.12 3205.36 8130.95 477.16 3484.63 39.32 892.31 0.22 * 675.007 445 , 94 30.38 102.85 3424.65 50617.37 16.71 195.1 37.08 15.72 0.31 406443.4 2052.43 12931.27 2367.72 2897.6 51.66 141.21 0, 21 * 675.007 277.88 5.68 14.3 5974.15 13911.28 20.41 433.52 53.83 174.33 0.31 44687.61 2482.88 17470.06 3043.65 2423.65 39, 86 279.11 0.54 * 675.007 532.09 14.18 15.62 4550.45 3092.5 24.54 327.34 41.47 24.12 0.28 62175.9 5856.49 13565.38 1606, 73 1917.52 100.53 509.69 5.46 * 675.007 1095.7 35.34 279.1 6030.6 4171.58 4.16 951.06 77.46 85.01 0.94 225877.64 13135.15 8130 , 95 2052.17 4560.12 133.03 430.08 1.1 4710.06 313.07 22.45 20.99 3960.53 3580.85 11.94 321.74 17.2 90.2 0.66 116303, 68 3394.98 164708.56 456.63 2080.9 9 68.22 964.78 1.19 * 675.007 1648.37 224.82 29.71 7652.48 28004.29 23.94 287.22 104.59 36.95 1.01 182581.12 5815.68 19275, 7 1727.7 1723.64 44.97 322.56 0.72 * 675.007 528.25 24.13 29.47 5485.14 2674.78 9.96 198.55 143 27.71 0.5 152169.69 4624 , 71 15426.21 2523.16 717.08 54.05 134.53 1.02 * 675.007 313.07 16.53 15.18 4251.53 25597.28 22.79 158.97 29 7.96 0.58 123509, 3 3918.68 8040.29 2002.57 2109.72 76.97 270.77 0.56 * 675.007 477.67 6.57 28.31 3889.25 5991.97 20.66 253.97 366.09 78, 5 0.47 62996.77 3078.6 39186.07 1823.75 2243.36 123.76

* 11.424 0.87 * 675.007 397.35 7.45 29.71 9096.77 4124.48 11.41 437.24 100.59 46.54 0.42 86098.83 27519.83 13766.64 1349.72 1199 , 67 128.34 382.45 0.27 * 675.007 * 27.419 * 0.647 8.7 4343.35 6066.02 20.78 321.74 235.29 7.68 0.25 63575.57 5406.71 11363.38 2006.38 1869.22 70.17 356.74 0.9 * 675.007 203.95 16.95 10.4 4262.3 20442.12 19.46 182.8 184.88 27.05 0.53 30888.07 2781.64 9627.2 791.31 1239.83 72.27 356.74 0.43 * 675.007 208.74 * 0.647 9.12 8399.05 44870.21 10.12 396.9 30.85 56.32 0 , 47 17 254.98 5713.59 21 130.08 1350.97 1399.38 71.28 113.91 0.35 * 675.007 213.51 10.51 * 1.397 1789.91 2674.78 9.15 313.26 69, 82 31.21 0.28 70726.15 6507.32 20582.84 603.23 955.98 112.27 120.89 2.4 * 675.007 232.32 7.45 9.12 4776.74 8617.59 6.9 338 , 42 13.87 12.04 0.34 77 251.55 5037.14 21550.72 1231.26 1409.3 61.21 * 11.424 1 4272.22 397.35 17.8 28.31 7278 36670.78 15.95 362 , 82 43.78 34.84 0.81 174406.14 8359.52 14 641.04 2043.26 2104.94 49.38 692.79 0.93 * 675.007 845.05 114.92 28.08 5682.71 1832 , 47 18.76 755.23 114.17 21.48 0.39 151269,41 4975.4 11246.51 3223.75 2579.3 37.6 514.4 1.69 9150.09 347.35 296.14 64.89 7069.78 28478.82 26, 13 290.16 100.02 7.39 0.58 172447.71 15524.25 9389.52 1794.66 3346.74 71.44 73.09 1.94 * 675.007 232.32 19.5 10.83 3918, 25 1489.82 9.58 131.48 21.7 7.68 0.35 118019.85 6669.44 20877.57 1558.95 632.39 50.69 992.57 0.58 * 675.007 364.19 15, 25 10.4 4580.63 35125.5 14.19 191.62 19.74 27.49 0.34 90150.36 1791.75 2820.95 2038.17 1012.27 26.49 92.24 0.08 4272 , 22 222.96 8.77 11.25 11692.44 9139.4 7.66 147.45 37.83 5.92 0.23 35370.3 3793.37 21690.88 941.33 1542.59 33.71 253.87 0.46 * 675.007 184.49 18.65 14.3 1715.82 37891.26 8.65 205.39 24.6 9.08 0.34 71537.8 7758.32 7280.21 1755.48 1027.57 36.26 420.18 1.12 * 675.007 347.35 26.63 27.39 7772.35 57025.24 7.13 118.95 104.02 8.81 0.88 119898.96 9832.65 27257.59 2045.81 1269.88 84.78 298.33 0.88 * 675.007 164.51 10.51 11.69 5841.11 18129.14 17.87 205.39 69.55 22.29 0.35 42951, 65 3120.88 2332.78 295.8 1073.36 50.49 69.03 0.75 * 679.575 163 , 77 * 0.555 14.31 4163.46 5385.85 35.02 1303.84 9.88 8.7 0.48 30137.5 32417.86 14156.45 5971.73 * 34.349 143.68 215.81 1.58 * 673.809 183.25 * 0.579 9.96 6725.95 24270.47 13.87 660.58 11.77 7.39 0.76 33729.83 4401.21 9344.18 1456.04 1156.97 81.01 69, 54 0.42 * 673.809 * 26.852 * 0.579 * 1.378 6880.3 55523.13 21.15 212.12 5.5 6.74 0.43 10637.34 3050.4 16611.51 1900.25 948.92 18, 92 * 37.47 0.33 * 684.082 216.75 * 2.588 * 1.393 3940.3 184832.91 14.54 278.84 5.72 9.66 * 0.42 70647.91 3059.77 28961.91 2012, 81 1478.44 160.06 123.26 0.28 * 684.082 194.67 * 2.588 * 1.393 5873.4 20761.76 27.57 192.23 7.91 8.07 0.52 52635.12 3952.81 35500 , 56 889.51 1561.68 124.43 * 37.47 0.9 * 684.082 248.91 * 2.588 19.96 8767.31 32999.53 25.03 195.11 9.04 8.61 0.47 112659 , 16 3487.98 363.88 1463.44 3462.76 60.63 306.26 0.92 * 684.082 238.31 * 2.588 8.36 9798.81 43507.43 14.87 585.12 11.31 * 0.555 0.47 58203.41 4017.29 96445,89 2902.99 1789.76 31.43 * 37.47 0.25 * 684.082 * 27.695 * 2.588 * 1.393 7001.2 1474.85 21.9 309.44 8.17 5 71 * 0.42 32786.1 1717.09 43161.95 1492.03 1510.51 56.33 * 37.47 0.2 * 684.082 * 27.695 * 2.588 * 1.393 6217.47 8329.69 31.86 125.48 2.22 * 0.555 * 0.42 21585.14 2683.25 12386.44 1903.2 4334.64 165.72 * 37.47 0.69 * 684.082 * 27.695 * 2.588 8.36 5014.81 24629.84 22.4 411.83 15.18 6 , 34 * 0.42 49334.08 4146.52 73325.41 1323.3 1060.22 17.52 * 37.47 0.56 * 684.082 * 27.695 * 2.588 * 1.393 8842.56 4460.8 37.72 195.11 9 , 74 * 0.555 * 0.42 38952.36 2157.41 19174.99 1430.91 1139.85 71.33 202.3 0.32 * 684.082 269.8 19.07 * 1.393 7978.06 6202.17 16, 71 214.74 9.39 * 0.555 * 0.42 50140.71 2450.1 14347.67 1873.05 1915.08 30.21 * 13.55 0.54 * 679.575 190.07 * 0.555 9.71 4919, 8 5518.58 25.3 463.79 16.5 8.7 0.23 26751.52 12162.4 4788.01 2560.41 * 34.349 54.39 * 37.47 0.35 * 684.082 238.31 * 2.588 11.6 5178.51 126867.91 14.4 233.54 12.91 7.51 * 0.42 63029.88 966.69 141909.82 632.4 1510.51 26.33 * 13.55 0.3 * 679.575 * 27.296 * 0.555 * 1.377 11177.02 9106.3 35.81 332.09 13.62 * 0.714 0.15 61407.17 5167.01 11740.75 1957.13 * 34.349 39.6 * 13.55 0 , 29 * 67 9.575 163.77 * 0.555 9.98 5676.17 6388.94 14.74 1030.46 15.2 * 0.714 0.21 44782.97 3398.77 18056.28 2993.97 * 34.349 18.64 277.17 0 , 52 * 684,082 205.78 * 2.588 8.89 8279.25 4460.8 37.31 665.11 35.26 * 0.555 * 0.42 131888.94 4912.19 72095.38 1426.85 2002.26 35, 92 128.57 0.22 4104.49 * 27.695 * 2.588 11.87 7765.65 6412.19 21.91 222.89 6.77 14.94 * 0.42 49430.8 1212.8 19348.14 790, 12 1133.23 115.3 255.84 0.7 * 684.082 398 76.32 20.25 2792.91 20443.86 24.25 217.48 23.71 9.66 0.47 107566.95 5954.07 59649.02 432.09 1459.15 33.21 129.98 1.73 * 682.921 206.25 * 1.666 * 1.361 9797.01 54365.11 83.26 1868.2 17.39 6.58 0.51 81720.87 6028.65 71571 , 44 975.53 * 34.126 256.57 * 37.47 0.28 * 684.082 * 27.695 * 2.588 * 1.393 6888.46 1712.43 15.02 152.57 8.75 * 0.555 * 0.42 81707.55 1212 , 8 73748.12 2366.27 2187.7 41.59 * 39.33 0.26 * 682.921 * 27.643 * 1.666 10.47 9052.78 24096.1 22.17 669.76 11.8 * 0.658 0.24 55,104.51 4018.54 13413.26 2389.46 * 34.126 149.82 * 39.33 0.32 * 682.921 * 27.643 * 1.666 * 1.361 3711.07 1626.04 42.98 563.55 8 , 93 * 0.658 0.18 44690.39 4811.35 10470.56 2295.61 * 34.126 232.94 * 37.47 0.19 * 684.082 * 27.695 * 2.588 * 1.393 13487.27 45418.61 25.46 276, 42 14.86 * 0.555 * 0.42 27101.35 3743.84 67389.05 897.75 1587.2 74.82 272.95 0.34 5195.35 183.4 * 2.588 * 1.392 2240.26 19961.76 16.05 351.69 12.47 12.99 * 0.42 44107.63 8390.18 15563 2443.42 2285.89 62.58 183.53 0.54 * 684.082 * 27.695 * 2.588 13.25 7286.47 9895.35 16.71 318.55 13.34 6.93 * 0.42 77885.11 3122.91 56782.56 2587.78 993.36 21.05 * 39.33 1.22 * 682.921 * 27.643 * 1.666 * 1.361 6365.78 25006.55 35.69 1147.42 7.09 * 0.658 0.93 42993.26 4221.47 75337.69 915.92 * 34.126 165.26 * 41.51 0.46 * 1970.97 192.36 * 16.134 9.48 3625.92 7900.08 16.56 337.82 9.44 5.4 0.33 77410.69 3535.92 187672.68 2784.36 * 33.371 104.49 * 37.47 0.3 * 684.082 * 27.695 * 2.588 * 1.393 8979.83 9195.42 34.66 177.49 10.03 * 0.555 * 0.42 38571.26 1463.14 54858 537.98 2407.99 29.12 * 39 , 33 0.74 * 682.921 206.25 * 1.666 * 1.361 5770.83 28504.78 20.26 1056.05 13.16 7.84 0.69 60716.49 9701.47 4547.56 2400.89 * 34.126 86.79 314.42 0.62 * 684.082 * 27.695 * 2.588 * 1.393 7156.47 12696.75 22.51 309.44 18.71 * 0.555 * 0.42 32722.35 1552.38 108007.3 1400 , 55 1368.79 14.47 * 39.33 0.96 * 682.921 373.54 * 1.666 8.74 6328.91 36805.02 28.85 621.66 20.89 * 0.658 0.64 38856.25 8062, 56 154052.99 1947.78 * 34.126 115.39 154.26 0.45 * 684.082 216.75 * 2.588 * 1.393 6387.8 22439.17 29.26 855.25 14.57 * 0.555 0.67 41781.35 7299.01 26161.92 2182.44 1073.53 60.63 * 39.33 0.8 * 682.921 280.4 * 1.666 13.39 3372.13 19010.58 16.4 500.43 16.8 6.58 0.45 70826.39 5636.19 47010.99 2894.78 * 34.126 127.04 346.47 0.89 10790.29 503.92 40.93 17.7 3638.84 16196.91 17.19 377.14 22.83 18 , 36 0.46 21 753.91 5654.72 89,445.3 1678.63 3788.64 68.79 * 39.33 0.56 * 682.921 297.95 * 1.666 * 1.361 6068.24 6405.21 41.35 5572, 35 17.74 * 0.658 0.62 44251.78 14207.93 65872.37 3701.31 * 34.126 116.22 * 37.47 0.34 * 684.082 * 27.695 * 2.588 8.36 7574.4 26465.51 25.26 307 , 14 9.45 * 0.555 * 0.42 66880.84 2295.67 228792.97 1835.51 1612.67 44.17 * 39.33 0.84 * 682.921 * 27.643 12.94 9.31 4876.38 13001.05 19.74 1164.02 10.78 * 0.658 0.53 17952.81 10708.59 88401.08 4307.99 * 34.128 478.84 * 39.33 0.61 * 682.921 * 27.643 * 1.666 13.98 7017.85 1078.06 20.53 299.38 12.41 * 0.658 0.2 53635.19 4176.31 24548.43 2329.64 * 34.126 142.68 141.78 0.71 8195.43 * 27.643 * 1.666 * 1.361 5469.28 39497.57 37.74 277.5 8.13 * 0.658 0.24 45376.66 5452.14 18357.91 1950.51 * 34.126 127.69 * 37.47 * 0.013 * 684.082 * 27.695 * 2.588 * 1.393 6482.84 13111.47 20.23 139.35 10.03 7.51 * 0.42 81779.55 1271.35 11388.65 1537.2 986.64 37.31 192.98 0.2 * 684.082 * 27.695 * 2.588 * 1.393 6641.9 3494.81 32.14 329.78 8.46 * 0.555 * 0.42 52018.15 1974.21 43925.89 3264.53 2101.39 39.07 * 41.51 0.9 * 1970.97 245.92 * 16.134 9.99 3164.6 7378.66 36.68 1153.23 7.2 5.01 0.53 86757.07 5146.48 26672 , 53 2758.25 * 33.371 142.19 362.17 0.28 * 684.082 * 27.695 * 2.588 * 1.393 3731.26 27471.09 14.35 452.83 12.62 * 0.555 * 0.42 80701.68 1403, 89 29112.33 1624.24 1000.06 57.22 381.52 0.52 * 684.082 * 27.695 * 2.588 12.15 8529.1 3641.9 41.42 368.75 13.95 * 0.555 0.43 109574.99 2855.32 71219.34 1684.93 1877.58 54.51 * 41.51 0.15 * 1970.97 271.86 * 16.134 8.47 4562.63 58209, 37 7.27 225.38 18.25 7.41 0.15 22011.85 4625.74 165630.04 2840.7 * 33.371 44.1 * 37.47 0.27 * 684.082 * 27.695 * 2.588 * 1.393 3937, 38 5544.98 17.77 186.4 7.5 * 0.555 * 0.42 46409.88 895.23 68777.42 1405.6 1205.73 50.02 122 0.86 8195.43 186.42 * 1.666 46.29 7412.88 41487.28 25.51 1004.54 25.63 6.58 0.42 61702.68 11311.18 35100.75 1895.99 * 34.126 54.06 488.74 0.72 * 684.082 * 27.695 * 2.588 * 1.393 2714.79 72815.35 19.09 825.56 57.33 7.51 0.53 61090.08 6960.68 11907.62 971.74 1013.47 117.94 * 37.47 0.23 * 684.082 * 27.695 * 2.588 * 1.393 3269.39 22590.49 28.8 103.14 10.44 * 0.555 * 0.42 23194.38 1552.38 35915.21 2905.35 551.22 23.28 * 39.33 0.36 * 682.921 * 27.643 20.53 8.45 5654.95 22045.27 21.81 601.52 12.06 * 0.658 0.2 34576.99 4606.57 17044.52 997.15 * 34.128 28.82 * 37, 47 0.32 * 684.082 259.41 * 2.588 * 1.393 5616.34 40709.72 30.62 349.53 9.56 9.66 0.5 41431.36 3952.81 81032.88 2473.06 959, 74 66.8 238.39 0.73 * 684.082 * 27.695 * 2.588 8.89 2927.37 21828.17 43.34 793.88 22.44 * 0.555 0.51 83 115.32 4405.96 18445.79 1984, 47 1777.18 78.57 164.19 0.31 * 684.082 183.4 * 2.588 8.62 9249.28 * 151.463 47.99 512.07 11.66 * 0.555 * 0.42 65185.4 3249.51 12643 , 11 3719.57 2033.29 132.31 * 37.47 0.46 * 684.082 * 27.695 * 2.588 * 1.393 5221.09 46533.29 41.34 243.95 14.42 * 0.555 * 0.42 66473 3808, 04 38,683.81 1865.53 1166.25 122.89 463.54 0.41 * 684.082 259.41 * 2.588 13.8 4431.1 54474.66 24.37 168.36 24.16 11.85 0.44 117984.63 14344.49 87123.29 1394.5 685.57 55.34 * 37.47 0.19 * 684.082 * 27.695 * 2.588 * 1.393 1995.69 9445.9 9.47 641.14 13.87 7, 23 * 0.42 29201.27 7197.37 34272.66 2447.98 544.05 38.45 * 39.33 0.66 6181.59 191.44 * 1.666 15.16 6917.93 42717.72 49.93 601.52 14 , 92 * 0.658 0.34 45899.02 3996.03 38382.69 1339.75 * 34.126 99.3 * 39.33 1 * 682.921 * 27.643 * 1.666 * 1.361 6004.76 2481.2 43.59 650.09 7 , 21 * 0.658 0.33 53973.58 4743.02 5948.14 2068.82 * 34.126 37.02 65.06 1.11 * 675.007 222.96 9.86 11.69 2367.83 25815.21 8.51 308.98 22.41 33.57 0.38 98936.11 3876.94 25594.26 2435.73 1552.42 120.47 554.03 0.24 * 675.007 562.64 * 0.647 10 , 83 919.71 33507.28 17.41 2666.16 16.5 28.37 0.16 30435.41 11234.71 20442.45 2538.61 2309.92 42.9 437.47 1.45 * 675.007 286.77 8.99 14.3 6485.29 2674.78 40.96 409.68 18.16 18.16 0.79 97140.54 14924.07 27313.51 1652.05 2028.23 39.42 664.35 0, 52 * 675,007 * 27,419 * 0,647 * 1,379 5150.11 43759.96 22.01 170.14 9.35 37.99 0.39 95357.72 2802.91 11392.58 1647.01 1124.03 29.06 873, 96 2.76 * 675.007 988.87 49.66 37.22 4646.67 12337.59 20.08 1086.19 24.92 17.56 1.01 285632.6 10070.35 75 223.62 1912.42 2640.39 41 72

412.71 2.67 * 675.007 286.77 30.17 18.29 4882.61 86041.38 12.85 170.14 10.46 29.36 0.42 73010.66 4624.71 17583.18 952.43 1542.59 63.7 615.47 0.6 * 675.007 938.02 5.45 * 1.379 2914.54 170529.54 15.39 1062.3 19.64 20.31 0.35 56449.14 3416.01 25803 , 92 1889,6 1419,21 25 776,33 0,38 * 675,007 184,49 27,47 * 1,379 5152,95 78769,16 14,91 212,13 17,84 51,67 0,23 24713,36 1791,75 19528.97 2177.08 1557.34 53.38 73.09 4.33 8958.96 338.85 82.57 84.95 8890.19 1297.59 16.13 273.82 12.56 7.1 1, 18 192111.42 17163.87 31849.25 2894.13 2588.7 14.84 576.99 0.6 * 675.007 * 27.419 18.01 9.12 5720.72 171568.64 19.75 306.12 15.09 69.3 0.5 38416.87 1922.34 16988.84 1360.96 3060.28 21.02 970.76 0.58 * 675.007 291.19 4.33 18.29 2956.57 140867.17 9.65 266.26 15.92 32.72 0.31 26120.36 4002.09 25943.69 1782.02 1517.98 30.07 * 11.424 2.46 5140.13 585.31 7.45 24.17 7293.86 2798, 26 13.59 621.17 23.82 139.21 0.34 154023.12 6811.12 64922 1358.46 2518.08 31.83 207.23 0.59 * 675.007 174.57 6.12 9.12 4037 .45 55206.99 8.37 201.98 7.7 6 45.04 0.3 42 951.65 2009.12 5193.31 2098.01 1264.87 30.87 346.32 0.71 * 675.007 493.35 14.18 10.4 1026.33 10657 16.33 296 , 01 14.45 33.57 0.27 120314.29 7918.88 15269.4 2243.51 1369.59 17.26 351.54 0.36 * 675.007 * 27.419 * 0.647 * 1.398 2823.49 26309.76 5.35 257.07 14.54 26.38 0.26 68582.61 5570.5 20624.96 978.34 685.43 66.73 346.32 0.86 4272.22 241.6 15.25 8.28 2776, 97 45229.89 5.98 208.77 11.51 38.51 0.45 35399.97 6709.94 55009.45 1562.72 695.99 22.53 495.47 0.71 * 675.007 477.67 13, 53 11.25 4316.29 161325.16 15.28 2075.22 20.54 11.51 0.31 123854.1 3226.46 16421.69 1745.38 3328.32 44.2 219.13 0.26 * 675.007 259.89 3.88 11.25 4699.13 20512.54 20.29 269.3 22.89 7.68 0.4 39006.72 3458.05 11158.78 1736.54 2729.47 116.37 367 , 08 0.26 * 675.007 184.49 * 0.647 * 1.379 5318.28 14021.12 9.67 135.55 4.08 13.84 0.34 49670.49 926.98 38079.13 1649.53 1179.54 62.88 325.23 0.47 * 675.007 * 27.419 * 0.647 * 1.379 1571.44 16020.4 10.59 284.27 5.22 10.45 0.38 28063.48 7517.13 6231.58 3975.31 1616, 2 256.75 * 11.424 2 , 5 * 675,007 347.35 41.5 19.18 5410.15 17365.47 30.42 195.1 30.17 4.37 0.67 83149.78 3709.71 5699.58 1917.5 595.02 48.57 * 11,424 1.81 * 675,007 724.88 70.21 20.99 3483.57 7605.05 6.96 301.8 68.08 5.31 1.04 155585.28 3709.71 20905.63 1301.04 955 , 98 57.19 2014.33 0.94 * 675.007 914.1 12.24 16.06 2465.33 8735.22 8.45 314.68 46.75 26.82 0.6 71787.98 6365.27 4777 , 47 2431.88 343.19 38.36 447.28 3.42 * 675.007 886.61 51.28 23.26 4165.56 13845.12 23.32 290.16 4.9 7.1 0.83 160729 , 21 5713.59 11392.58 1164.11 1999.4 34.51 748.76 1.47 * 675.007 982.12 26.63 106.98 8543.52 1832.47 30.01 223.71 32.54 5 , 31 0.49 108185.6 8079.27 10484.03 1577.8 1364.62 21.52 69.11 3.86 31900.6 907.25 37.4 29.71 4208.49 55718.88 20.53 131.48 158.33 5.62 1.08 214818.39 12764.14 65231.15 932.7 3800.13 12.68 * 11.424 2.65 * 675.007 338.85 36.58 8.7 3586.58 1084 , 37 10.36 247.73 75.57 7.96 0.52 161173.68 5488.64 8732.65 1623.09 1394.42 18.28 * 11.424 0.33 * 675.007 194.28 * 0.647 * 1.379 3068 , 44 128578,71 15.34 129.43 10.27 5.31 0.25 42245.57 2117.3 22951.27 1422.24 1625.99 26.94 372.22 0.19 4272.22 313.07 6.57 23.71 2968.95 16402.66 6.21 296.01 27.18 13 , 07 0.14 39596.17 3458.05 16847.16 349.61 3590.04 47.42 * 10.951 3.28 * 674.132 1230.1 638.75 111.67 1665.16 9373.88 19.42 458, 83 43.93 61.78 0.99 141133.19 13669.11 19682.52 1498.39 1337.13 222.47 * 10.951 0.61 * 674.132 289.24 6.51 9.39 4561.69 107417.75 8, 69 142.95 14.04 4.06 0.19 52224.38 2999.04 9141.76 805.18 1308.91 35.93 * 10.951 0.9 * 674.132 534.7 12.13 19.95 4232.72 70,136.31 17.39 303.84 35.1 8.64 0.73 119064.81 8725.79 21506.43 915.26 6235.15 66.16 130.66 0.35 * 674.132 390.5 17.59 12.66 3629 , 84 21818.82 14.8 167.49 56.27 6.82 0.32 42927.17 2369.17 52342.32 1056.81 1652.98 188.41 429.8 0.63 6414.89 779.77 119.8 48.44 3533.47 4013.23 12.73 229.51 43.32 9.22 0.61 130644.72 6649.42 19174.43 912.65 4065.37 127.45 * 10.951 0.3 5477.33 320.18 18.13 13.1 1907.19 50649.3 6.24 195.08 28.68 20.86 0.21 51520.39 6564.95 16517.06 752.09 1438.69 62.62 * 10,951 0.09 * 674.132 28 9.24 7.65 12 4450.3 70164.65 9.38 127.35 26.08 3.67 0.2 33409.08 2248.17 61805.31 1556.69 2154.37 13.94 * 10.951 0, 99 * 674,132 360.65 8.22 8.96 2598.39 18515.73 28.23 142.95 83.94 14.19 0.43 200278.77 2248.17 26182.93 343.14 1094.36 232, 16 97.06 0.33 * 674.132 247.06 11.02 14 4611.05 15029.64 19.82 203.92 19.91 4.8 0.31 101939.77 4884.93 15081.1 1283.3 2345 , 75 83.02 * 10.951 0.63 4044.79 370.65 17.05 10.68 2090.26 17797.1 20.72 172.22 21.79 5.51 0.46 43178.71 5560.59 11512.97 950.25 1822.06 127.45 390.75 0.45 * 674.132 330.37 22.95 15.12 2219.59 134083.73 15.28 241.86 72.36 9.5 0.61 143541.2 8517.29 41837.6 593.48 2936.34 68.66 595.79 0.76 8713.38 477.91 36.06 16.03 1348.18 7530.28 15.82 267.76 69.37 22.74 0 , 37 76815,07 9126.89 14702.69 606.27 1957.27 245.34

117.06 0.29 * 674.132 192.48 8.78 15.8 7238.38 21308.78 2.73 172.22 18.16 11.41 0.2 45842.94 4445.22 28699.6 525.7 1681, 16 35.85 48.5 0.22 5947.63 284.03 14.33 16.49 5597.73 9602.01 9.15 245.92 13.87 24.16 0.18 39349.18 6700.16 39275 , 71 288.95 1681.16 81.93 1335.73 0.91 4526.33 1004.53 41.21 44.83 2762.42 5280.65 12.41 181.51 47.5 10.61 0.27 133945.72 7124.52 9525.44 812.82 2458.29 70.36 874.91 0.48 5003.69 340.52 4.18 11.12 2791.92 8718.87 33.6 265.81 55, 84 35.28 0.42 156991.06 9899.37 12198.38 1018.35 1714.98 253.64 * 10.951 0.33 * 674.132 192.48 10.74 * 1.37 1478.89 10313.78 9, 99 216.87 15.82 8.93 0.22 74218.17 5328.84 24042.36 672.65 783.45 114.77 * 10.951 0.99 * 674.132 268.29 12.13 10.68 1324.86 1137.61 17.64 339.96 25.31 29.11 0.21 64563.39 8309.32 179234.95 1303.92 437.89 120.17 206.32 0.41 * 674.132 268.29 7.36 17.4 11446 , 14 2474.07 16.98 353.96 302.91 25.85 0.17 79373.42 3108.07 156129.56 1029.06 2840.76 32.91 278.59 0.17 8713.38 330.37 12.13 16.94 985.97 2184.37 4 525.33 125.9 71.2 7 0.18 10107.51 1537.64 361806.01 * 40.515 1433.04 103.88 * 10.951 0.81 * 674.132 273.55 * 0.598 21.12 7365.7 990.37 16.44 352.23 159, 98 38.81 0.32 44033.64 3295.92 118347.51 2158.89 1173.43 31.02 525.62 0.54 * 674.132 299.61 8.22 15.57 3925.49 5677.42 5, 85 239.82 63.42 78.36 0.31 42474.29 5361.89 188158.35 * 40.515 1726.25 101.39 237.23 0.19 4044.79 268.29 * 0.598 27.09 4216.59 15546.26 8.59 195.08 82.25 33.51 0.3 56757.66 3516.5 431872.86 390.98 1872.77 50.99 263.52 0.9 * 674.132 370.65 28.76 17.63 3205.19 3411.71 6.36 352.23 191.65 53.1 0.34 77805.47 2490.83 135071.6 1133.7 2345.75 78.03 487.57 0.27 * 674.132 458, 74 39.67 17.86 5985.78 1137.61 9.68 519.43 151.17 51.42 0.14 43882.8 3738.55 111782.02 2451.07 1116.95 42.26 343.25 0.6 * 674.132 * 27.362 5.93 * 1.37 4068.84 * 138.366 20.96 332.87 39.24 68.87 0.28 66297.9 8343.95 123347.67 2740.67 2413.28 82.95 98 , 61 0.6 * 674.132 164.17 7.65 15.8 5775.13 * 138.366 25.05 157.87 52.49 5.51 0.24 33664.6 3611.49 158680.47 410.16 1320, 2 48.75 * 10.951 0.15 * 674,132 192.48 9.9 10.25 4460.1 47744.6 6.36 206.1 27.31 14.67 0.17 64665.29 4445.22 9256.92 407.2 1241.18 32.64 5276 , 72 2.55 11407.53 636.36 1824.77 209.46 2101.45 3567.13 34.62 311.2 18.7 28.36 0.69 248629.18 19647.66 2007.22 730.4 1071.76 97.79 296.27 0.6 * 674.132 419.97 27.71 13.32 1678.6 2474.07 11.56 249.95 64.16 15.63 0.3 49 108.89 7826.23 4364.67 1462.69 1049.17 85.05 82.9 2.01 * 674.132 1946.39 250.29 29.77 2854.06 21609.51 7.27 614.84 222.63 22.22 1.44 433959.55 7107.49 11322.36 886.6 2593.31 80.46 * 10.951 0.57 4044.79 268.29 10.46 12.66 8825.93 4769.19 20.15 199.52 9.76 31.43 0.22 63443.45 2127.85 15213.49 471.02 1720.62 110.64 51.87 0.87 * 677.552 196.62 12.04 16.23 5511.89 2329.49 13.57 541.31 36.28 6.31 0.29 48438.37 15249.66 190679.51 3187.47 * 32.367 190.26 470.97 0.28 * 674.132 214.6 4.77 12.44 2051.17 6289 , 02 9.79 387.99 17.23 35.8 0.26 60499.13 10838.38 479349.32 1608.59 1602.24 106.47 * 10,951 1.52 * 674.132 419.97 20.82 13.77 951 , 51 1647.7 15.54 601.01 37.77 74.85 0.76 78 693.34 9214.33 170863.29 640.1 2165.63 64.3 * 10,951 0.38 4044.79 236.33 8.22 * 1.37 3153.66 12031.53 12.64 371.14 86, 49 138.23 0.31 21553.32 4934.05 189248.29 479.42 1071.76 45.69 311.12 0.55 * 674.132 192.48 23.22 15.12 5559.67 2379.63 8, 99 176.89 35.96 22.64 0.21 51922.64 2127.85 134551.9 649.83 1218.6 64.52 * 10.951 0.14 * 674.132 257.72 * 0.598 8.75 1681.29 64256 , 49 3.29 281.28 50.15 36.85 0.13 28042.95 6043.84 177396.43 224.76 1145.19 37.94 71.68 0.37 * 674.132 186.88 3.59 * 1.37 1186.39 21729.23 8.09 332.87 26.2 24.06 0.23 31974.6 3850.1 ** 100666.874 1598.77 1410.48 55.15 1074.44 0.71 4044.79 268.29 180.6 44.06 3748.74 1761.91 14.57 277.45 48.68 13.95 0.18 39349.18 1508.68 126351.83 692.34 1658.61 102, 05 * 10,951 1.22 5003.69 458.74 9.34 22.07 2135.08 1872.26 10.51 372.84 19.33 27.21 0.47 115610.33 3264.53 117048.36 677, 57 5103.16 46.32 199.22 0.21 4286.12 289.24 10.46 9.82 1335.22 6485.9 6.08 249.95 26.88 13.2 0.15 47 751.87 4737 , 88 436633.87 669.38 1568.4 102.53 106.36 0.99 * 677.552 225 , 27 * 0.496 17.17 5922.55 4437.09 20.77 1319.22 9.48 108.58 0.24 82115.8 17505.37 134384.78 5132.19 * 32.367 138.75 * 10.951 0.64 * 674.132 257.72 5.35 * 1.37 1005.74 830.19 4.17 152.97 27.87 23.25 0.21 126 133.03 4106.34 96390.14 715.46 1133.9 57.83 NA 0.19 * 674.132 203.59 8.22 10.25 5118.53 20208.36 8.8 132.63 12.22 5.16 0.12 17665.67 1222.48 221931.82 1776.54 1838.96 77.29 * 10.951 0.2 * 674.132 * 27.362 * 0.598 * 1.37 5597.73 26778.09 3.57 181.51 24.08 29.11 0.15 23659.32 943.19 199279.82 1062, 2 823.05 21.08 103.26 0.95 7800.81 2169.4 9.9 28.55 1762.37 2283.12 12.58 409.45 105.28 13.08 0.29 30688.03 5032 , 45 128310.15 560.96 4166.4 87.39 35.53 0.93 * 677.552 409.26 8 19.07 6072.84 1486.82 24.45 1021.32 35.09 87.42 0.25 28 252.83 19050.3 47236.2 3221.54 * 32.367 96.9 106.35 0.26 5947.63 299.61 4.47 10.03 7645.28 28 510.68 9.29 148 19.24 21, 6 0.22 36159.77 1712.56 139222.37 753.77 1133.9 24.1 205.55 0.75 * 677.552 191.76 25.93 19.07 3465.23 2117.69 19.35 782.45 19 , 25 23.88 0.39 125933.05 13507.09 144 211.6 1478.35 * 32.367 95.5 * 10.36 0.98 * 677.552 225.27 9.37 11.18 4886.5 * 130.377 24.96 635.81 52.65 23.35 0.46 77145 , 52 13636.25 81653.93 3272.72 * 32.367 166.06 * 10.951 0.81 * 674.132 562.72 4.18 78.89 7748.13 4434.16 9.13 269.71 618.29 63.07 0.37 227364.35 4445.22 168672.17 1103.71 3863.28 45.09 507.91 0.41 * 674.132 214.6 9.9 14 3229.5 21878.44 13.97 233.66 12, 66 12.95 0.22 55043.25 6043.84 8988.14 1756.47 45460.4 63.3 550.03 3.27 * 677.552 859.07 351.63 61.32 10145.3 65257.18 42, 49 967.07 61.97 32.87 0.42 148042.08 24447.51 20033.2 3320.17 * 32.367 79.6 141.76 2.45 7420.62 683.35 20.38 85.71 8852, 97 1211.97 8.55 807.73 29.72 35.51 0.42 62730.63 16990.07 82256.17 2457.42 * 32.367 77.25

124.46 1.25 * 677.552 417.53 24.1 16.23 3498.48 1816.63 24.96 1268.31 38.13 104.09 0.25 29266.23 25345.7 95499.84 2916.11 * 32.367 57.79 * 10.36 0.34 6946.9 * 27.835 2.98 * 1.385 2046.14 1306.9 10.84 417.1 21.76 6.31 0.17 28252.83 10888.54 85386.22 3028, 91 * 32.367 49.33 * 10.36 0.74 * 677.552 225.27 23.48 11.86 6109.23 1486.82 46.6 642.8 70.33 40.92 0.5 88855.03 10240, 99 27240.59 2779.37 * 32.367 115.91 * 10.36 0.63 * 677.552 537.99 13.03 20.27 4249.87 3034.27 40.41 593.08 100.84 32.62 0, 3 86245.89 14798.1 4125.46 2387.09 * 32.367 209 169.25 0.36 * 677.552 215.82 25.32 15.3 4588.64 21889.4 17.59 563.77 15.3 27.75 0 , 35 98081.99 7418.55 10149.45 2331.66 * 32.367 64.58 * 10.951 0.33 * 674.132 203.59 4.77 * 1.37 5024.04 14005.28 4.94 162.71 7, 4 27.02 0.18 27990.77 1508.68 21262.1 1416.65 1714.98 18.67 706.65 1.04 * 677.552 490.53 350.23 51.09 5793.61 60563.32 24, 2 495.01 12.09 9.57 0.34 116777,02 7353.54 6527.91 1192.81 * 32.367 152.69 474.19 0.48 36813 333.16 63.53 15.3 6559.98 17802 , 52 39.71 266.27 13.17 9.35 0.22 66 286.98 3537.24 36330.57 3667.71 * 32.367 99.23 482.47 0.27 4526.33 273.55 5.35 16.03 2703.61 26425.33 11.74 203.92 18.64 9.36 0.19 44435.82 2490.83 5542.3 * 40.515 1827.69 90.01 813.95 0.88 13010.4 280.35 41.68 20.03 5706.24 37178.01 16.8 740.55 16.2 29.26 0.47 72437.66 10726.69 12203.11 3187.47 * 32.367 135.06 * 10.951 0.66 4526.33 553.4 49.38 22 , 07 1428.99 47266.8 14.99 229.51 83.03 24.86 0.41 100340.73 6060.58 17026.52 575.99 2447.04 19.66 139.63 0.49 4044.79 289.24 10.46 12 2880.78 20609.06 14.75 172.22 29.89 14.67 0.19 32538.91 6161.11 7485.32 1495.49 1331.48 54.42 266.26 0 , 99 69901.97 2786.85 228.37 87.44 1314.52 3252.39 17.33 443.16 81.76 2.84 0.49 252627.63 7141.55 26746.26 1059.5 1878.4 52, 38 144.08 0.26 * 674.132 268.29 5.93 8.54 2166 42317.21 9.59 152.97 12.8 12.7 0.16 45239.97 4591.3 163702.84 1133.7 3526.35 16.18 * 10.951 0.68 * 674.132 979.02 13.78 24.92 2845.17 4191.36 16.85 195.08 197.87 21.6 0.25 99790.86 5760.04 19419.09 711 , 32 2390.77 39.26 * 10.951 0.22 5477.33 299.61 9.9 9.39 3548.98 76223.33 13.26 110.89 11.1 11.67 0.18 28407.84 1393.45 46836.53 512.5 1523.29 23.06 453, 02 0.25 * 674.132 * 27.362 5.06 9.39 5583.89 2566.62 12.19 132.63 9.47 8.64 0.18 52425.56 1566.66 9141.76 2042 2278.21 59, 6 348.56 0.58 * 674.132 * 27.362 * 0.598 11.34 2163.18 4296.32 12.06 210.44 15.56 29.21 0.35 22583.76 7637.29 9678.77 1014.79 1658 , 61 55.4 177.72 0.52 * 674.132 792.99 13.23 19.95 2163.18 3252.39 10.45 199.52 97.86 4.8 0.67 125838.2 4884.93 5893 , 67 596.67 3672.37 67.73 * 39.33 1.33 * 682.921 196.42 * 1.666 12.21 6837.42 16011.77 25.41 478.83 12.47 * 0.658 0.33 30240, 28 4538.45 24811.78 923.04 * 34.126 107.93 289.74 0.37 * 684.082 * 27.695 * 2.588 * 1.393 7512.01 18134.61 33.48 171.43 7.21 11.61 0.61 42513.57 1702.06 285689.52 2671.06 926 34.98 * 37.47 0.45 * 684.082 * 27.695 * 2.588 * 1.393 4645.29 24389.14 19.99 132.5 10.2 11.61 0 , 58 36983.59 2035.12 13081.33 2856.98 1080.18 23.7 * 37.47 0.63 4104.49 * 27.695 * 2.588 * 1.393 10103.99 68005.24 41.44 211.99 9, 36 6.34 0.47 49559.79 1672.04 222959.33 1742.92 2529.4 73.76 381.52 0.6 * 684.082 * 27.695 * 2.588 * 1.393 8121.6 7019.43 18.98 311.73 10 9.66 0.47 49753, 34 1883.12 8066.85 1169.78 1238.53 68.49 * 39.33 1.55 * 682.921 280.4 * 1.666 12.5 9232.49 948.17 31.19 3263.98 16.06 6, 58 0.86 108343.31 7685.58 14933.77 1574.42 * 34.126 217.05 * 39.33 0.34 6181.59 186.42 * 1.666 9.89 7036.94 5495.03 24.09 1870.28 20 , 29 12.88 0.33 33683.82 13058.52 6004.77 2924.66 * 34.126 163.27 198.56 0.56 7469.65 165.86 * 1.666 9.02 6989.25 9517.05 18.61 789 , 64 7.9 6.8 0.5 54425.4 4447.72 12657.69 1540.45 * 34.126 50.88 * 39.33 0.35 5621.44 * 27.643 * 1.666 10.47 5315.96 2916, 86 33.47 444.26 10.55 6.58 0.2 25945.46 4674.76 6950.68 2914.19 * 34.129 89.03 277.17 0.73 * 684.082 189.06 * 2.588 * 1.393 2074, 21 5430.99 17.14 529.9 10.96 8.88 * 0.42 32913.6 3281.23 31879.77 1313.34 685.57 39.34 * 39.77 0.31 * 706.944 * 28.096 * 21.183 12 , 65 8504,98 59396.14 21.68 422.51 13.58 9.49 0.29 38297.49 6004.39 13521.3 963.71 * 32.555 41.25 * 39.77 0.42 * 706.944 177 , 01 * 21.18 3 * 1.362 5429.28 8175.91 28.79 364.11 11.17 7.79 0.36 22505.3 4280.58 55869.87 1463.74 * 32.555 54.22 * 37.47 0.36 * 684.082 * 27.695 * 2.588 75.59 6192.31 4838.05 43.01 259.18 11.19 6.93 * 0.42 81923.61 8682.08 14867.63 1822.68 1695.14 36.72 * 39, 77 0.75 * 706.944 268.55 * 21.183 10.14 5841.53 4734.97 19.43 1573.83 14.91 6.32 0.56 31061.35 11941.06 4106.81 2932.64 * 32.555 98 , 43 * 41.51 2.05 * 1970.97 237.16 * 16.134 66.57 6377.63 19232.69 19.51 519.94 51.34 13.51 0.67 93604.17 4799.47 7238, 43 1715.83 * 33.371 81.36 463.54 0.86 * 684.082 340.23 16.42 35.04 4150.88 1931.78 20.85 338.62 25.01 12.08 * 0.42 86321, 35 8630.51 139739.29 1136.87 1066.88 18.13 * 39.77 0.69 * 706.944 * 28.096 * 21.183 10.14 4273.32 65005.34 33.71 399.58 7.95 6.92 0.37 59738.54 5814.44 130705.49 2501.59 * 32.555 92.94 197.65 0.69 * 684.082 216.75 * 2.588 8.89 4398.52 1474.85 29.44 325.31 16, 99 6.93 0.63 56819.21 7146.6 18062.49 3864.82 1231.98 49.5 * 37.47 1.15 4762 290.32 20.8 12.15 4720.02 41463.5 11, 9 189.33 15.44 19.5 0.63 65 253.02 5208.16 4651.93 * 40.56 1218.86 92.17 * 11.332 2.43 * 682.162 494.65 8.86 30.49 2560.48 24291.27 46.35 1625.85 26.95 6.77 1.05 95 755.39 21 994.79 17082.25 2610.72 3961.12 125.97 202.3 0.81 * 684.082 183.4 * 2.588 27.71 8856.26 14507.57 18.12 731.63 15 , 15 8.61 * 0.42 42099.64 2403.68 26060.96 2585.47 959.74 17.04 * 37.47 0.5 4104.49 259.41 19.94 8.62 4888.12 4198.47 16.5 311.73 19.06 23.29 0.5 51 304.98 3297.09 47 903.31 344.19 1303.84 99.81 * 41.51 2.17 * 1970.97 201.47 * 16.134 11 , 01 5445.63 10,107.73 25.59 657.26 10.78 6.35 0.43 91396.13 7646.94 16797.16 2219.55 * 33.371 72.38 * 37.47 0.45 * 684.082 171 , 96 * 2.588 11.6 2581.44 8594.74 11.67 318.55 9.27 5.71 * 0.42 51726.24 1492.84 92569.41 2647.87 1231.98 44.38 1022.35 0.27 * 684.082 194.67 * 2.588 * 1.393 2927.37 44042.28 11.11 808.36 8.26 * 0.555 0.54 33678.04 4211.25 179033.91 1724.95 664.56 19.16 * 41.51 0.27 * 1970.97 * 27.42 * 16.134 * 1.329 5226.01 65086.66 28.79 217.25 2.93 7.41 0.22 50373.51 3579.66 193235.55 2481.1 * 33.371 43.25 * 41.51 1.15 * 1970.97 192.36 * 16.134 10.5 7858.27 20858.06 41.65 1443.2 3.71 9.39 0.64 117300.12 8846.29 101081.98 4190.41 * 33.371 109.59 * 37.47 0.46 * 684.082 183.4 * 2.588 * 1.393 4300.98 76161.68 16.72 569.85 6.6 * 0.555 * 0.42 48046.52 4503.58 178149.74 1330.28 1720.43 35, 82 * 41.51 1.03 * 1970.97 183.18 * 16.134 14.14 3894.45 2604.15 31.92 603.02 14.25 7.41 0.5 79868.15 7990.06 26655.18 2441.54 * 33.371 99.93 * 41.51 0.96 * 1970.97 263.27 * 16.134 9.23 9368.94 32931.72 19.37 1855.19 26.73 3.97 0.39 68626, 25 6787.42 103932.02 2060.19 * 33.371 56.9 * 41.51 0.89 * 1970.97 201.47 * 16.134 9.48 5455.23 14021.9 45.89 753.53 14.96 8 , 75 0.53 60211.85 18369.23 46530.22 3987.43 * 33.371 83.43 164.19 0.32 * 684.082 194.67 * 2.588 * 1.393 7107.86 8059.59 19.55 165.26 3 , 79 10.16 * 0.42 25994.7 1169.04 12312.96 2166.9 1026.85 132.09 * 41.51 0.27 * 1970.97 192.36 * 16.134 * 1.329 5990.13 29502, 9 22.88 1885.79 8.84 5.6 0.34 80077.04 6141.01 218721.76 4401.15 * 33.371 78.33 * 10.36 1.05 5195.1 * 27.835 * 0.496 9.41 6491.41 4187.1 29.19 1163.75 4.1 4.24 0.32 72872.94 5367.55 7430.62 1498.27 * 32.367 94.55 * 41.51 1.8 * 1970.97 * 27.42 * 16.134 11.01 4700.95 10416.63 30 , 53 522.99 14.81 8.75 1.05 91139.93 7131.53 24270.31 2945.71 * 33.371 149.43 * 41.51 0.97 * 1970.97 * 27.42 * 16.134 10, 5 2424.89 4099.46 22.98 1004.57 4.57 7.75 0.51 16888.52 4886.28 179152.9 1405.65 * 33.371 168.58 338.54 0.41 4543.89 269, 8 * 2.588 39.19 7725.98 38310.47 21.11 1326.01 8.11 18.36 0.43 20223.86 895.23 19001.66 601.12 878.52 68.58 * 37.47 0 .9 * 684.082 * 27.695 * 2.588 * 1.393 8011.38 59363.57 22.32 259.18 15.62 7.51 0.51 60124.86 2004.65 6950.58 928.12 643.49 35.43 * 37.47 0.47 * 684.082 * 27.695 * 2.588 * 1.393 2492.94 63274.17 15.47 281.25 7.47 5.71 * 0.42 50981.24 5987.41 2398.57 * 40.56 775 , 81 24.5 * 37.47 0.55 * 684.082 183.4 * 2.588 13.25 5054.14 52769.98 39.34 1058.5 30.98 * 0.555 * 0.42 62225.66 12013.05 76650 , 35 3494.13 1271.23 46.27 * 37.47 1.33 4543.89 290.32 * 2.588 33.86 8121.6 25249.76 15.19 610.14 20.27 10.65 * 0, 42 207 689.38 3376.52 29 479.82 974.54 1106.74 41.18 * 41.51 1.8 * 1970.97 263.27 * 16.134 22.24 7183.81 166949.49 43.46 470.44 15.62 5.6 0.65 115825.39 10041.59 33729.73 1933, 02 * 33,371 114,93 238,39 0,42 * 684,082 * 27,695 * 2,588 * 1,393 5854.75 13585.96 15.39 241.37 3.79 * 0.555 * 0.42 58566.92 2188.07 41801.06 2852.27 1297.32 15.48 66.72 0.28 * 677.552 * 27.835 * 0.496 21.95 6078.03 66236.02 22.83 350.33 2.7 3.97 0.35 24146.52 4028, 33 79 189.88 4272.19 487.7 65.83 * 10.36 0.27 6471.56 * 27.835 4.47 8.53 4089.25 3153.14 46.21 1066.03 13.46 1.85 0 , 15 26446.8 3635.51 7629.82 4444.74 * 32.367 33.94 * 39.77 0.14 * 706.944 185.41 * 21.183 10.64 2941.83 16859.74 18.16 309.27 11, 37 5.41 0.46 18389.63 11900.46 65597.42 1897.82 * 32.555 100.93 * 39.77 0.36 5257.04 04.01 * 21.183 366.89 5711.56 24063.48 19, 21 361.69 8.12 5.72 0.59 57678.79 6962.42 26508.01 2057.07 7723.6 58.7 * 39.77 0.34 20544.07 * 28.096 * 21.183 20.51 5757, 29 3238.87 27.61 359.27 7.78 4.78 0.37 56405.17 4631.95 251439.88 904.42 * 32.555 57.83 * 10.36 0.37 7735.6 * 27.835 * 0.496 * 1.385 5675.47 1486.82 22.12 1226.94 8.5 2.81 0.27 49915.41 6377.67 6393.22 4104.45 * 32.367 66.6 * 10.36 0.43 * 677.552 * 27.835 * 0.496 * 1.385 8978.61 37011.76 28.64 571.16 5.39 3.97 0.23 30390.73 13765.4 112311.22 4810.49 * 32.367 37.76 * 10.36 0.33 * 677.552 225.27 * 0.496 * 1.385 6517.76 23874.19 25.18 454.8 17.5 3.4 0.2 31792.69 9981.85 17853.6 2991.27 * 32.367 47.56 51.87 0.77 * 677.552 * 27.835 * 0.496 24.64 8450.99 4535.04 28.9 600.3 9.11 4.51 0.25 64644.47 4420.74 14434.43 3419.09 * 32.367 44.15 * 39.77 0.73 * 706.944 185.41 * 21.183 14.71 4722.91 7347.43 31.8 520.15 15.48 5.56 0.4 31097.5 5173.11 4925.93 1328.49 * 32.555 81.93 * 39.77 0.37 * 706,944 * 28,096 * 21,183 * 1,362 5650.83 6301.77 18.82 1008.46 15.7 6.62 0.27 32362.45 2993.28 11 753.2 1126.3 * 32.555 30.35 * 41.51 1.32 * 1970.97 164.52 * 16.134 14.14 4691.7 9793.78 16.57 892.16 5.55 7.06 0.74 56388.45 10212.05 7742.61 4749.02 * 33.371 190.44 * 41.51 0.56 * 1970.97 173.9 * 16.134 14.67 3595.36 1262.41 29.16 428.9 18.67 4.81 0.19 23892.6 12506.77 41741 79 6151.12 * 33.371 87.97 * 41.51 1.03 * 1970.97 * 27.42 * 16.134 14.67 5706.42 9039.6 19.84 637.46 26.95 12.39 0.5 39352.77 4016.34 53998.29 1704.49 * 33.371 16.43 112.42 0.68 * 684.082 * 27.695 * 2.588 * 1.393 8675.21 1931.78 17.63 197.98 23.86 * 0.555 * 0.42 117942.9 3344.73 94 501.21 1941.04 782.7 29.78 * 41.51 1.63 * 1970.97 297.36 * 16.134 14.67 2873.71 4427.59 31.92 679.66 4.76 13.79 0 , 43 82598.68 6011.53 231693.15 2431.66 * 33.371 340.13 * 41.51 2.42 * 1970.97 288.9 * 16.134 69.34 6217.83 21000.45 22.98 464.13 48, 51 13.65 0.82 170193.68 9998.97 20118.7 2694.11 316.02 86.2

* 41.51 0.71 * 1970.97 * 27.42 * 16.134 * 1.329 7293.13 151276,22 47.89 316.47 8.15 4.4 0.51 36931.5 5838.78 21215.44 4742 , 54 * 33.371 58.87 178.75 0.25 * 684.082 * 27.695 * 2.588 * 1.393 5579.35 2516.55 38.25 528.13 8.93 8.07 * 0.42 47115.23 2996.74 15349 , 5 2146.95 1827.46 26.03 * 39.77 2.58 * 706.944 243.77 * 21.183 24.84 5410.67 52399.02 37.19 2989.61 20.47 13.78 0.58 89855 , 97 12266.36 7639.1 2646.24 * 32.555 34.64 51.87 0.71 4227.58 626.76 28.97 20.99 4445.77 10111.74 12.92 354.97 79.55 13 , 28 0.27 77488.85 4812.77 4961.31 5762.74 * 32.367 35.73 640.5 0.67 83217.69 333.16 130.24 21.47 6740.09 3384.18 34.88 287.09 32.9 14.79 0.64 82892.22 5465.39 6139.96 3633.17 * 32.367 42.59 * 10.36 0.29 25206.22 191.76 8.69 12.31 6088.43 3327, 21 16.37 611.05 14.04 49.09 0.22 62794.1 13797.68 63907.13 3445.78 * 32.367 58.68 210.57 0.59 12703.56 * 27.835 * 0.496 11.18 10074 , 03 782.26 40.35 611.05 6.08 10.23 0.26 117868.89 12990.32 315067.27 2479.66 * 32.367 164.04 * 10.36 0.67 4873.67 * 27.835 4 , 1 11.63 4006.86 * 130.377 50 , 47 820.23 17.7 29.42 0.27 41834.64 10014.25 63843.55 2993.15 * 32.367 98.75 * 10.36 0.51 8049.95 * 27.835 5.19 20.27 8978.61 2260.04 20.21 510.66 9.31 17.31 0.19 86209.9 5921.72 63240.05 2529.74 * 32.367 41.53 1075.6 1.07 4526.33 1425.64 226.28 41, 26 1381.97 1761.91 8.98 277.45 37.61 41.08 0.6 115495.74 9284.35 5346.74 1237.64 907.87 56.82 148.52 0.38 * 674.132 169, 9 9.9 9.82 2439.2 8036.79 11.54 116.48 14.52 3.27 0.23 30171.69 2766.81 7639.91 1029.06 1286.33 47.68 1379.84 0 , 71 5947.63 534.7 102.15 29.77 1245.07 43618.3 9.38 132.63 23.74 3.67 0.36 142119.1 3611.49 6283.18 1046.94 1246.82 31.17 106.35 0.18 * 674.132 294.43 5.93 12.44 1724.45 6117.63 12.2 221.12 127.77 5.51 0.24 72614.69 7175.63 9793.71 1816, 83 2036.12 43.38 * 10.951 0.26 * 674.132 * 27.362 * 0.598 * 1.37 5425.29 29345.97 8.82 87.17 8.26 3.87 0.15 38540.77 1393.45 3174 , 63 1070.3 743.84 10.23 597.61 4.34 19683.38 253.14 122.98 18.59 9064.58 41573.64 60.04 495.01 50.5 7.39 0.8 155371.01 10402.92 5715.64 2232.09 * 32.367 126.56 * 10.95 1 0.27 * 674.132 192.48 3.59 * 1.37 1371.56 58311.05 6.41 142.95 24.08 5.51 0.2 29654.29 2127.85 12692.7 888.34 1410 , 48 16.01 434.97 0.64 5947.63 1629.4 183.88 46.89 4710.16 25408.95 9.52 360.88 247.16 6.34 0.27 242220.21 5032.45 11570.13 1259.03 2857.63 30.86 1236.14 1.41 15300.91 177 73.47 26.61 10282.37 20573.63 33.69 690.79 36.24 4.78 0.43 192692 , 59 6377.67 7927.74 3282.2 * 32.367 151.56 288.12 1.05 * 674.132 257.72 113.53 28.31 3985.87 3567.13 17.93 132.63 19 3.27 0.45 30,481.62 3722.64 24643.25 1090.14 2081.17 34.56 163.87 0.6 * 677.552 243.93 10.72 10.29 6628.72 36745.02 21.78 510.66 189.33 14.79 0.26 112958.01 12990.32 4493 2274.46 * 32.367 97.48 210.57 0.72 5515.54 253.14 36.71 14.84 4976.46 22040.28 26.41 302, 24 15.52 8.9 0.24 180820 6345.11 18844.68 2710.27 * 32.367 133.78 2412.35 1.2 8676.85 1701.13 607.13 80.09 5157.21 45277.33 19 , 97 899.5 345.25 33.03 0.56 143939.12 10532.44 7097.53 3637.01 * 32.367 50.88 56.91 0.2 * 674.132 * 27.362 5.35 * 1.37 4027, 31 38 636.43 12.32 116.48 11.86 5, 85 0.29 60296.49 1321.93 19136.78 1011.23 913.53 15.38 * 10.36 0.7 * 677.552 * 27.835 * 0.496 11.41 4440.86 * 130.377 25.89 487.09 78 , 72 14.39 0.26 136 125.74 9528.17 195 801.14 3373.4 * 32.367 216.42 * 10.36 1.02 5034.51 201.46 8.69 19.07 6549.42 3211.71 38.2 663.57 38.97 27.41 0.26 21670.17 9755.04 166425.51 1681.53 * 32.367 114.75 * 10.36 0.35 16969.71 * 27.835 * 0.496 * 1.385 5415, 2 899.46 31.29 1040.98 200.68 61.34 0.23 26173.18 6247.43 115287.76 1520.01 * 32.367 81.55 * 10.36 0.96 * 677.552 167.01 4, 47 10.29 5762.74 2531.68 35.1 704.22 11.7 29.51 0.24 42921.18 8912.09 173509.26 2609.66 * 32.367 125.62 * 10.951 0.8 * 674.132 225 , 51 5.93 8.54 2393.25 990.37 15.03 162.71 118.08 6.18 0.36 69987.08 4267.36 190619.6 250.05 2953.2 40.84 * 10, 36 0.35 19983.71 206.27 2.98 * 1.385 4128.1 2788.89 23.52 914.38 2.37 42.02 0.2 33696.77 13248.73 ** 101405.397 3773.46 * 32.367 41.38 * 10.36 0.67 4227.58 * 27.835 * 0.496 8.97 14644.61 6393.65 8.48 471.08 6.57 31.16 0.22 30171.16 13959.09 150558 , 82 4770.75 * 32.367 64.85 93, 75 1.04 * 677.552 229.97 12.04 13.91 4856.58 1306.9 46.76 1120.78 12.26 38.18 0.28 66384.06 19661.45 271770.76 3660.03 * 32.367 305.82 * 10.951 2.7 * 674.132 739.89 53.43 15.12 3979.51 9072.87 17.66 345.24 206.64 12.45 0.42 170 105.52 6666.33 90649.11 807.73 3520.73 26.64 632.96 0.53 21339.63 779.77 73.94 40.24 1978.95 5587.2 8.92 281.28 66.44 7.14 0.32 53733.83 5527, 42 98 316.69 877.96 3380.3 84.81 * 10.36 0.4 4873.67 333.16 3.73 * 1.385 6182.14 135623.3 45.56 1040.98 49.61 51.41 0 , 24 22 552.86 22071.95 141769.48 1808.94 * 32.367 43.68 249.72 1.22 * 674.132 350.61 18.13 16.49 1434.23 1529.06 4.23 311.2 10, 81 9.22 0.29 101995.02 7929.49 130,108.01 1700.55 1478.17 70.75 608.42 0.48 * 677.552 177 * 0.496 * 1.385 6370.46 11174.89 32.81 949.65 77.08 70.28 0.31 46651 25089.11 259655.7 3943.37 * 32.367 128.31 * 10.36 0.47 * 677.552 167.01 * 0.496 9.41 3247.73 22787.01 26 1104, 48 15.78 72.73 0.23 39366.24 11341.58 240 143.37 3535.52 * 32.367 63.71 * 10.36 0.53 * 677.552 167.01 5.91 15.3 5258.11 4535, 04 38.09 851.12 6.64 47.55 0.23 36196.36 13022.62 82792.45 4838.34 * 32.367 71.46 * 10.36 1.82 57252.79 234.64 11.38 20.51 6926.63 28794, 37 37.71 911.41 6.11 3.54 0.77 76017.01 13474.8 18705.49 3032.68 * 32.367 90.26 * 10.36 0.53 44783.2 215.82 3.73 13.45 5056.65 23124.26 22.55 585.82 13.91 3.4 0.25 60899.21 6833.26 12807.29 3744.58 * 32.367 39.99 * 37.47 1.15 7314.95 449, 35 20.8 12.15 6454.3 84970.2 24.13 320.81 20.47 8.61 0.81 110423.32 6054.14 16994.48 1089.8 1796.05 23.97 * 10.36 0.56 21 930.57 * 27.835 * 0.496 * 1.385 7395.43 11835.59 24.76 364.15 12.22 4.51 0.32 47586.67 7418.55 7696.11 4100.56 * 32.367 124.78 * 37.47 1.67 * 684.082 238.31 * 2.588 11.6 11288.8 11178.15 47.94 946.85 29.24 11.14 0.61 102539.61 5323.64 11722.68 1182.41 1465.58 11.33 * 39.77 0.42 * 706.944 177.01 * 21.183 9.15 7111.39 106888.06 43.83 540.83 14.23 6.62 0.79 22355.83 8167.86 14385.5 2733.07 * 32.555 49.42 * 10.36 0.44 26243.94 * 27.835 5.19 12.54 6766.67 5055.48 64.44 563.77 3.41 5.43 0.43 34888.53 13862.25 13282.72 2717.73 * 32.367 127.2 5 * 39.77 0.75 * 706.944 * 28.096 * 21.183 * 1.392 7918.28 7548.39 17.05 390.25 13.75 10.32 0.67 28526.31 5266.97 21485.34 1625.06 * 32.555 41 , 5 * 39.77 0.17 * 706.944 * 28.096 * 21.183 * 1.392 6117.01 77941.49 10.39 229.82 6.76 7.5 0.24 26524.6 3351.41 167517.74 1036.78 * 32.555 76, 86 * 39.77 0.77 * 706.944 193.8 * 21.183 9.15 11268.64 6474.12 41.93 422.51 9.99 5.41 0.33 42309.11 5229.41 17049.51 2989, 08 * 32.555 66.29 * 37.47 0.73 * 684.082 205.78 * 2.588 * 1.393 6138.93 14987.67 23.95 316.29 10.58 * 0.555 * 0.42 71476.85 5027.12 9846 , 35 1899.97 1218.86 13.03 * 39.77 0.57 * 706.944 * 28.096 * 21.183 * 1.362 5605.47 9977.18 35.19 238.54 13.24 5.1 0.57 25720.07 2603.99 65000.25 3217.2 * 32.555 91.45 * 39.77 0.29 * 706.944 * 28.096 * 21.183 * 1.392 4772.32 42062.78 15.5 249.96 9.56 10.32 0.35 38007, 02 6558.42 38834.82 2204.94 * 32.555 49.79 143.82 0.23 * 706.944 177.01 * 21.183 9.39 4779.4 44901.61 21.87 392.59 7.84 5.41 0 , 29 41576.95 3136.07 62128.08 1580 * 32.555 44.7 123.26 2.06 4324.74 216.75 * 2.588 10.51 11203.71 1761 8.21 26.77 634.68 5.08 5.71 0.47 48657.66 4373.46 203448.56 2103.87 1638.09 28.98 * 39.77 0.94 5073.25 202.17 * 21.183 29.81 2750.67 22844.66 27.22 583.28 36.34 16.09 0.36 48446.85 17656.95 147363.31 2139.52 * 32.555 27.15 * 37.47 0.67 * 684.082 * 27.695 * 2.588 * 1.393 4499.35 1712.43 15.05 900.93 17.89 6.93 * 0.42 33359.63 2620.91 26834.31 2191.33 885.32 16.14 * 37.47 0 , 58 * 684.082 453.95 * 2.588 15.46 6718.54 3188.93 25.26 316.29 11.95 8.61 0.69 81204.11 2745.72 105903.1 844.02 1497.69 12, 77 * 37.3 0.48 * 639.193 272.36 * 0.68 * 1.36 3378.44 10126.42 17.56 208.67 11.14 * 0.751 0.28 46858.57 2831.81 10974.05 * 39.613 11,609.29 74.07 * 39.77 0.4 * 706.944 177.01 * 21.183 9.89 3951.86 5680.67 30.61 1973.87 18.55 4.78 0.41 37245.2 5833.41 28857.24 2821.83 * 32.555 36.43 * 37.3 0.34 5936.13 300.56 9.55 10.51 5043.01 50544.21 13.04 384 13.31 * 0.751 0.19 180603, 14 2679.11 9601.33 552.84 2573.12 72.01 66.72 0.79 7104.98 234.64 6.26 8.53 5167.28 11387.26 19.44 1648.87 7.3 5 , 81 0.16 83600.83 4747.45 20926 1328.22 * 32.367 147.09 * 10.36 0.24 * 677.552 * 27.835 4.47 10.29 3655.76 4916.41 54.39 690.79 8.6 3.4 0.2 27486.5 8944.53 183005, 88 2208.15 * 32.367 66.83 * 10.36 0.46 35646.96 * 27.835 5.19 * 1.385 3427.28 8607.49 17.09 541.31 16.29 3.4 0.18 22387.58 4976, 01 70 172.35 2285.53 * 32.367 43.1 * 10.36 0.24 10699.64 * 27.835 * 0.496 9.85 5654.97 1486.82 36 1158.41 10.51 5.56 0.22 43552, 68 15475.39 9987.64 1237.93 * 32.367 79.83 * 39.77 0.91 23608.79 * 28.096 * 21.183 16.27 6902.8 13234.11 20.89 601.11 7.21 4.78 0.2 31856.56 7485.12 166421.43 636.21 * 32.555 90.12 249.53 0.35 * 639.193 340.83 5.3 30.79 1956.47 3151.2 16.15 633.67 23 , 37 8.63 0.3 62764.25 7366.19 10878.88 985.27 1267.24 139.63 * 10.36 0.87 * 677.552 * 27.835 * 0.496 19.07 6793.27 1486.82 29, 65 998.64 6.5 2.96 0.39 56244.8 9171.55 283580 1909.3 * 32.367 64.11 66.72 0.35 7420.62 * 27.835 14.33 9.85 3257.15 6555, 51 16.34 510.66 3.22 5.56 0.14 26173.18 4289.98 23670.51 1975.12 * 32.367 123.87 * 37.3 1.07 * 639.193 474.63 12.16 21, 11 1244.76 44235.81 17.41 384 21.3 9 * 0.751 0.46 212771.08 5279.84 13583.06 709.15 1968.17 114.2 * 39.77 0.54 8158.53 * 28.096 * 21.183 16.53 11892.51 1937.47 182.36 722.58 12.53 6.62 0.45 27290.32 10829.38 224025.12 2582.18 * 32.555 65.24 * 39.77 0.23 * 706.944 * 28.096 * 21.183 * 1.362 4300.51 27007.29 19.75 190.21 6.93 5.72 0.31 40189.44 6808.24 58383.15 3057.93 * 32.555 101.4 215.55 0.4 16060.52 * 27.835 5.91 9.85 5157 , 21 12,188.03 39.29 331.5 3.61 1.85 0.2 45777.68 2782.74 133290.1 987.26 * 32.367 46.24 * 39.77 0.39 * 706.944 177.01 * 21.183 12.4 6799.61 1630.07 46.44 664.8 19.04 3.82 0.52 57253.51 3968.5 23858.01 885.15 * 32.555 127.73 * 39.77 0.56 * 706.944 210.52 * 21.183 8.66 7494.39 208689.43 25.91 636.15 26.43 7.79 0.55 33374.1 14938.43 20943.16 3059.28 * 32.555 114.58 309.11 0.37 5204.4 427.97 * 0.68 * 1.36 2815.75 8921.21 16.93 553.52 11.25 * 0.751 0.2 79145.15 6133.28 26094.45 1032.53 2995 , 24 63.79 * 10.36 0.57 8989.45 * 27.835 * 0.496 8.53 5440.61 32834.71 30.42 1012.84 9.38 5.56 0.32 43064.54 5563.21 15250 , 78 3028.91 * 32.367 92.02 * 10.36 0.4 4551.19 * 27.835 * 0.496 * 1.385 5562.91 5282.96 29.03 386.63 4.03 4.51 0.25 26720.4 4812.77 32227.65 1953.17 * 32.367 39.02 * 10.36 0.41 * 677.552 * 27.835 * 0.496 8.75 6470.34 1656.02 31.83 1263.17 * 0.03 4.78 0.31 34001.23 4747.45 97572, 47 4391.7 * 32.367 105.65 * 39.77 0.39 * 706.944 202.17 * 21.183 * 1.392 4104.8 10082.54 22.81 258.38 8.74 5.72 0.51 27217.48 2709.65 6578.31 1406.81 * 32.555 32.73 * 10.36 0.26 4873.67 * 27.835 * 0.496 * 1.385 7211.35 3384.18 23.59 483.11 * 0.03 3.11 0.22 34250 , 54 3831.98 10633.61 2951.79 * 32.367 47.59

* 39.77 0.25 * 706.944 210.52 * 21.183 * 1.362 8390.47 28227.97 32.56 322.07 16.56 5.72 0.44 71306.32 5795.48 38072.25 2684.32 * 32.555 74.92 171.99 0.48 * 706.944 193.8 * 21.183 * 1.362 5073 60492.53 19.27 657.21 9.45 5.41 0.51 46956.95 10608.3 4583.26 4450.71 * 32.555 51.3 * 39.77 0.75 * 706.944 * 28.096 * 21.183 * 1.392 4754.65 5861.15 18.54 464.75 10.61 3.49 0.68 51828.08 4817.91 40675.92 1063, 17 * 32.555 35.69 * 39.77 0.44 * 706.944 177.01 * 21.183 14.71 6207.44 10117.55 14.65 406.51 13.86 4.14 0.31 54068.15 8698.29 280140, 05 1122.96 * 32.555 51.63 * 10.36 0.36 4873.67 167.01 3.73 8.53 4613.35 4727.73 32.35 391.05 * 0.03 2.66 0.17 58340.43 3078.22 210367.87 1654.28 * 32.367 29.63 * 10.36 0.79 * 677.552 * 27.835 * 0.496 14.37 7882.69 3716.14 39.57 1278.56 8.7 3, 11 0.38 67292.56 6312.55 25838.79 3295.49 * 32.367 105.9 * 10.36 0.43 * 677.552 215.82 * 0.496 8.75 4806.77 45383.22 26.91 690.79 10.98 4.38 0.25 50330.97 7548.54 29933.12 3673.46 * 32.367 56.96 * 39.77 0.45 * 706.944 * 28.096 * 21.183 * 1.362 4579.31 59804.41 16.6 282.97 9.42 5.25 0.39 36266.97 7601.76 21877.86 2952.77 * 32.555 151.35 * 39.77 0.3 * 706.944 * 28.096 * 21.183 * 1.362 7646 75891, 24 21.03 277.59 7.04 4.47 0.32 32868.28 4557.76 25239.35 2111.97 * 32.555 78.68 * 10.36 0.74 6471.56 * 27.835 * 0.496 * 1.385 4956 , 44 2117.69 32.82 857.23 11.44 * 0.114 0.34 60867.79 14217.3 143261.27 2522.32 * 32.367 110.85 * 39.77 0.26 * 706.944 * 28.096 * 21.183 * 1.392 3391.22 8,405.63 42.11 282.97 5.9 4.78 0.28 38079.62 3387.43 36565.47 2357.41 * 32.555 54.1 * 37.3 0.28 * 639.11 93 168.99 * 0, 68 * 1.36 4225.97 21239.74 17.68 239.84 8.94 * 0.751 0.22 56201.06 5128.52 10513.29 555.91 1638.58 49.43 * 37.3 0.47 * 639.193 * 26.646 * 0.68 * 1.36 3703.77 171646.44 28.09 1123.75 6.5 * 0.751 0.24 137674.76 1840.92 18962.3 1728.14 2349.71 41.54 3187, 8 0.78 * 701.526 389.5 161.69 28.57 4579.14 2230.49 20.29 159.96 17.48 12.28 0.34 135654.82 2927.44 9373.07 1728.04 809.47 32 , 93 707.85 0.77 * 701.526 838.64 88.91 30.59 1148.27 109 394.34 5.94 152.74 42.67 10.59 0.66 78 503.31 5579.59 24 558.79 93 0.46 1683.04 116.56 278.5 0.38 * 701.526 247.04 9.9 9.47 4320.5 1536.03 18.75 197.48 65.7 39.16 0.26 81405 2285, 05 3868.67 1445.79 784.8 28.26 * 35.8 2.42 * 701.526 201.85 37.65 15.24 5867.32 3789.09 14.34 101.33 16.44 9.18 0 , 48 89310.46 5234.48 10625.73 337.93 1670.65 39.46 * 35.8 1.29 * 701.526 247.04 26.16 20.57 3410.49 7927.53 5.55 187.57 19.89 12.93 0.4 37647.14 2717.14 7775.98 862.33 743.7 20.43 2703.01 2.02 * 701.526 470.83 550.6 85.89 9724.71 6411.97 20, 14 216.68 33.82 8.08 1.6 90631 3900.85 22342.61 961.53 579.42 49.09 553.35 1.08 * 701.526 562.93 72.35 45.85 4268.78 5506 , 51 13.77 257.46 318.17 10.25 0.41 115385.73 3472.71 26352.47 411.05 834.14 26.32 12827.98 0.97 6413.16 2349.16 2818.46 374.65 1634.63 1786.06 21.17 528.57 487.73 39.16 2.64 224636.7 12915.76 10139.58 476.61 480.96 49.63 443.02 0.62 * 682.162 161.46 5.36 16.8 3384.52 6461.66 14.74 554.42 11.7 33.15 0.47 31964.31 11041.69 8988.67 1545.94 1711.28 121.91 * 11.332 0.41 * 682.162 172.39 4.16 * 1.364 3037.38 13001.65 18.51 247 , 03 29.64 7.53 0.29 50182.78 5102.97 16667.68 1366.72 1426.13 132.03 * 11.332 0.6 * 682.162 193.73 * 0.642 * 1.364 5164.97 3862.75 23 , 86 385.16 88.6 26 0.36 80038.54 10904.71 10384.56 1608.49 1584.96 111.41 2784.15 0.09 * 682.162 * 26.909 67.26 * 1.364 2892.15 * 129.267 22.19 1817.91 52.8 9.39 0.09 11462.8 11745.91 1294.4 2832.13 217.02 29.43 * 11.332 0.83 * 682.162 * 26.909 * 0.642 8.18 5739.91 47316.41 26 , 64 275 11.42 9.93 0.4 40604.93 8866.47 5177.78 1794.08 2580.79 61.61 454.36 2.18 * 682.162 511.14 392.81 36.14 3739.27 5331.94 22.01 577.61 367.67 28.64 1.4 146181.48 5543.62 8743.07 2001.61 1110.19 50.16 181.13 0.85 4889.12 969.15 * 21.183 38.3 4403.02 12922.72 10.06 612.87 253.96 88.12 0.8 83989.12 6099.58 6843.69 1275.97 * 32.555 39.11 692.16 1.42 * 682.162 486 , 35 327.82 24.83 4141.88 3688.37 10.9 660.96 89.53 32.99 0.65 136842.98 6658 11757.19 1906.64 810.21 57.49 * 35.8 0 , 86 * 701.526 * 26.487 5.4 9.74 2139.36 29069.89 3.11 105.64 6.44 * 0.692 0.35 113674.01 4642 7864.2 882.42 1377.74 112.91 1237, 09 2.9 9313.19 1123.36 270.71 52.4 6714.91 933.19 20.62 370.05 30.52 23.38 0.77 187585.8 5626.76 21242.6 1420.02 4366 , 49 31.75 173.32 1.09 * 701.526 389.5 19.08 19.72 3098.19 7093.1 9.15 187.57 103.11 16.94 0.66 74445.21 2478.13 9512 , 69 738.9 904.06 14.15 * 11.332 1.76 * 682.162 1958.03 * 0.642 17.75 2537.17 77258.87 16.59 7114.38 25.96 22.27 0.43 71534.95 8335, 8 18065.63 921.88 2102.32 114.18 765.6 1.05 * 682.162 161.46 * 0.642 * 1.364 1818.49 21095.87 25.15 432.58 11.19 40.62 0.71 66895 , 43 15638.87 4522.56 1339.61 2097.14 172.68 * 11.332 0.57 * 682.162 283.36 * 0.642 16.33 3117.77 26994.19 20.05 2890.85 14 26.27 0.33 43250 , 9 8394.81 22038.35 2088.43 1415.5 59.86 478.15 0.39 * 682.162 311.54 * 0.642 * 1.364 1948.31 9638.32 25.61 2993.79 15.9 55.38 0.29 38582.94 11941.47 15188.2 2592.63 649.18 64.55 771.49 0.38 * 682.162 * 26.909 12.25 * 1.364 2187.62 43146.37 6.52 504.52 13, 12 37.45 0.29 28311.53 9474.83 9141.71 2165.87 1018.11 63.09 631.11 0.86 * 682.162 820.82 5.96 10.12 4213.15 41745.96 10, 64 8210, 46 74.24 31.62 0.55 72052.51 7902.71 152386.93 3491.7 2355.2 36.82 * 11.332 0.41 * 682.162 214.45 9.15 19.17 2406.53 19093.38 16.69 387.48 11.9 16 0.32 90568.96 10650.28 74,101.62 1871.07 1951.73 108.56 392.11 0.5 * 682.162 444.33 4.76 * 1.364 2809.54 15,169.88 22.85 334.77 109.48 37.77 0.23 30432.08 7488.63 20925.49 2660.2 1319.6 24.85 83.35 0.6 * 682.162 1240.11 8.86 10.6 3114.79 352156.03 28.7 7427.82 61.91 48.18 0.72 55880.74 13056.06 11326.7 2973.21 2463.04 144.55 136.67 1.12 * 682.162 1509.84 6.55 11.56 7311.84 136135.33 19.82 8466.81 67.9 48.33 0.48 52857.34 9259.06 19385.35 2075.33 1789.91 62.1 170, 51 0.29 4873.53 919.2 * 0.642 * 1.364 3279.29 109905.9 14.78 6151.53 69.9 59.42 0.42 63739.15 5383.61 4163.41 1547.12 2169.59 118.57 811.42 0.7 * 682.162 339.09 7.13 * 1.364 2989.87 48834.38 25.37 609.73 46.23 112.94 0.37 41850.64 12997.4 4386.31 2799 , 37 2200.6 55.09 * 11.332 0.59 * 682.162 254.43 11.69 10.6 2560.48 5769.53 27.59 443.5 22.66 5.13 0.35 142037.31 10082, 42 4809.92 2170.64 1574.41 86, 48 348 0.51 * 682.162 193.73 * 0.642 * 1.364 4163.55 10,402.94 15.17 396.69 65.97 11.24 0.3 26678.42 12078.36 18406.78 2318.78 1196.39 122.05 197.13 0.29 * 682.162 * 26.909 8.86 9.4 4487.43 2787.64 23.14 354.37 25.15 29.53 0.33 42531.54 10199.95 6962.76 1555 , 38 1056.09 209.61 * 11.332 0.46 * 682.162 418.66 13.91 8.18 2110.39 38652.12 5.9 291.15 42.58 7.68 0.33 254 304.71 8001, 2 23092.89 1911.38 1637.67 79.85 * 41.51 0.36 * 1970.97 210.5 * 16.134 15.2 7895.72 15910.44 28.37 1019.25 12.95 3.97 0.22 89499.94 7646.94 18927.98 6007.89 * 33.371 108.34 * 11.11 0.64 4448.53 319.59 * 1.463 21.44 7033.05 3124.4 30.45 856.55 6.49 13.91 0.6 126916.4 17718.57 42874.64 3559.01 696.93 57.22 * 10.48 0.69 * 623.86 * 26.389 * 0.50 30.02 6231.92 71618, 97 14.56 477.55 13.17 7.58 0.34 48558.49 9288.45 34454.15 1896.91 * 33.693 35.86 * 41.51 2.76 * 1970.97 559 * 16.134 40.45 7142.29 89110.15 29.7 2623.56 20.93 5.79 1.04 216304.43 10893.19 27885.13 1902.35 * 33.371 126.09 * 41.51 0.73 * 1970.97 254 , 62 * 16,134 12.05 5590.11 3144.29 2 3.03 1467.27 15.65 7.06 0.28 31487.52 6615.2 21621.12 3827.73 * 33.371 93.96 * 41.51 0.47 * 1970.97 * 27.42 * 16.134 * 1.329 7235.8 68084.23 23.69 344.83 4.99 6.16 0.29 26919.27 5536.17 73943.71 3069.53 * 33.371 55.52 * 41.51 0.44 * 1970.97 164.52 * 16.134 9.48 8817.91 23058.97 35.01 479.85 7.69 6.71 0.48 72100.95 11997.81 183844.16 5183.49 * 33.371 205.73 * 41.51 1, 8 * 1970.97 * 27.42 * 16.134 * 1.329 7350.61 1995.44 56.72 419.12 2.14 13.51 0.85 28844.68 9956.34 7626.55 5760.69 * 33.371 109, 06 * 41.51 0.72 * 1970.97 210.5 * 16.134 12.57 4622.45 34657.41 40.59 737.33 16.45 9.71 0.51 56515.06 6959.53 56788.31 3779.58 * 33.371 286.57 * 41.51 0.09 * 1970.97 * 27.42 * 16.134 72.43 5716.14 18202.8 30.5 570.87 4.32 4.81 0.17 44646 , 37 4277.77 170978.66 2033.13 1335.61 103.4 645.57 4.27 * 706.944 419.11 180.81 45.82 7511.64 3120.64 24.66 378.46 111.97 8 , 36 0.82 121287,44 2957.68 29844.87 1201.29 * 32.555 24.62 587.78 1.44 * 706.944 276.78 159.62 20.77 5080.25 1466.21 15.37 309, 27 26.89 8.08 0.55 95058.18 2780.3 18491.99 579.89 * 32.555 23.15 1334.86 1 9587.58 1103.69 198.99 33.18 3687.4 2083.03 15.48 339.65 52.05 6.32 0.7 165232.7 3136, 07 6312.79 824.6 * 32.555 15.87 445.93 2.67 * 706.944 358.48 * 21.183 21.58 5810.83 21346.89 29.25 244.28 21.7 8.36 0.51 107529 , 76 3895.39 37049.16 1751.98 * 32.555 67.26 181.13 1.18 * 706.944 342.22 * 21.183 29.81 4950.43 1293.56 10.85 520.15 18.44 4.78 0.53 105314.73 8737.7 16299.62 1932.17 * 32.555 65.43 453.26 1.35 * 706.944 463.29 169.21 36.3 2872.73 66568.7 26.68 288.31 31 , 45 48.91 0.63 45289.5 2133.76 3228.27 307.48 * 32.555 15.19 * 11.332 0.34 * 682.162 * 26.909 * 0.642 * 1.364 6521.59 59840.27 17.92 337.24 10, 13 6.46 0.49 34893.15 7449.16 26936.78 2819.99 1468.59 30.46 * 11.332 2.95 * 682.162 244.6 4.76 29.55 2010.71 20851.3 21.6 687.63 18.14 4.78 0.53 138773.92 9141.33 65067.69 2067 1595.51 37.97 70.61 0.44 * 682.162 214.45 * 0.642 * 1.364 5002.15 43607.72 28 , 59 337.24 15.5 7.08 0.49 32639.23 7607.02 11638.62 2263.77 1023.54 68.78 * 11.332 0.43 * 682.162 * 26.909 * 0.642 * 1.364 2510, 98 22510.06 23.35 403.54 12.75 4.96 0.36 57411.43 6974.86 221980.56 2486.7 1700.77 132.84 * 11.332 0.29 * 682.162 * 26.909 * 0.642 * 1.364 3538.59 50738.58 14.93 165.51 67.93 5.81 0.4 35966.77 5263.43 5738.37 2081.28 777.08 30.77 * 11.332 0.32 * 682.162 172.39 * 0.642 * 1.364 3605.32 12968.98 14.93 351.94 18.42 9.66 0.56 23026.07 6539.02 12023.47 3039.1 952.79 116.82 * 11.332 0.76 * 682.162 * 26.909 * 0.642 * 1.364 3849.35 3776.45 45.62 773.21 19.03 7.38 0.4 56351.72 9572.88 24272.32 2269.74 925.48 93.55 * 11.332 0.41 * 682,162 * 26,909 * 0.642 * 1.364 2147.54 4999.87 14.22 285.82 6.94 6.14 0.49 32386.55 6082.06 22615.03 2433.85 930.95 72.13 * 11.332 0, 19 * 682,162 264.16 * 0.642 * 1.364 3279.29 8782.54 14.93 306.88 6.87 7.68 0.29 34893.15 5902.84 13695.45 1952.91 352.96 88.08 * 11.332 0.4 * 682.162 * 26.909 * 0.642 * 1.364 3499.24 4999.87 32.55 301.68 5.52 5.48 0.38 25962.34 4619.89 10429.66 2732.74 1018.11 212, 33 * 11,332 1.61 5880.65 273.8 11.13 23.42 3339.37 12337.23 42.79 959.43 10.37 5.48 0.57 173912.46 2391 8.47 33540.65 1671.09 1362.28 121.24 * 11.332 0.87 * 682.162 * 26.909 * 0.642 * 1.364 3990.62 9269.93 36.29 1572.88 10.07 6.77 0.74 47974 , 21 9474.83 6096.57 1768.04 559.23 45.95 * 11.332 1.88 * 682.162 * 26.909 4.16 11.08 5968.85 32326.57 31.85 481.82 17.8 6.14 0, 69 69945.18 14934 16209.28 1804.73 1158.73 72.53 * 11.332 0.57 * 682.162 * 26.909 * 0.642 * 1.364 3996.78 45241.44 27.57 301.68 8.57 4.78 0, 19 32048.87 8394.81 148145.86 2068.19 688.27 69.67 * 11.332 0.35 48747.27 * 26.909 * 0.642 8.67 5473.76 10498 456.18 1646.81 6.4 21.88 1.52 36706.17 8001.2 10 309.35 2170.64 693.84 45.4

* 11.332 0.79 * 682.162 193.73 * 0.642 * 1.364 3052.25 7691.45 32.73 929.16 14.75 7.97 0.47 47618.59 6836.3 5310.51 2369.08 5184.27 51.92 * 11.332 0.39 * 682.162 * 26.909 * 0.642 * 1.364 2354.48 26527.01 16.3 434.77 12.03 7.68 0.38 26989.75 11569.88 35887.48 1647.46 777 , 08 28.61 * 11.332 0.94 * 682.162 161.46 * 0.642 9.15 4393.64 19247.48 26.61 389.79 15.63 9.93 0.37 40685.47 7843.6 8496.52 3623.29 382 16.37 * 11.332 0.43 * 682.162 183.14 * 0.642 * 1.364 2110.39 27425.63 11.44 354.37 18.48 11.75 0.45 7141.58 4982.45 9019, 31 2611.93 626.77 81.02 416.88 0.44 * 692.51 182.2 3.86 * 1.373 6000.15 25648.56 25.44 399.29 20.65 23.56 0.76060 , 93 9639.64 11392.19 4914.5 * 33.406 230.06 114.61 0.25 * 692.51 164.52 * 0.491 * 1.373 4497.41 8142.25 15.89 318.41 41.57 68, 32 0.33 17419.88 9137.27 5702.81 3235.9 * 33.406 38.86 * 10.766 0.26 * 692.51 246.57 3.86 * 1.373 9861.23 50918.69 14.2 305.27 19.17 15.45 0.3 26360.73 10171.71 6145.76 2032.69 * 33.406 51.79 * 10.766 0.31 * 692.51 258.22 * 0.491 * 1.373 6313.3 15768.8 22, 8 340.0 7 29.64 13.32 0.36 55002.26 8103.45 5968.83 3528.23 * 33.406 59.96 139.79 0.35 4336.24 202.75 6.67 9.36 3139.5 77675, 43 13.52 488.91 14.62 16.64 0.45 28194.7 16032.93 14721.73 2274.14 * 33.406 39.37 370.51 0.99 4155.06 258.22 7.36 * 1.373 5333.07 3541.91 14.54 484.91 22.32 17.38 0.6 18755.14 20748.29 24864.46 2051.56 * 33.406 124.67 70.64 0.34 * 692.51 * 26.929 * 0.491 * 1.373 4630.85 3235.82 17.87 309.66 6.96 17.17 0.38 14060.68 16744.26 21698.77 5135.34 * 33.406 52.84 * 10.766 0.63 * 692.51 * 26.929 * 0.491 * 1.373 6350.78 2453.16 18.7 494.89 10.92 17.7 0.69 14182.91 6126.48 12096.96 2762.51 * 33.406 114.2 * 10.766 0.58 * 692.51 199.82 3 , 13 * 1.373 6773.09 4477.59 13.19 598.37 13.47 21.99 0.58 26432.81 25172.34 5835.91 2340.73 * 33.406 84.89 * 10.766 0.33 * 692, 51 350.82 3.13 * 1.373 7118.68 33662.5 10.16 726.12 26.18 37.17 0.36 38343.89 17722.59 5824.83 3124.99 * 33.406 78.82 486.84 0.26 * 692.51 * 26.929 * 0.491 * 1.373 6459.56 12313.76 10.26 278.62 8.25 10.98 0.31 37623.09 9491.87 3769.93 5196.5 * 33.406 37, 45 500.65 0.41 * 692.51 * 26.929 * 0.491 * 1.373 6467.96 98746.25 16.52 324.94 18.3 24.53 0.62 37929.3 7896.77 7769.13 4580.42 * 33.406 79.07 * 10.766 0.41 * 692.51 185.14 3.49 * 1.373 6359.13 5374.72 15.56 655.33 12.04 25.67 0.6 13361.55 29481.62 5157 , 24 3876.93 * 33.406 34.11 306.7 0.87 * 692.51 217.39 * 0.451 * 1.373 8022.7 11745.39 36.18 1049.35 33.64 28.85 0.51 10 376.03 8575 , 97 8714.19 3715.34 * 33.406 97.31 44.76 0.49 * 692.51 * 26.929 * 0.491 * 1.373 4332.4 43721.84 45.7 607.93 8.19 20.99 0.42 10737.58 10112.58 15695.56 2678.98 * 33.406 22.67 84.16 0.34 * 692.51 * 26.929 * 0.451 * 1.373 7569.47 22294.84 15.13 395.12 17.53 26.43 0 , 51 19584.14 9403.21 6718.71 3100.58 * 33.406 86.04 * 10.766 0.33 * 692.51 * 26.929 * 0.491 * 1.373 5061.92 18078.61 13.23 324.94 14.15 31 , 75 0.35 8921.03 2859.53 8573.34 1615.19 * 33.406 28.51 392.01 0.31 4155.06 199.82 60.29 * 1.373 4894.01 54863.75 14.54 534, 41 17.93 30.67 0.4 15269.28 14373.83 11947.61 1354.66 * 33.406 60.19 469.49 0.31 * 692.51 226.16 69.86 * 1.373 6434.39 74112.02 15.19 592.61 10.01 74.95 0.42 28983.8 9935.21 12693.67 1323.74 * 33.406 69.29 * 10.766 0.67 8015.56 240.75 11.73 11.19 4701 , 8 54517.17 14.16 424.11 16.54 17.7 0.47 26108,34 8132.98 20286.15 2010.68 * 33.406 43.99 * 10.766 0.23 * 692.51 * 26.929 * 0.491 * 1.373 6275, 9 2764.78 11.14 374.15 10.39 24.53 0.36 20443.64 10733.5 3701.47 2592.48 * 33.406 63.62 265.62 0.31 * 692.51 * 26.929 * 0.491 * 1.373 5525.84 23808.14 42.01 419.99 6.26 15.34 0.44 23243.24 15795.85 17903.05 2638.9 * 33.406 69.87 63.74 0.5 4875.71 234 , 91 * 0.491 * 1.373 6543.72 50376.57 36.42 594.53 30.36 37.51 0.44 40078.53 8635.05 12267.56 3771.87 * 33.406 28.02

317.76 0.52 * 692.51 164.52 3.13 * 1.373 8151.64 3541.91 34.04 454.72 10.76 15.99 0.35 16177.63 14966.25 5625.08 4833.2 * 33.406 209.91 103.88 0.47 6637.57 237.83 3.13 11.19 6151.85 42375.52 16.59 917.06 25.62 23.17 0.45 26918.96 34746.42 7234.08 4277.29 * 33.406 60.85 368.71 0.62 * 692.51 167.47 * 0.491 * 1.373 6405.08 38894.11 31.36 504.83 15.67 14 0.52 35609.93 21252.82 7157.46 4323.06 * 33.406 131.47 108.19 0.84 5232.26 269.84 4.57 10.27 5325.26 2764.78 23.95 759.17 31.44 76 0.53 22877, 79 12 597.35 4517.86 3989.07 * 33.406 79.32 * 10.766 0.76 * 692.51 * 26.929 5.28 * 1.373 7740.37 16897.96 25.49 470.87 16.76 19.77 0.47 20592.45 12390.18 6377.52 1774.14 * 33.406 131.25 88.6 0.21 * 692.51 * 26.929 * 0.451 * 1.373 5585.33 5212.69 13.93 468.86 11.04 20, 39 0.42 9660.28 11561.68 4551.65 3903.67 * 33.406 39.64 * 10.766 0.43 * 692.51 246.57 6.67 * 1.373 11660.88 252141.5 26.03 2894.61 14.22 43.86 0.43 22199.63 2389.91 6090.5 3118.48 * 33.406 17.73 148.03 0.82 * 692.51 * 26.929 4.57 * 1.373 6854.54 12366.37 39.99 335 , 76 16.63 10.98 0.47 40622.12 2654.02 9524.54 2907.63 * 33.406 62.29 * 10.766 0.35 * 692.51 223.24 3.13 * 1.373 3468.71 146095, 13 21.99 540.28 32.4 25.48 0.59 29485.53 14373.83 7921.61 3051.82 * 33.406 56.4 * 10.766 0.32 5054.28 191.02 11.73 * 1.373 7524 , 77 28,730.34 57.62 357.19 8.09 11.46 0.42 9963.05 1101.4 35430.85 221.76 * 33.406 11.53 * 10.766 0.62 * 692.51 164.52 * 0.491 * 1.373 6127.15 12523.52 27.9 456.74 20.28 29.95 0.61 19490.29 12094.26 3127.41 2429.73 * 33.406 41.32 93 0.63 4155.06 301, 74 3.13 * 1.373 5411.46 6299.38 17.86 413.8 48.24 23.36 0.65 18451.83 11413.77 9373.55 2065.73 * 33.406 33.99 75.18 0.49 * 692 , 51 * 26,929 * 0.491 * 1.373 9137.98 33356.13 17.46 278.62 13.78 15.12 0.35 18603.62 5802.2 59254.67 3226.1 * 33.406 105.34 190.33 0.77 * 692.51 170.42 * 0.491 18.53 10089.45 19838.55 27.11 1190.45 88.92 80.24 0.67 29790.01 29511.35 40646.52 5044.71 * 33.406 69.14 339.73 0.3 * 692.51 164.52 3.86 * 1.373 6760.26 21124.13 14.98 582.99 12.66 26.81 0.63 7348.82 12804.54 9265.62 2946, 44 * 33.406 33, 83 125.19 0.66 * 692.51 794.07 * 0.491 11.81 9471.59 37494.34 27.33 582.99 135.05 16.96 0.48 24299.08 34270.32 15050.05 3845 , 2 * 33,406 65,96 84,16 0,41 * 692,51 176,31 * 0,491 * 1,373 7302,91 60023,79 31,07 327,11 15,39 17.28 0,49 36076,68 8812,29 15124 , 16 2053.14 * 33.406 132.32 118.85 0.58 4155.06 164.52 8.04 * 1.373 5585.33 6925.59 48.03 260.56 17.81 15.34 0.43 13278.53 5124 , 48 10342.21 3178.76 * 33.406 106.4 204.12 0.41 * 692.51 * 26.929 * 0.491 * 1.373 4601.09 12418.86 24.39 464.83 28.87 36.05 0.41 9542.76 15469.91 5190.74 2730.36 * 33.406 55.91 72.92 0.42 * 692.51 193.95 21.09 9.96 8398.16 18511.74 16.51 278.62 39, 35 13.78 0.91 34462.24 4977.21 6278.26 5861.91 * 33.406 10.8 429.24 0.51 * 692.51 * 26.929 * 0.451 * 1.373 11258.52 17933.11 47.14 337 , 92 6.1 11.7 0.41 42 950.63 4623.86 11 221.07 3820.18 * 33.406 30.03 * 10.766 0.61 * 692.51 * 26.929 * 0.491 9.06 2971.84 14306.62 11.25 305.27 14.5 12.52 0.45 8799.26 5271.78 8876.56 2410.64 211.32 54.31 * 10.766 1.05 5232.26 193.95 3.86 * 1.373 6653 77 21 766.38 36 , 18 413.8 17.99 18.33 0.38 65502.69 13130.16 7540.09 2337.56 * 33.406 54.01 172.39 0.26 * 692.51 176.31 5.98 * 1.373 7524.77 28,296.09 15.85 434.36 22.69 62.91 0.3 18774.07 10112.58 10556.87 1930.62 * 33.406 31.86 * 10.766 0.77 5232.26 229.08 9.4 8 , 46 6251.01 29091.93 21.79 318.41 15.77 17.28 0.41 31615.7 7040.84 8399.82 1401.08 * 33.406 50.77 * 10.766 0.56 * 692.51 205.68 * 0.491 * 1.373 2975.11 34141.58 17.8 766.48 32.46 25.48 0.43 8578.12 14255.36 3449.69 4421.64 * 33.406 17.55 765.77 1.06 * 692.51 234, 91 * 0.491 21.86 6164.21 65898.27 15.69 466.84 25.87 32.29 0.55 38235.7 14373.83 6300.32 2904.39 * 33.406 14.2 * 10.766 0.39 7157 , 17 188.08 25.17 151.59 4833.43 2572.3 11.64 604.11 10.11 25.67 0.76 19809.02 5095.03 3495.56 3157.56 6428.38 46.98 141.85 0.33 * 692.51 * 26.929 * 0.491 27.31 9977.56 4506.98 32.79 853.15 18.74 14.12 0.44 36364.07 19353.78 8963.09 4209.61 552.13 148.84 * 10.766 0.53 * 692.51 164.52 3.13 * 1.373 6209.62 2611.39 14.56 357.19 13.35 17.49 0.52 10961.11 16536.77 4326, 06 2010.68 * 33.4 06 23.44 176.4 0.38 * 692.51 * 26.929 * 0.491 9.36 8963.61 17167.03 30.41 346.51 18.43 28.02 0.41 17150.06 11591.27 9610, 76 4883.34 * 33.406 76.08 * 10.766 0.92 * 692.51 182.2 40.16 10.27 3279.46 126361.12 6.88 296.44 17.19 17.07 0.8 8329.28 6863.81 7091.75 661.23 * 33.406 12.64 56.73 0.47 * 692.51 188.08 * 0.451 * 1.373 5975.68 8271.61 35.99 651.57 14.22 31.21 0.36 29986 , 99 16447.85 22415.81 2923.79 * 33.406 75.32 88.6 0.43 * 692.51 * 26.929 * 0.451 * 1.373 6675.01 28858.88 18.06 506.81 36.54 42.38 0.51 34605.62 5389.62 10127.34 1337.65 * 33.406 51.15 * 10.766 0.28 * 692.51 * 26.929 4.57 * 1.373 4985.36 4085.85 17.98 411.73 10.51 20 , 79 0.4 32743.1 10378.66 5413.76 5243.77 * 33.406 39.53 77.44 0.38 * 692.51 * 26.929 5.98 * 1.373 5008.29 13006.27 22.7 386, 76 16.39 41.47 0.58 11315.11 12538.16 5525.04 2323.28 * 33.406 122.3 176.4 0.56 5586.56 182.2 12.72 88.18 6275.9 5999, 39 10.29 563.66 8.91 50.74 0.84 9870.31 8280.63 7354.38 4073.05 2414.3 48.99 70.64 0.27 * 692.51 * 26.929 3.86 * 1.373 4266.95 27275.01 13.85 204 , 66 7.56 10.62 0.29 12881.8 3741.14 6399.56 4057.91 * 33.406 26.56 180.39 2.32 * 692.51 * 26.929 3.13 20.52 9284.47 13362 , 52 49.11 867.43 33.83 10.98 0.58 40549.59 12893.35 27785.29 2455.2 * 33.406 113.7 * 10.766 0.31 * 692.51 164.52 * 0.491 * 1.373 7201.78 7204.93 12.89 342.22 32.28 22.58 0.59 6750.39 10230.84 13820.4 5214 * 33.406 128.21 172.39 0.26 * 692.51 * 26.929 * 0.491 * 1.373 5427.18 2289.44 30.47 411.73 28.13 32.29 0.36 43096.69 11443.35 6641.76 3758.56 * 33.406 42.48 354.26 0.43 * 692.51 * 26,929 * 0.491 * 1.373 5458.68 1850.02 49.79 386.76 12.1 15.56 0.51 19019.7 14492.3 12491.34 4302.71 * 33.406 163.78 * 10.766 0.32 * 692.51 176.31 * 0.491 * 1.373 5050.41 146506.56 10.63 411.73 13.47 17.07 0.38 8725.84 8251.1 6123.66 3561.26 * 33.406 53.63 127.29 0 , 59 * 692.51 188.08 * 0.491 * 1.373 8151.64 1941.96 24.73 799.19 21.61 19.26 0.51 24843.19 10940.51 4382.53 2965.87 * 33.406 85, 14 * 10.766 0.5 * 692.51 * 26.929 * 0.491 * 1.373 4541.75 6831.19 17.23 357.19 9.85 14.56 0.56 8076.9 9403.21 4585.43 3455.69 * 33.406 49.79 * 10.766 0.34 * 692.51 229.08 3.49 * 1.373 1518.32 6249.9 11.86 204.66 40.06 43.53 0.43 30309.25 13041.36 6013.09 3856.89 * 33.406 67.72 1053.74 1.21 * 692.51 229.08 * 0.491 * 1.373 6371.65 1117.1 14.69 380.46 26.55 11.93 0.57 44524.37 7808.2 7572.83 2650.12 * 33.406 74.38 94.31 2.05 * 653.597 285.04 4.44 * 1.363 9628.54 23889.96 24.92 538.53 13.17 29.53 0.89 32269, 28 9183.01 10773.25 6669.4 * 34.389 10.24 * 13.654 1.15 * 653.597 229.06 5.53 * 1.363 13647.46 6132.89 13.51 527.45 7.58 43 0.96 57623 , 27 10,143.03 20577.45 2427.72 * 34.389 80.49 40.96 0.63 8942.83 266.48 5.81 * 1.363 5776.71 118 181.57 29.52 254.57 14.23 50 0 , 86 39454.74 2822.91 9645.61 3595.32 * 34.389 46.6 * 13.654 0.85 * 653.597 241.58 * 0.601 * 1.363 5589.29 35999.91 39.47 482.96 24.46 24, 67 0.83 25239.49 6330.13 4717.14 3128.62 * 34.389 75.72 55.01 0.64 5661.76 229.06 4.99 * 1.363 5119.25 30316.88 13.27 258.48 22, 48 56.18 0.86 46195.51 14774.07 9483.99 917.23 * 34.389 73.3

96.4 0.96 * 653.597 184.82 4.99 * 1.363 8602.15 25143.66 33.61 436.26 5.18 23.11 1.24 30792.21 3214.43 4429.92 1479.57 * 34.389 66.01 207.65 0.9 * 653.597 247.82 9.84 12.25 4469.49 5011.03 18.46 419.35 69.29 66.28 0.89 18261.45 19193.51 12792, 34 2796.35 * 34.389 86.97 311.34 1.13 * 653.597 235.33 7.7 11.92 7601.06 154530.79 25.15 387.26 20.52 29.9 1.19 8726.91 7,108.72 8059.72 389.6 * 34.389 33.14 * 13.654 1.14 * 653.597 222.79 * 0.601 * 1.363 7352.43 91516.72 35.24 413.7 13.7 26.57 1.01 33914 , 46 6973.71 8385.75 2544.38 * 34.389 56.01 * 13.654 1.36 * 653.597 312.72 4.44 * 1.363 8972.78 127 246.84 33.73 324.35 8.9 46.78 0 , 94 29084.68 3673.31 5545.63 2846.01 * 34.389 17.54 * 13.654 1.13 * 653.597 281.95 * 0.601 12.42 6742.17 17810.43 40.25 778.31 39.36 199.24 1.13 35973.22 4926.65 16995.85 6584.75 * 34.389 24.13 * 13.654 3.97 * 653.597 232.2 9.31 10.94 5375.15 86006.06 41.25 285.75 6, 93 36.76 1.22 55869.94 3426.7 14433.31 351.15 * 34.389 6 70.7 1.05 * 653.597 260.27 * 0.601 * 1.363 8972.78 136323.44 37.61 383.48 5 , 83 41.77 1.01 50298.89 10275 10970.91 4887 * 34.389 29.45 476.13 0.23 * 656.313 * 27.445 * 6.138 9.43 6908.36 6250.96 14.08 331.06 8.56 36.47 * 0.029 18043.84 8385.66 5800.54 3908.72 1941.5 85.37 326.83 3.88 * 656.313 183.34 * 6.138 22.88 13256.38 162098.82 50.83 672, 87 13.21 13.32 * 0.029 31692.15 14611.79 25478.32 5672.53 2334.14 137.24 * 13.654 2.33 4584.83 321.9 8.78 14.41 10234.64 77827.3 59.53 527.45 15.68 53.46 1.38 28242.83 4719.3 4207 3365.31 * 34.389 18.1 * 13.654 0.64 * 653.597 * 27.335 * 0.601 * 1.363 7380.93 110609.24 17.65 291.56 8.45 23.31 0.59 36326.61 10,307.97 136895.06 2283.03 * 34.389 39.4 * 13.654 1.03 * 653.597 178.44 * 0.601 * 1.363 7853.38 5282.38 27 , 98 471.78 11.63 55.43 1.14 24109.45 2463.99 9359.56 1342.96 * 34.389 36.6 * 13.654 0.54 * 653.597 235.33 * 0.601 * 1.363 7390.44 29073, 76 33.98 293.5 24.17 77.3 0.75 29522.21 2607.93 2888.16 4920.33 * 34.389 12.56 * 13.654 1.25 * 653.597 254.05 * 0.601 8.98 9548, 03 35,608.54 25.22 250.65 8.28 27.88 0.76 87258.03 8784 24961.84 2313.25 * 34.389 82.1 1 * 13.654 0.95 * 653.597 241.58 3.61 52.94 8404.45 217265.26 47.86 697.32 9.74 82.5 1.04 34839.34 16831.83 13062.2 3549.51 1060.66 43.88 * 13.654 0.55 * 653.597 * 27.335 * 0.601 * 1.363 6637.38 14676.72 20.24 289.63 9.46 35.73 0.86 64373.28 10176.03 25954.82 3696 , 03 * 34.389 120.72 48.07 1.38 * 653.597 278.86 * 0.601 8.5 7442.85 17480.23 18.83 571.65 19.23 48.43 0.77 15710.14 8149.93 9247.5 1720.16 * 34.389 59.02 * 13.654 0.54 * 653.597 172.04 * 0.601 * 1.363 2592.88 1452.43 21.71 415.59 12.68 46.62 0.74 21581.98 25816 , 19 9085.5 3756.29 * 34.389 112.05 43.35 2.33 * 653.597 184.82 * 0.601 9.96 9574.83 37887.49 78.65 717.18 13.56 41.69 2.33 52982, 25 18652.47 14359.96 3811.35 * 34.389 60.32 55.01 0.84 * 653.597 216.49 * 0.601 * 1.363 9403.93 18517.72 20.01 486.68 17.02 68.27 0.89 48990.17 5955.69 6135.56 1756.34 * 34.389 14.21 123.06 1.34 * 653.597 303.51 * 0.601 9.96 7276.66 41836.95 20.28 411.82 21.3 108.92 1, 34 72588.76 5202.27 5261.93 4790.95 * 34.389 29.17 * 13.654 1.12 * 653.597 * 27.335 * 0.601 * 1.363 6939.96 14962.61 30.23 343.51 15.5 51 0.69 41653.41 9249.41 9483.99 3703.11 * 34.389 68.08 * 13.654 0.77 5234.78 216.49 8.78 * 1.363 4860 , 18 3428.15 21.71 549.59 6.44 72.13 1.07 40538.03 2101.71 13723.79 1961.15 * 34.389 72.14 * 13.654 0.82 * 653.597 254.05 * 0.601 * 1.363 14627.66 20056.03 40.74 183.2 29.51 67.35 0.83 35548.56 2028.8 28032.79 981.43 * 34.389 16.31 385.28 0.69 * 656.313 340.51 * 6.138 9.12 4987.89 43168.82 57.38 425.48 11.84 29.42 0.33 35177.61 14790.72 20545.86 4290.61 1514.67 74.8 262.08 0.41 * 656.313 263.92 * 6.138 18.77 7194.33 86805.19 26.87 467.21 8.81 15.77 * 0.029 29592.46 11659.37 17693.23 2744.36 1521.81 83.23 134, 58 1.18 6259.77 306.87 * 6.138 13.14 9176.59 199685.76 43.94 1061.44 26.04 43.71 0.57 96120.47 11507.05 18454.28 3002.69 4863, 77 138.5 387.33 0.2 4733.45 311.11 * 6.138 31.51 5725.06 49584.02 21.81 447.21 18.16 28.75 * 0.029 21618.96 10250.79 115330.99 4136.52 5160.37 37.56 333.18 1.52 7722.36 398.14 * 6.138 20.66 7520.86 3473.83 43.29 812.14 14.05 12.3 * 0.029 133358.12 149 99.23 32858,98 3068.09 2572.67 160.06 181.46 1.8 4802.84 222.79 * 0.601 8.5 8129.26 2943.88 37.53 635.64 20.37 37.61 1.88 46.299,17 4337.65 11044.99 3934.31 * 34.389 37.78 137.05 0.41 6753.4 406.26 356.64 18.77 36598.31 3873.96 55.08 8636.17 12.75 25.16 1.29 35208.03 7818.82 43772.63 6664.44 1698.62 94.76

217.34 * 0.01 4733.45 272.6 * 6.138 10.04 7781.19 12846.42 24.61 413.76 14.89 15.29 * 0.029 55638.44 9508.88 21223.29 1993.72 1747, 6 228.35 * 9.827 0.37 * 656.313 272.6 * 6.138 16.88 5953.75 12144.67 13.62 574.71 28.74 61.47 * 0.029 23953.13 13714.17 11190.93 2559, 23 6058.24 67.53 228.66 0.59 4733.45 365.39 * 6.138 11.9 7817.4 12001.37 16.79 438.86 21.91 14.96 * 0.029 43633.36 10343.17 10959, 42 1258.88 13277.45 94.39 75.07 0.96 * 653.597 466.7 * 0.601 12.25 9855.73 34080.92 45.34 1082.56 18.73 26.01 1.05 28572.77 17311.97 8034.61 4079.39 * 34.389 30.08 98.49 1.36 4802.84 436.82 4.44 9.63 10,407.19 123869.87 36.87 831.94 16.42 64.13 1.03 24260.74 10966.21 6924.58 2921.54 * 34.389 11.48 101.64 0.53 * 656.313 298.36 * 6.138 16.88 7808.34 96161.28 70.48 482.18 10, 42 15.29 * 0.029 49222.39 8699.01 10111.46 3549.16 1747.6 145.29 70.7 0.81 * 653.597 213.34 * 0.601 * 1.363 8010.5 1944.37 13.58 232, 95 10.26 11.99 0.49 41931.84 4337.65 5132.62 1036.07 * 34.389 60.46 90.09 4.37 * 653.597 260.27 * 0.601 9.96 12141.18 14 564.07 45.55 492.25 6.58 56.48 1.5 31261.73 3953.88 12669.61 3781.14 * 34.389 27.54 133.06 1.95 * 653.597 266.48 * 0.601 * 1.363 5296.21 7832.69 33.66 326.27 16.71 48.2 1.28 48714.48 3107.97 9110.43 1721.8 * 34.389 28.34 83.13 0.15 4733.45 201.69 * 6.138 9.43 7317.14 2308.79 46.89 442.2 9.14 46.41 * 0.029 23147.99 9167.06 14470.93 3317.72 2186.51 89.97 111.95 0.38 6259.77 323, 76 * 6.138 21.3 10192.69 37608.47 55.7 805.69 85.3 35.71 * 0.029 25230.98 10926.53 20050.44 912.54 1172.73 107.02 158.96 0.33 * 656.313 670.14 * 6.138 19.4 6656.86 98961.19 35.56 538.47 30.75 41.46 * 0.029 54642.06 12024.21 16957.22 1749.74 814.3 28.27 * 13.654 1.06 4365.35 238.46 13.02 * 1.363 11426.28 13904.07 45.29 2066.15 23.17 54.37 1.48 28389.55 8049.55 6696.16 2782.67 * 34.389 33 , 83 374.56 0.95 * 653.597 168.83 * 0.601 * 1.363 8993.59 13554.85 22.43 276.04 9.14 45.02 0.78 45607.87 5476,98 17531.99 3142.48 * 34.389 23.84 92.2 1.18 * 653.597 197.54 * 0.601 * 1.363 5168.4 5079.68 22.44 434.38 15.54 44.54 0.72 56419.95 5133.45 10278 , 35 1613.53 * 34.389 23.39 * 13.654 1.83 * 653.597 197.54 * 0.601 8.98 4884.25 29693.21 24.08 5417.39 23.38 34.34 1.37 60134.43 8584.08 12792.34 1677.46 * 34.389 60.98 455.77 1.27 * 653.597 165.62 * 0.601 * 1.363 8208.88 4390.05 38.11 670.16 16.95 46.06 1.17 71300.82 19416.04 7908.98 4814.93 * 34.389 73.91 108.83 0.92 * 653.597 175.24 * 0.601 * 1.363 9531.96 22066.86 10.24 632 11.17 77.57 0.9 40538, 03 11981.84 9185.21 3811.35 * 34.389 60 673.92 * 0.01 * 637.141 189.4 * 2.564 14.54 9836.42 11219.47 28.32 267.79 16.46 20.61 * 0.029 10084.38 1966.99 15275.68 2103.6 1973.81 62.35 102.65 0.69 5873.54 172.04 4.88 190.15 6296.06 47668.4 17.49 727.99 10.75 78 , 73 1.27 43980.69 3602.96 10 501.19 1721.8 5754.35 23.51 228.66 * 0.01 * 656.313 306.87 * 6.138 12.52 4796.53 67502.26 25.72 776 , 65 10.68 23.02 * 0.029 59059.39 9694.85 36078.01 1721 2347.49 27.1 795.27 0.41 * 656.313 * 27.445 * 6.138 * 1.342 5752.96 3262.54 16.65 221 , 04 13.82 18.81 * 0.029 12988.97 3405.23 7636.26 1533.31 1007.45 107.06 228.66 * 0.01 4205.98 509.65 * 6.138 12.21 5978.03 14486.59 20.12 317.45 59.98 71.17 * 0.029 22564.9 4585.29 17788.74 1027.54 2367.49 63.94 180.35 0.25 * 656.313 306 , 87 * 6.138 8.21 4748.19 17206.04 15.26 189.54 20.82 18.35 * 0.029 38693.18 8448.42 22125.27 434.86 2084.97 52.14 * 13.654 0.84 * 653.597 * 27.335 * 0.601 * 1.363 6278.31 4058.9 33.83 242.8 9.21 24.28 0.86 25949.89 4372.43 7606.96 3145.95 * 34.389 70.28 366.67 0 , 12 * 656.313 * 27.445 * 6.138 8.21 7614.66 2172.16 24.7 8580.24 6.44 19.04 * 0.029 22921.59 4552.02 9150.88 2785.85 1607.05 41.84 255 , 46 0.19 * 656.313 255.19 * 6.138 11.28 7740.53 10865.66 59.18 281.54 18.88 9.65 * 0.029 64361.26 5573.28 17693.23 3305.86 1927.76 42.45 * 13.654 1.28 5234.78 172.04 10.91 16.24 4629.81 3069.31 38.51 211.18 17.17 32.59 1.1 17530.94 3883.86 11686.1 2205, 96 211.06 54.44 163.75 1.36 6259.77 259.56 * 6.138 56.9 5193.5 18231.82 51.3 296.96 8.17 19.19 0.47 66016.7 5898, 73 17709.16 1204 1406.98 58.33 61.81 0.84 4365.35 178.44 9.58 10.61 6429.89 2879.92 24.96 354.96 10.05 36.76 1.06 84797.43 11360 8310.58 2175.87 * 34.389 67.42 114.5 1 * 656.313 242.01 * 6.138 14.38 12823.56 3576.49 38.53 650.04 11.55 6.07 0.63 97150.15 5703.67 30869.19 1749.74 4994.61 37.48 94.31 0.88 * 653.597 382.69 * 0.601 12.25 6251.71 44,108.7 14.79 662.9 23.52 40.87 0 , 97 122519.34 2822.91 8185.21 1876.73 * 34.389 22.81 * 13.654 0.34 * 653.597 235.33 5.26 * 1.363 10218.01 3655.04 32.67 211.18 11.38 21 , 33 0.67 109025.27 2283.3 8335.64 4935.16 * 34.389 28.34 * 13.654 3.81 * 653.597 172.04 * 0.601 * 1.363 7409.48 31434.99 19.02 215.15 11, 13 28.07 1.39 82561.19 5614.01 10637.26 667.62 * 34.389 31.66 50.4 1.31 4365.35 297.36 4.44 * 1.363 6403.03 2104.59 49.19 281.87 23.24 121.49 0.85 121739.02 4650.06 7732.89 3166.76 * 34.389 38.12 736.06 0.85 11483.87 315.78 10.91 156.56 8591.96 4754.3 17.93 1315.2 13.35 84.43 2.2 60065.6 6023.87 22,309.09 1362.44 6301.66 26.09 85.84 1.49 7736.34 487.53 8.78 16 , 58 16764.53 4006.5 53.49 690.08 15.61 79.34 0.81 146999.84 13154.89 18628.3 3100.91 * 34.389 25.68 1246.95 * 0.01 4733.45 183.34 * 6 , 138 43.81 4987.89 25014.42 25.69 587.85 9.59 21.13 * 0.029 22467.43 5866.26 31331.84 1913.22 5053.91 83.11 50.4 0.88 * 653.597 346.31 5.53 10.61 7755.9 5125.14 25.21 270.19 10.26 38.95 0.6 69390.32 10637.4 21016.24 4601.68 * 34.389 77.38

117.04 2.75 * 656.313 164.67 * 6.138 13.14 10 754.83 14314.78 57.22 513.68 7.08 15.45 0.76 52772.76 8322.86 12951.08 1199.3 3103.95 168.63 * 13.654 0.53 6084.24 337.17 7.16 20.63 6673.75 74526.01 27.74 549.59 11.1 33.12 0.73 60685.18 11752.97 17117.72 3004, 17 389.32 90.24 161.36 0.59 * 656.313 183.34 * 6.138 9.43 7587.81 4249.6 50.39 356.5 10.07 16.4 * 0.029 30706.32 6480.28 51970 , 47 2196.05 1733.63 51.03 34.26 0.64 6753.4 219.77 * 6.138 11.9 6083.68 34175.48 38.86 398.65 4.21 24.52 * 0.029 18994, 2 5311.6 15430.39 2118.17 1844.95 140.56 74.98 0.61 6259.77 183.34 * 6.138 10.66 9947.72 4157.7 28.29 370.02 16.69 16, 56 0.3 157 671.14 8542.47 33 304.17 * 41.29 3078.38 11.15 527.24 0.23 * 656.313 348.84 * 6.138 52.31 5590.32 27875.5 20.19 224, 52 10.88 12.81 * 0.029 61186.81 7184.39 14086.33 3007.72 1290.68 63.67 185.04 * 0.01 6507.41 210.76 * 6.138 9.74 4897.5 8691, 42 12.94 331.06 12.3 24.03 * 0.029 31322.91 10127.49 1763.73 4096.39 2664.5 32.54 40.96 0.72 5234.78 184.82 8.78 * 1.363 5283.79 33517.51 14.38 473, 65 13.38 62.39 0.89 19701.02 2028.8 15166.33 2966.27 * 34.389 21.72

40.96 2.88 * 653.597 367.56 * 0.601 8.98 8561.42 8788.34 70.08 562.47 32.13 96.14 1.24 43946.02 15901.31 10921.51 2642.84 * 34.389 136.01 * 13.654 1.08 4365.35 266.48 * 0.601 * 1.363 6637.38 16670.22 19.76 268.25 11.73 36.84 1.31 25239.49 2210.78 4691.09 2299 , 82 * 34.389 18.02 * 9.827 0.64 5250.73 556.22 * 6.138 34.73 6284.8 2172.16 63.48 906.87 15.42 23.81 * 0.029 113487.39 42344.35 15758 , 01 3681.08 1113.07 148.72 170.9 0.13 4205.98 192.55 * 6.138 11.28 7361.22 10217.11 46.18 267.79 12.1 27.52 * 0.029 77 203.05 4916 , 72 12261.64 3936.44 2294.02 91.08 415.94 * 0.01 * 656.313 246.42 * 6.138 8.21 8435.17 10916.52 16.2 450.55 15.61 20.83 * 0.029 13138.41 6897.18 11,102.03 2974.24 2016.85 17.01 * 13.654 1 * 653.597 300.44 * 0.601 * 1.363 8858.75 1775.47 23.26 482.96 21.19 78.11 0.92 27765.02 2715.55 9906.4 6215.57 * 34.389 16.66 * 13.654 0.73 * 653.597 188 * 0.601 * 1.363 5325.24 5458.73 18.54 215.15 9.46 46.3 0.7 26952.93 3036.86 3505.84 3156.36 * 34.389 15.2 52.71 1.35 * 653.597 178.44 4.99 9.63 4956.73 6688.57 39.25 753.18 98.88 175.75 1.29 43842.02 31237.88 7606.96 4178.35 * 34.389 106.27 * 13.654 0.99 * 653.597 229.06 * 0.601 * 1.363 5927.56 12881.86 30 , 5 600.97 12.29 87.73 1 41583.78 12079.84 8661.04 4.344 * 34.389 38.27 782.56 0.35 * 656.313 174.05 * 6.138 9.43 2524.27 30921, 3 18.93 145.25 9.59 16.09 * 0.029 14858.85 3712.03 13190.24 1746.55 2334.14 143.23 538.9 1.81 4205.98 430.46 * 6.138 65.48 6392.59 73975.7 20.47 597.69 29.31 23.6 * 0.029 33531.23 11171.3 31071.7 1024.43 1030.22 59.62 * 9.827 * 0.01 5250.73 228.71 * 6.138 17.51 8862.11 2928.24 13.28 224.52 34.25 25.02 * 0.029 20133.26 2678.14 12951.08 1511.13 2585.82 31.39 264.28 * 0.01 4733, 45 255.19 * 6.138 13.14 7754.08 2504.69 26.96 324.26 21.81 35.2 * 0.029 16594.13 3712.03 12848.26 1732.17 2300.72 92.8 50.4 1.73 * 653.597 291.21 * 0.601 8.66 7936.67 20620.01 43.42 479.23 36.79 220.38 1.75 37876.73 3848.82 10129.65 3987.95 * 34.389 30, 64 * 13.654 0.65 * 653.597 197.54 * 0.601 * 1.363 5534.39 13723.58 26.47 266.29 22.38 70.66 1.26 28352.89 3107.97 10736.17 3128.62 * 34.389 25.6 * 13.654 0.6 * 653.597 181.63 * 0.601 * 1.363 8169.03 36279.85 13.13 362.58 10.05 68.84 0.85 19383.43 3249.87 9980 , 85 906.01 * 34.389 28.26 262.11 1.21 * 653.597 210.19 7.16 * 1.363 4956.73 78924.57 20.12 289.63 10.19 61.22 0.73 18984.62 4511.38 55847.21 3253.66 * 34.389 11.01 50.4 1.34 18345.96 502.38 22.89 343.08 8745.41 206400.41 63.62 874.66 21.48 75.59 2.58 38859.4 2283.3 7101.89 2828.88 13184.62 4.85 77.25 0.72 * 653.597 * 27.335 * 0.601 * 1.363 9192.41 10438.29 18.12 234.93 8.55 51.77 0.69 31261.73 1955.73 11735.35 2973.15 * 34.389 7.46 127.12 0.67 6259.77 340.51 * 6.138 19.4 5441.19 217627.4 14.65 666 , 35 12.56 12.73 0.45 32735.68 8948.92 19208.79 3760.2 2159.51 60.21 464.21 0.63 4733.45 332.16 * 6.138 10.66 5078.89 3776 , 45 21.87 307.21 18.13 34.62 * 0.029 23599.52 3336.69 6095.87 3356.72 1052.91 24.48 424.05 0.19 6259.77 263.92 * 6.138 12, 52 5721.08 41450.97 7.55 308.92 28.91 17.35 * 0.029 22109.29 4552.02 42755.53 850.56 1775.5 55.78 569.8 0.12 5250.73 281.23 * 6.138 14.38 4 026.67 60344.06 14.16 422.13 16.72 15.69 * 0.029 28846.74 7311.69 15397.54 4419.09 1246.65 111.97 111.95 0.84 * 656.313 298.36 * 6.138 15.01 13103.39 2928.24 39.15 300.38 14.96 5.11 * 0.029 34568.7 6480.28 22399.09 2780.87 1831.09 141.93 226.03 0.59 3921, 58 * 27.335 * 0.601 * 1.363 5674.18 13697.69 27.3 351.15 6.93 58.27 0.82 56076.19 12112.5 9197.67 3414.3 * 34.389 31.19 168.14 1, 18 4365.35 184.82 6.62 * 1.363 5798.16 14108.86 11.57 7125.36 17.1 38.95 0.82 6296.3 3953.88 13282.85 2780.96 * 34.389 43.51 * 13.654 0.82 * 653.597 229.06 * 0.601 * 1.363 6528.78 29802.02 22.75 199.22 8.45 19.08 0.53 26210.65 6669.34 5866.76 2097.44 * 34.389 70.56 * 13.654 1.24 * 653.597 321.9 * 0.601 * 1.363 4442.33 23412.57 12.49 417.47 12.15 50.69 1.01 93628.97 17631.65 7076.58 3563.6 * 34.389 22.89 * 13.654 1.72 * 653.597 235.33 * 0.601 * 1.363 9542.67 15477.56 45.47 358.78 10.33 34.17 1.5 51193.71 5339.73 12755.53 3628.85 * 34.389 33 , 68 * 13.88 3.22 * 611.926 680.28 15.23 46.31 5171.92 23200.15 17.23 768.68 16 27.99 1.16 140665.2 4 8567.4 15320.67 986.12 1791.79 62.36 64.39 1.51 * 611.926 267.46 7.26 13.94 15119.71 117806.55 55.7 442.23 16.6 * 2.511 0.62 56247.06 13930.42 23303.32 3954.67 4779.82 33.03 * 41.51 0.18 * 1970.97 164.52 * 16.134 * 1.329 8829.03 119235.66 37.49 358, 71 2.36 8.75 0.21 42815.41 5968.36 20791.45 5341.22 * 33.371 20.08 * 41.51 0.12 * 1970.97 201.47 * 16.134 * 1.329 4691.7 9510, 21 20.13 241.33 1.85 7.75 0.18 79312.06 3492.17 38229.33 2415.87 * 33.371 51.67 * 10.36 0.27 * 677.552 * 27.835 5.91 * 1.3885 8009 , 91 43026.39 16.46 412.82 21.66 1.85 0.24 58774.81 4976.01 13377.65 3858.3 210.3 64.62 * 10.36 0.12 * 677.552 * 27.835 * 0.496 * 1.385 7006.94 7106.51 33.15 321.89 * 0.03 3.11 0.24 46884.52 2256.59 19061.07 1657.91 * 32.367 42.52 * 10.36 0.2 * 677.552 * 27.835 * 0.496 * 1.385 5963.93 18136.75 16.6 302.24 * 0.03 3.69 0.22 62667.17 3570 14324.29 2820.52 * 32.367 157.73 * 10.36 0, 18 * 677.552 234.64 * 0.496 * 1.385 5056.65 7106.51 22.33 386.63 15.36 2.81 0.23 99117.32 10208.6 15172.42 2620.82 * 32.367 74.56 130.3 0 , 2 * 677,552 * 27,835 * 0.496 * 1.385 4667.77 899.46 43.33 302.24 8.43 2.5 0.29 45661.52 3831.987070.75 2676.7 * 32.367 161.69 * 10.36 0, 29 4227.58 * 27.835 * 0.496 * 1.385 5425.36 30919.87 32.96 471.08 * 0.03 2.5 0.25 33558.47 6084.6 16750.22 4356.39 * 32.367 99.38 155 , 49 0.13 * 692.174 * 27.599 * 0.553 * 1.373 5976.16 7295.19 26.24 281.02 8.7 * 0.712 0.21 27564.64 2826.14 17306.69 2256.95 * 33.243 19.64 59.36 0.23 * 692.174 230.64 * 0.553 10.92 5033.86 2475.07 23.24 415.99 13.71 9.72 0.26 60798.83 9789.1 10730.93 3493.4 * 33.243 27.98 130.43 0.14 5589.23 258.3 5.3 * 1.373 5312.38 38860.48 25.14 235.87 14.78 * 0.712 0.19 34729.57 6389.18 26298.32 1621.07 * 33.243 41.25 * 36.49 0.19 * 644.123 * 27.061 * 0.591 * 1.31 6846.32 5970.33 16.72 224.93 5.48 8.73 * 0.029 18753.88 3088, 6 6419.84 1488.53 881.67 15.44 145.13 0.14 * 644.123 * 27.061 4.18 * 1.31 4493.86 2364.65 17.56 220.49 5.13 12.07 0.32 24814 , 47 1668.46 54747.6 1528.91 297.5 15.76 118.66 0.18 * 644.123 * 27.061 * 0.591 * 1.31 4016.05 989.21 32.5 149.05 8.35 4, 17 * 0.029 31517 .58 6408.6 879.66 3109.92 1314.27 21.97 * 36.49 0.25 * 644.123 * 27.061 * 0.591 * 1.31 8018.71 * 135.54 19.26 169.95 4.76 5.3 * 0.029 33508.7 2396.9 11344.05 2484.23 917.03 2.38 * 36.49 0.15 8649.81 306.19 28 10.72 5063.86 9321.53 3.68 229.33 7.18 13 * 0.029 6571.09 2742.41 5478.58 837.82 1011.36 15.35 * 36.49 0.23 * 644.123 * 27.061 * 0.591 * 1.31 4721.87 2129.14 20.09 176.64 9.03 4.17 * 0.029 26512.01 4990.3 2642.63 1598.49 1365.44 30.84 431.09 0.24 7207.66 221.27 4.12 81.45 10742.72 13721 31.85 771.43 13.09 12.53 0.32 45430.45 8402.91 12680.62 4122.45 2166.42 42.84 * 14.11 0.2 * 692.174 * 27.599 * 0.553 12.96 6754.5 18978.6 27.28 547.45 6.08 * 0.712 0.3 49752.89 2967.51 72982.56 4409.84 * 33.243 41.41 103.84 0.16 * 692.174 258.3 3, 72 * 1.373 9429.09 11848.16 38.42 422.76 14.07 * 0.712 0.19 82779.26 5129.23 17058.64 3987.28 * 33.243 21.71 89.78 0.16 * 692.174 202, 28 6.82 9.9 5789.04 54649.86 38.89 284.97 10.05 * 0.712 0.32 41570.73 2543.8 10349.77 3743.25 * 33.243 53.01 * 14.11 0, 18 * 692.174 * 27.599 * 0.553 * 1.37 3 4033.99 2038.82 12.35 227.3 5.8 * 0.712 0.19 50616.21 4415.82 15774.96 1099.98 * 33.243 79.1 42.33 0.11 * 692.174 * 27.599 * 0.553 * 1.373 7955.17 32663.17 24.96 209.75 4.43 * 0.712 0.18 35618.16 4558.34 8996.8 2406.96 * 33.243 37.72 * 14.11 0.16 11419.51 * 27.599 * 0.553 * 1.373 4624.81 25101.73 20.74 132.17 5.4 * 0.712 0.19 30328.07 2346.5 8089.26 3945.79 * 33.243 57.51 * 14.11 0.14 6678 , 44 225.96 * 0.553 * 1.373 5219.15 10125.66 12.59 345.64 11.83 * 0.712 0.19 56901.35 25566.49 16632.27 2210.3 * 33.243 64.04 143.12 0 , 09 6410.22 * 27.599 * 0.553 * 1.373 7127.15 2864.56 19.88 1241.89 23.51 * 0.712 0.17 49081.25 9760.18 23619 1858.41 * 33.243 41.06 * 10.951 0, 21 5003.69 257.72 * 0.598 * 1.37 5528.58 5092.08 5.49 102.27 7.11 1.91 0.24 43430.2 1422.17 11055.34 1050.53 1681.16 39 , 19 * 10,951 0.11 * 674.132 * 27.362 * 0.598 * 1.37 1153.43 990.37 14.83 74.25 8.32 1.91 0.31 25257.28 7826.23 19005.02 1394.71 681.58 41.52 * 11.332 0.17 * 682.162 * 26.909 * 0.642 * 1.364 2380.49 9249.14 22.42 205.06 8.5 2.84 0.34 68874.29 5783.2 3 19908.86 2520.37 1632.4 63.12 * 11.332 0.31 * 682.162 * 26.909 * 0.642 * 1.364 4846.51 73346.59 14.1 261.19 20.39 3.67 0.36 27564.92 5263.43 7938.09 * 41.6 593.06 8.39 * 11.332 0.53 * 682.162 * 26.909 * 0.642 * 1.364 4219.36 2218.01 18.77 504.52 9.73 6.46 0.35 18425.71 6618.35 22446.3 2217.18 1314.26 50.67 * 11.332 0.15 * 682.162 161.46 * 0.642 * 1.366 3678.31 59321.16 15.07 217.44 10.07 8.83 0.29 24234 , 09 5843.05 15173.77 3210.53 2906.69 13.59 257.67 0.16 * 656.313 * 27.445 * 6.138 * 1.342 4331.83 210876.41 13.16 238.42 8.49 8.63 * 0.029 16244.15 4318.47 14035.98 1963.1 1356.29 36.48 343.7 0.1 * 656.313 * 27.445 * 6.138 * 1.342 4181.84 6179.03 39.68 139.88 6.25 8, 73 * 0.029 24497.7 3982.66 12882.55 1993.72 2091.76 82.47 * 9.827 0.11 * 656.313 * 27.445 * 6.138 * 1.342 3811.42 1184.8 19.92 283.25 4.18 6 , 38 * 0.029 19866.62 4184.46 12538.54 3180.81 462.81 55.31 177.99 * 0.01 * 656.313 * 27.445 * 6.138 * 1.342 6669.55 106,105.09 23.34 327.66 7 , 95 9.65 * 0.029 15217.35 3061.12 25567.48 1574.55 1327.19 35.19 156.55 0.33 * 656, 313 * 27.445 * 6.138 * 1.342 6891.18 3153.6 39.06 247.08 6.7 11.96 * 0.029 39206.54 4351.91 15331.79 1908.4 2186.51 153.65 161.36 0, 27 * 656.313 * 27.445 * 6.138 * 1.342 8790.28 2630.07 86.25 267.79 2.68 11.61 0.37 17219.26 4684.95 13394.39 2483.57 3078.38 97.66 529, 18 0.26 * 656.313 * 27.445 * 6.138 * 1.342 8881.32 19728.37 20.32 324.26 2.12 10.56 * 0.029 28940.1 6608.82 13394.39 4012.82 1726.63 6.62 379.09 * 0.01 * 656.313 * 27.445 * 6.138 * 1.342 8491.53 13436.36 32.16 317.45 22.17 10.2 0.3 10355.19 9260.4 6878.16 1400.49 1948.37 65 , 29 405.77 0.24 * 656.313 * 27.445 * 6.138 12.83 4253.03 1724.17 31.13 157.72 8.56 7.38 * 0.029 32060.9 5344.38 16311.45 3332.97 1471 , 74 108.16 208.2 0.26 * 656.313 * 27.445 * 6.138 8.82 6467.65 1724.17 13.4 221.04 8.69 6.38 * 0.029 37665.22 3061.12 7279.9 1901 , 97 2904.9 28.49 88.49 0.19 * 656.313 * 27.445 * 6.138 * 1.342 5040.9 7532.95 21.82 228 8.27 9.65 * 0.029 47389.32 6801.19 18042.89 1884 , 3 1128.03 86.11 255.46 0.21 4205.98 183.34 * 6.138 9.43 4931.33 89488.71 36.04 154.16 20.72 1 2.3 * 0.029 20564.87 2111.01 9836.95 794.87 2050.95 69.49 144.41 0.18 * 656.313 * 27.445 * 6.138 8.82 5224.22 2567.86 22.71 193.06 6.66 8.82 * 0.029 34050.35 3678.06 11173.17 1251.03 2506.77 42.49 440.19 0.24 4205.98 164.67 * 6.138 13.14 5562.73 7596.6 41 , 63 175.44 6.86 8.16 * 0.029 57725.52 6287.01 15953.84 1552.34 2807.82 345.4 122.1 0.1 4471.09 * 27.445 * 6.138 * 1.342 5378.86 1724 , 17 55.14 245.35 9.78 10.56 * 0.029 19162.69 4083.69 3385.93 1625.4 4540.25 59.78 275.24 * 0.01 * 656.313 * 27.445 * 6.138 * 1.342 5464 , 63 6393.31 41.64 189.54 10.07 11.61 * 0.029 50004.05 3847.58 11965.71 1649.27 1202.38 109.52

203.61 0.15 * 656.313 * 27.445 * 6.138 41.21 7592.28 2029.86 14.08 366.64 6.12 9.65 0.29 36726.31 8761.55 11226.44 3288.93 2781, 85 129.89 * 9.827 * 0.01 4205.98 183.34 * 6.138 * 1.342 6409.24 7910.06 22.33 178.98 7.24 12.04 * 0.029 19263.62 6801.19 17709.16 3581 , 65 2781.85 84.62 * 36.49 0.15 * 644.113 164.99 * 0.591 * 1.31 2915.7 1120.54 28.54 199.15 4.25 2.41 * 0.029 68137.01 5638 , 03 6023.2 767.36 3674.73 36.56 329.13 0.23 * 644.123 217.42 5.83 * 1.31 6133.27 16962.83 27.42 232.24 12.36 3.45 0, 39 50146.57 5088.19 15408.13 2970.75 1247.36 42.51 * 36.49 0.19 * 644.123 * 27.061 * 0.591 * 1.31 3463.03 11492.22 21.01 186.45 7, 8 2.94 * 0.029 41476.15 7484.61 13471.56 1734.08 1448.12 33.04 * 36.49 0.21 * 644.123 * 27.061 * 0.591 * 1.31 4126.75 17140.94 16.05 249.35 3.43 6.97 0.36 25413.38 4132.6 14240.89 3119.93 2130.04 51.19 * 36.49 0.12 * 644.123 * 27.061 9.86 * 1.31 1576, 32 37,985.32 8.59 164.84 7.41 14.66 0.32 10764.72 2953.88 20477.59 319.93 901.31 90.24 * 36.49 0.19 * 644.123 * 27.061 * 0.591 * 1.31 3980.97 2387.63 19.51 243.71 6.34 4.74 * 0.029 33240.59 8611.55 5795.34 754.33 4977.06 53.86 * 36.49 0.12 * 644.123 * 27.061 * 0.591 * 1.31 8878.42 9848 , 62 14.6 230.79 14.37 11.98 * 0.029 42718.62 6091.8 12744.21 434.85 2003.82 20.6 * 36.49 0.14 * 644.123 * 27.061 * 0.591 * 1, 31 3891.09 63883.52 17.43 191.26 3.55 3.69 * 0.029 22425.29 4794.77 8848.84 409.72 2587.88 60.19 * 36.49 0.1 4319.13 164.99 * 0.591 39.5 5304.27 30710.3 6.73 196.01 6.79 8.73 * 0.029 26569.75 1630.1 15174.38 1725.33 1735.72 7.03 * 36.49 0.14 5035.74 191.23 6.24 * 1.31 5463.51 103229.08 6.56 192.85 4.74 5.3 * 0.029 31632.44 1936.84 13825.66 1738.46 1582.04 13, 3 * 36.49 0.22 * 644.123 * 27.061 * 0.591 * 1.31 4177.3 31897.5 12.51 191.26 3.19 3.69 * 0.029 10400.81 5460.99 13835.78 2412.23 2130.04 29.49 * 36.49 0.21 * 644.123 * 27.061 * 0.591 * 1.31 4863.18 24063.43 24.1 223.46 13.51 4.4 0.37 47592.56 4463.19 4697.88 604.94 2055.09 35.76 * 36.49 0.14 * 644.123 164.99 8.27 * 1.31 6719.02 67927.74 13.38 136.17 9.66 4.17 * 0.029 30177.34 1841.01 17396.21 2362.93 2374.7 8.47 * 36.49 0.1 * 644.123 * 27.061 * 0.591 * 1.31 5379.68 16943 17.69 202.27 5.4 4.4 * 0.029 36807.4 3358.43 11 756.5 2470.03 1483.57 12.08 * 36.49 0.14 * 644.123 * 27.061 3.55 * 1.31 2673.11 54028.47 8.62 178.29 1.95 5.94 * 0.029 23632.46 3127.11 20487.91 2682.64 755.99 365.8 * 36.49 0.16 * 644.123 * 27.061 * 0.591 * 1.31 4006.02 74752.26 12.7 276.66 5.88 4.05 * 0.029 16653.45 4521.63 21946 , 27 1554.53 1003.5 29.85 145.13 0.15 * 644.123 * 27.061 * 0.591 26.36 3176.35 2786.19 6.82 310.33 6.54 4.51 * 0.029 35789.72 3938 , 58 23171.94 550.96 975.98 9.62 * 36.49 0.23 * 644.123 * 27.061 5.83 * 1.31 5199.72 112370.79 9.4 208.43 4.59 7.37 * 0.029 21 956.15 2358.54 11515.01 1327.21 1988.04 12.45 * 36.49 0.11 * 644.113 * 27.061 4.18 13.55 4617.88 7793.41 3.34 186.45 2.64 3.45 * 0.029 26916.05 2512.01 15062.64 930.61 1526.89 20.42 * 36.49 0.1 * 644.123 * 27.061 6.24 * 1.31 3881.13 34445.77 8.48 118.69 6.13 10.03 * 0.029 24291.9 2473.63 13856.02 3338.11 1617.5 4.19 * 36.49 0.12 * 644.113 * 27.061 * 0.591 * 1.31 2555.5 10509.3 19 157.92 4.37 * 0, 138 0.35 35866.48 8449.87 8928.75 2098.31 1475.69 61.06 * 36.49 0.13 * 644.123 * 27.061 6.65 32.14 3985.98 1245.4 18.76 159, 66 3.91 4.85 * 0.029 44785.2 2973.11 13754.81 1341.72 1400.87 15.16 * 36.49 0.12 * 644.123 * 27.061 3.76 * 1.31 5888.11 6274, 45 7.95 173.31 4.93 4.85 * 0.029 22327,65 2703.99 7443.22 1218.4 1223.75 9.14 * 36.49 0.1 * 644.123 * 27.061 * 0.591 * 1.31 7494.67 4115.21 15.17 136.17 4.34 2.54 * 0.029 33240.59 2665.58 8429.59 1146.57 1838.2 7.61 * 36.49 0.14 * 644.123 * 27.061 * 0.591 * 1.31 3767 14276.65 24.28 309.07 5.53 2.54 * 0.029 27761.56 5186.17 10490.41 2791.68 614.72 22.06 * 36.49 0.17 * 644.123 * 27.061 * 0.591 * 1.31 6569.58 14636.22 16.45 118.69 5.06 4.4 0.3 44902.52 1630.1 7841.44 1600.64 1522.95 7.37 * 36, 49 0.15 * 644.123 * 27.061 * 0.591 * 1.31 5954.68 * 135.54 26.14 235.14 7.2 2.41 * 0.029 29027.75 5559.31 3732.19 2105.15 1641.14 45.38 * 36.49 0.17 * 644.123 * 27.061 * 0.591 * 1.31 5662.34 1305.8 13.67 287.23 4.3 4.17 * 0.029 26146.14 12266.26 5607.23 3778 , 79 1972,27 35,72 * 36,49 0,18 * 644,12 3 * 27.061 * 0.591 * 1.31 5379.68 79161.47 21.04 120.69 8.42 1.69 * 0.029 55283.69 2320.19 13117.81 1679.57 905.24 84.22 * 36.49 0 , 2 * 644,123 * 27,061 * 0.591 * 1.31 8141.69 1536.23 11.33 226.4 7.62 2.68 * 0.029 50863.25 2876.95 18974.43 2254.61 1475.69 11.61 * 36.49 0.1 * 644.123 * 27.061 * 0.591 * 1.31 4205.16 1957.28 18.71 217.51 6.18 3.45 * 0.029 32359.87 4970.73 11837.03 2394.6 1365.44 32.95 * 36.49 0.11 * 644.113 196.47 6.65 * 1.31 4380.9 2271.74 11.12 216.01 4.25 9.57 0.31 18192.53 5225.39 4165 , 36 1006.53 1349.69 21.52 * 36.49 0.18 * 644.123 * 27.061 * 0.591 * 1.31 6189.41 9001.73 12.45 145.42 5.48 5.73 0.3 30943 , 27 3977.36 28919.79 1560.95 1507.2 25.02 * 36.49 0.21 * 644.123 * 27.061 * 0.591 * 1.31 4468.13 2612.36 28.43 302.73 4.74 2 , 94 * 0.029 37788.04 7845.62 20725.35 2524.58 1794.84 41.14 * 36.49 0.3 * 644.123 * 27.061 * 0.591 * 1.31 3090.59 * 135.54 33.73 249 , 35 6.56 5.19 * 0.029 22073.54 7544.7 3751.86 536.07 1145.06 29.22 * 36.49 0.2 * 644.123 175.49 * 0.591 * 1.31 3565.39 26592 , 05 7.65 485.01 10.95 4.17 * 0.029 24988.46 15129.64 6717.61 2457.03 1003.5 12.12 * 36.49 0.14 * 644.123 * 27.061 * 0.591 * 1.31 7862.9 32509.18 17.65 163.12 12.14 4.4 * 0.029 29909.2 2301.01 21200.75 1843.09 1172.6 7.22 194.44 0.19 * 644.123 * 27.061 * 0.591 * 1.31 * 27.314 7943.56 6.93 * 12.996 5.58 3, 69 * 0.029 36884.26 8268.26 4609.06 2824.59 1633.26 17.43 * 12.46 0.16 * 676.132 214.42 6.49 * 1.37 6050.48 8114.82 23.76 242.15 4.52 10.01 0.29 48823.62 3599.6 10502.05 768 1025.75 46.16 97.88 0.12 * 676.132 * 27.292 * 0.628 * 1.37 7455.53 4592.5 24.97 223.38 5.06 6.69 0.22 35755.21 2838.59 28371.54 3899.12 2169.75 5.01 186.61 0.25 4056.79 * 27.292 * 0.628 * 1.37 2878.95 5376.38 20.47 280.43 11.8 8.5 0.29 29030.25 6166.57 6007.76 2288.03 835.15 204.89 * 12.46 0.11 * 676.132 * 27.292 6.9 * 1.37 5411.35 5257.6 8.01 172.23 8.17 12.98 0.23 24825.57 4493.61 11903.63 2461.27 1690.47 4.33 * 36.49 0.11 * 644.123 * 27.061 * 0.591 * 1.31 7032.88 83921.82 25.94 275.33 7.1 8.45 * 0.029 14543.47 2396.9 14423.34 1998.51 913.1 31.6 * 36 , 49 0.17 * 644.123 * 27.061 4.18 * 1.31 3930.98 2997.03 16.23 273.99 5.83 8.45 0.36 54393.77 4755.71 13683.98 2073.26 1550.53 38.7 * 36.49 0.1 * 644,123 * 27,061 * 0.591 * 1.31 3467.89 1536.23 7.51 211.47 2.18 2.13 * 0.029 17020.19 8348.94 4007.77 1701.33 1007.43 21.79 * 36 , 49 0.22 * 644.123 * 27.061 4.18 * 1.31 8599.67 2007.04 10.35 173.31 2.96 5.94 * 0.029 27761.56 1052.68 5330.2 979.15 1255, 23 13.86 * 36.49 0.22 4678.92 * 27.061 * 0.591 * 1.31 4139.38 4435.12 21.07 370.59 5.18 7.77 0.35 51881.65 5107.78 7592 , 5 344.84 2082.7 20.79 * 36.49 0.17 * 644.123 * 27.061 7.06 * 1.31 4406.51 122585.52 26.71 220.49 7.41 2.13 * 0.029 19769 , 7 6210.49 5369.76 1978.23 367.89 11.28 * 36.49 0.16 * 644.113 * 27.061 * 0.591 * 1.31 3911.02 6951.12 15.13 208.43 3.76 2 , 41 * 0.029 36576.86 5854.82 6280.96 3127.43 1247.36 22.16 * 36.49 0.16 * 644.123 * 27.061 * 0.591 * 1.31 3147.71 3478.69 5.05 173, 31 7.62 4.4 * 0.029 41166.11 3377.72 15255.67 2819.71 842.38 31.92 * 36.49 0.13 * 644.123 * 27.061 * 0.591 * 1.31 4101.53 2828.9 14.26 141.76 6.29 1.53 * 0.029 48026.62 1936.84 5112, 7 1679.57 1846.09 14.6 * 36.49 0.25 * 644.123 * 27.061 * 0.591 * 1.31 4695.82 * 135.54 33.47 143.6 5.73 4.85 * 0.029 26377, 25 5657.72 44503.09 2898.07 1156.87 57.27 * 36.49 0.11 * 644.113 * 27.061 * 0.591 * 1.31 1503.7 1055.77 6.23 173.31 7.31 8, 16 * 0.029 34696.57 2915.41 12320.58 1012.42 1156.87 24.7 * 36.49 0.13 * 644.123 * 27.061 * 0.591 * 1.31 4740.13 849.69 7.66 217.51 4, 54 2.54 * 0.029 23477.05 3977.36 4579.46 3121.18 211.57 51.23 * 12.46 0.13 * 676.132 * 27.292 * 0.628 * 1.37 9217.04 1184.5 24.2 242, 15 6.42 6.09 0.25 64645.41 6009.84 7978.72 5599.08 1744.73 23.32 * 12.46 0.13 * 676.132 * 27.292 * 0.628 * 1.37 5514.91 1388, 01 34.35 213.68 12.14 6.19 0.18 36139.87 9084.5 19038.11 2292.94 1250.53 25.66 * 12.46 0.16 * 676.132 * 27.292 * 0.628 51.97 5879.35 4050.14 13.81 1345.76 3.02 8.59 0.35 35216.05 2212.42 1354.48 1992.79 2183.05 40.22 106.36 0.16 * 676.132 * 27.292 5 , 87 * 1.37 3044.68 26385.76 17.43 1262.22 5.17 4.37 0.17 33799,98 3878.48 10931.39 4191.93 911.8 8.77 106.36 0.1 * 676,132 211.55 * 0.628 * 1.37 6292.62 4416.5 19.54 245.21 3.56 9.13 0.32 21411.43 3679.35 7591.65 2825.39 1586.05 12.61 * 12.46 0.13 * 676.132 * 27.292 * 0.628 * 1.37 4645.89 1482.38 19.52 260.19 11.51 5.15 0.19 18456.19 4552.98 15246.51 2354.28 987.89 44.22 * 12.46 0.25 * 676,132 * 27,292 * 0.628 * 1.37 5556.4 953.3 21.65 242.15 9.45 5.88 0.38 24087.07 8542.41 15761.99 3565.47 825.53 32.37 106, 36 0.21 * 676.132 * 27.292 * 0.628 * 1.37 6699.24 43752.03 18.15 175.88 4.29 6.29 0.35 17768.69 5676.33 5756.3 1893.34 3151.98 13.61 * 12.46 0.12 * 676.132 * 27.292 8.53 * 1.37 5449.29 32657.37 7.64 220.17 13.27 7.85 0.19 26045.13 3479.85 6098, 87 1051.98 1213.32 20.92 * 12.46 0.4 * 676.132 * 27.292 * 0.628 11.53 5980.55 17835.48 16.55 506.76 25.43 4.59 0.3 126900.99 7843.85 3313.64 4116.88 1481.05 14.21 * 12.46 0.17 * 676.132 * 27.292 * 0.628 * 1.37 6155.59 62725.91 15.3 305.39 6.76 6.09 0.23 31415.25 4632.09 7824.14 2640.36 1581.5 11.6 * 36.49 0.11 * 644.113 * 27.061 * 0.591 * 1.31 2065.15 8677.02 20.91 205.36 3.57 2.54 * 0.029 4747.63 4404.78 2720.98 951.91 653, 95 13.2 * 36.49 0.21 * 644.123 * 27.061 * 0.591 * 1.31 4486.14 46227.7 22.38 189.66 8.37 4.85 0.37 48481.06 3165.64 13228, 95 553.59 1357.57 23.57 * 36.49 0.16 3956.06 * 27.061 * 0.591 41.89 3231.38 28370.29 9.69 176.64 3.67 3.81 0.3 26203, 93 3744.89 9288.55 1784.56 1972.27 23.98 * 36.49 0.13 * 644.123 * 27.061 4.6 * 1.31 3284.18 49531.88 16.43 159.66 4.25 5 , 51 * 0.029 66199.42 2973.11 6956.01 1560.95 897.38 24.25 * 36.49 0.22 5741.42 * 27.061 9.86 * 1.31 4675 22099.45 16.05 221, 98 2.25 15.07 0.32 11371.2 7164.69 19509.02 1207.22 948.47 39.99 258.8 0.11 * 644.123 * 27.061 9.06 8.76 5403.98 89581.29 16.69 174.98 8.35 2.94 * 0.029 35425.2 3783.6 33889.3 2387.55 1341.82 40.96 83.8 0.08 * 661.68 * 27.223 * 0.573 * 1.345 NA 39954 , 92 NA NA 5.11 10 0.41 14834.1 NA 19336.75 NA * 33.139 58.99 76 0.1 * 661.68 * 27.223 * 0.573 * 1.345 NA 40923.03 NA NA 16.96 10.8 0.26 22247.37 NA 21203.16 NA * 33.139 31.61 70.09 0.07 * 661.68 * 27.223 * 0.573 * 1.345 NA 20916.17 NA NA 58.77 9 0.29 13691.59 NA 8333 , 72 NA * 33,139 69,6 131,15 0,06 * 661.68 * 27.223 * 0.573 * 1.345 NA 43168.71 NA NA 28.08 7.44 0.36 13847.31 NA 14646.69 NA * 33.139 34.2 83.8 0.07 * 661.68 * 27.223 * 0.573 * 1.345 NA 171654.44 NA NA 13.7 9.17 0.27 10900.31 NA 67273.13 NA * 33.139 132.45 * 11.086 0.16 * 661.68 * 27.223 * 0.573 * 1.345 NA 4176.13 NA NA 7.83 7.97 0.2 27398.77 NA 12344.12 NA * 33.139 72.19

75.5 0.24 4040.95 * 27.042 * 1.87 48.5 3916.07 58784.62 10.32 503.8 13.58 9.96 0.45 16092.58 5047.78 13466.93 1535, 61 577.28 20.24 37.85 0.15 * 673.492 * 27.042 * 1.87 * 1.369 3383.99 78014.38 18 172.82 3.48 * 0.745 0.3 21219.67 4708.08 13020.96 1255.78 644.52 41.93 * 12.021 0.16 5597.94 * 27.042 * 1.87 * 1.369 3027.58 128426.22 9.68 226.32 4.16 11.03 0.33 20471.8 3489 , 71 10437.51 1160.6 857.43 19.9 46.67 0.17 * 673.492 * 27.042 * 1.87 * 1.369 3617.97 76489.57 9.63 238.75 5.07 * 0.745 0.27 17004, 65 4516.6 15765.02 3568.81 1139.31 97.99 * 12.46 0.16 * 676.132 * 27.292 * 0.628 * 1.37 5875.86 18114.98 28 210.39 2.49 12.34 0 , 49 13635.07 1579.77 18555.28 4648.24 1157.33 77.75 47.1 0.21 * 676.132 * 27.292 * 0.628 * 1.37 3371.73 137699.6 14.69 128.94 8, 62 8.77 0.3 40887.14 3938.15 10856.35 2398.51 1185.35 27.5 * 12.46 0.09 * 676.132 * 27.292 * 0.628 * 1.37 6208.24 16634.09 17.21 274 , 73 4.06 6.29 0.17 26475.67 3938.15 4899.45 3482.87 1110.49 40.75 * 12.46 0.16 * 676.132 * 27.292 * 0.628 * 1.37 3993.91 109656 , 6 13.17 210.39 3.4 9.83 0.26 21042.1 16424.66 6201.16 2371.47 1676.88 39.03 * 12.46 0.09 * 676.132 * 27.292 * 0.628 * 1.37 7451.91 90006.24 10.18 137.33 4.7 7.66 0.28 22480.79 2435.25 19 227.07 3754.27 1586.05 15.8 * 12.46 0.07 * 676.132 * 27.292 * 0.628 * 1.37 2940.61 120095 28.93 305.39 3.44 8.59 0.23 19223.04 6323.17 8000.78 1823.18 1110.49 33.67 144.68 0.1 * 676.132 * 27.292 * 0.628 * 1.37 4412.85 10394.94 45.99 210.39 3.71 6.89 0.23 16021.65 4057.39 43333.8 1911.51 1063.48 32.18 * 12.46 0.08 * 676.132 * 27.292 4.83 * 1.37 4348.9 166897.99 22.54 183.05 5.46 9.66 0.32 13568.23 5302.79 54131.06 1138.18 1072.9 34.34 * 12.46 0.12 * 676.132 * 27.292 * 0.628 * 1.37 5013.25 38887.48 8.61 235.98 7.5 6.39 0.23 48288.51 4513.4 23774.88 2141.3 1626.97 44 , 41 * 12.46 0.1 * 676.132 * 27.292 4.41 * 1.37 3572.56 239398.47 10.28 200.37 10.12 9.57 0.31 37700.77 5656.69 10867.08 1588.4 1444.38 21.76 80.57 0.09 * 676.132 * 27.292 * 0.628 * 1.37 4318.64 6590.48 30.54 377.07 2.86 7.08 0.26 40021.64 7318 , 65 10416.01 1980.64 2143.12 122.49 * 12.46 0.08 * 67 6.132 * 27.292 * 0.628 * 1.37 3807.59 134574.94 10.72 213.68 9.05 6.99 0.23 35421.53 5892.2 10792 2595.81 1490.2 46.12 * 12.46 0.09 * 676.132 * 27.292 * 0.628 * 1.37 8684.8 51454.68 10.05 260.19 9.1 7.28 0.19 25613.02 2677.52 10995.68 2378.84 728.74 17.98 * 12.46 0.1 * 676.132 * 27.292 * 0.628 * 1.37 5266.78 3296.59 26.46 266.05 3.33 6.29 0.35 18870.4 5479.84 6110.24 1631.62 1011.56 22.88 * 12.46 0.12 * 676.132 * 27.292 * 0.628 * 1.37 4497.14 84007.17 11.65 266.05 4.1 6.89 0.24 19631.8 3778.96 17022.78 547.05 820.71 11.81 * 12.46 0.43 4056.79 * 27.292 8.12 * 1.37 2086.87 34828.39 5.7 268.96 15.25 21.37 0 , 52 88047.03 2959.19 15846.11 2886.45 796.6 235.29 212.07 0.1 * 676.132 * 27.292 * 0.628 * 1.37 2904.9 16122.94 16.75 291.66 5, 27 10.69 0.22 23259.84 13429.31 29037.01 4120.76 1199.34 94.24 71.7 0.08 * 676.132 * 27.292 * 0.628 * 1.37 4476.89 14438.95 22.33 254.25 4.1 7.66 0.23 29846.18 2798.36 3997.41 2332.18 1231.93 14.61 114.73 0.11 * 676.132 * 27.292 6.08 * 1.37 2178.92 50,605.63 17.47 377.07 11.88 11.19 0.33 30919, 71 10801.35 10308.39 1806.27 1567.83 193.49 * 12.46 0.1 * 676.132 * 27.292 * 0.628 * 1.37 4639.12 128 553.09 6.69 216.94 7.52 9, 66 0.26 19046.99 1743.78 26 224.76 2376.39 1608.8 5.89 59.6 0.09 * 676.132 * 27.292 * 0.628 * 1.37 2969.85 146315.39 11.01 226.57 5.65 5.88 0.21 17618.01 5381.5 35507 2097.41 1231.93 12.86 103.55 0.14 * 676.132 176.07 5.25 * 1.37 3044.68 160637.63 9 , 7 326.7 * 0.027 6.49 0.25 16021.65 12586.97 11 797.07 1460.3 978.41 * 0.032 * 12.46 0.07 * 676.132 * 27.292 14.83 * 1.37 2810, 92 32625.61 12.48 271.85 3.51 7.47 0.31 14296.02 5184.66 6990.11 1565.62 1380.02 14.71 * 12.46 0.1 * 676.132 * 27.292 * 0.628 * 1.37 4399.38 14002.5 14.39 183.05 5.86 4.59 0.26 18277.7 5361.83 21 749.61 2981.39 1054.06 20.63 * 12.46 0.13 * 676,132 197 * 0.628 * 1.37 3325.78 66798.76 13.9 196.97 11.25 7.28 0.22 31649.58 4275.69 17253.76 1632.82 1068.19 36.02 * 12 , 46 0.14 * 676.132 166.86 4.83 * 1.37 3240.58 200750.94 17.07 223.38 4.91 8.22 0.28 11559.51 6967.84 6956.52 486.9 1704, 04 28.56 * 12.46 0.08 * 676.132 * 27.292 * 0.628 * 1.37 6966.53 17755.26 7.95 260.19 4.01 10.35 0.3 10828.77 3918.26 14203.26 3905.55 1278.37 37.84 * 12.46 0.12 * 676,132 * 27,292 6.49 * 1.37 6564.45 73552.58 12.3 266.05 3.96 7.28 0.27 11050.43 3579.65 3474.11 2450.19 757.89 18.67 * 12.46 0.09 * 676.132 * 27.292 5.25 * 1.37 2759.17 23428.29 19.49 223.38 7.5 9.83 0.29 20985.11 5715.6 12382.42 1424, 48 968.92 59.18 37.37 0.08 * 676.132 * 27.292 5.25 * 1.37 3675.01 37408.62 18.13 291.66 5.79 10.26 0.24 13366.84 6244, 89 12233.59 2867.75 1255.17 62.09 * 12.46 0.1 4056.79 * 27.292 13.67 * 1.37 2765.63 57668.46 9.91 210.39 4.66 12.58 0.34 14230.51 4236.03 10222.22 1671.28 1035.19 92.06 133.91 0.14 * 676.132 * 27.292 4.41 * 1.37 4666.22 51186.33 16.57 235.98 7.15 8.4 0.34 14099.12 6401.42 14213.81 1245.97 1604.25 24.54 157.96 0.06 * 676.132 * 27.292 * 0.628 * 1.37 6713.46 56221.96 13 , 74 260.19 7.5 8.5 0.21 22 452.83 2979.27 17736.63 2573.56 1119.87 32.61 * 12.5 0.21 * 696.339 * 27.577 * 0.61 * 1.358 6035 , 19 1820.83 21.99 377.89 12.2 4.89 0.41 14437.83 7719.62 17880.12 2 243.26 2298.9 170.22 * 12.5 0.12 * 696.339 * 27.577 4.79 * 1.358 3598.83 847.66 9.57 123.18 7.09 6.32 0.22 24111.16 5422 , 28 15917.43 2388.69 1340.98 46.11 94.52 0.06 * 696.339 * 27.577 * 0.61 * 1.358 2742.76 847.66 16.92 151.56 6.88 4.27 0.14 19216 , 67 4869.46 4500.04 2409.53 674.22 81.59 * 12.5 0.16 * 696.339 * 27.577 * 0.61 * 1.358 4407.45 8996.62 10.74 395.32 7.52 10 , 31 0.38 18682.39 3293.97 9337.49 1060.49 2042.03 100.05 44.86 0.21 * 696.339 * 27.577 * 0.61 * 1.358 5573.4 3858.33 18.29 288, 49 5.98 6.19 0.41 26170.78 4830 7910.97 1998.19 2857.06 56.71 58.89 0.04 * 696.339 * 27.577 * 0.61 * 1.358 3135.55 2126.38 9, 17 188.63 6.41 2.54 0.16 14561.87 4199.13 6194.76 2340.94 1431.49 108.34 62.28 0.12 * 696.339 * 27.577 * 0.61 * 1.358 1978.51 1820.83 29.35 251.8 8.47 7.91 0.3 30700.76 6193.41 5760.63 2802.35 2003.04 103.76 44.86 0.09 * 696.339 * 27.577 * 0.61 * 1.358 6450.53 3959.89 14 247.03 13.87 6.6 0.28 10911.51 4317.35 80030.94 1597.8 1964.04 88.57 91.41 0.15 * 696.339 * 27.577 * 0, 61 * 1.358 5019.47 12 754.13 39.9 26 5.83 7.69 7.13 0.21 45277.79 5995.56 13997.01 3140.39 1682.62 41.28 * 12.5 0.23 * 696.339 * 27.577 3.89 * 1.358 8482.74 50701 , 45 37.35 214.94 9.15 8.41 0.31 27604.34 4672.2 18392.81 3406.24 2127.73 45.88 124.61 0.23 * 696.339 * 27.577 * 0.61 * 1.388 3886 969.9 54.93 207.21 46.14 4.58 0.42 49295.43 11525.65 14586.6 4219.46 2057.62 75.95 191.63 0.11 * 696.339 * 27.577 * 0 , 61 * 1.358 4432.54 47830.13 8.03 225.04 6.41 26.79 0.29 28140 6668.59 20478.83 2461.08 1971.84 16.11 100.68 0.12 * 696.339 * 27.577 * 0.61 * 1.358 4552.06 * 141.277 8.12 275 9.84 5.33 0.32 16223.85 3864.37 5508.15 4903.47 1807.83 92.61 65.64 0.09 * 696.339 * 27.577 * 0.61 * 1.358 3984.52 54638.59 11.79 227.53 7.52 8.16 0.15 25198.01 3451.23 16637.05 * 40.84 766.24 58.58 * 12.5 0.11 * 696.339 * 27.577 * 0.61 * 1.358 3532.1 31568.85 14.27 185.91 10.14 8.16 0.3 18317.62 6292.37 5920.85 2015.1 1815.64 19.12 * 12.5 0.12 * 696.339 * 27.577 * 0.61 * 1.358 6302.46 12305.8 23 180.4 6.71 6.05 0.3 36579.68 4475.02 14717.56 2766.29 2127 , 73 65.76 * 12.5 0.12 * 696.3 39 * 27.577 * 0.61 * 1.358 5189.98 910.03 18.6 222.54 9.75 7.91 0.22 20910.95 5797.8 5531.14 2277.41 1270.04 44.59 * 12 , 5 0.1 * 696.339 * 27.577 * 0.61 * 1.358 7952.6 2795.7 24.25 177.62 9.07 6.05 0.26 37856.48 2979.63 11104.58 2949.57 1455, 08 53.35 * 12.5 0.14 * 696.339 * 27.577 * 0.61 * 1.358 4789.53 6976.15 15.09 212.38 9.41 3.61 0.22 20406.04 8037.35 8982, 12 4,117.48 2562.83 34.06 * 12.5 0.18 * 696.339 * 27.577 12.62 * 1.358 5099.77 5952.17 12 292.92 11.77 9.85 0.31 46780.2 7283.06 21387 , 15 1933.05 1936.72 51.34 * 12.5 0.19 * 696.339 * 27.577 * 0.61 * 1.358 4460.8 2407.47 20.82 191.34 7.27 6.6 0.27 37146 , 91 4869.46 14171.75 1008.02 2388.25 77.15 * 12.5 0.26 * 696.339 * 27.577 * 0.61 * 1.358 5041.93 41533.43 15.57 275 13.74 10.66 0.2 23108.28 6292.37 11698.06 1984.91 379.71 21.29 * 12.5 0.16 * 696.339 * 27.577 25.88 * 1.358 3735.9 138778.81 15.18 157.52 11 , 38 9.38 0.34 11864.99 5185.27 8726.21 2480.75 877.89 108.86 * 12.5 0.42 * 696.339 * 27.577 * 0.61 * 1.358 2757.4 14276.16 9 , 15 207.21 6.49 9.02 0.42 18950 7223.56 63 31.37 1312.53 1537.56 156.5 * 12.5 0.17 * 696.339 * 27.577 * 0.61 8.72 4910.65 1780.47 10.35 356.05 8.29 7.26 0, 5 17054.45 4534.16 14193.59 2201.86 1651.28 35.34 * 12.5 0.28 * 696.339 * 27.577 10.49 * 1.358 4665.78 969.9 7.71 389.55 10.87 7.13 0.33 11026.85 11665.68 4207.01 4411.33 937.55 27.87 41.22 0.11 * 696.339 * 27.577 * 0.61 * 1.358 2518.54 969.9 10.89 212 , 38 13.36 4.11 0.26 57202.7 6193.41 13297.57 1857.26 861.96 45.06 * 12.5 0.2 * 696.339 * 27.577 * 0.61 * 1.358 6349.49 * 141.277 14.33 201.99 7.18 6.05 0.26 43926.25 8713.21 8123.63 2513.97 869.93 76.82 58.89 0.22 * 696.339 * 27.577 4.79 * 1.358 4681 , 62 131189.14 12.01 151.56 15.68 9.62 0.51 33764.7 4159.73 7417.06 2957.08 1470.8 25.6 * 12.5 0.14 * 696.339 * 27.577 * 0, 61 40.16 5045.14 4948.12 11.15 207.21 5.64 9.38 0.24 22539.79 3293.97 7966.96 548.94 630.09 26.23 * 12.5 0.12 * 696.339 * 27.577 * 0.61 * 1.358 5316.12 42692 20.92 232.48 14.9 3.95 0.23 15379.93 2783.32 12082.12 2111.95 818.13 110.42 * 12, 5 0.16 * 696.339 * 27.577 * 0.61 * 1.358 6396.6 53376.98 9.4 1 132.95 4.61 6.87 0.38 18529.03 4987.86 36461.77 779.39 1056.59 43.17 * 12.5 0.11 * 696.339 * 27.577 * 0.61 * 1.358 6021, 89 7439.51 11.81 225.04 21.78 5.48 0.29 21283.3 7005.46 10057.15 3731.26 925.63 76.39 * 12.5 0.19 * 696.339 * 27.577 5, 91 * 1.358 2898.46 1899.97 10.55 169.14 18.25 4.58 0.19 35357.59 4711.64 7079.05 2070.75 1226.64 39.13 * 12.5 0.13 * 696.339 * 27.577 * 0.61 * 1.358 4824.53 1137.48 22.64 194.03 5.64 5.48 0.26 33216.08 4277.94 9957.67 830.31 2166.66 44 115.75 0 , 4 * 696,339 * 27,577 * 0.61 * 1.358 7435.22 1083.39 30.34 183.16 10.78 6.32 0.39 43638.61 6134.05 5600.07 3472.47 1266.1 152, 3 * 12.5 0.18 * 696.339 * 27.577 * 0.61 * 1.358 4542.61 3154.02 13.13 225.04 6.92 7.91 0.35 53635.03 7183.9 10289.1 3459, 72 985.21 59.6 * 12.5 0.15 * 696.339 * 27.577 * 0.61 * 1.358 2728.13 4231.63 10.48 180.4 4.87 7.13 0.14 13260.84 5778, 03 7180.56 2407.08 1639.53 59.6 * 12.5 0.16 * 696.339 * 27.577 * 0.61 * 1.358 3827.69 2978.08 20.43 232.48 6.41 5.91 0, 34 45 982.83 5 303.76 22 977.56 3948.34 810.15 72.56 * 12.5 0.14 * 69 6.339 * 27.577 * 0.61 * 1.358 2623.07 3268.13 18.77 191.34 6.37 6.05 0.37 55449.38 5284.01 28756.21 3875.79 1266.1 60.95 124, 61 0.11 * 696.339 * 27.577 * 0.61 * 1.358 3073.08 26499 14.34 217.49 10.35 4.74 0.28 8467.91 6450.74 12235.62 2438.98 383.77 24.96 * 12.5 0.12 * 696.339 * 27.577 * 0.61 * 1.358 3486.71 11,103.07 14.57 237.38 8.34 6.19 0.27 27854.43 4790.54 8948.76 4185.43 1525 , 78 14.34

55.45 0.08 * 696.339 * 27.577 * 0.61 * 1.358 3141.51 4059.94 3.18 237.38 12.58 5.48 0.26 18124.87 5936.22 6183.36 858.12 1210 , 85 42.52 * 12.5 0.08 * 696.339 * 27.577 * 0.61 * 1.358 2245.21 19143.44 11.18 232.48 5.64 8.65 0.26 15258.16 5264.26 10675 , 14 2531.21 838.06 28.75 * 12.5 0.13 * 696.339 * 27.577 * 0.61 * 1.358 2783.78 10124.06 11.14 284.03 8.34 6.32 0.24 12109 , 4 4908.93 13188.21 2704.23 670.21 29.5 91.41 0.17 * 696.339 * 27.577 * 0.61 * 1.358 2077.37 13143.04 10.96 212.38 16.15 10, 08 0.34 23564.99 4691.92 7473.29 3340.17 1329.16 48.85 109.78 0.07 * 696.339 * 27.577 * 0.61 * 1.358 3932.13 1899.97 13.59 212.38 5.72 5.48 0.16 32047.66 11185.76 5829.34 2226.2 702.25 34.49 * 12.5 0.12 * 696.339 * 27.577 * 0.61 * 1.358 2833.7 19079.19 29.69 154.55 4.61 4.89 0.19 40078.89 3077.83 4791.47 2451.26 1218.74 21.23 115.75 0.16 * 696.339 * 27.577 * 0.61 8.87 3399.18 15743.63 9.49 263.52 14.56 10.31 0.29 28888.48 6292.37 6194.76 2797.37 989.18 53.06 * 12.5 0.12 * 696.339 * 27.577 * 0.61 * 1.358 3417.27 85907.39 17.3 299.51 17.0 5 6.87 0.28 34313.29 9190.84 5577.1 1964.39 1167.39 37.57 * 12.5 0.16 * 696.339 * 27.577 * 0.61 * 1.358 4457.66 2269.54 12, 68 331.48 9.5 8.65 0.31 11595.97 5778.03 6046.51 2512.74 1281.87 18.79 * 12.5 0.19 * 696.339 * 27.577 * 0.61 * 1.358 1927, 88 2269.54 32.71 232.48 11.73 8.9 0.26 43028.15 3038.55 13406.91 1711.08 1368.54 42.35 * 12.5 0.09 * 696.339 * 27.577 * 0 , 61 * 1.358 3093.88 2918.04 17.4 163.38 7.61 7.39 0.22 10 957.71 4830 11763.93 2562.02 2182.23 44.12 44.86 0.12 * 696.339 * 27.577 * 0.61 * 1.358 5481.94 5421.66 10.03 261.19 7.48 8.41 0.3 13196.72 2312.62 10079.25 2794.88 949.47 14.13 * 38.55 0, 14 * 682,952 206.43 * 0.576 * 1.363 4353.29 17573.05 21.69 322.15 15.65 8.15 * 0.03 22672.02 12101.67 13128.4 2413.18 653.38 87.78 * 38.55 0.13 * 682.952 * 27.584 * 0.576 * 1.363 5640.83 1277.35 22.46 210.47 8.32 6.8 * 0.03 12434.32 4414.71 5442.05 4560.48 1735 , 21 152.94 * 38.55 0.1 * 682.952 * 27.584 * 0.576 * 1.363 4960.77 23658.81 15.72 123.02 9.6 6.34 * 0.03 19256.44 3913.89 9392, 72 3716.43 792.22 73.29 * 38.55 0.14 * 6 82.952 * 27.584 16.39 * 1.363 4539.6 27212.36 15.62 540.65 11.59 10.23 0.3 9634.67 18319.79 20722.31 2612.85 880.05 50.89 * 38, 55 0.11 * 682.952 * 27.584 * 0.576 * 1.363 6051.18 972.84 15.21 191.79 7.12 12.55 * 0.03 33525.99 4183.96 8255.66 3017.29 1441.68 49.12 * 38.55 0.26 * 682.952 246.57 * 0.576 8.37 4909.53 124925.08 13.68 239.16 105.17 6.8 * 0.03 11728.56 5350.78 21504.44 1745, 93 1547.93 56.51 * 38.55 0.11 * 682.952 * 27.584 * 0.576 * 1.363 6212.58 54313.02 14.65 182.11 8.65 7.93 * 0.03 12943.94 5388.79 8626.42 3119.52 1678.3 45.2 * 38.55 0.08 * 682.952 * 27.584 * 0.576 * 1.363 3999.38 47396.22 17.74 194.18 14.04 14.77 * 0.03 9906.35 5065.2 6251.14 2347.74 1224.09 75.52 * 38.55 0.19 * 682.952 * 27.584 * 0.576 * 1.363 3530.79 61186.09 11.57 358 10.52 12.65 * 0.03 19994.65 7741.84 16534.85 2084.96 1905.51 47.33 * 38.55 0.13 * 682.952 * 27.584 * 0.576 * 1.363 7814.04 3956.16 16.63 201.24 8.03 5, 86 0.3 15169.07 6127.54 51705.74 2390.94 1633.53 83.19 * 38.55 0.12 * 682.952 * 27.584 * 0.576 * 1.363 4604.45 888.06 14.76 182.11 6.51 6.1 * 0.03 12720.61 6899.42 10283.9 3318.57 1170.49 81.2 120.47 0.4 * 682.952 180.96 16.39 * 1.363 3281.88 225030.98 10.71 156.66 8.5 8.79 0.44 42131.57 5995.29 27883.7 3809.2 1531.6 77 * 38.55 0.12 * 682.952 * 27.584 7.45 * 1.363 5955.59 3284.63 9.63 201.24 7.05 5.36 * 0.03 15207.48 4068.34 6723.19 1653.1 716.62 45.31 * 38.55 0.16 * 682.952 * 27.584 * 0.576 * 1.363 6239.58 8628.14 22.47 232.7 5.47 6.57 * 0.03 26409.3 7180.74 17041.71 1331.42 1252.91 117.94 * 38.55 0.11 * 682.952 * 27.584 4.82 * 1.363 7262.76 11448.07 10.11 145.89 6.98 6.8 * 0.03 7152.74 4644.83 14343.76 2607.87 1945.96 16.95 * 12.5 0.09 * 696.339 * 27.577 * 0.61 * 1.358 2898.46 1083.39 13.47 237.38 8.72 10.77 0.29 26008.97 6450.74 5300.9 2932.06 2279.46 114 , 13 * 12.5 0.14 * 696.339 * 27.577 * 0.61 * 1.358 3132.57 2638.11 24.04 185.91 12.58 10.08 0.39 39295.62 7362.41 8670.53 1774 , 49 1266.1 73.33 62.28 0.24 * 696.339 * 27.577 * 0.61 * 1.358 3393.16 52584.29 15.71 237.38 8.77 6.05 0.31 10064.33 4731, 37 7,304.51 2560.79 2636.46 29.5 * 12.5 0.16 * 696 , 339 * 27.577 * 0.61 * 1.358 2945.68 12858.44 13.39 214.94 14.47 7.13 0.34 11954.1 4948.39 5519.65 2847.18 1392.16 36.49 * 36.49 0.14 * 644.123 * 27.061 5.01 28.17 3240.97 48244.27 18.56 257.69 3.91 8.16 * 0.029 6005.23 3628.82 24967.29 1868.62 1546, 59 72.05 * 36.49 0.57 * 644.123 * 27.061 * 0.591 * 1.31 7978.89 52845.75 23 271.31 3.34 4.4 * 0.029 24930.47 3281.28 25218.65 1090, 53 1168.67 41.45 * 36.49 0.12 * 644.123 * 27.061 3.76 * 1.31 4612.7 26466.45 19.08 257.69 7 7.18 * 0.029 35828.1 2588.78 21759 , 73 1224.51 712.81 8.76 174.38 0.16 * 644.123 * 27.061 * 0.591 * 1.31 4076.35 26256.65 20.78 281.97 4.93 4.4 * 0.029 31900.44 8127.2 13471.56 1409.5 1243.42 47.19 * 36.49 0.16 * 644.123 * 27.061 8.67 * 1.31 6638.42 70339.02 31.54 173.31 5.38 4, 85 0.39 41592.48 2128.49 12663.48 1893.11 681.41 24.79 * 36.49 0.17 * 644.113 * 27.061 6.24 * 1.31 6088.47 58764.72 30.75 243 , 71 4.39 5.3 * 0.029 23165.93 3957.97 49275.61 1541.71 1617.5 12.17 * 36.49 0.12 * 644.123 * 27.061 * 0.591 * 1.31 3366.12 74287.37 13 , 18 161.4 4.49 6.97 0.34 29 334.42 2780.84 10681.05 391.46 834.53 2.18 * 36.49 0.11 * 644.113 * 27.061 * 0.591 * 1.31 6082.88 63340.29 24.15 196.01 6.54 5.3 * 0.029 18933.75 1745.17 3869.94 1187.95 1448.12 16.83 * 36.49 0.12 * 644.123 * 27.061 * 0.591 * 1.31 2537.13 30343.22 18.15 156 , 16 3.72 7.57 * 0.029 99448.14 3861.06 13512.01 2080.08 869.88 81.12 118.66 0.14 * 644.123 * 27.061 * 0.591 * 1.31 3727.48 122859.46 25.02 205.36 8.4 3.45 * 0.029 35310.12 8086.94 9528.62 1753.8 1330.01 23.8 232.92 0.1 * 644.123 * 27.061 4.18 * 1.31 1873 , 17 132863.04 5.44 166.55 9.06 2.94 * 0.029 26454.26 5382.4 13208.74 2184.19 1101.8 14.14 433.67 0.13 5035.74 * 27.061 10, 26 * 1.31 2291.35 49434.16 7.33 166.55 4.96 1.53 * 0.029 8905.05 4054.95 11826.96 1000.65 1113.6 69.88 * 36.49 0.17 * 644.123 * 27.061 * 0.591 * 1.31 5846.62 84134.87 11.28 342.31 9.19 1.84 * 0.029 34159.99 2857.72 8010.81 2603.33 1361.5 24.61 * 36 , 49 0.13 * 644.123 * 27.061 * 0.591 * 1.31 5069.17 83975.08 12.85 192.85 5.08 3.2 * 0.029 27338.97 4016.15 26205.22 1657.86 1479.63 14.14 * 12.5 0.15 * 696.3 39 * 27.577 * 0.61 * 1.358 2795.52 20801.6 14.82 169.14 9.07 6.6 0.26 35640.87 3726.61 9404.04 1748.15 893.81 18.86 * 12 , 5 0.13 * 696.339 191.94 4.34 11.22 3314.98 12819.37 8.93 496.8 5.98 9.38 0.32 16601.14 13750.39 7585.67 3886.14 798 , 18 44.71 * 12.5 0.19 * 696.339 * 27.577 * 0.61 * 1.358 7620.72 1899.97 20.66 196.7 14.3 5.91 0.19 9823.24 6648.78 5462 , 15 1301.93 1565.03 7.07 * 12.5 0.15 4907.79 168.42 9.19 * 1.358 2605.61 3959.89 32.06 284.03 23.45 14 0.49 29208, 8 4869.46 30290.31 1506.33 1313.4 72.67 48.44 0.16 * 696.339 * 27.577 * 0.61 * 1.358 3345.02 2339.12 31.34 301.69 2.43 5.91 0.39 43728.47 4908.93 7169.29 3411.33 1376.41 32.47 * 12.5 0.25 * 696.339 186.09 * 0.61 * 1.358 2020.81 52660.59 9.81 318, 88 15.33 9.14 0.32 18336.87 6312.16 10884.45 2019.94 1737.42 24.71 * 12.5 0.16 * 696.339 * 27.577 * 0.61 * 1.358 4577.29 11271, 02 12.13 194.03 8.85 7.13 0.32 17309.12 3018.91 3805.59 1809.24 1175.3 24.84 * 12.5 0.11 * 696.339 * 27.577 * 0.61 * 1.358 3090.91 9416.49 10.82 180.4 5.72 8.65 0.25 9382.1 3844.69 11895.6 3 3401.16 1557.18 10.75 * 12.5 0.09 * 696.339 283.61 * 0.61 * 1.358 2414.52 1899.97 17.56 660.38 24.1 8.9 0.44 19539.28 3687.26 5691.86 2207.94 1604.25 13.99 * 12.5 0.1 * 696.339 * 27.577 * 0.61 * 1.358 3216.13 * 141.277 12.74 217.49 14.73 2.54 0.28 59,103.85 6292.37 12520.53 2235.95 1155.54 45.23 100.68 0.19 * 696.339 * 27.577 * 0.61 * 1.358 3742.01 1655.91 24.02 268.14 10.1 6 , 87 0.37 11483.11 5303.76 7866.15 1590.66 1455.08 48.91 * 12.5 0.14 * 696.339 * 27.577 7.89 * 1.358 1944.74 24071.82 12.3 272, 72 5.72 15.22 0.38 6490.55 3844.69 8614.82 821.05 861.96 22.4 41.22 0.12 * 696.339 168.42 * 0.61 * 1.358 2582.36 79706, 22 5.57 201.99 7.52 5.48 0.26 19140.58 4790.54 9415.12 1507.52 977.27 44.94 * 12.5 0.15 * 696.339 * 27.577 * 0.61 * 1.358 6362.94 7156.37 11.84 247.03 14.47 5.33 0.27 13790.78 2351.82 8112.45 4776.3 1490.44 7.82 * 12.5 0.17 * 696.339 * 27.577 * 0.61 * 1.358 2475.13 969.9 6.59 247.03 9.8 4.89 0.28 11049.85 4790.54 5023.55 3408.79 1447.22 38.77 * 12.5 0.09 * 696.339 * 27.577 * 0.61 * 1.358 5629.08 * 141.277 13.2 9 196.7 9.41 3.95 0.19 27711.54 2979.63 8235.42 2904.57 1329.16 31.79 * 12.5 0.08 * 696.339 * 27.577 * 0.61 * 1.358 3907, 52 3037.4 12.19 169.14 6.07 6.05 0.16 22686.74 2940.36 6898.33 2392.37 806.16 13.16 * 12.5 0.12 * 696.339 * 27.577 * 0 , 61 * 1.358 5261.06 2857.26 12.47 148.53 6.15 5.48 0.17 10016.32 1490.71 9503.81 2776.23 806.16 4.4 * 12.46 0.12 * 676,132 * 27,292 * 0.628 * 1.37 3854.09 10047.31 24.06 235.98 6.13 5.04 0.15 32843.59 3599.6 9638.97 2472.36 1672.35 74.86 * 12.46 0.09 * 676.132 * 27.292 * 0.628 * 1.37 3427.6 3296.59 8.78 183.05 4.52 10.09 0.24 26958.26 2192.13 43633.59 2413.27 1352 , 37 18.37 * 12.46 0.11 * 676.132 * 27.292 * 0.628 * 1.37 3506.6 62510.51 15.28 288.88 4.24 4.25 0.15 25937.25 3958.03 12350 , 54 2851,54 796,6 23,32 755,53 0,24 * 666,122 * 27,161 * 0,536 * 1,359 5617.08 3106.56 18.91 217.06 8.38 46.1 0.32 35120.9 1425.48 9712, 47 2792.8 2139.28 14.5 518.83 0.28 * 666.122 * 27.161 * 0.536 * 1.3959 3903.91 3335.87 17.23 211.59 4.47 42.7 0.39 28913.67 5831.47 34848 , 4 2170.96 1834.04 38.9 447.72 0, 37 * 666,122 * 27,161 * 0,536 * 1,359 3381.74 10475.39 10.57 424.67 11.51 58.31 0.41 40900.92 14329.36 14245.33 2523.58 1792 34.2 326.78 0 , 3 * 666,122 * 27,161 * 0,536 * 1,359 2824,29 6831.11 9.07 173.11 9.29 40.5 0.27 33875.85 9349.77 4808.53 1335.66 2402.02 72.75 670 , 1 0.26 * 666.122 * 27.161 * 0.536 * 1.359 2429.91 6147.25 6.7 102.37 12.06 26.37 0.42 23614.46 1158.7 8739.94 2868.47 1776.72 20 , 21 672.8 0.35 * 666.122 * 27.161 * 0.536 9.4 1841.64 2291.22 11.93 143.39 14.45 34.03 0.35 18379.91 25733.8 14596.3 2419.97 2024.9 60.65 359.86 0.25 * 666.122 * 27.161 * 0.536 * 1.359 2578.18 5734.16 13.28 127.31 10.75 17.94 0.32 14114.92 8585.57 7448.91 1698, 46 2265 37.15 386.55 0.22 * 666.122 * 27.161 15.23 45.95 2141.54 1939.26 6.73 1121.43 3.27 13.36 0.32 17699.55 8712.58 5077, 33 2142.73 260.21 41.92 604.86 0.21 * 666.122 * 27.161 * 0.536 * 1.359 1550.56 5245.63 6.75 194.75 7.6 47.16 0.3 24705.73 11225, 33 23763,32 2511,1 1259,28 37,38 401,25 1,66 * 666,122 * 27,161 * 0,536 * 1,359 5170,44 4036,6 12,79 337,33 13,43 103, 41 0.47 23265.64 16340.37 15848.31 2780.06 1807.29 42.48 350.89 0.38 * 666.112 * 27.111 * 0.536 * 1.398 4892.7 28697.41 9.11 251.89 32, 64 40.16 0.33 7892.41 12,303.08 13705.86 1791.35 1883.69 29.76 258.89 0.86 * 666.122 245.14 * 0.536 * 1.396 2486.73 8891.5 25.27 540 , 99 15.37 18.6 0.49 15 458.79 12 396.35 2766.36 885.19 1213.13 52.88 227.08 0.3 * 666.122 * 27.161 * 0.536 * 1.391 2183.09 15109.42 9 , 95 248.52 10.46 20.1 0.5 17198.72 17238.54 5304.8 2335.68 2043.97 72.43

410.03 0.58 * 666.122 * 27.161 * 0.536 * 1.359 2155.37 7870.54 14.33 293.84 10.3 30 0.5 33588.22 16035.5 8996.51 2275.41 1929.51 97, 96 239.88 0.66 * 666.122 * 27.161 4.7 * 1.359 1342.22 4231.78 17.22 200.44 16.58 16.38 0.54 33444.35 8151.19 6509.06 1934.39 2154.53 41.98 119.3 0.43 * 666.122 * 27.161 * 0.536 * 1.397 1987.8 1939.26 34.66 200.44 8.83 18.05 0.43 38609.17 9916.96 3441.09 976.82 1616.05 87.75 181.26 0.51 * 666.128 208.26 * 0.536 * 1.359 2526.65 9110.86 26.25 169.02 19.38 13.24 0.57 41068.07 7413.29 11046, 78 1025.85 1738.49 68.46 177.92 0.62 * 666.122 * 27.161 * 0.536 43.65 2618.39 8191.34 9.97 107.58 18.46 50.05 0.44 32484.34 6325 , 95 11211.36 2554.83 1224.67 70.97 377.69 0.29 * 666.122 223.09 * 0.536 * 1.359 3454.85 35912.71 15.92 222.45 37.01 42.03 0.3 24012.14 4609.77 5451.52 1166.35 1573.93 23.79 467.81 0.24 * 666.122 * 27.161 * 0.536 * 1.359 3390.86 7111.31 8.35 217.06 4.28 18.27 0, 25 24903.38 6255.12 17623.89 2448.89 1570.1 108.88 227.08 0.48 * 666.122 * 27.161 * 0.536 * 1.359 7464.88 55811.73 14, 76 368.51 17.34 28.87 0.26 23962.48 9386.28 4996.82 3461.14 2379.19 26.35 271.43 0.13 * 666.122 * 27.161 * 0.536 * 1.359 2393.12 2431.55 16 , 2 173.11 16.29 21.66 0.23 37892.93 5550.33 11893.79 1556.16 1792 123.94 377.69 0.3 * 666.112 * 27.161 * 0.536 * 1.396 3506.84 1476.26 18.98 * 12.834 12.62 21.46 0.22 40709.91 1221.09 2205.55 1770.95 2040.16 17.61 293.16 0.11 * 666.122 * 27.161 * 0.536 28.5 4128.39 7827, 16 11.75 117.66 8.73 11.89 0.21 51678.25 2116.38 11034.12 4267.46 24657.52 45.19 191.22 0.09 * 666.122 * 27.161 * 0.536 * 1.395 2205.31 2824 , 25 8.76 334.42 10.75 9.71 0.14 25026.8 6716.57 4862.4 2897.36 24504.64 13.07 59.24 0.3 * 666.122 * 27.161 * 0.536 * 1.359 1493 , 45 13,185.84 15.9 152.17 59.16 29.34 0.31 53229.69 7198.35 16060.97 2495.52 2036.34 32.25 174.57 0.24 * 666.122 * 27.161 4, 7 * 1.359 3577.46 19223.92 12.97 124.94 10.33 13.96 0.36 29253.01 973.11 5398.21 454.78 566.63 9.74 86.35 0.3 * 666.122 * 27.161 * 0.536 * 1.359 2373.36 1345.1 9.71 160.71 9.45 11.01 0.18 27600.9 8060.92 8714.25 717.54 2150.72 36.31 214.16 0.37 * 666.122 * 27.161 * 0.536 * 1.396 6045.71 23020.78 20.55 115.18 14.58 28.2 0.34 25642.64 1189.86 8379.89 1008.29 1278.51 10.75 323.75 0.22 * 666.122 * 27.161 * 0.536 47.64 6176.41 7429.76 13.72 1153.12 8.7 18.38 0.46 34546.51 4644.36 9060, 57 2342.86 1573.93 41.7 236.69 0.14 * 666.122 * 27.161 * 0.536 * 1.398 3800.6 6543.53 5.78 117.66 5.77 34.75 0.21 13444.56 820.47 15109 , 87 3767.08 1879.87 7.1

217.4 0.1 * 666.122 * 27.161 * 0.536 * 1.359 3454.85 102495.08 7.59 124.94 6.42 21.35 0.23 26844.16 1252.37 10005.58 3083.92 1661.98 24, 93 112.12 0.1 * 666.122 * 27.161 * 0.536 * 1.359 1367.65 6592 7.15 94.28 5.81 14.55 0.18 19930.45 1840.79 3021.14 744.92 1466.63 25 , 7 181,26 0,08 * 666,122 * 27,161 * 0,536 * 1,359 1879,34 75096,28 14,64 198,55 13,3 15,93 0,14 55078,91 3311,26 8970,87 2461,31 2363 , 97 75.14 347.89 0.36 * 666.112 * 27.161 * 0.536 66.29 3433.49 17802.98 14.38 781.37 8.38 22.79 0.38 35957.91 4506.11 10577.66 1119.76 2489.5 99.58 112.12 0.26 * 666.122 * 27.161 * 0.536 * 1.359 1367.65 31021.53 6.82 192.83 27.02 34.75 0.23 21659.91 8948.84 6574.66 1052.28 2649.15 48.59 55.22 0.21 * 666.112 193.3 4.11 * 1.359 3966.84 19094.74 7.36 134.32 55.64 48.61 0.28 32580 , 42 1696.13 4673.62 275.88 1960.04 10.28 174.57 0.53 * 666.122 200.79 * 0.536 10.84 2045.2 59617.2 5.56 204.19 8.51 21.56 0 , 34 22465.17 14782.83 15798.26 2090.51 2835.24 46.07 541.24 0.52 * 666.126 266.97 * 0.536 * 1.395 1411.08 37713.52 12.79 241 , 7 16.45 49.49 0.29 20135.34 17334.31 8560.04 1875.35 1792 35 476.37 0.2 * 666.122 * 27.161 3.51 * 1.396 1812.13 12178.01 9.58 164 , 89 7.92 22.39 0.28 18744.03 5165.54 6495.93 1197.9 1658.16 44.3 347.89 0.12 * 666.122 * 27.161 * 0.536 139.04 3266.71 1053.03 10.43 200.44 11.11 35.64 0.21 21962.47 2775.25 6587.77 832.32 827.11 19.66 305.45 0.35 * 666.128 178.21 * 0.536 * 1.359 1919, 88 39 614.42 9.59 256.9 16.16 34.75 0.3 37033.2 10652.53 14846.87 1664.82 1608.4 25.23 507.56 0.14 5828.81 * 27.161 13, 08 * 1.359 3345.32 7696.11 14.07 104.99 9.74 47.64 0.26 43768.32 1189.86 8199.47 477.12 441.45 30.98 290.07 0.84 * 666.122 170.61 * 0.536 * 1.359 3442.64 171119.8 50.35 215.24 11.64 17.72 0.57 68033.85 10837.05 10590.35 3317.03 1860.78 32.94 262.03 0.25 * 666.122 * 27.161 * 0.536 * 1.359 1833.59 59768.18 12.49 213.42 12.94 20.73 0.24 22941 7269.94 4389.13 1603.65 1485.8 44.08 187.91 0.17 * 666,122 237.82 * 0.536 * 1.396 2632.79 31389.68 5.73 181.13 9.32 36.17 0.29 29495.16 6503.28 10806.01 2344.91 216.55 36.98 314.6 2 0.47 * 666.122 * 27.161 * 0.536 * 1.359 3197.52 1476.26 48.75 348.83 3.2 11.64 0.36 57917.34 4575.2 12725.53 698.55 1631.37 22.59 479 , 22 3.39 * 666.122 * 27.161 * 0.536 * 1.359 4462.62 3410.05 15.46 194.75 34.43 13.84 0.41 80 900 13594.87 7552.75 1669.62 1776.72 80 122, 86 0.67 * 666.112 223.09 24.11 * 1.359 4391.38 20529.22 8.79 99.71 7.53 34.48 0.47 45899.41 805.32 11994.73 2516.3 3555.51 35.17 174.57 0.17 * 666.122 * 27.161 * 0.536 12.67 2039.72 2243.18 6.36 82.91 7.73 9.44 0.2 40757.66 3683.73 5584.61 723, 33 1902.78 23.19 42.83 0.29 * 666.122 * 27.161 * 0.536 * 1.359 2384.65 16890.09 25.14 188.97 17.01 10.37 0.3 16320.85 5673.2 13278.85 1348 , 57 1688.77 48.53 * 39.91 0.57 7301 272.46 9.03 * 1.38 5587.62 24663.78 12.72 172.16 10.53 15.25 0.58 54471.76 3301.93 6872.17 1400.76 2449.69 35.47 * 39.91 0.53 * 682.77 227.88 * 0.64 10.39 4513.28 6077.12 15.3 149.74 244, 78 14.8 0.49 29936.89 5998.89 12346.24 1473.72 3949.24 50.97 * 39.91 0.62 * 682.77 328.83 * 0.64 * 1.38 3536.03 18,208.05 12.91 274.83 15.68 9.02 0.89 31089.02 5648.02 9143.57 2346.59 3616.82 85.89 * 39.91 0.51 * 682.77 286.85 * 0.64 10.39 3845.93 20197.64 18.2 315, 52 19.9 30.84 0.68 20797.54 10808.2 13459.7 2813.13 2977.92 88.69 * 39.91 0.38 * 682.77 163.97 * 0.64 * 1.38 3420.79 5506.25 15.01 206.36 6.6 * 0.756 0.58 47401.37 4780.93 10095.19 3173.5 2326.68 293.3 * 39.91 0.51 * 682.77 279 , 68 * 0.64 * 1.38 2847.41 12886.15 41.01 397.69 20.65 23.95 0.69 23416.16 14592.22 4346.71 4015.6 2988.79 76.78 * 39.91 1.34 * 682.77 550.69 * 0.64 16.58 3851.84 12010.85 27.73 409.97 14.97 9.02 1.21 58523.37 12401.64 10189.4 2331.6 3064.76 51.57 193.28 0.35 * 682.77 * 27.33 * 0.64 * 1.38 5361.96 19880.16 28.83 303.26 16.47 27.66 0 , 48 46043.92 9501.72 15878.42 1476.19 2505.35 59.61 219.02 0.79 4592.21 395.64 4.91 9.83 2629.08 17491.33 9.82 318.55 115.63 37 0.87 20582.68 12992.05 14203.62 1824.08 2038.22 56.25 169.51 0.44 * 682.77 315.02 4.48 * 1.38 4448.62 134965, 36 29.12 217.21 28.77 17.67 0.91 15107.43 4780.93 8757.58 4466.13 1917.95 23.87 * 39.91 0.51 * 682.77 * 27.33 12 , 89 * 1.38 2156.43 2441.64 14.52 258.48 18.19 9.21 0.55 39532.58 6887.32 9143.57 3825.71 2066.72 106.57 * 39.91 0 , 64 * 682.77 163.97 * 0.64 * 1.38 5245.4 7822.61 18.79 202.69 8.98 10.46 0.59 35526.54 6367.18 10844.39 3258.63 3759.73 76.16 * 39.91 0.4 * 682.77 434.2 * 0.64 12.64 2050.15 50713.96 7.09 187.7 34.29 15.69 0.59 54236, 1 6887.32 7327.53 2122.19 1609.91 103.25 158.73 1.43 * 682.77 180.56 * 0.64 * 1.38 4865.78 3402.9 17.41 340.92 19.16 19.82 0.62 30357.5 9107.64 10564.6 3162.08 2298.61 34.78 * 39.91 0.34 * 682.77 163.97 * 0.64 9.27 4467.07 9237, 58 14.38 329.07 26.87 17.11 0.66 5672.87 13636.59 20944.45 3008.78 1580.48 147.67 * 39.91 0.47 * 682.77 * 27.33 4.04 * 1.38 7976.75 29122.66 11.68 345.31 10.08 10.98 0.47 37145.75 2989.23 10032.3 2853.46 1761.89 46.37 * 39.91 2.06 * 682.77 163.97 * 0.64 8.71 3620.17 2441.64 14.04 358.36 14.49 16.55 0.73 35225.62 14133.05 46583.47 4013.01 1917.95 81, 42 * 39.91 1.01 * 682.77 321.95 * 0.64 * 1.38 6017.56 3840.85 16.11 297.05 24.67 14.5 0.93 64944.42 15161 16009.53 2 948.11 2583 119.59 * 39.91 0.52 * 682.77 196.7 * 0.64 * 1.38 4120.23 7420.8 17.03 290.78 9.01 16.12 0.74 26105, 69 14,702.35 13,758.03 2925.38 2759.4 139.75 * 39.91 0.53 7301 293.96 6.6 * 1.38 3701.86 7778.87 5.72 365.51 18.41 18 , 89 0.81 68219.42 3135.63 10782.33 1809.21 2786.83 48.5 * 39.91 0.42 * 682.77 * 27.33 * 0.64 * 1.38 7448.58 6901 , 71 21.65 180 12.83 9.93 0.63 45160.23 8410.43 11522.95 3771.62 2146.26 97.41 * 39.91 0.56 * 682.77 * 27.33 * 0 , 64 * 1.38 4790.27 40903.92 32.8 213.62 9.17 12.64 0.84 48565.06 4461.72 6220.02 1193.03 1324.28 30.31 * 39.91 0 , 48 * 682.77 212.46 * 0.64 * 1.38 4069.17 37132.65 16.33 198.99 34.44 11.32 0.53 25967.04 5314.71 3850.73 1680.36 3075.59 26.48 * 39.91 0.27 * 682.77 212.46 * 0.64 9.27 4687.01 14165.37 11.81 265.07 16.47 8.06 0.67 45876, 89 11198.33 10937.36 4364.11 1491.76 76.37 * 39.91 0.83 * 682.77 163.97 * 0.64 * 1.38 6499.68 11013.18 17.45 379.65 8, 63 16.12 0.53 50270.53 3998.42 9111.53 2781.65 2174.57 46.02 494.54 0.66 * 682.77 328.83 * 0.64 25.6 4341.43 37980, 93 11.94 617.9 3 25.83 11.49 0.73 51735.8 10771.01 9685.04 3503.49 3328.82 91.65 * 39.91 0.44 * 682.77 279.68 8.23 * 1.38 4192 , 6 25618.88 24.63 1040.85 13.1 8.83 0.41 55484.86 9051.26 10626.89 1742.29 1894.94 81.3 383.92 0.66 * 682.77 180, 56 4.04 10.11 6708.77 10071.9 18.55 189.6 29.29 12.48 0.49 50814.41 4099.62 49215.44 4154.38 3414.47 68.7 347.2 0 , 36 * 682.77 196.7 * 0.64 * 1.38 6197.27 10141.62 12.37 153.92 139.69 11.99 0.4 22391.61 3590.61 12754.36 3807.67 2168.91 19.27 * 39.91 0.83 8942.04 355.98 12.89 10.95 4045.2 104010.96 12.72 467.88 120.4 22.61 1.15 114784.86 4180 , 38 7594.56 1001.18 2682.4 26.67 439.93 0.5 * 682.77 257.86 * 0.64 * 1.38 4998.77 19437.12 17.29 290.78 5.4 11, 32 0.46 81335.38 6483.08 8902.71 3989.71 2885.31 23.28 * 39.91 0.6 * 682.77 * 27.33 * 0.64 * 1.38 4846.87 1875, 93 12.82 202.69 9.13 15.98 0.52 23676.4 5687.11 8660.55 3345.19 2304.23 49.5 * 39.91 0.92 * 682.77 163.97 9, 82 * 1.38 5113.62 36925.26 14.2 172.16 10.68 14.5 0.69 24193.21 5960 9031.33 4077.79 1756.07 58.42 * 39.91 0.38 10496 23 196 , 7 11.37 * 1.38 5120.03 7188.99 5.94 261.79 8.4 21.86 0.67 33405.36 8334.82 13295.17 397.31 1521.41 55.52 * 39 , 91 0.27 * 682.77 * 27.33 * 0.64 * 1.38 2532.02 6998.64 11.19 168.18 11.1 13.27 0.58 33073.79 7385.03 10283, 44 2331.6 1848.8 86.79 419.94 0.46 * 682.77 * 27.33 * 0.64 48.25 5663.51 20158.17 15.54 180 11.25 12.32 0.68 38060.02 6521.68 10220.76 2369.07 1883.42 39.57 * 39.91 0.52 7720.93 315.02 4.04 * 1.38 4090.17 9716.81 11.46 236.51 13.32 29.76 0.7 37739.36 7651.88 12 194.51 1954.37 2516.46 20.79 * 39.91 0.21 * 682.77 227.88 * 0.64 * 1.38 3261 , 01 11332.98 18.53 278.05 8.67 13.43 0.61 23503.05 13654.99 9574.05 3391.09 2236.7 45.67 * 39.91 0.47 * 682.77 163 , 97 * 0.64 * 1.38 6234.8 5854.81 18.78 158.05 9.47 8.64 0.39 38797.77 3856.25 6080.86 2847.16 2405.05 28.85 * 39.91 0.56 * 682.77 * 27.33 * 0.64 * 1.38 3103.03 14 451.09 13.4 220.77 9.85 14.5 0.73 19522.86 4939.7 8967 , 06 1853.85 1738.62 73.83 * 39.91 0.57 * 682.77 382.55 * 0.64 * 1.38 4758.91 76663.64 16.59 160.1 15.38 13, 59 0.45 42 976.71 5 804.21 8 302.91 343 0.65 2292.99 13.11

* 39.91 1.3 * 682.77 * 27.33 * 0.64 * 1.38 5078.45 78610.63 11.36 309.42 16.66 10.98 0.77 75694.14 1956.83 11813.75 1094.05 1721.14 71.29 * 39.91 0.31 * 682.77 * 27.33 * 0.64 * 1.38 4947,98 106647.08 12.72 164.16 11.06 8.64 0.49 40775.58 5334.37 18532.22 3073.33 1521.41 16.05 * 39.91 0.3 * 682.77 196.7 * 0.64 * 1.38 4460.91 61681 , 15 13.31 258.48 7.26 16.55 0.6 53010.14 6154.2 14203.62 2750.18 1825.67 20.19 * 39.91 0.32 * 682.77 328.83 * 0.64 12.07 3498.48 9498.24 13.96 366.94 26.01 17.39 0.56 87089.51 14224.92 12315.92 3346.47 2416.22 102.63 408.75 0.42 * 682.77 * 27.33 * 0.64 * 1.38 4967.01 7734.92 34.53 409.97 5.71 15.69 0.5 40897.05 7289.54 11859.55 3762.61 2510, 9 57.31 166.83 0.46 * 682.77 180.56 4.04 * 1.38 4765.17 47804.05 8.53 241.66 13.32 17.67 0.53 39557.02 7193.94 13160 , 31 2052.53 1491.76 58.08 * 39.91 0.43 * 682.77 163.97 * 0.64 12.07 4476.3 1875.93 18.89 661.48 19.64 188.39 0.55 38207.82 3322.65 20492.71 3493.26 2377.1 46.2 * 39.91 0.84 * 682.77 257.86 * 0.64 * 1.38 5326.26 8814.46 15 76 215,42 14,15 12 , 64 1.26 55272.84 7708.97 20506.84 3313.35 3393.08 117.7 335.57 5.11 * 682.77 180.56 * 0.64 * 1.38 5423.8 27582.81 17.49 315 , 52 19.64 11.32 0.84 44466.05 7251.31 11584.27 2153.31 2321.07 40.46 457.54 0.8 * 682.77 196.7 * 0.64 * 1.38 7152.06 25119.45 11.61 390.79 16.24 30.41 0.7 0.9999.33 7059.93 13130.31 1447.75 2988.79 22.49 * 39.91 0.54 * 682.77 243 4.48 9.83 3728.22 19760.09 27.32 310.95 13.4 39.14 0.87 61824.19 9894.74 15411.03 3715.03 1802.51 34.24 778.23 0 , 44 * 682.77 301.03 5.77 * 1.38 6142.83 9534.97 12.89 309.42 15.5 19.82 0.67 60974.78 7308.65 8188.47 3093.6 2577 , 46 15.14 * 39.91 0.44 * 682.77 272.46 * 0.64 * 1.38 3553.4 33558.34 13.86 234.79 25.79 14.5 0.65 76515, 94 9838.66 11061.1 3587.98 2197.19 89.27 * 39.91 0.34 * 682.77 272.46 * 0.64 * 1.38 4928.97 25152.97 19.78 245.06 23 17.94 0.68 46020.06 6656.59 14529.02 5780 1662.72 15.51 * 39.91 0.84 * 682.77 408.6 * 0.64 * 1.38 2863.93 28880.08 9 , 62 271.6 16.84 19.29 0.7 52845.03 11661.85 13668.64 2643.35 949.1 37.05 * 39.91 0.49 6438.09 328.83 5.77 * 1 , 38 7627.97 42357.71 5.08 200.84 243.82 23.83 0.75 11529.16 3528.99 10063.76 3098.67 1946.67 12.67 * 39.91 0.3 * 682 , 77 * 27.33 * 0.64 * 1.38 5866.38 15009.02 9.05 265.07 4.89 9.21 0.52 52679.89 5608.91 15790.94 3609.77 2360.31 55.44 * 39.91 0.58 * 682.77 272.46 * 0.64 * 1.38 4544.16 68242.83 9.12 241.66 46.51 15.69 0.84 18237.22 3672 , 58 10968.32 2843.37 1011.75 34.56 * 39.91 1 * 682.77 227.88 * 0.64 16.58 5666.81 12829.37 12.78 246.76 9.85 17.11 0 , 82 26,657.44 8410.43 10611.33 3393.64 1825.67 62.65 * 39.91 0.39 5538.56 180.56 7.42 * 1.38 3763.43 4386.38 23.03 227 , 83 10.8 12.48 0.58 30723.96 2437.05 8040.84 3585.41 1779.31 40.91 * 39.91 0.96 * 682.77 163.97 * 0.64 * 1, 38 5229.27 1875.93 23.43 253.49 18.93 12.15 0.66 35300.91 8674.7 7843.14 2853.46 1998.23 52.31 231.66 0.96 * 682.77 315.02 * 0.64 * 1.38 5209.94 9931.18 11.36 315.52 27.72 19.29 0.68 68124.27 10286.8 9367.26 3680.34 1545.07 49.37 * 39.91 1.99 * 682.77 180.56 * 0.64 8.71 4909.99 32247.2 17.4 455.01 14.67 18.89 0.86 24931.56 11049.8 15323.17 3139 ,2 4 1998.23 58.84 * 39.91 0.28 5993.44 227.88 * 0.64 * 1.38 2800.71 7601.71 14.24 359.79 13.62 36.01 0.68 20087 , 41 13986.01 6770.11 2780.39 1098.64 35.97 * 39.91 0.79 * 682.77 328.83 * 0.64 * 1.38 3778.13 8656.12 11.07 297, 05 14.97 19.82 0.6 35376.16 9445.49 12980.14 2898.87 1123.3 25.47 295.39 0.49 * 682.77 243 * 0.64 * 1.38 4516.37 37904.28 21.21 238.23 30.85 28.67 0.48 30958.8 7136.54 8595.75 2123.43 1306.18 27.72 * 39.91 0.52 * 682.77 * 27, 33 * 0.64 * 1.38 5779.62 10618.57 36.37 345.31 12.34 14.2 0.88 24563.55 11587.75 13100.3 3131.63 1998.23 27.67 435, 5 0.52 9728.52 408.6 4.04 * 1.38 5126.43 14959.01 14.49 431.42 19.98 * 0.756 0.37 41236.83 9088.85 17528.75 2186.93 2814 , 23 21.94 524.59 1.11 * 682.77 301.03 * 0.64 47.68 4702.61 61711.46 18.54 1034.52 9.58 20.72 1.03 20766.91 9239 , 13 11215.43 2281.68 2365.91 30.92 226.62 0.44 * 682.77 * 27.33 * 0.64 * 1.38 6934.43 3494.52 27.07 287.62 7, 18 16.83 0.64 39434.77 8012.87 9937.82 2967.06 2021.1 38.49 * 39.91 0.27 * 682.77 * 27.33 * 0.64 9.27 4878.4 46,522.17 34.21 23 8.23 17.74 8.45 0.55 23964.11 4541.74 22041.75 3969.01 1912.2 26.38 * 39.91 0.65 * 682.77 * 27.33 * 0.64 * 1, 38 4216.81 57121.57 21.85 166.18 11.21 10.64 0.37 32484.79 2372.36 6736.02 4236.32 993.01 9.29 466.3 0.68 * 682.77 * 27.33 * 0.64 * 1.38 4932,14 8656.12 8.63 315.52 4.93 17.67 0.52 38675.02 4361.47 4737.84 2838.33 1686.12 29.7 581 , 61 0.47 * 682.77 212.46 4.48 * 1.38 4658.96 6803.55 11.58 202.69 41.02 22.36 0.61 33277.95 4099.62 7444.6 2509 , 17 1562.79 20.49 * 39.91 0.31 * 682.77 * 27.33 * 0.64 * 1.38 6085.16 18014.82 13.52 215.42 14.3 10.64 0 , 58 44 753.47 4541.74 9937.82 2965.8 1715.31 25.61 * 38.55 0.16 * 682.952 * 27.584 * 0.576 * 1.363 4496.47 6704.54 15.36 196.55 12.63 11.8 * 0.03 36132.48 9710.35 3820.19 4621.18 1025.68 95.96 * 38.55 2.88 * 682.952 165.5 * 0.576 * 1.363 7970.09 20140.03 36.51 432.81 18.7 8.15 0.55 14666.91 14220.11 7473.47 2949.32 2744.89 291.25 * 38.55 0.27 * 682.952 421.01 * 0.576 * 1.363 2916.2 18623 , 59 8.93 3460.57 47.06 11.02 * 0.03 28009.96 6861.87 16444.35 1156.25 1865.02 87.42 * 38, 55 0.27 * 682.952 * 27.584 * 0.576 * 1.363 3807.19 39656.51 16.73 172.16 7.19 5.24 * 0.03 14355.23 6146.42 8218.53 1220.01 1800.16 118 , 3 * 38.55 0.36 * 682.952 165.5 * 0.576 * 1.363 3807.19 35000.36 16.8 228.34 14.79 9.42 * 0.03 33423.24 6466.96 11052.53 1972 , 71 2388.91 89.23 * 38.55 0.28 * 682.952 * 27.584 9.24 * 1.363 3026.14 3387.88 20.75 237.02 18.62 14.73 0.39 10352.11 6278, 5 5298.28 2535.8 1384.34 109.22 * 38.55 0.28 * 682.952 * 27.584 * 0.576 * 1.363 5352.35 3609.18 9.52 284.16 11.78 10.83 * 0.03 46909.85 6655.16 13455.16 296.79 1531.6 105.8 * 38.55 3.31 21645 165.5 * 0.576 14.11 16411.49 1130.96 55.78 560.35 77.09 38 , 45 0.96 146962.79 26608.72 19768.84 2528.36 3794.89 124.55 * 38.55 0.86 * 682.952 * 27.584 * 0.576 * 1.363 6499.44 25345.79 46.83 282.2 11 7.03 0.54 11284.43 8579.99 14325.64 1790.66 4164.45 85.64 * 38.55 0.38 * 682.952 * 27.584 * 0.576 * 1.363 7302.92 26019.53 21.65 239 , 16 9.35 7.93 0.32 33080.79 7068.28 8700.45 2901.57 3019.6 41.24 322.3 0.73 * 682.952 * 27.584 * 0.576 11.88 3835.03 11847.58 3.77 307.24 113.43 16.81 0.41 29416.86 7218.21 6099.51 2700.06 1974.26 222.04 362.3 0.26 * 682.952 * 27.584 * 0.576 * 1.368 3862.92 8236.52 31.43 266.22 30.11 9 0.44 10593.81 12412.92 13799.87 4065.63 1848.81 68.18 122.87 0.22 * 682.952 * 27.584 * 0.576 * 1.363 4683.96 38835, 8 25.93 266.22 12.55 16.13 0.39 30958.31 15166.17 9668.8 4176.3 1355.63 249.93 * 38.55 0.42 * 682.952 170.67 * 0.576 10, 25 3971.31 111847.64 13.9 376.97 26.31 7.03 * 0.03 11602.35 6353.92 17304.21 2943.03 963.38 100.07 * 38.55 0.32 * 682.952 * 27.584 * 0.576 * 1.363 7254.74 3609.18 26.67 290.02 13.41 23.94 0.31 28833.89 9377.44 8811.43 3563.64 825.72 100.79 * 38.55 0 , 77 * 682,952 * 27,584 * 0,576 * 1,363 5449,38 5,304.75 27.5 266.22 11.92 7.26 0.38 8892.57 5483.78 * 137.987 3319.85 1560.17 74.78 * 38 , 55 0.27 * 682.952 * 27.584 * 0.576 * 1.363 4979.1 21364.1 23.51 228.34 16.14 9 0.42 14237.79 4568.19 6118.48 3043.76 1621.32 140.79 * 38.55 0.47 4247.36 170.67 7.97 * 1.363 6239.58 76322.75 16.97 312.87 21.95 20.72 0.57 33217.76 4683.13 5183.04 2351.44 2 236.29 174.33 * 38.55 0.16 * 682.952 * 27.584 * 0.576 * 1.363 4232.32 4465.34 20.66 228.34 35.64 14.29 * 0.03 11199.06 4874.36 5009 , 76 2257.84 1038.13 186.86 * 38.55 0.52 * 682.952 * 27.584 * 0.576 * 1.363 3943.29 45393.66 10.45 333.11 9.6 10.63 0.5 17391.93 11441.53 5566.39 1456.49 2465.07 55.41 * 38.55 1.05 * 682.952 * 27.584 * 0.576 * 1.363 6220.29 49735.5 23.21 342.1 13.26 13.84 0, 43 13566.21 8710.02 10723.43 2727.52 3747.62 33.81 * 38.55 0.59 * 682.952 * 27.584 * 0.576 * 1.363 4862.05 6197.44 15.29 286.12 13.52 8 , 36 * 0.03 35205.55 5938.56 14869.19 3289.34 1625.39 65.82 * 38.55 0.27 * 682.952 * 27.584 * 0.576 9.71 8222.45 12367.2 14.18 323 , 99 8.07 11.22 0.45 11134.85 5862.89 9051.59 2371.19 1824.49 41.92 * 38.55 0.56 * 682.952 211.49 6.93 * 1.363 6726.52 3569 , 51 8.25 325.82 19.11 16.64 0.54 36373.01 7629.78 6364.67 2266.45 863.35 41.75 * 38.55 0.35 * 682.952 165.5 * 0.576 * 1.363 7512.69 10953.14 13.3 237.02 8.29 7.26 0.34 13366.57 4931.65 5795.35 2657.67 1824.49 52.22 * 38.55 0.68 * 682.952 186 , 08 * 0.576 13.69 840 1.74 207680.85 15.64 393.8 32.43 23.79 0.32 25078.05 12979.75 6345.76 1865.76 1490.75 36.65 * 38.55 0.76 * 682.952 * 27.584 11.01 * 1.363 4246.52 3529.59 18.28 370.13 3.9 9.1 0.33 9080.68 5198.57 3443.12 1117.38 1844.76 44.53 * 38.55 0.49 * 682.952 * 27.584 * 0.576 * 1.363 3359.96 2428.82 20.45 345.67 8.21 11.8 0.43 15265.06 13454.47 8617.16 3725.43 1856.92 239.7 * 38.55 0, 4 * 682.952 * 27.584 * 0.576 11.06 3167.04 21605.77 30.01 437.56 14.64 11.41 0.68 42427.95 11845.12 7939.68 2282.44 2095.36 238.71 * 38.55 0.64 * 682.952 * 27.584 10.25 * 1.363 2363.92 92105.13 17.09 205.88 14.64 11.41 0.43 5398.34 7330.56 9641.21 2239.4 2155.81 29 , 33 * 38.55 0.25 * 682.952 * 27.584 * 0.576 * 1.363 3194 50047.5 11.2 223.94 10.45 11.12 0.35 10962.91 5730.35 5528.15 2930.46 804, 79 52.99 * 38.55 0.19 * 682.952 165.5 * 0.576 * 1.362 4211.04 31 677.71 8.9 223.94 16.89 10.93 * 0.03 32772.64 6542.27 8700, 45 2206.23 788.03 71.91 * 38.55 0.16 * 682.952 * 27.584 * 0.576 * 1.363 3575.45 2574.17 14.11 320.31 12.26 11.12 0.4 0.4001.98 9451 46 553 7.71 2522.16 1100.25 80.41 * 38.55 0.41 * 682.952 * 27.584 * 0.576 * 1.363 5921.27 36921.52 19.67 397.12 22.64 12.74 0.35 12021, 06 7517.64 12710.54 3146.07 2083.26 32.93 198.33 0.27 * 682.952 * 27.584 * 0.576 167.13 5285.4 9338.12 26.45 309.12 15.39 8.25 0.33 27477.12 8021.67 9153.08 3773.09 1318.69 80.47 * 38.55 0.21 * 682.952 * 27.584 * 0.576 * 1.363 5505.53 15484.21 12.42 342.1 198.99 14, 56 0.38 12862.89 8969.83 9549.22 725.82 1527.52 92.76 * 38.55 0.21 * 682.952 * 27.584 * 0.576 * 1.363 7258.75 4605.07 17.02 352.74 12.26 8.36 * 0.03 8797.82 15129.82 6685.52 2735.01 905.08 20.37 * 38.55 0.38 * 682.952 * 27.584 * 0.576 * 1.363 3957.3 33657.82 10.52 172 , 16 15.5 7.26 0.33 22 987.67 6899.42 6986.44 3773.09 708.2 39.3 * 38.55 0.26 * 682.952 * 27.584 * 0.576 * 1.363 4274.94 9469.75 33.06 219.5 13.3 8.04 * 0.03 22619.35 6429.29 5890.53 3950.13 1384.34 45.88 * 38.55 0.27 * 682.952 * 27.584 * 0.576 11.61 6511.14 2621.61 22.78 388.79 33.12 13.29 0.37 14899.32 8561.41 10961.16 3998.1 1408.92 79.42 * 38.55 0.27 * 682.952 * 27.584 * 0.576 14.67 6699.01 2806.79 13.33 331.29 86.06 8.79 * 0.03 13625.9 7704.49 8032.7 3778.25 1832.6 62.76 * 38.55 0, 13 * 682,952 * 27,584 * 0,576 * 1,363 4150.86 13870 15.7 258.05 9.35 5.61 * 0.03 10854.89 10522.15 2676.43 5284.94 838.27 42.83 * 38, 55 0.59 * 682.952 * 27.584 * 0.576 * 1.363 4924.16 9314.09 22.63 544.9 12.41 10.23 0.65 14937.94 9192.26 6430.83 3181.51 1584.64 50, 45 * 38.55 0.39 * 682.952 * 27.584 * 0.576 * 1.363 2480.02 22292.08 17.16 215.01 11.15 9.93 0.34 13366.57 10558.98 5719.11 2288.6 1425.3 182.33

* 38.55 0.33 9648.28 329.97 21.39 33.79 3076.34 21796.73 6 345.67 8.76 9.62 0.43 11006 4491.49 9365.09 2158.4 1539, 77 58.53 * 38.55 0.28 * 682.952 * 27.584 * 0.576 * 1.363 4428.35 53915.1 31.3 264.19 13.22 5.98 * 0.03 11854.25 5407.8 4486.73 2776.26 1678.3 9.92 127.64 0.32 6292.49 201.36 59.47 13.27 5543.04 2175.03 14.74 1302.86 10.63 10.23 0.94 9725, 6 9599.44 4340.51 1992.2 1478.49 127.02 * 38.55 0.26 4545.02 * 27.584 * 0.576 * 1.363 4431.93 2428.82 17.74 243.42 10.34 13.84 0, 43 11623.42 3312.2 8737.45 3118.25 1219.97 77.31 * 38.55 0.28 * 682.952 * 27.584 * 0.576 * 1.363 2979.42 26965.87 17.99 156.66 10.63 7.93 0.33 14628.06 7853.83 2460.99 2340.34 758.66 59.51 * 38.55 0.32 * 682.952 * 27.584 * 0.576 * 1.368 5810.99 19409.23 18.64 278.25 11, 52 7.49 * 0.03 28507.9 5141.43 9272.94 3957.91 5188.18 26.03 * 38.55 0.16 * 682.952 * 27.584 * 0.576 * 1.363 5252 26330.99 7.17 312, 87 4.82 1.1 * 0.03 8169.53 11221.16 5680.96 3144.81 1269.37 33.16 * 38.55 0.43 * 682.952 * 27.584 * 0.576 * 1.3663 7472.22 21247.07 22.07 453.21 7.34 14.56 0.35 14413.83 6993.25 10045.43 2807.54 1796.11 69.04 360.32 0.62 * 682.952 * 27.584 12.49 11.75 5430.69 4376.8 18.56 400 , 43 18.39 15.09 * 0.03 25233.99 2387.94 4525.65 1348.06 1108.52 46.44 * 38.55 0.45 * 682.952 * 27.584 * 0.576 * 1.363 3606.43 11599, 99 15.31 258.05 19.07 9.52 0.4 21047.75 5407.8 8218.53 3562.36 1617.24 34.22 * 38.55 0.2 * 682.952 * 27.584 * 0.576 * 1.363 5822.38 2941.34 16.66 290.02 17.26 2.88 * 0.03 16196.72 6466.96 11052.53 4050.04 804.79 38.21 * 38.55 0.48 * 682.952 * 27.584 * 0.576 13, 27 5584.36 2761.15 21.44 373.56 28.84 15 0.56 15303.4 7255.67 12037.43 3850.52 758.66 38.96 301.97 0.25 * 682.952 * 27.584 * 0.576 13.41 6797.4 2668.57 14.3 343.89 66.17 5.86 * 0.03 12923.69 6936.96 7678.84 3700.99 1523.44 31.64 * 38.55 0.2 * 682,952 * 27,584 * 0,576 * 1,363 4676,72 18623,59 18,87 278,25 9,13 5.61 * 0.03 9336.44 10042.78 3770.78 5557.67 896.74 34.1 * 38 , 55 0.56 * 682.952 * 27.584 * 0.576 * 1.363 5573.09 8017.76 20.58 579.71 9.75 8.79 0.58 13804.47 9414.45 5852.47 2740.01 1363.84 24.75 * 38.55 0.39 * 682.952 * 27.584 * 0.576 * 1.363 2583.93 29716.85 17.88 230.53 11.15 3.77 * 0.03 6167.86 9081.08 7454.78 2202.55 859.17 115.97 * 38.55 0.32 10194.37 344.47 23.26 32.39 3452.03 20995.49 5.69 350.98 12.22 7.03 0.33 11241.78 4568.19 9291.38 2292, 29 1417.12 58.85 * 10.229 0.75 * 674.175 206.41 * 0.603 * 1.26 5904.17 1576.89 43.71 721.66 7.53 41.55 0.91 65718.38 14426.2 16274, 43 3573.62 2261.85 182.28 * 10.229 0.61 * 674.175 254.9 3.62 * 1.26 5834.74 35312.05 50.68 532.04 165.99 49.28 0.68 52076, 46 9851.64 13888.41 1027.6 2007.34 169.18 112.31 0.67 * 674.175 * 27.506 * 0.603 * 1.26 5565.77 39698.24 20.47 546.89 5.95 13.42 0.62 174 302.47 7118.76 21 948.02 1727.01 2223.26 72.96 * 10.229 0.48 5755.18 222.96 3.62 * 1.26 4559.71 5775.6 29.65 327, 18 17.57 16.89 0.61 52813.19 8451.26 7753.57 1814.58 2007.34 78.87 47.86 0.51 * 674.175 * 27.506 * 0.603 * 1.26 7364.3 17527.75 26, 13 364.96 10.73 28.86 0.52 36761.33 10977.79 6993.44 2421.83 2157.68 66.57 * 10.229 0.13 * 674.175 * 27.506 * 0.603 * 1.26 6142.76 10949 , 08 15.74 647.66 6.51 50.43 0.32 34566.52 19345.4 17085.6 2288.28 2169.25 48.81 * 10.229 0.25 * 674.175 * 27.506 * 0.603 * 1.26 3405.92 6185, 78 13.37 443.13 6.29 52.48 0.33 31453.69 9713.69 21069.16 1996.21 1127.41 41.67 * 10.229 0.56 * 674.175 197.97 4.59 * 1, 26 4288.51 13188.84 43.93 493.89 12.76 25.57 0.61 44855.92 13693.78 4014.23 2480.13 1818.58 159.16 * 10.229 0.84 * 674.175 180.67 4.59 * 1.26 2490.04 20251.52 13.61 270.96 8.2 23.48 0.43 36166.8 6807.84 7542.33 1098.3 1945.7 47.88 * 10.229 0, 37 4806.21 254.9 11.23 * 1.26 6158.77 36338.81 14.03 310.74 12.43 21.91 0.52 23715.24 3193.22 6516.48 875.65 1518.03 26.9 * 10.229 0.2 * 674.175 * 27.506 * 0.603 * 1.26 3076.83 88931.75 10.66 534.53 9.1 49.14 0.36 16206.65 9331.55 19248.53 2813, 84 1251.5 35.51 * 10.229 0.39 * 674.175 * 27.506 * 0.603 * 1.26 2932.1 1464.03 13.96 643.16 14.12 49.21 0.65 27675.01 14011.72 10753 , 13 2118.07 971.57 161.18 * 10.229 0.1 * 674.175 * 27.506 * 0.603 * 1.26 3364.76 13732.06 16.87 568.77 5.74 89.18 0.25 14540.84 14938.08 9296.07 3626.47 869, 67 24.48 174.38 0.34 * 674.175 * 27.506 4.11 * 1.26 6390.82 44763.06 18.2 394.88 6.96 23.67 0.31 40082.51 12776.24 10459, 83 3585.1 1976.52 31.95 * 10.229 0.33 4304.54 * 27.506 * 0.603 250.23 9637.23 5691.29 25.79 612.38 43.78 16.89 0.36 48749.97 12210 , 19 11021.8 1990.1 1568.15 81.09 * 10.229 0.19 * 674.175 * 27.506 * 0.603 * 1.26 3356.54 6003.45 26.22 504.21 2.89 63.33 0.29 34154.97 13852.66 10031.67 3609.22 1382.96 114.58 * 10.229 0.18 * 674.175 * 27.506 * 0.603 * 1.26 3326.44 22824.14 28.07 415.11 4.32 27, 87 0.33 36807.07 12492.88 5526.56 2278.87 2354.5 59.84 346.55 0.23 * 674.175 * 27.506 * 0.603 * 1.26 6222.95 12420.23 16.92 409.39 5.52 32.68 0.24 35138.05 8511.68 12680.74 2767.04 1583.57 95.82 * 10.229 0.32 * 674.175 * 27.506 * 0.603 * 1.26 3710.88 30625.11 23, 14 283.39 8.53 38.27 0.49 21969.29 7222.71 12205.19 2793.14 1598.99 52.89 * 10.229 0.79 * 674.175 308.03 10.76 8.3 2250.6 80,490.44 28.68 551.79 14.23 41.4 0.55 8238.84 8966.21 4829.93 1777.66 1822.43 88.17 * 10.229 0.3 * 674.175 180.67 * 0.603 * 1 , 26 5553 , 35 41988.24 31.47 488.68 10.12 27.6 0.56 20388.29 13566.83 8137.73 2740.99 948.1 62.62 * 10.229 0.17 * 674.175 * 27.506 * 0.603 * 1.26 5051.27 170 381.21 22.98 509.33 6.96 60.18 0.3 26838.79 4603.52 8951.91 2971.73 908.93 33.61 * 10.229 0.32 * 674.175 * 27.506 * 0.603 * 1.26 4404.65 7750.84 21.42 374.07 8.69 15.08 0.41 17351.94 4419.24 3036.12 2609.36 2939.49 63.41 * 10.229 0, 34 5288.63 231.08 10.29 * 1.26 8035.49 10776.13 17.44 456.78 5.31 21.41 0.64 27535.86 2422.52 5037.5 479.71 1868.65 109, 1 * 10.229 8.56 * 674.175 * 27.506 * 0.603 * 1.26 2409.81 28783.58 13.51 283.39 7.72 15.08 0.58 16808.66 1908.47 88220.62 2999.32 1371 , 38 286.41 315.31 0.75 * 674.175 197.97 3.62 * 1.26 5102.68 16983.85 19.24 364.96 8.58 25.39 0.39 27350.19 8572.14 7205.91 1144.05 2261.85 65.44 * 10.229 0.17 * 674.175 180.67 * 0.603 * 1.26 4248.05 84155.72 17.43 589.02 8.15 41.71 0.33 19573.81 9,301.05 6516.48 2539.78 1150.71 34.21 544.79 0.56 * 674.175 * 27.506 * 0.603 * 1.26 5036.18 37644.64 24.51 443.13 11.76 21.71 0 , 82 225916,71 8859,94 7243.35 2430.29 4143.21 152.64 160.59 0.55 * 674.175 206.41 * 0.603 149.38 1974.67 12473.99 26.31 486.07 7.12 36.82 0.55 41392 , 24 8029.56 1025.65 1768.7 1606.69 40.1 * 10.229 0.2 * 674.175 * 27.506 * 0.603 * 1.26 5060.33 26523.66 10.48 252.66 26.48 18.41 0.33 30811.2 6483.66 63095.45 1407.13 814.53 88.4 * 10.229 0.43 * 674.175 197.97 * 0.603 8.97 6625.66 17389.55 20.94 813.74 10, 92 79.45 0.51 40702.54 6029.62 8853.45 2192.48 2084.43 60.07 * 10.229 0.13 5755.18 189.39 * 0.603 * 1.26 5117.83 3654.73 17.16 493.89 4.16 41.86 0.4 38363.16 7089.09 14963.56 942.66 1545.02 66.23 201.77 0.15 * 674.175 * 27.506 * 0.603 * 1.26 5528.54 116101.08 12 , 55 279.87 7.39 35.44 0.31 15355.43 1157.37 17689.33 2929.89 857.87 28.53 * 10.229 0.2 * 674.175 * 27.506 * 0.603 180.87 4550.9 10248 , 09 26.9 559.1 11.64 111.92 0.34 32003.82 10405.42 11497.78 2918.9 2250.27 50.76 * 10.229 0.46 * 674.175 197.97 * 0.603 11.7 4135.82 271826.22 45.43 454.07 14.37 107.28 0.71 37104.41 13677.91 7716.32 3518.66 1957.25 90.16 * 10.229 0.23 * 674.175 * 2 7.506 4.59 12.74 3905.04 17573.71 13.2 493.89 3.5 23.58 0.3 35801 7431.05 28476.57 1650.08 1444.74 134.46 * 10.229 0.38 * 674.175 * 27.506 * 0.603 9.3 7909.29 12820.62 26.15 448.61 5.95 57.7 0.39 12148.56 2663.61 11265.94 3126.93 1026.23 18.25 * 10.229 0 , 3 * 674,175 * 27,506 * 0.603 * 1.26 4930.87 10805.05 14.06 422.21 15.34 29.22 0.25 17145.65 4334.44 5308.41 1384.28 1537.31 11, 33 * 10.229 0.18 * 674.175 * 27.506 * 0.603 * 1.26 5923.15 2475.93 11.93 549.35 5.95 60.84 0.26 23263.4 10575.25 15832.86 3745.43 1115 , 75 16.81 * 10.229 0.13 * 674.175 176.26 * 0.603 10.32 5800.13 56952.09 21.6 412.26 17.31 14.84 0.28 45598.67 10745.37 5820.22 2488, 63 1471.75 51.34 258.32 0.27 * 674.175 * 27.506 * 0.603 * 1.26 4173.15 24695.51 24.67 586.66 7.8 22.11 0.32 31178.44 15291.09 10520.95 3564.45 1216.64 102.44 566.89 0.19 4806.21 * 27.506 * 0.603 52.54 4817.22 4628.24 17.18 608.9 7.1 40.94 0.45 9503.31 14139, 11 8236.67 4585.65 1937.99 51.91 * 10.229 0.38 * 674.175 * 27.506 * 0.603 * 1.26 4689.48 149622.94 11.94 493.89 12.45 53.1 1 0.35 18472.93 9240.08 13107.57 595.6 1131.29 19.19 * 10.229 0.4 * 674.175 * 27.506 * 0.603 * 1.26 2596.78 18975.67 20.6 423.62 8 , 5 33.02 0.38 35366.65 7103.92 9296.07 1533.04 1173.99 98.4 * 10.229 0.19 * 674.175 231.08 * 0.603 * 1.26 5000 14888.21 24.5 430 , 64 7.66 24.63 0.26 36349.71 8240.13 5218.29 1277.64 2586.5 22.28 * 10.229 0.22 4304.54 185.05 5.56 * 1.26 3260.96 13,306.14 26.84 479.49 7.93 22.4 0.29 61508.02 8059.61 24342.39 1549.52 2571.02 318.1 * 10.229 0.18 5288.63 * 27.506 * 0.603 137, 21 4044.28 3852.09 15.99 310.74 10.04 31.66 0.39 20339.17 10776.33 9332.91 3288.28 1606.69 130.21 118.73 0.25 * 674.175 * 27.506 * 0.603 * 1.26 7367.7 21007.04 8.39 445.88 2.89 100.89 0.37 16469.34 1445.76 4358.72 2577.2 1220.51 * 0.03 NA 0.15 * 674.175 * 27.506 * 0.603 * 1.26 3613.24 17204.58 10.56 645.41 6.16 44.29 0.24 24236.42 8059.61 8162.48 2410.2 1290.2 20.06 * 10.229 0.21 4304.54 * 27.506 3.62 * 1.26 3299.12 5691.29 33.49 366.48 5.76 38.58 0.54 27651.82 8405.97 7542.33 2506.72 790 83 109.75 * 10.229 0.19 * 674.175 * 27.506 * 0.603 * 1.26 2990.91 2437.31 21.17 394.88 7.02 50.29 0.3 17480.46 8390.88 6162.99 3353.76 1127.41 18.81 85.09 0.14 * 674.175 206.41 * 0.603 * 1.26 3052.62 55850.81 9.07 383.06 11.67 31.49 0.23 17813.21 5811.13 11192.71 1892.88 834.24 24.05 505.1 0.12 7081.66 214.74 42.16 34.74 2312.04 2908.34 17.74 667.67 12.2 27.33 0.44 27489.45 5347.8 10398.7 798 , 32 1352.06 47.77 32.77 0.15 * 674.175 * 27.506 * 0.603 * 1.26 3613.24 4983.99 17.91 445.88 16.48 25.2 0.26 11364.1 4490.02 5024 , 56 2712.83 940.28 35.74 * 10.229 0.4 * 674.175 * 27.506 * 0.603 * 1.26 4662.86 9175.83 23.03 445.88 6.27 43.61 0.42 17634.29 3940.93 21357.48 2001.3 2223.26 50.25 * 10.229 0.2 * 674.175 206.41 * 0.603 29.77 7023.36 11807.21 16.98 616.99 13.41 34.2 0, 25 31889.25 6219.62 6491.29 2658.84 2153.83 10.81 * 10.229 0.53 4806.21 180.67 3.62 * 1.26 3806.28 10337.1 23.38 290.35 9 , 95 27.24 0.56 21145.84 7222.71 4094.07 2327.03 1367.51 63.01 250.99 3.94 * 674.175 * 27.506 * 0.603 * 1.26 4012.92 43544.42 12, 72 426.44 11.2 26.32 0.35 21 896.92 7148.44 23515.24 2763.78 822.42 260.16 98.11 0.36 * 674.175 * 27.506 * 0.603 * 1.26 4300.09 31530.91 21.4 488.68 19.88 51.77 0 , 29 27675.01 10405.42 6591.99 1452.08 869.67 87.9 * 10.229 0.21 * 674.175 * 27.506 * 0.603 * 1.26 4150.17 9426.85 15.42 413.69 12.06 32 0.26 17991.56 5738.48 6137.66 2481.19 651.59 56.71 * 10.229 0.16 * 674.175 * 27.506 * 0.603 * 1.26 2412.39 3768.41 29.99 620.44 5 , 21 23.09 0.36 24330.96 8466.36 10643.17 2882.7 1429.3 141.49 328.55 0.62 * 674.175 285.62 * 0.603 8.47 3271.85 60518.25 27, 11 819.68 14.65 79.57 0.76 30420.7 18440.97 6390.45 4096.69 1104.09 89.11

* 10.229 0.11 5288.63 * 27.506 * 0.603 * 1.26 5325.11 12965.89 22.84 1260.57 15.17 25.2 0.37 25531 4263.9 5295.54 1534.98 857.87 74.64 147.98 0.09 * 674.175 * 27.506 * 0.603 * 1.26 1984.71 53805.17 18.76 272.76 2.14 17.33 0.26 31339.02 9362.07 9798.97 4285.71 979.39 138.33 * 10.229 0.2 * 674.175 * 27.506 * 0.603 * 1.26 3882.42 25443.57 17.05 327.18 7.02 43.84 0.27 30765.28 8662.92 4829.93 2572 , 92 530.4 56.83 * 10.229 0.21 * 674.175 * 27.506 3.86 7.97 3282.76 11375.31 9.56 219.75 6.8 18.19 0.54 19722.56 2529.39 5603.32 886.18 1398.41 33.32 * 10.229 0.2 * 674.175 214.74 * 0.603 * 1.26 6056.52 23652.61 12.07 827.56 15.14 49 0.31 26092.62 16776.79 9296.07 2733.41 1002.82 55.23 * 10.229 0.27 4806.21 171.8 * 0.603 * 1.26 2892.12 16738.48 9.04 320.66 2.19 40.78 0.3 33 102.28 3289.25 5449.68 1697.35 822.42 97.96 * 10.229 0.21 * 674.175 * 27.506 * 0.603 * 1.26 4515.69 4354.95 11.47 305.71 4, 11 19.04 0.26 8529.36 1369.93 17936.15 3867.56 1111.86 16.69 * 10.229 0.86 * 674.175 * 27.506 * 0.603 * 1.26 3978.78 12366.37 31.72 696 , 02 15.76 58.98 0.4 10741.87 7849.52 4894.91 1538.85 885.38 29.19 341.76 0.2 * 674.175 * 27.506 4.59 * 1.26 10227.74 84498.17 14.75 1115.48 5.79 47.11 0.44 17762.14 1934.53 23566.02 2882.7 798.73 38.87 * 10.229 0.24 * 674.175 206.41 * 0.603 * 1.26 3682 , 93 57057.79 25.72 556.67 12.17 26.6 0.26 25531 15467.94 7517.45 3070.2 1760.8 72.23 * 10.229 0.79 5288.63 180.67 * 0.603 * 1, 26 3851.36 9978.5 23.13 708.9 8.15 79.45 0.44 38638.01 9667.75 7790.8 2069.77 711.5 31.59 261.98 0.27 * 674.175 * 27.506 * 0.603 * 1.26 3837.26 13136.55 23 386.03 5.55 25.39 0.42 44438.77 8405.97 5500.94 1628.51 286.95 66.34 34.16 0.12 * 674.175 * 27.506 * 0.603 12.74 3671.76 3042.01 19.11 742.64 4.71 47.4 0.37 30075.85 10698.94 6943.37 2732.32 1247.63 58.14 * 10.229 0 , 17 * 674,175 * 27,506 * 0.603 * 1.26 3326.44 19020.2 18.11 269.16 4.76 29.04 0.49 11486.48 3578.98 5590.54 3955.33 278.19 87, 73 * 12.021 0.18 * 673.492 * 27.042 * 1.87 * 1.369 3671.17 19464.18 18.75 946.82 5.8 46.32 0.28 47371.5 5576.97 3357.48 3471.11 1288 , 54 43.59 * 12.021 0.19 4 040.95 181.31 * 1.87 65.39 3036.84 22906.36 18.05 594.65 6.57 50.17 0.4 28190.88 9765.67 10417.46 1110.57 1391.82 53.56 * 12.021 0.4 * 673.492 * 27.042 * 1.87 * 1.369 3172.9 20710.16 22.21 490.81 77.84 42.12 0.43 19628.35 11594.57 13137.49 4702.35 1295, 01 278.59 * 12.021 0.21 * 673.492 * 27.042 * 1.87 * 1.369 2686.29 7214.43 13.04 256.83 3.48 28.64 0.33 26640.46 6756.58 2889.24 2745 , 6 949.67 162.76 * 12.021 0.28 * 673.492 * 27.042 * 1.87 8.44 3934.96 65371.45 17.39 588.67 12.81 52.81 0.33 39836.6 8515.08 8504 , 16 2320.39 791.23 22.08 * 12.021 0.23 * 673.492 * 27.042 * 1.87 * 1.369 2827.39 62101.85 7.62 318.63 12.28 26.6 0.39 26066.21 6462.33 8895.85 3804.6 651.22 61.42 195.46 0.18 * 673.492 * 27.042 * 1.87 * 1.369 6899.3 10938.62 10.37 434.82 6.77 47.62 0 , 22 22847.89 11760.61 14218.8 6771.84 386.61 11.02 * 12.021 0.43 6345.45 * 27.042 * 1.87 9.85 3784.04 18421.24 29.85 339.7 20 , 24 24.97 0.42 27097.06 5280.85 9080.45 1806.57 1145.82 117.65 * 12.021 0.22 7076.83 181.31 * 1.87 8.44 2226.43 3317.13 23.48 592 66 1.52 38 , 98 1.1 17896.53 13854.88 11354 1478.37 1067.59 149.54 112.68 0.55 * 673.492 * 27.042 * 1.87 * 1.369 3039.93 27608.79 23.61 404.31 5 , 23 26.12 0.46 30047.91 16527.47 4836.02 4210.23 1015.27 128.94 * 12.021 0.2 * 673.492 * 27.042 * 1.87 * 1.369 4966.67 16722.97 9.5 350 18.04 41.88 0.33 13902.01 1878.78 7374.39 2168.05 590.76 17 * 12.021 1.1 * 673.492 285.61 * 1.87 * 1.369 4995.66 75637.15 12, 66 679.31 10.81 45.56 0.54 22201.84 3058.37 3430.28 2514 393.49 9.03 213.28 0.42 * 673.492 193.67 * 1.87 * 1.369 4518.08 40674 , 79 22.79 820.01 * 0.027 95.23 0.49 54628.68 11137.79 8400.61 2687.19 617.67 49.48

317.24 0.96 4040.95 * 27.042 * 1.87 9.85 4121.14 62945.44 11.06 684.9 16.84 25.46 0.79 11646.07 6357.14 6691.49 2162.72 975 , 94 35.63 * 12.021 0.2 * 673.492 * 27.042 * 1.87 * 1.369 3680.56 39365.97 9.86 318.63 2.81 23.11 0.36 12899.79 11573.81 5287.62 3770.81 764.67 56.31 * 12.021 0.12 * 673.492 * 27.042 * 1.87 * 1.369 3517.99 2710.36 16.46 288.52 6.69 14.15 0.17 45286.37 4729.33 6605 , 32 2894.21 916.78 50.17 * 12.021 0.43 7437.19 263.55 * 1.87 9.85 4327.13 17116.07 5.14 574.61 8.51 52.23 0.59 36,991.84 9349.2 5017.52 1906.73 1152.33 108.02 * 12.021 0.27 * 673.492 * 27.042 * 1.87 * 1.369 3530.48 8885.38 17.02 512.36 5.64 33.7 0.27 31292.28 5872.56 13292.65 2168.05 1571.62 95.69 * 14.138 0.23 * 629.29 * 26.683 * 0.516 203.54 5239.94 60079.18 16.64 920.21 4.82 57 , 03 0.35 23 973.73 8250.91 20048.04 4489.53 1242.12 186.5 84.44 0.14 * 629.29 * 26.683 * 0.516 58.34 3026.36 11440.72 15.47 1174 , 51 3.31 24.69 0.61 28012.27 6502.95 17938.93 2480.76 874.99 466.04 90.99 0.22 * 629.29 * 26.683 * 0.516 * 1.328 3803.45 32865, 19 16.87 543.44 8.63 22.9 0.36 9346.68 14757.28 11850.79 3978.15 1455.42 411.33 114.29 0.28 * 629.29 * 26.683 * 0.516 * 1.328 3084.07 13929 , 99 16.31 467.96 7.56 69.98 0.33 57909.31 10096.12 8343.14 1580.5 1346.53 140.18 105.93 0.35 * 629.29 206.96 * 0.516 86.79 4717.77 8748.79 33.81 1058.4 6.58 38.06 0.26 53490.68 10564.75 7412.92 1876.73 1935.46 93.95 NA 0.32 * 629.29 166 5 , 31 * 1.328 6491.71 143387.1 12.37 441.2 10 16.25 0.45 38470.16 9194.12 10143.79 2665.43 2442.53 52.12 * 14,138 0.2 * 629.29 * 26.683 * 0.516 62.86 3676.24 3026.95 12.14 752.84 7.59 33.43 0.5 15972.32 12973.01 8271.92 4067.41 1794.18 133.33 120.48 0 , 24 * 629.29 * 26.683 * 0.516 * 1.328 5589.45 2759.65 9.52 562.55 6.79 25.02 0.26 30640.69 5621.09 7220.88 3557.54 2274.21 19, 35 275.64 0.12 * 629.29 * 26.683 * 0.516 * 1.328 4062.52 42743.34 16.85 533.74 3.31 28.21 0.26 35593.24 7663.93 11746.81 3180.68 915.6 134.72 555.18 0.37 * 629.29 * 26.683 * 0.516 * 1.328 5579.6 33660.02 14.28 518.99 3.48 27.45 0.73435.14 9795.8 12289 , 14 2758.76 1242.12 106.06 240.28 0.63 * 629.29 177.75 * 0.516 9.63 10014.29 85891.36 38.35 407.79 5.77 77.4 0.51 18540.02 2904.99 19980.47 3865, 62 3853.92 13.54 * 14.138 0.4 * 629.29 * 26.683 4.22 * 1.328 2929.39 5919.74 23.44 460.02 4.23 24.3 0.39 33617.43 10714.56 7795, 71 1674.94 1759.99 155.42 * 14.138 0.13 * 629.29 * 26.683 * 0.516 * 1.328 4858.77 15520.02 19.04 378.69 3.68 45.53 0.38 18788.47 8439 , 89 8863.95 1557.23 467.16 51.82 73.28 0.09 * 629.29 * 26.683 10.95 18.2 3218.11 9029.73 15.52 372.71 4.64 9.76 0.27 24615.62 4245.64 16615.2 2981.77 1391.14 27.94 * 14.138 0.17 * 629.29 * 26.683 * 0.516 * 1.328 2583.35 85593.26 9.12 714.31 9, 63 70.86 0.32 8913.64 14571.65 16331.81 3804.29 910.53 136.48 * 14.138 0.16 * 629.29 * 26.683 * 0.516 * 1.328 4367.93 12472.93 10.77 734 , 73 5.32 66.3 0.26 31478.12 18980.32 10610.23 2993.21 1361.41 120.57 * 14,138 0.18 * 629.29 * 26.683 * 0.516 * 1.328 5275.7 27661.84 20.79 473.2 1.74 21.14 0.2 35904.71 9852.13 16286.45 2023.76 2820.18 126.66 * 14.138 0.25 * 629.29 * 26.683 * 0.516 8.41 3263 , 94 30810.4 17.5 4 387.55 7.74 21.55 0.3 28275.99 7796.63 42547.45 2522.13 1415.88 48.05 * 14,138 0.23 * 629.29 * 26.683 * 0.516 * 1.328 4015.54 46948 , 79 20.46 329.19 1.44 10.64 0.33 35852.81 6005.27 6082.78 2323.23 639.27 75.9 * 14.138 0.18 * 629.29 247.47 * 0.516 * 1.328 2523.55 13380.59 25.49 488.74 32.74 24.1 0.39 21603.41 12526.02 8911.16 2130.36 535.33 86.1 * 14,138 0.14 * 629.29 * 26.683 * 0.516 * 1.328 4634.74 51880.21 19.69 562.55 6.31 29.88 0.24 20734.07 7036.92 22703.43 3520.05 1267.03 60.31 166.13 0.25 * 629.29 * 26.683 * 0.516 * 1.328 3414.1 33985.96 17.19 533.74 3.16 48.07 0.42 15689.79 8968.12 13804.26 1927.33 803.68 91.4 * 14.138 0 , 23 * 629.29 * 26.683 * 0.516 * 1.328 6075.79 1851.94 15.69 562.55 5.8 62.59 0.37 41908.64 10770.72 7484.82 4505.53 1401.04 30, 29 * 14,138 0.22 * 629.29 * 26.683 * 0.516 * 1.328 4125.25 1919.29 16.67 697.72 5.47 15.65 0.28 39091.17 9043.48 13483.77 3440.03 2110 , 05 26.31 * 14.138 0.56 * 629.29 * 26.683 3.66 * 1.328 3949.9 39043.96 10.32 435.73 14.9 21.96 0.8 * 536.361 7074.99 6835.41 1292.13 1 192.24 51.72 * 14.138 0.09 * 629.29 * 26.683 * 0.516 * 1.328 2196.66 9833.27 11.38 460.02 3.71 18.03 0.35 15859.4 7055.96 18571, 25 2669.21 951.04 321.41 296.43 1.08 * 629.29 * 26.683 * 0.516 * 1.328 4348.95 28072.29 18.06 467.96 7.5 23.24 0.53 14980.1 7834.53 6180.36 2958.89 905.45 64.64 101.71 0.14 * 629.29 * 26.683 * 0.516 109.91 4386.91 6934.19 13.31 523.93 5.59 63.35 0.21 26928.73 6903.61 10552.01 3576.96 30354.09 56.54 * 14.138 0.4 * 629.29 * 26.683 * 0.516 * 1.328 3629.82 38262.57 31.08 523.93 5, 59 35.54 0.48 16422.86 6732.03 6423.68 2640.25 1157.25 74.28 571.88 0.12 * 629.29 * 26.683 * 0.516 15.92 5298.47 35616.61 9, 13 509.02 * 0.026 46.17 0.25 38 754.83 4576.85 16 354.49 1815.11 849.56 127.68 90.99 0.1 * 629.29 * 26.683 * 0.516 * 1.328 1966.92 102819 , 17 7.98 348.23 1.74 31.4 0.18 29801.6 4245.64 21781.24 1673.71 1711.08 120.01 * 14.138 0.46 * 629.29 224.37 23.71 23.19 3666.95 52113.04 22.98 676.65 4.14 25.86 0.36 120403.44 6808.31 5321.33 1688.45 925.73 28.93 * 14,138 0.2 * 629, 29 * 26.683 * 0.516 * 1.328 3887, 5 9132.11 12.9 653.03 2.57 30.19 0.32 8758.08 6770.18 8248.16 2695.67 849.56 74.32 * 14,138 0.33 * 629.29 * 26.683 * 0.516 * 1.328 5992.57 8523.69 18.24 590.53 4.82 67.89 0.37 52687.01 8099.61 9017.33 3009.75 1465.3 32.57 * 14.138 0.25 * 629.29 166 * 0.516 * 1.328 5071.42 41446.26 20.55 430.23 13.68 17.59 0.45 18429.45 8232.01 15173.19 2835.89 1954.9 72.74 * 14,138 0.76 * 629.29 * 26.683 * 0.516 * 1.328 2847.81 25337.51 35.44 555.43 9.13 28.33 0.58 57492.51 17648.13 8651.21 2943.65 1031.78 65.31 NA 1.06 * 629 , 29 * 26,683 * 0,516 * 1,328 4529,66 9059,04 14,14 687,23 5,71 57,03 0,51 33357 15313,88 66013,68 1994,07 680,58 356,73 649,63 0, 17 * 629.29 * 26.683 * 0.516 * 1.328 4469.31 12696.6 22.02 354.44 3.36 22.09 0.32 25255.78 6330.89 11109.96 1262.89 1311.78 126.75 156.44 0.18 * 629.29 166 * 0.516 * 1.328 3524.86 6593.68 20.79 413.47 9.04 43 0.26 36942.08 12 376.93 35 827.56 3740.47 472.43 39 , 01 NA 0.16 * 629.29 * 26.683 * 0.516 * 1.328 2893.11 8778.57 17.08 438.47 4.94 42.51 0.22 42347.76 9870.91 18085.78 3039.03 696 , 03 45.71 120.48 0.24 * 629.29 * 26.683 * 0.516 * 1.328 3484.82 127726.95 8.91 531.3 8.54 18.61 0.3 23464.26 19664.66 14307, 03 3208.82 1425.77 61.53 * 14.138 0.53 * 629.29 171.88 5.31 * 1.328 3735.13 67700.64 12.45 583.61 5.06 56.53 0.56 14150, 4 8911.58 7004.29 2509.59 1465.3 21.25 * 14.138 0.2 * 629.29 * 26.683 3.66 18.2 7115.29 71252.7 20.7 2456.94 6.04 33, 38 0.24 37745.25 14979.97 8141.2 4308.88 556.2 88.56 * 14.138 0.13 * 629.29 * 26.683 * 0.516 * 1.328 3568.04 88885.74 11.3 413.47 6 , 86 10.29 0.3 10535.14 5505.58 17554.65 2753.71 1102.14 35.86 NA 0.2 * 629.29 * 26.683 * 0.516 * 1.328 5520.56 129406.92 22.49 473 , 2 * 0.026 30.86 0.29 17736.34 4206.58 4322.77 4804.17 * 32.158 15.94 * 14.138 0.28 * 629.29 * 26.683 * 0.516 * 1.328 2862.9 7034.65 26, 38 295.96 8.82 10.64 0.37 25335.69 5467.05 7028.38 2274.61 1366.36 79.01 * 14,138 0.36 * 629.29 * 26.683 * 0.516 * 1.328 2808.6 35392 , 04 11.36 338.79 3.94 62.15 0.54 10414.36 4187.05 10528.71 2778.98 910.53 224.3

* 14,138 0.55 * 629.29 * 26.683 * 0.516 * 1.328 3352.72 4386.3 25.77 452.01 4.82 17 0.42 39220.52 10808.16 5048.7 2998.3 900.38 47 , 65 * 14,138 0.25 * 629.29 * 26.683 3.66 * 1.328 3193.7 34310.86 15.83 595.12 7.37 45.59 0.28 11522.86 9589.13 4974.1 3816, 03 1162.25 29.04 * 14.138 0.36 * 629.29 * 26.683 * 0.516 * 1.328 3227.27 40978.19 11.32 648.69 7.53 64.78 0.42 11582.25 11144.89 11642 , 76 4075.3 1097.12 31.43 194.57 0.38 * 629.29 * 26.683 * 0.516 * 1.328 5191.23 9059.04 17.47 413.47 3.88 16.55 0.48 34502.08 3084 , 16 4998.98 1902.64 4988.69 55.56 214.92 0.35 * 629.29 * 26.683 * 0.516 * 1.328 3035.46 8793.45 21.56 560.18 5 50.82 0.26 11105 , 66 8836.18 14032.92 2840.95 NA 118.29 313.54 0.13 * 629.29 * 26.683 * 0.516 * 1.328 2010.97 24498.04 15.73 390.48 2.07 22.43 0 , 26 35151.78 5235.59 20757.36 3757.39 859.74 156.16 * 14,138 0.13 * 629.29 * 26.683 * 0.516 * 1.328 2896.13 21411.11 16.17 278.5 2.74 13.47 0.26 13833.74 3834.54 19045.06 2366.92 976.31 84.1 * 14,138 0.34 * 629.29 * 26.683 * 0.516 * 1.328 3978.01 2592.39 21.99 424, 6 8 2.1 23.7 0.38 51314.21 6827.38 6094.98 3261.34 1351.49 62.99 327.09 0.33 * 629.29 * 26.683 * 0.516 * 1.328 3549.53 4386.3 19.35 488.74 6.49 25.08 0.28 8913.64 11574.68 9851.43 4677.93 1072.02 32.15 * 14,138 0.22 * 629.29 * 26.683 * 0.516 * 1.328 4431, 26 11,904.91 34.05 332.41 9.63 28.46 0.26 20352.33 4964.9 7244.91 5158.71 * 32.158 22.91 909.47 0.19 * 629.29 * 26.683 * 0.516 300.83 3429.46 1077.26 23.81 665.98 7.56 22.3 0.29 31948.5 8118.53 7783.77 2112.99 1016.67 147.48 * 14.138 0.26 * 629, 29 * 26.683 * 0.516 * 1.328 3781.7 6136.73 11.75 430.23 6.83 12.74 0.25 21467.85 10695.84 5271.87 2219.83 844.47 37.79 * 14,138 0, 36 * 629.29 * 26.683 * 0.516 * 1.328 3968.64 3282.12 20.24 260.39 9.69 22.9 0.28 15122.44 3341.92 5222.35 1732.68 1137.23 118.99 507.57 0.26 * 629.29 * 26.683 * 0.516 12.1 2899.15 53129.51 11.57 467.96 12.02 28.89 0.36 26875.77 10058.6 5568.02 2637.73 1588.45 47.95 NA 0.25 * 629.29 * 26.683 * 0.516 * 1.328 3407.95 46331.31 16.91 435.73 37.95 22.63 0.43 17179.15 6693.88 17622.49 2693.15 * 32.158 24.93 229.48 0.58 * 629.29 * 26.683 * 0.516 * 1.328 4644.31 2814.19 48.6 604.24 8.51 42.73 0.39 19064.02 15833.05 14032.92 5330.66 824 , 09 52.61 * 14.138 0.25 * 629.29 * 26.683 6.37 * 1.328 4558.29 4103.3 9.78 1094.14 5 37.55 0.35 8508.13 3302.34 7938.83 655 , 12 711.46 66.86 * 14.138 0.26 * 629.29 * 26.683 * 0.516 * 1.358 5711.19 6136.73 28.55 402.07 2.63 28.21 0.48 41443.63 2685.13 29969.39 6379.89 808.79 37.59 * 14.138 0.23 * 629.29 * 26.683 * 0.516 * 1.328 5035.88 24516.41 17.93 282.04 4.29 36.92 0.34 10263.05 2201 , 58 30342.72 2487.03 472.43 13.65 * 14,138 0.24 * 629.29 * 26.683 * 0.516 * 1.328 2793.54 10793.41 14.28 504 7.71 26.05 0.33 51624, 91 4615.73 16717.17 2993.21 608.2 72.45 * 14.138 0.21 * 629.29 * 26.683 * 0.516 * 1.328 3035.46 28938.16 10.21 613.28 7.59 76.86 0.37 26955.2 15202.59 7148.75 3144.89 649.61 82.11 158.38 0.28 * 629.29 * 26.683 * 0.516 * 1.328 2917.29 36669.34 23.32 478.41 8.69 51.7 0.55 17207.07 6998.85 3623.04 3418.12 925.73 55.17 * 14.138 0.12 * 629.29 * 26.683 * 0.516 * 1.328 2493.69 26718.54 15.66 796 , 09 12.5 24.03 0.43 10111.35 14645.91 13116.92 2647.8 * 32.158 65.94 * 14.138 0.3 * 629.29 * 26.683 * 0.516 * 1.328 2862.9 65836.89 9 , 51 332.41 8.05 21.55 0.36 13225.98 366.92 8461.74 3496.8 608.2 23.87 * 14,138 0.26 * 629.29 * 26.683 6.37 * 1.328 4075, 05 44842.98 25.01 309.5 2.24 12 0.38 11165.42 4304.18 8378.74 2989.4 1237.14 60.85 214.92 0.41 * 629.29 * 26.683 9.45 22.86 3592.74 20538.34 10.92 363.64 7.28 22.03 0.59 5619.16 1525.59 4574.35 1779.41 1237.14 24.3 612.48 0.17 * 629 , 29 * 26,683 * 0,516 * 1,328 4405,91 21,128.44 23,67 329,19 5,83 23,04 0,26 30876,38 4401,66 20262,01 1823,73 623,75 14,6 * 14,138 0 , 39 * 629.29 * 26.683 3.1 * 1.328 6212.73 72027.35 14.37 341.95 2.18 21.41 0.31 17374.45 2242.1 14147.17 2334.46 1142.23 28 , 83 * 14,138 0.24 * 629.29 * 26.683 * 0.516 * 1.328 1794.26 35022.11 15.67 452.01 3.83 107.78 0.8 8789.23 2765.2 7938.83 3263.91 * 32,158 109.81 49.25 1.1 * 658.785 368.54 14.09 10.77 6098.91 8222.07 37.33 653.68 32.87 105.09 0.82 191545.75 11413.72 27876 , 48 8189.64 2914.41 33.36 49.25 0.21 * 658.785 * 26.892 4.25 16.19 5020.5 14650.7 31.24 329.71 3.71 31.1 0.36 34145.99 8062.16 33620.34 2595.93 2185.73 200 , 83 * 14.32 1.14 5769.95 2199.05 19.9 261.13 2999.44 7070.72 30.22 1674.35 30.94 28.88 0.45 62381.56 10447.22 5445, 34 452.49 5795.05 37.1 49.25 1.28 * 658.785 386.5 10.22 54.32 6068.87 26244.55 26.48 456.72 17.22 15.37 0.78 79132.64 7450 , 28 5679.09 667.86 6064.21 46.9 119.33 0.79 * 658.785 313.09 9.07 13.6 5280.69 89940.63 35.3 651.56 11.53 49.08 0 , 31 47933.84 6586.21 18671.15 3564.15 4529.97 19.88 61.23 0.48 * 658.785 792.66 8.5 15.67 5146.02 14241.27 24.4 597.51 19 , 24 48.03 0.72 129831.73 6145.8 14633.79 2270.49 3411.1 22.9 94.13 0.33 4670.14 430.41 11.06 42.01 7990.84 17790.96 20.95 506.88 13.05 32.22 0.45 63825.29 10784.75 3177.6 2414.66 2381.48 15.79 72.62 3.09 * 658.785 464.64 * 0.628 30.86 5838 .8 24282.69 36.36 1819.75 7.16 50.2 1.1 161058.77 14992.2 6368.66 1746.64 6153.42 18.71 42.97 0.92 * 658.785 439.03 * 0.628 40.41 6402.1 54072.1 28.41 2541.64 15.59 32.44 0.6 15 4906.07 13316.23 34007.97 2256.92 5266.04 54.56 72.62 1.83 * 658.785 460.4 4.25 128.85 5077.07 8660.47 12.37 1040.31 14.51 27.03 0.91 66698.53 14029.11 23335.02 3248.46 8291.15 26.49 161.59 0.58 * 658.785 331.86 11.9 12.06 7059.95 1770.58 20.49 599.71 9.42 28.52 0.36 55734.17 13095.58 8674.09 3451.22 2234.97 65.27 205.4 0.59 7337.7 202.85 9.07 11.28 5544, 99 39,912.86 29.35 623.67 8.87 38.69 0.9 147693 8014.96 21294.19 3940.57 3805.72 62.71 * 10.89 1.68 * 678.454 * 26.439 * 0.651 * 1.488 2192.21 3652.72 17.94 403.89 18.64 27.09 0.51 15849.3 10560.26 7111.51 2393.73 1053.21 49.73 * 10.89 0.47 * 678.454 238.68 4 , 55 9.84 4004.67 11715.96 27.37 328.09 8.35 23.65 0.51 49507.6 9221.6 10871.3 4079.31 585.35 46.37 57.63 0.52 * 678.454 178.12 * 0.651 53.24 2127.43 13134.48 23.85 952.79 27.92 8.68 0.51 59615.8 8857.78 7817.35 2617.05 2033.13 71.72 46.43 0, 81 * 678.454 232.87 4.55 9.34 7232.68 43724.27 21.19 427.87 8.9 10.96 0.28 48824.12 4618.13 208750.83 2476.87 1269.39 88, 18 * 10.89 0.36 * 678.454 203.06 8.32 * 1.348 10863.85 19190.71 19.85 301.23 7.59 9.04 0.4 44395.78 6911.43 109824.4 5436.23 890.28 12.15 * 10.89 0.9 * 678.454 411.93 5.51 21.2 5478.95 5143.78 35.84 1479.58 34.42 18.93 0.56 51758.85 20507.87 120351.77 5171.24 1117.38 47.87 124.91 0 , 49 5832.85 482.99 14.36 9.34 7189.69 4385.56 21.9 558.53 26 16.83 0.35 111331.94 9439.45 11059.34 1620.6 1027.39 65, 62 * 10.89 4.16 * 678.454 2967.99 7.08 87.09 9550.05 8060.92 21.35 26080.88 62.31 10.79 0.65 237322.24 11745.79 482233.48 2231 , 97 2033.13 78.5 34.68 1.81 * 678.454 11432.98 8.94 25.17 7039.83 10937.8 22.16 ** 9253.687 24.68 28.27 0.6 39089, 77 3925.01 39790.07 2472.5 507.7 42.52 * 10.89 0.52 * 678.454 261.48 * 0.651 10.85 9135.69 70420.78 50.82 439.66 10.78 11, 46 0.44 64279.61 4885.24 17499.31 1865.18 599.31 34.28 * 10.89 0.87 * 678.454 * 26.439 4.55 * 1.348 5928.62 15557.83 22.21 661.31 10.28 36.55 0.48 32041.76 10018.88 4635.78 3980.65 682.17 56.62 34.68 0.92 * 678.454 283.65 * 0.651 18.32 4259.78 2948.38 18 , 92 518.93 2.38 51.42 0.54 50 231.22 15 762.6 34574.23 4238.92 5308.4 7.23 50.69 0.89 13213.84 356.88 51.29 24.08 5214.5 19248.93 27.9 464.22 29.41 17.68 0 , 87 78838.92 3260.59 7697.98 289.71 3333.73 7.51 * 13.46 0.71 * 687.243 520.07 * 2.09 17.84 14930.81 18927.01 24.58 1144.23 8 , 33 17.68 0.76 64459.35 10972.02 8424.95 2007.07 2442.5 18.72 62.5 2.44 5724.33 362.95 24.2 43.05 11622.34 4174.49 52, 63 1240.08 20.38 47.86 0.83 116584.75 20472.49 25861.06 2488.62 3757.13 67.77 * 13.46 5.62 14817.3 543.22 36.38 133.86 18598, 28 101386.05 44.19 2686.99 59.47 26.4 1.14 336611.21 7496.55 8152.74 508.32 3448.75 14.13 * 13.46 0.29 * 687.243 321.63 * 2.09 8.71 8612.94 34134.58 40.21 862.1 6.31 21.57 0.53 97531.79 4100.98 10209.3 391.26 2591.77 13.22 * 13.46 0 , 83 20689.84 189.13 39.85 52.17 5780.17 6308.98 39.3 674.84 3.97 25.51 0.52 70448.56 8216.57 12508.49 859.25 2207.54 62.02 91.05 0.35 * 666.198 255.09 * 1.988 11.9 4962.11 37969.34 43.2 438.38 13.85 11.56 0.71 50834.17 7087.96 10268.77 2258 , 05 3337.71 91.76 67.41 2.78 * 666.198 798.71 23.66 102, 47 5001.18 25577.43 24.34 800.19 21.1 15.12 1.12 100220.96 10222 25528.52 3535.17 3351.27 64.41 105.97 0.78 * 666.198 471.38 17.24 25.63 5090.65 159850.34 42.26 603.81 8.22 27.82 0.82 51979.19 16951.15 12132.28 3592.3 2475.03 41.25 50.46 1.12 * 666.198 482.75 22.11 90.14 7970.52 10385.91 20.86 1174.7 17.57 33.74 0.46 43724.12 8009.05 8315.73 3444.33 4535.81 71.4 * 39.09 10.08 * 666.198 770.4 * 1.988 138.65 4731.01 2964.65 37.47 1150.47 14.73 20.2 1.12 127 393.13 15 582.08 14585.81 4427.45 7327.9 148 , 15 75.49 1.43 * 666.198 494.02 * 1.988 42.56 6276.03 42319.64 15.24 1059.1 17.18 41.96 0.74 49289.04 4142 116327.6 2394.78 2198.7 97.75 105.97 0.72 * 666.198 400.7 5.54 14.22 12532.73 32764.91 26.67 537.81 13.53 15.76 0.52 76217.05 9439.03 6794.7 5133, 83 2334.26 59.7 130.05 1.13 8340.53 653.05 25.19 48.71 7390.32 40496.85 34.32 1061.6 40.85 36.49 0.73 85484.55 22939 , 29 13221.26 8576.92 2191.51 210.25 * 39.09 2.11 12917.59 808.07 41.97 76.45 4828.51 46479.84 10.83 1084.08 117.94 31.5 1.01 83016.74 154 70.25 10829.79 1642.82 1611.18 128.7 * 39.09 2.93 7274.99 388.45 39.32 73.02 6108.24 24715.68 27.28 1089.05 25.69 26 , 49 0.75 74679.41 21238.43 17924.22 3751.33 3507.1 66.42 67.41 0.51 * 666.198 225.02 * 1.988 8.13 4212.08 16792.13 34.12 368, 63 7.75 23.69 0.44 39828.39 6545.76 16755.14 4377.89 1533.49 86.55 * 39.09 0.94 * 666.198 255.09 * 1.988 36.81 6868.01 12081, 14 28.91 537.81 21.23 20.97 0.61 50547.46 11169.02 11458.43 7614.37 2593.23 101.19 105.97 0.5 * 666.198 400.7 * 1.988 14.69 7842.76 49724.31 13.5 401.9 64.43 36.89 0.58 39881.6 11449.04 7626.46 1871.69 3272.75 76.13 * 39.09 3.74 7816.89 424 , 77 23.66 46.96 7818.48 13386.08 34.35 1026.28 16.48 23.32 0.63 97064.64 11806.27 11067.02 5541.28 3193.81 137.05 75.49 1.39 * 666.198 269.57 4.4 31.92 10393.86 27681.73 26.85 550.91 33.24 52.27 0.8 65798.33 11614.41 14190.15 4090.27 2927.38 102.36 136.23 0.46 55874.62 538.91 27.53 19.64 4806.3 6059.81 33.56 524.16 12.72 30.82 0.56 27894.67 7978.33 7444, 75 4077.44 2826.06 131.52 67.41 0.76 * 666.198 47 1.38 * 1,988 15.61 7384.33 45634.44 22.05 514.55 12.56 28.51 0.47 43230.02 7909.65 6580.66 5300.73 2133.8 53.8 * 39, 09 2.97 * 666.198 538.13 * 1,988 16.07 6444.65 60745.05 34.67 767.69 29.02 19.61 0.72 38648.49 9987.87 9655.01 7114.11 2920.65 72.82 59.59 0.71 * 642.838 304.45 * 1.988 16.99 8834.56 20461.36 39.51 612.4 25.85 26.61 0.68 93935.1 17380.68 14871.35 3549 , 97 3595.29 55.55 137.83 1.4 18927.15 406.77 25.19 23.83 8162.37 7181.6 16.92 1550.19 8.14 36.67 0.84 39719.04 8531.95 19497.41 2735.65 6362.8 101.68

92.22 0.71 * 642.838 234.12 * 0.537 11.71 7674.76 10750.74 28.34 539.72 10.53 18.99 0.59 42483.77 19157 10540.74 4246.62 2739.43 107.42 116.83 3.03 13329.36 363.46 17.24 24.73 9110.63 32623.01 21.16 551.21 9.25 19.66 0.97 78153.79 12798.66 31620.79 2177 , 36 4,187.62 68.82 81.72 4.46 7274.99 516.26 14.65 47.84 5309.22 5007.14 46.68 568.96 15.49 29.13 0.93 67806.64 12860.17 32233.27 2858.71 4268.09 236.18 * 12.79 0.96 9135.14 263.37 23.78 14.9 5541.67 6532.69 25.09 516.06 10.03 25 , 09 0.88 25,106.87 18860.02 10727.08 1021.52 3445.5 97.37 * 13.82 1.36 * 642.838 324.6 * 1.988 81.17 11766.64 7971.15 30.45 775 , 5 16.9 41.27 0.76 77207.42 9935.6 11536.14 4337 4738.24 48.95 150.29 2.81 * 642.838 240.25 3.61 39.47 9035.12 20525.65 33.9 878.17 18.29 17.97 1.16 69506.52 21695.36 21418.24 3452.77 5055.11 181.74 59.59 3.67 * 642.838 642.93 * 1.988 66.12 6229.66 3827.24 30.03 1046.52 36.57 66.25 1 124 141.83 20348.46 9943.16 3770.45 7323.1 73.9 206.16 1.44 10 285.61 209.34 11.93 80 , 74 8616.52 54995.91 45.01 603.81 13.53 16.92 0.92 42915.15 11614.41 30168.88 3915.17 5499.51 32.78 122.13 1.61 148841.07 286.95 22.56 145.06 4127.42 31242, 6 86.95 707.92 64.81 7.61 1.29 209 138.09 25 318.22 18 227.06 799.19 4508.52 50.73 76.34 2.72 14 151.42 642.93 18.9 305 , 42 9009.99 100192.07 30.82 1644.13 25.77 29.27 1.27 171693.59 14835.07 20906.58 3225.29 4286.99 50.2 47.75 0.44 4194 * 27.635 * 0.537 * 1.39 3887.78 7880.37 12.07 268.54 13.25 33.25 0.47 36638.47 5848.72 4025.5 5175.22 1577.28 100.16 1178.65 1, 98 45,661.62 527.24 49.63 16.53 7703.13 43447.69 34.33 429.54 9.88 45.7 1.95 79214.68 49041.88 19382.79 2260.12 3638.58 73 , 33 * 13.82 0.63 * 642.838 193.15 * 1.988 10.96 6318.16 10361.73 32.67 325.71 3.41 20.49 0.74 35159 10643.4 9447.24 3469.66 2221.32 51.17 59.59 2.35 12521.36 255.09 * 1.988 23.83 9491.8 9693.49 33.86 533.24 10.62 11.29 0.56 131222.75 16350.89 18175.26 4409.55 1980.24 21.64 92.22 0.65 * 642.838 225.02 4.37 46.08 5292.84 12293.57 21.21 568.96 15.13 42.3 0.56 39536.5 13581.93 31006.3 3185.37 3275.55 29.69 * 13.82 1.96 4849.69 692.96 * 1,988 50.47 7776.03 43262.12 42.55 833.16 42.59 28.84 1.18 64132.63 14670.14 11145.69 1630, 45 3435.81 32.5 * 12.79 0.96 * 642.838 * 27.635 * 0.537 * 1.39 8809.98 23288.17 42.23 510.1 4.37 23.86 0.59 23728.11 21133, 34 8,108.62 4168.07 4007.13 25.35 109.39 2.69 7722.36 647.61 * 6.138 30.23 4396.67 59172.7 16.11 5977.18 34.92 31.75 0, 53 29 716.49 9539.9 6593.01 1711.42 1913.99 198.65 187.38 0.37 * 656.313 * 27.445 * 6.138 * 1.342 5685.3 9542.27 22.68 351.42 6.19 11, 09 * 0.029 17529.63 7375.26 7714.83 5115.64 1521.81 35.31 * 11.332 1.38 * 682.162 273.8 31.42 10.6 2653.94 11647.33 18.18 351.94 22 , 29 10.72 0.95 101276,66 11432.96 8310.97 1590.78 1126.38 72.53 * 11.332 2.6 * 682.162 2409.48 * 0.642 30.96 1528.38 8021.53 23.05 8499.62 33.81 8.26 1.22 235828.51 12997.4 100817.11 2371.48 4362.87 249.5 150.72 2.26 * 682.162 * 26.909 * 0.642 8.67 4859.19 6181.04 26 , 66 464.97 9.01 7.38 0.57 44167.93 12176.14 24721.29 3181.07 2703.3 237.06 143.93 1.77 10953.75 421.4 * 1.75 32, 08 5252.11 2949, 13 48.54 615.41 50.48 28.16 0.74 88460.86 21178.4 20337.62 2567.7 * 34.54 80.83 243.95 1.25 40649.13 215.96 * 1, 75 40.72 15589.17 6391.8 66.76 1173.66 14.22 16.82 1.19 138409.3 20725.25 147148.97 2132.83 * 34.54 221.62 * 10.934 0.62 21180 , 26 425.31 * 1.75 13.67 10891.25 9354.03 54.45 619.77 39.44 24.76 0.76 103741.35 9997.34 8107.72 1535.62 * 34.54 62 , 43 * 10,934 0.61 5864.77 215.96 50.3 16.65 8659.21 8942.8 16.99 278.3 18.07 10.77 0.37 65971.41 8569.6 92498.13 1412 , 69 230.98 66.28 * 10.934 0.46 8216.81 189.37 * 1.75 10.53 22243.35 3195.46 54.64 411.91 36.37 27.51 0.61 109679.38 12 234.44 7555.71 2147.91 * 34.54 190.73 * 10.934 0.4 5244.07 * 27.402 * 1.75 10.95 6543.01 1404.24 34.33 271.4 10.32 6, 28 0.33 107694.94 7581.42 225427.66 2102.7 * 34.54 80.07 * 10.934 0.48 6470.61 358.02 * 1.75 12.41 7084.61 5214.94 28.4 417.37 24.99 20.25 0.69 71566.32 10315.37 100926.21 2521.93 * 34.54 128.09 * 10.934 2.33 * 685.577 325.63 34.69 10.53 8029.12 6671, 07 29.48 459.71 26.21 9.31 0.46 39237.34 9678.75 127810.81 1974 , 96 * 34.54 204.71 1462.07 5.02 * 685.577 1140.34 129.57 18.37 4780.98 19685.75 13.85 1004.74 25.1 10.25 1.95 135486.33 13470.7 81967.49 2794.88 * 34.54 288.52 122.05 1.53 11481.27 603.22 11.98 25.82 53356.84 1661.26 144.3 755.11 37.71 33 , 2 0.85 224797.05 19116.73 63118.53 2812.81 * 34.54 81.64 * 11.332 0.88 * 682.162 339.09 * 0.642 * 1.364 2386.27 22920.83 22.06 366.36 27.09 18.4 0.51 29052.26 6300.79 4437.48 1354.93 61146.1 51.37 * 10.934 1.15 7645.23 341.89 36.3 22.29 14650.11 3764.15 24.38 360.71 43.87 52.64 0.5 67383.06 7171.02 17972.78 1922.5 * 34.54 136.35 * 10.934 1.47 47763.23 288.51 25.56 15, 8 36906.99 75266.57 99.61 1335.36 115.27 14.9 0.95 82674.39 13512.52 73535.09 1424.96 * 34.54 10.16 * 10.934 0.68 * 685.577 224, 69 * 1.75 8.27 14123.23 17158.54 74.87 348.77 21.01 14.2 0.45 84532.94 6563.52 30335.68 1983.71 * 34.54 274 60.83 1 , 39 5864.77 241.98 * 1.75 * 1.397 3711.52 119171.67 9.51 219.89 43.69 14.44 0.53 65680.51 4628.06 96665.7 1265.9 * 34, 54 73.61 505.85 1.4 4605.82 292.67 * 1.75 * 1.379 7 403.32 449756.55 25.88 271.4 22.32 193.87 0.89 116819.06 8569.6 49963.73 2112.74 * 34.54 69.94 638.98 0.43 7645.23 233 , 36 * 1.75 12.41 3514.63 7256.31 ** 116.65 727.62 47.04 56.33 0.59 106683.62 17421.7 31916.27 2667.07 * 34.54 102, 72 * 10,934 6.07 4927.34 * 27.402 48.78 19.24 6336.29 1780.5 33.62 383.94 10.95 * 0.115 0.8 244617.9 8783.51 38066.02 996.56 * 34, 54 120.43 * 10.934 0.69 * 685.577 171.28 * 1.75 * 1.398 4800.93 6918.7 17.44 400.85 15.1 4.99 0.41 28427.02 11350.77 81610.33 1251, 27 * 34.54 84.86 * 10.934 0.9 * 685.577 317.44 * 1.75 8.27 3857.44 21475.3 24.21 298.44 61.31 9.72 0.65 38603.84 10273 34043, 36 2848.7 * 34.54 112.15 76.14 0.89 * 682.162 527.51 19.88 29.55 9458.44 12738.71 42.16 677.02 87.09 28.99 0.5 73967 , 55 7725.34 72500.96 1763.31 1196.39 73.19 99.4 0.97 * 685.577 250.55 33.47 14.52 9604.7 7676.43 46.79 570.83 8.71 3 .18 0.29 79454.55 5138.39 297293.15 699.48 * 34.54 71.91 * 10.934 1.09 * 685.577 189.37 * 1.75 9.91 4446.63 56783.45 13.28 278.3 13.73 7.19 0.41 68 792.81 14 556.09 56 508.78 1 795.48 * 34.54 99.15 * 10.934 0.77 48517.3 401.78 * 1.75 14.73 8680.83 27504.97 58.33 632.74 251.6 10.51 0.77 49530.98 7710.73 39289.45 2086.39 * 34.54 47.41 * 10.934 0.71 * 685.577 513.64 20.33 69.57 35786.03 12490.91 52.45 1095 23.89 78.45 0, 73 48582.34 9848.73 18207.63 1353.88 * 34.54 64.72 80.56 0.59 * 685.577 * 27.402 * 1.75 21.86 4515.93 72005.48 41.12 219.89 10 , 57 4.65 0.48 64391.06 5469.36 61965.61 2349.57 535.47 17.18 * 10.934 1.95 21634.59 284.34 * 1.75 32.08 18783.17 111429.53 41, 87 597.81 15.95 7.48 0.99 73426.55 18207.79 39008.18 2350.83 * 34.54 54.5 113.1 0.97 8216.81 740.92 28.1 26.26 5792.76 14780.26 20.2 464.85 48.43 10.77 0.68 45759.48 10992.13 32564.66 1412.69 * 34.54 53.01 * 10.934 0.27 14540.93 * 27.402 * 1.75 10.33 7879.91 4348.41 63.12 538.41 14.92 10.77 0.35 64099.61 5359.19 111278.75 1497.46 * 34.54 30.26 * 10.934 0 , 83 7063.57 349.97 * 1.75 22.07 10486.09 21438.1 84.8 444.1 21.9 5.32 0.69 72351.96 8076.41 110049.23 2017.48 * 34.54 48.04 * 10.934 0.26 13539.21 309.22 * 1.75 16.65 3517 .85 21,673.09 60.02 304.99 20.48 7.34 0.58 39913.39 5182.6 22792.89 1390.62 * 34.54 127.83 65.87 0.88 92710.59 * 27.402 * 1.75 13.67 15240.93 1780.5 64.79 881.4 10.53 8.06 0.39 32394.73 6759.16 62694.9 1543.01 * 34.54 153.13 * 10.934 0 , 87 * 685.577 284.34 23.84 11.16 28496.64 6770.8 126.27 422.79 14.68 5.97 0.59 81642.23 4783.76 71610.67 575.13 * 34.54 116.78 * 10.934 3.2 90918.89 456.35 47.26 24.27 3082.77 2688.78 7.38 219.89 11.72 7.78 0.76 61053.08 5359.19 92871.04 1093.27 * 34.54 160.15 37.02 1.81 * 685.577 * 27.402 32.24 13.25 10587.07 3195.46 46.68 467.4 9.41 5.65 0.39 42192.7 5094.16 78862.03 1867.65 * 34.54 146.82 150.37 0.54 16014.51 610.58 16.72 37.07 12452.76 14173.75 35.91 5270.96 18.46 27 , 14 0.46 100 767.56 16530.77 74834.98 2173.05 * 34.54 77.33 452.54 0.57 * 685.577 * 27.402 * 1.75 * 1.379 6137.3 33310.78 14.95 223 , 78 2.3 4.47 0.39 81270.7 6061.81 303759.07 2571.52 * 34.54 235.56 133.08 0.41 * 685.577 284.34 * 1.75 12.83 5376, 57 66095.36 17.71 679.06 30.18 9.72 0.46 38603.84 16717.38 58266.97 2542.26 * 34.54 115.24 * 10.934 0.34 * 685.577 374.03 * 1.75 * 1.379 6774.89 75175.83 21.95 524.17 39.62 79.03 0.44 29994.01 2656.02 187828.79 1670.22 * 34.54 147.17 * 10.934 0.86 48893.33 309.22 * 1.75 11.16 7294.52 10713.71 37.91 467.4 11.93 4 , 3 0.29 93601.15 7127.74 241440,02 1194.03 * 34.54 104.63 104 0.69 16014.51 1153.7 20.33 20.54 6212.68 6468.71 8.26 916 , 6 64.83 19.52 0.52 145116.62 8954.41 140145.7 1607.16 * 34.54 73.55 * 10.934 0.75 17932.12 224.69 11 24.71 25193.04 93442, 56 59.06 597.81 15.1 19.31 0.62 103657.44 9827.49 111737.24 1245.17 * 34.54 72.22 sHER2 / sEGFR2 / CancerSEEK CancerSEEK sErbB2 sPECAM-1 TGFa Trombospondina-2 TIMP -1 TIMP-2 Score Result (pg / ml) (pg / ml) (pg / ml) (pg / ml) (pg / ml) (pg / ml) Regress.

Logist. Test 6832.07 9368.53 * 2.681 21863.74 56428.71 39498.82 0.681 Negative 5549.47 6224.55 * 2.681 29669.66 73940.49 41277.09 0.986 Positive 3698.16 4046.48 179.03 6020 , 47 22797.28 28440.6 0.978 Positive 5856 6121.93 * 2.681 4331.02 20441.19 25896.73 0.693 Negative 5447.93 6982.32 * 2.681 2311.91 56288.51 49425.2 0.515 Negative 6259.35 7935 , 75 * 2.681 9564.37 45870.23 48382.43 0.707 Negative 9336.65 7965.05 26.02 12153.76 18718.19 24998.06 0.117 Negative

4845,24 4512,37 * 2,681 * 406,606 19099,46 27730,8 0,381 Negative 4232,62 4609,75 * 2,681 * 406,606 18330 29151,57 0,892 Positive 4474,35 5445,12 * 2,681 2229,02 11941,18 23460, 29 0.557 Negative 3477.67 4980.39 * 2.681 9633.14 22641.94 21701.45 0.698 Negative 4465.13 5743.23 * 2,681 * 406.606 62470.75 41075.16 0.356 Negative 7077.06 4152.68 * 2.681 2666, 43 45976.53 37632.81 0.878 Positive 3836.11 2682.27 * 2.681 * 406.606 50429.23 38164.65 0.998 Positive 4585.15 5308.6 * 2.681 9335.4 70380.06 47344.38 0.117 Negative 4095.8 4520 , 46 * 2,681 * 406,606 13765,62 21637,54 0,967 Positive 5505,01 3648,55 * 2,681 3534,48 17332,57 21829,29 0,915 Positive 4305,85 6838,52 * 2,681 4028,31 18682,82 24805,66 0.574 Negative 3962.64 4015.16 * 2.681 9564.37 19326.5 21861.25 0.934 Positive 6239.88 6081.92 * 2.681 10922.95 20296.31 23268.28 0.307 Negative 3211.9 3485.85 * 2,681 * 406.606 23132,96 31845,7 0,750 Negative 4687,07 6458,7 * 2,681 2624,55 41369,48 38231,2 0,999 Positive 5280,43 4508,32 * 2,681 12270,35 36672,51 2911 9.22 0.981 Positive 4260.06 4789.87 * 2.681 2063.89 15524.68 19113.57 0.439 Negative 7767.91 8023.77 * 2.681 16206.63 35140.87 30285.27 0.969 Positive 4951.14 6769.32 * 2.681 * 406.606 27145.36 22980.34 0.996 Positive 3185.46 3232.72 * 2.681 13370.08 20750.13 26635.99 0.951 Positive 3674.24 3855.74 * 2.681 * 406.606 28075.81 29054.55 0.606 Negative 4758, 3 3399.66 * 2.681 16206.63 31100.85 32497.95 0.116 Negative 5353.01 5376.72 * 2.681 * 406.606 20016.52 25704.04 4.904 Positive 3176.66 2717.42 224.47 * 406.606 13501.38 21925.17 0.973 Positive 6184.78 6522.23 * 2.681 4396.09 19949.01 26378.74 0.117 Negative 3471.74 4585.34 * 2.681 7653.96 34875.33 31064.63 0.988 Positive 5435.25 4467.94 * 2.681 * 406.606 52458.17 38164.65 0.991 Positive 5120.06 5479.43 * 2.681 * 406.606 54220.89 46070.49 0.397 Negative 4412.97 5962.43 * 2.681 * 406.606 17132.66 22564.56 0.840 Negative 1924.08 1420.52 17.34 6688.42 29093.45 27279.69 0.906 Positive 4863.9 3909.97 * 2.681 4962.71 15558.96 24292.85 0.739 Negative 3276.62 4279, 58 * 2,681 8924.27 93675.88 79745.19 0.448 Negative 5878.46 4822.84 * 2.681 * 406.606 20750.13 28795.93 0.116 Negative 5965.24 6778.53 * 2.681 * 406.606 50998.21 38664.19 0.330 Negative 4640.7 3886.7 * 2.681 15517.57 142686.13 51912.48 0.990 Positive 5258.37 4480.04 * 2.681 * 406.606 26438.57 28150.09 0.944 Positive 3599.62 3565.05 * 2.681 * 406.606 54716.49 37632, 81 0.543 Negative 4863.9 7906.49 * 2.681 13510.95 16868.16 20583.18 0.308 Negative 6647.78 4255.7 * 2.681 * 406.606 41300.46 30934.62 0.490 Negative 4223.48 3625.73 * 2.681 * 406.606 15182.2 22692.47 0.121 Negative 3135.61 5257.68 * 2.681 6844.97 54734.6 31845.7 0.993 Positive 3890.27 3875.08 * 2.681 6666.08 87140.85 34495.42 0.999 Positive 3510.34 2731 , 52 * 2,681 * 406,606 21,829.76 24164,71 0,323 Negative

7017.31 9135.14 * 2.681 * 406.606 15075.3 23492.3 0.680 Negative 4130.76 3791.3 * 2.7 3558.8 57625.12 32837.86 0.818 Negative 5889.09 7047.41 * 2.7 * 129, 5 69 646.43 28 875.35 0.343 Negative 5662.55 5030.11 35.52 * 129.5 36705.4 30318.69 0.480 Negative 5843.74 3929.21 * 2.7 * 129.5 36588.8 27259.35 0.300 Negative 3226.59 3251.24 * 2.7 4704.4 65232.48 24608.24 0.125 Negative 6142.21 7544.99 * 2.7 9811.52 106771.02 34442.57 0.868 Positive 5301.27 8310.67 * 2.7 20465.66 167486.78 33103.83 0.994 Positive 3330.45 4773.51 * 2.7 3128.9 79659.34 36638.81 0.998 Positive 5578.55 5642.96 * 2.7 2244.33 49487 , 04 36023.47 0.640 Negative 6431.92 5622.96 * 2.7 20489.78 88506.88 28298.68 0.644 Negative 5063.37 4252.86 * 2.7 * 129.5 47856.77 34106.48 0.318 Negative 4372.56 4071.69 * 2.7 6326.58 72633.21 28319.99 0.999 Positive 4678.93 4268.02 * 2.7 * 129.5 54661.21 25832.27 0.117 Negative

2637.17 5273.52 * 2.7 732.01 88506.88 32660.88 0.974 Positive 4452.25 6254.04 * 2.7 * 129.5 37598.75 27301.59 0.598 Negative 9589.39 8117.28 * 2, 7 * 129.5 57938.7 31407.43 0.868 Positive 6546.11 5855.85 * 2.7 6469.71 43498.28 33592.97 0.451 Negative 4493.71 6172.29 * 2.7 * 129.5 73025, 64 33325.92 0.983 Positive 4343.89 5839.72 * 2.7 * 129.5 84791.49 27153.82 0.968 Positive 5031.27 4730.99 * 2.7 * 129.5 51888.67 25499.04 0.521 Negative 4858.13 5045.74 * 2.7 * 129.5 73538.54 29799.64 0.937 Positive 4723.7 4162.11 * 2.7 * 129.5 49334.58 25665.53 0.338 Negative 4528.81 4154.56 * 2.7 * 129.5 60034.86 34240.8 0.116 Negative 4938.25 5839.72 * 2.7 683.74 81693.71 31889.66 0.956 Positive 4551.15 5077.04 * 2.7 * 129, 5 48203.95 28277.37 0.540 Negative 5084.34 6154.94 * 2.684 6264.92 136363.01 57377.78 0.926 Positive 5778.95 5027.85 * 2,684 5095.23 72112.49 59669.66 0.621 Negative 4853.14 5082.54 * 2.684 * 445.855 73931.88 59388.49 0.999 Positive 6935.33 5545.11 * 2.684 * 445.855 86067.68 57704.59 0.936 Positive 2106.35 471 0.58 * 2.684 * 445.855 81139.74 66179.4 0.779 Negative 5807.42 3096.21 * 2.684 * 445.855 63180.71 52544.71 0.614 Negative 3263.91 1905.96 * 2.684 * 445.855 59700.78 48440.06 0.985 Positive 1759.17 2168.53 * 2.684 5631.7 98251.75 40124.55 0.926 Positive 4991.64 3985.13 * 2.684 * 445.855 82000.48 80450.38 0.362 Negative 5411.01 3971.52 * 2.684 * 445.855 81441, 1 57564.51 0.966 Positive 3778.83 4322.61 * 2.684 * 445.855 93552.46 62914.06 0.852 Negative 8955.64 5343.78 * 2.684 * 445.855 66284.65 45779.35 0.965 Positive 9649.19 7351.94 * 2.684 6868.37 79321.23 58312.06 0.121 Negative 5140.1 4032.86 * 2.684 2259.98 64350.35 53053.97 0.990 Positive 2829.49 3470.64 * 2.684 2752.86 90969.03 54073.83 0.397 Negative 8914 , 82 6435.85 * 2.684 7984.31 76794.75 54630.88 0.824 Negative 4152.81 5392.04 * 2.684 4372.28 58036.95 36633.24 0.996 Positive 6652.66 5388.33 * 2.684 * 445.855 53084.78 33786.36 0.998 Positive 7854.7 4476.64 * 2.684 3155.42 80793.43 43357.07 0.606 Negative 5434.47 5354.91 * 2.684 6423.56 65765.79 42718.58 0.972 Positive 4152.81 5432.98 * 2.684 * 445.855 57034.01 46924.9 0.593 Negative 4491.59 3301.37 * 2.684 * 445.855 75200.09 56584.9 0.981 Positive 5940.59 4561.31 * 2.684 * 445.855 76805, 7 59,107.47 0.50 Negative 5177.33 3552.82 * 2.684 * 445.855 55145.06 46099.91 0.328 Negative 7249.49 6732.74 * 2.684 6772.98 123349.14 65989.51 0.940 Positive 5561.47 4094.47 * 2.684 * 445.855 53819.75 40033.68 0.371 Negative 8711.17 7774.9 * 2.684 * 445.855 49080.16 45596.25 0.117 Negative 5993.04 4.8585.7 * 2.684 15648.46 210938.12 80205.49 0.997 Positive 6150, 86 5170.41 * 2.684 2721.96 75393.67 60232.44 0.581 Negative 6492.72 4703.44 * 2.684 * 445.855 60130.57 39261.74 0.801 Negative 5821.66 7591.45 * 2.685 3590.78 78892.7 56584 , 9 0,788 Negative 4747,39 3836,14 * 2,684 * 445,855 48888,33 36995,34 0,449 Negative 4692,38 4177,02 * 2,684 10649,53 43699,45 32929,27 0,993 Positive 5093,63 3748,84 * 2,684 * 445.855 49859.03 43357.07 0.387 Negative 4027.5 2844.75 * 2.684 * 445.855 37123.82 38082.49 0.388 Negative 4614.63 3001.26 * 2 , 684 * 445.855 40397.27 41534.26 0.997 Positive 2691.37 3210.18 * 2.692 * 404.102 103949.9 32314.4 0.979 Positive 3375.1 4401.83 51.57 * 404.102 107297.72 31572.38 1,000 Positive 3390 , 67 3073.44 * 2.692 * 404.102 88191.99 27763.62 0.989 Positive 3754.37 3016.4 73.44 * 404.102 34135.04 4.8585.88 0.977 Positive 6950 3688.73 * 2.684 * 445.855 74090.65 60138.6 0.639 Negative 5826.41 2177.23 * 2.684 * 445.855 43615.5 46283.15 0.122 Negative 3823.04 2171.43 * 2.684 * 445.855 57503.59 55002.55 0.123 Negative 7111.76 6279.29 * 2.684 * 445.855 87948, 95 65372.89 0.877 Positive 8477.96 5133.74 * 2.684 * 445.855 66506.63 58639.44 0.886 Positive

11896.89 5148.4 * 2.684 7824.57 67449.27 51573.7 0.121 Negative 5902.49 4448.52 * 2.684 * 445.855 70241.74 67939.39 0.121 Negative 4651.19 2665.98 * 2.684 * 445.855 41459.11 35864.25 0.404 Negative 5149.4 3405.27 * 2.684 * 445.855 60697.85 62489.71 0.332 Negative 3441.38 3220.81 * 2.684 * 445.855 60851.55 54584.44 0.803 Negative 2816.88 1606.14 * 2.684 * 445.855 26612.04 28427.48 0.620 Negative 4854.77 6754.36 * 2.692 * 404.102 61463.72 41502.75 0.975 Positive 3468.72 4652.09 * 2.692 * 404.102 31681.29 29890.83 0.993 Positive 5576.42 6832, 34 * 2.692 4584.26 31278.71 27219.12 0.961 Positive 4942.49 9039.61 * 2.692 * 404.102 71685.17 34343.83 0.571 Negative 6487.61 7272.48 * 2.692 * 404.102 49446.57 25633.78 0.727 Negative 5426.3 4956.85 * 2.692 * 404.102 39031.77 28951.08 0.938 Positive 4971 11411.77 * 2.692 2598.33 100677.89 52666.14 0.564 Negative 5421.47 9817.65 * 2.692 * 404.102 43243.57 26179.01 0.681 Negative 3090.25 5778.54 * 2.692 * 404.102 55148.74 29890.83 0.997 Positive 4824.01 5158.07 * 2.692 * 404.102 66888.79 27813.11 0.514 Negative 3533.61 6155.27 23.09 12052.02 45807.58 27021.08 0.997 Positive 6677.95 6648.88 * 2.692 * 404.102 72192.94 51287.1 1,000 Positive 2846.39 6170 , 29 * 2,692 * 404,102 110600,08 38512,16 0,972 Positive 4197,87 7592,86 * 2,692 * 404,102 39138,24 27714,13 0,986 Positive 3876,97 4417,83 * 2,692 * 404,102 78925,95 45916,35 0,861 Negative 6993, 41 5342.29 * 2.736 3460.43 77503.17 44475.85 1,000 Positive 7212.01 4790.14 * 2.736 * 170.39 36750.11 36519.82 0.955 Positive 3169.33 4242.81 * 2.692 * 404.102 58878.61 27120.1 0.990 Positive 6060.8 6372.83 * 2.692 7945.95 150551.59 38711.14 1,000 Positive 4975.76 4698.34 * 2.692 10282.1 48099.87 24840.11 0.992 Positive 5612.85 5825.51 * 2.692 25272.12 102897.67 26922.04 0.999 Positive 6935.33 5218.19 * 2.684 * 445.855 76849.55 67225.11 0.984 Positive 5223.93 3937.55 * 2.684 * 445.855 79740.72 67605.92 0.997 Positive 4954, 64 2957.21 * 2.684 * 445.855 79027.78 61830.32 0.117 Negative 3927.1 3721.59 * 2.692 8667.06 80544.65 25435.43 0.993 Positive 2781.63 2479.29 * 2.692 3621.58 28157.71 24393.3 0.997 Positive 2747.19 2392.4 19.98 * 404.102 35027.41 25881.65 0.940 Positive 3381.77 3772.22 52.68 2867.14 166,373.66 32215.45 0.997 Positive 2493.27 3643.53 16.15 * 404.102 33181.7 30484.3 0.999 Positive 3266.41 4990.19 17.44 7125.77 76534.79 27021.08 0.999 Positive 3612.2 4965, 18 * 2.692 3378.36 168850.33 38164.07 0.967 Positive 3881.52 5344.97 * 2.692 2846.55 58056.64 34046.69 0.848 Negative 2310.19 2966.74 * 2.692 * 404.102 78322.9 31918.64 0.997 Positive 2665.67 4546.58 * 2.692 14682.17 205809.28 34938.3 0.993 Positive 2152.12 5988.05 * 2.692 * 404.102 87703.88 27367.64 0.792 Negative 2262.17 5586.26 * 2.692 5199.62 46897 , 03 24144.94 0.652 Negative 4294.98 12623.3 * 2.692 21853.23 141816.83 34690.57 0.997 Positive 4142.55 7710.81 * 2.692 * 404.102 69417.16 26575.35 0.308 Negative 2125.27 6164.28 * 2.692 22645.87 123979.69 42152.55 0.992 Positive 3770.23 4303.54 * 2.692 9328.93 39118.27 31077.78 0.793 Negative 5576.42 10199.82 * 2.692 * 404.102 1020 23.27 34641.03 0.977 Positive 4781.48 6242.55 * 2.692 3133.81 91119.7 34839.2 0.583 Negative 4621.43 4752.95 * 2.692 * 404.102 82164.25 26030.34 0.983 Positive 4373.88 5362, 08 * 2.692 * 404.102 83202.49 28901.61 0.975 Positive 3895.19 7096.99 * 2.692 * 404.102 61775.36 30830.49 0.984 Positive 3063.97 4525.04 * 2.692 3459.57 123163.09 33155.6 0.997 Positive 3596.46 4227.02 * 2.692 * 404.102 116937.17 35433.91 0.972 Positive 3587.47 4508.9 * 2.692 * 404.102 88147.47 29594.08 0.938 Positive 3551.55 5265.39 * 2.692 * 404.102 205865.48 30088 , 66 1,000 Positive 4318.16 4221.76 * 2.692 * 404.102 93261 27813.11 0.998 Positive 2896.2 3299.89 * 2.692 * 404.102 70186.37 30533.76 1,000 Positive

4576.88 6330.3 64.78 2681.3 146501.5 32264.93 1,000 Positive 3215.6 4306.19 109.37 * 404.102 165139.1 36078.52 0.983 Positive 2550.53 6080.41 * 2.692 * 404.102 239878 , 22 40055.65 0.919 Positive 2870.19 4114.37 24.93 * 404.102 113206.96 27862.6 0.998 Positive 2738.59 4295.6 66.96 * 404.102 160534.27 38362.96 0.996 Positive 2567.54 6342, 44 27.95 * 404.102 235646.9 34145.73 1,000 Positive 3306.27 5085.09 * 2.692 3297 132467.33 30088.66 0.999 Positive 4068.98 3236.77 58.21 * 404.102 98697.41 30088.66 0.9999 Positive 3024.61 4525.04 34.47 * 404.102 126679.47 36376.17 1,000 Positive 2825.11 3770.49 * 2.795 5588.16 89036.61 33120.88 1,000 Positive 4546.75 6383.08 * 2.795 * 434.712 63685, 39 24648.53 0.999 Positive 3503.43 3726.96 38.86 * 434.712 99433.42 32923.46 0.982 Positive 3758.12 5593.59 36.34 7906.56 89010.7 37353.84 0.993 Positive 2212.93 3005, 47 36.84 * 434.712 154617.01 36026.64 0.918 Positive 3157.72 3208.52 37.34 11492.17 99907.71 46486.08 0.990 Positive 3677.11 5103.9 51.67 * 434.712 84894.84 396 19.29 0.993 Positive 2805.72 6126.97 47.54 5845.76 ** 4142.527 38818.39 0.999 Positive 3007.83 6650.38 36.34 * 434.712 118170.46 29124.36 0.984 Positive 3920.98 4438 , 98 80.21 * 434.712 102522.67 28993.96 0.998 Positive 6253.43 4323.12 107.45 * 434.712 179920.4 34439.28 1,000 Positive 2798.46 4353.51 106.34 6796.6 143728.69 22204 , 14 0.992 Positive 3256.62 3916.69 * 2.795 * 434.712 57442.23 23231.56 0.965 Positive 2718.74 3651.89 * 2.795 16847.76 121601.51 25811.53 0.998 Positive 2103.46 2983.75 53.75 * 434.712 74155.75 33581.87 1,000 Positive 2412.91 3378.75 * 2.795 * 434.712 101393.46 29842.31 0.999 Positive 1914.46 3505.99 * 2.795 * 434.712 63800.93 25552.8 0.934 Positive 5305.73 4061 , 64 55.83 * 434.712 44468.41 21243.34 0.953 Positive 2946.73 4711.15 57.4 * 434.712 198939.23 30103.67 0.998 Positive 5687.35 4714.27 71.65 * 434.712 91484.07 29450, 55 1,000 Positive 3598.91 4941.05 44.46 7331.58 86624.09 32528.9 1,000 Positive 3298.8 3202.98 30.34 * 434.712 106770.12 32594.63 0.988 Positive 3346.04 4488 , 06 72.72 * 434.712 94271.78 43847.97 0.369 Negative 3712.52 5415.44 22.5 * 434.712 106640.43 38152.08 0.836 Negative 4250.03 6871.66 51.15 7331.58 70306.17 27562 , 13 1,000 Positive 3768.26 6893.23 * 2.795 13058.05 101727.48 27302.32 0.980 Positive 3500.92 5484.47 * 2.795 3659.28 122388.98 5959.63 0.852 Negative 3709.99 3975.65 * 2.795 4178.19 112770 28537.85 0.890 Positive 2129.01 4283.71 * 2.795 * 434.712 105339.21 40488.58 0.920 Positive 3212.06 7586.92 * 2.795 9655.25 ** 4142.527 31085.23 0.998 Positive 3207, 11 3626.01 * 2.795 * 434.712 110883.77 30365.2 0.934 Positive 5016.72 6175.11 * 2.795 * 434.712 148858.21 27562.13 0.467 Negative 3192.28 5666.77 * 2.795 2286.3 122885.16 33647, 76 0.995 Positive 4691.11 4925.18 60.54 * 434.712 154911.14 39485.71 0.962 Positive 3995.14 8065.03 98.11 * 434.712 ** 4142.527 60547.3 1,000 Positive 2781.52 4611.55 31 .33 * 434.712 79616.69 46486.08 0.965 Positive 2412.91 4442.04 74.85 * 434.712 94799.48 36756.1 0.992 Positive 3219.48 6306.52 96.47 * 434.712 68341.61 3206 9.03 1,000 Positive 7792.66 5814.18 58.44 7708.24 147256.2 25811.53 1,000 Positive 3872.51 6739.25 35.33 * 434.712 ** 4142.527 49481.36 0.999 Positive 2565.14 5547 , 2 92.11 * 434.712 92452.05 51329.43 1,000 Positive 3651.86 7323.07 43.44 * 434.712 88519.94 33581.87 0.997 Positive 3687.22 5461.43 177.36 11993.16 110832.49 33120 , 88 1,000 Positive 2842.09 3005.47 29.35 * 434.712 84428.46 29254.81 0.967 Positive 2353.83 4648.82 51.15 * 434.712 61225.08 26459.05 0.998 Positive 3824.14 4292.8 * 2.795 * 434.712 154911.14 51947.17 0.921 Positive 4494.45 9027.85 106.34 25787.35 104341.88 35232.24 1,000 Positive 3177.47 6278.76 64.23 * 434.712 85761.06 30954.23 0.9999 Positive

4136.42 7923.31 57.92 * 434.712 97535.48 46621.79 0.983 Positive 4198.33 6257.98 29.35 * 434.712 101971.29 47097.11 1,000 Positive 4038.72 4792.6 91.03 10253.15 154 382.44 45401.88 1,000 Positive 2675.4 5474.59 * 2.795 * 434.712 129785.49 33977.39 0.994 Positive 3259.1 5620.16 103.05 * 434.712 205773.08 49071.72 0.983 Positive 3184.87 4390, 07 159.05 * 434.712 98800.56 38818.39 1,000 Positive 3999.88 7012.76 32.2 24317.51 234457.39 47733.94 0.988 Positive 5052.14 4748.15 85.44 * 125.26 92649.9 43533, 06 0.947 Positive 6229.77 9326.05 66.39 * 125.26 99524.99 71815.92 0.992 Positive 6320.02 6761.89 55.65 * 125.26 100444.72 44313.98 0.968 Positive 4236.99 8237, 5 47.84 * 125.26 61293.07 33385.5 0.966 Positive 4227.22 6540.45 78.11 * 125.26 150519.92 46328.8 0.955 Positive 4723.62 5785.58 66.39 906.59 88779 , 64 42531.09 0.998 Positive 5230.15 9270.38 * 2.749 9793.28 157304.19 36618.51 1,000 Positive

4717.07 8266.36 18.46 * 125.26 126306.22 33549.5 0.992 Positive 6226.43 4690.61 59.55 * 125.26 43195.57 31856.56 0.936 Positive 3938.32 4011.02 72.73 * 125.26 53208.88 26524.38 0.984 Positive 4599.18 6463.19 43.93 2347.83 155032.98 36124.24 0.995 Positive 6893.74 5883.45 49.79 70277.28 220061.94 73517.95 0.997 Positive 3721 , 74 4305.84 55.65 16796.85 175160.85 46946.45 0.957 Positive 5693.57 7844.72 44.42 * 125.26 90923.17 37828.57 1,000 Positive 7803.4 7182.65 109.44 92343 141665.25 50786.69 0.997 Positive 4658.1 9875.83 61.5 * 125.26 144554.36 54954.71 0.992 Positive 5979.56 7004.88 * 2.749 * 125.26 100513.29 37498.29 0.991 Positive 4110 , 2 6000.63 * 2.749 2424.71 110 329.99 27 719.52 0.993 Positive 2604.57 3146.9 * 2.749 * 125.26 63604.78 28045.6 1,000 Positive 6286.58 6382.32 * 2.749 5271.36 40247 , 39 35465.85 0.982 Positive 5889.67 8452.63 * 2.749 2725.51 65271.73 28915.52 0.935 Positive 4393.37 3641.15 * 2.749 4314.67 85790.26 40644.55 0.903 Positive 4894.26 8669, 46 * 2.749 * 125.26 44321.53 30711.7 6 0.981 Positive 3986.92 4316.44 * 2.749 * 125.26 70139.27 42419.9 0.900 Positive 4103.71 5561.17 * 2.749 18295.89 68822.93 47959.23 0.994 Positive 3489.26 2813.71 * 2.684 * 445.855 82917.96 39670.33 0.982 Positive 3956.18 1579.27 * 2.684 * 445.855 57521.03 46191.53 1,000 Positive 4148.33 3702.07 * 2.684 2475.23 73205.52 61077.76 1,000 Positive 4834.72 3437, 91 * 2.684 * 445.855 27671.72 31171.86 0.50 Negative 2393.75 219.83 * 2.684 * 445.855 91984.3 40169.98 Positive 11635.02 4253.11 * 2.684 31446.72 111716.25 46237.34 0.997 Positive 4036.43 1948.19 * 2.684 * 445.855 46917.78 46420.62 0.117 Negative 5079.7 3153.56 * 2.684 * 445.855 53752.7 41716.34 0.919 Positive 5926.3 1142.68 * 2.684 11650.76 155288.59 50512, 01 0.293 Negative 2365.14 2177.23 * 2.684 * 445.855 102380.6 49314.05 0.968 Positive 5954.89 4818.05 * 2.684 * 445.855 47894.68 43493.96 0.984 Positive 3389.28 3304.61 * 2.684 * 445.855 86129 , 26 54909.61 0.999 Positive 5646.4 3695.4 * 2.684 * 445.855 47468.2 40215.42 1,000 Positive 6937.21 7419.06 20.86 8195.49 40342.29 34489.03 0.125 Negative 3567.85 3314.31 * 2.684 * 445.855 42211.75 52174.61 0.117 Negative 4809.45 5947.89 * 2.748 9620.89 54411.19 44887.06 0.434 Negative 7088.41 9909.04 * 2.748 7198.97 82593.82 52297.05 0.999 Positive 5429.56 6361.35 * 2.748 * 96.428 62792,98 49611.26 0.987 Positive 4772.06 7489.91 * 2.748 8182.27 48825 , 08 69666.15 0.370 Negative 7599.51 6956.06 * 2.736 6441.32 58278.75 44381.77 0.982 Positive 7843.43 5289.75 * 2.736 5514.11 70046.17 44758.11 0.908 Positive 6238.48 4011, 46 * 2.736 * 170.39 93057.29 66534.01 0.998 Positive 3236.27 4063.89 * 2.748 1531.03 70878.55 38895.83 0.314 Negative 5163.31 8513.53 * 2.748 4421.88 82635.3 44118, 53 0.972 Positive 11361.52 10465.44 * 2.736 10865.97 51004.71 49183.25 0.453 Negative

7212.01 7262.12 * 2.736 5706.09 54596 49183.25 0.985 Positive 5603.06 7729.55 * 2.736 3814.28 55642.4 41277.04 4.983 Positive 3735.65 7171.86 * 2.748 6612.86 52074.83 32117.01 0.994 Positive 8312.89 7883.07 * 2.736 * 170.39 50876.92 54471.2 0.218 Negative 8237.75 4779.82 * 2.736 * 170.39 56079.81 55418.11 0.950 Positive 9341.23 17753.74 * 2.736 27297.74 96575,04 39818.17 0.991 Positive 4452.6 7458.94 * 2.748 4398.12 82912.49 39361.05 0.991 Positive 6637.63 3201.12 * 2.736 * 170.39 28287.27 40900.63 0.503 Negative 7562 6735.29 * 2.736 * 170.39 47793.33 44758.11 0.942 Positive 5170.88 5708.47 * 2.748 3712.73 62184.55 45656.8 0.256 Negative 3966.21 5490.69 * 2.748 4850.83 35237 , 59 34890.1 0.123 Negative 4020.58 4011.8 * 2.748 * 96.428 34224.23 33544.8 0.705 Negative 2712.23 3325.37 * 2.748 * 96.428 53254.88 44161.2 0.672 Negative 5655.73 6853.14 * 2.748 * 96.428 46395.13 43649.45 0.117 Negative 2529.95 2881.67 29.72 618.01 38443.68 31277.72 0.930 Positive 4496.95 8165.73 * 2.748 10896.99 72750.81 47200.04 0, 76 7 Negative 7799.64 8226.8 * 2.736 4617.05 70289.29 53856.2 0.995 Positive 4559.9 6390.84 * 2.748 8182.27 58134.12 34721.85 0.618 Negative 6769.67 4549.22 * 2.748 * 96.428 52224, 47 37 248.92 0.125 Negative 3733.13 3986.38 18.11 * 170.39 62265.61 42735.4 0.609 Negative 3069.11 3843.67 * 2.748 1111.96 59125.96 38557.69 0.998 Positive 5193.62 6400 , 68 * 2.748 * 96.428 61591.86 51558.85 0.125 Negative 5315.17 4693.03 * 2.748 * 96.428 54456.78 49784 0.335 Negative 8338.58 9614.85 * 2.748 26203.44 97570.42 37755.26 1,000 Positive 5536 , 7 8389.9 * 2.748 4019.09 58482.46 49007.19 0.116 Negative 8344.21 7150.49 16.75 * 170.39 102415.79 64660.82 0.984 Positive 5310.56 6071.31 * 2.736 * 170, 39 66 259.06 60072.77 0.967 Positive 7280.87 7531.26 * 2.748 1442.14 41182.39 37839.69 0.347 Negative 6182.38 4056.67 * 2.736 5386.08 78432.48 59501.36 0.724 Negative 5910.59 6898.4 * 2.748 6175.35 93776.84 45314.54 0.593 Negative 5614.53 6310.4 * 2.848 16078.04 76981.43 35311.59 0.918 Positive 3415.49 3719.83 * 2.748 1934.77 65101.57 52,993.1 0.998 Positive 5584.38 3215.72 * 2.736 * 170.39 28702.09 43911.37 0.620 Negative 8993.35 6126.87 24.66 * 96.428 65780.81 37164.56 0.996 Positive 4386.22 6519.1 * 2.748 4731.43 123241.53 34763.91 1,000 Positive 4393.58 5780 * 2.748 1287.4 46799.31 48533.17 0.521 Negative 7393.22 6663.87 * 2.736 2363.63 54113.95 36236.72 0.896 Positive 2725 , 82 4335.71 * 2.748 2665.02 67648.9 37375.47 0.804 Negative 3825.43 3458.44 * 2.748 1287.4 69685.17 35479.22 0.772 Negative 6799.88 7150.49 * 2.736 * 170.39 86042 , 81 58549.95 0.996 Positive 5022.51 5420.08 * 2.749 * 125.26 66627.11 27502.16 0.939 Positive 4110.2 5041.65 * 2.749 15463.74 87068.7 25112.64 0.940 Positive 4687.58 6084 , 12 * 2,749 * 125.26 52903.32 41753.34 0.896 Positive 3016.52 6309.36 * 2.749 * 125.26 90321.46 27067.54 0.886 Positive 4308.61 8648.53 * 2.749 4379.95 99474.22 24678.34 0.924 Positive 4396.63 5446.01 * 2.749 * 125.26 55342.75 33002.97 0.917 Positive 5740.06 4918.01 * 2.749 * 125.26 40298.07 29459.52 0.989 Positive 4025.82 7950, 54 * 2.749 2 1460.47 65146.05 26415.77 0.960 Positive 5392.04 4305.53 * 2.749 15875.26 60439.06 27719.52 0.955 Positive 4589.37 6871.35 * 2.749 14973.37 201433.49 32620.65 0.997 Positive 6189 , 69 7147 * 2.749 13830.03 60497.6 23049.57 1,000 Positive 3634.69 7658.51 * 2.749 * 125.26 185344.35 36124.24 1,000 Positive 3048.37 5898.54 * 2.749 * 125.26 282904, 47 36728.41 0.944 Positive 4645 5830.7 24.36 * 125.26 47227.99 27936.9 0.974 Positive 4295.59 6590.79 43.93 * 125.26 63563.89 33877.62 0.913 Positive 3980.44 7052 , 17 114.35 13227.53 193889.7 26578.69 0.999 Positive 3522.05 4500.98 41.98 * 125.26 71688.59 27991.25 0.977 Positive

3899.47 4651.12 70.29 * 125.26 34566.42 27828.21 0.751 Negative 6066.22 4921.64 60.53 20960.91 55823.35 29296.29 0.939 Positive 3419.26 5342.45 55.16 * 125.26 48471.94 29894.91 0.982 Positive 3464.2 3026.91 * 2.749 11369.02 33798.58 33494.83 0.123 Negative 6611.46 4715.77 * 2.749 * 125.26 33262.1 37003.24 0.113 Negative 4815.46 3675.44 * 2.749 * 125.26 42482.33 35191.73 0.116 Negative 4268.63 4419.91 * 2.748 * 96.428 64578.02 62789.75 0.997 Positive 2979.2 5732.29 * 2.748 * 96.428 70470 , 67 39,107.25 0.374 Negative 6658.35 6678.04 * 2.713 6585.19 88080.1 47648.14 1,000 Positive 11651.47 6570.42 * 2.713 12973.51 61823.22 47985.52 0.836 Negative 9519.73 7270, 63 * 2.713 2104.18 55540.03 44377.13 0.930 Positive 3010.27 4212.4 * 2.748 * 96.428 50040.76 51472.09 0.125 Negative 3785.88 3762.42 * 2.748 * 96.428 33558.37 37755.26 0.877 Positive 9323.56 7412.48 * 2.713 * 137.094 56736.07 44329.11 0.933 Positive 5023.51 5261.32 * 2.748 4516.99 46766.88 46084.96 0.992 Positive 7422.15 6035.22 * 2.713 1279.27 64947, 29 432 73.07 0.954 Positive 8122.39 7211.23 * 2.713 4297.57 55014.72 56519.03 0.993 Positive 6910.27 7672.84 48.15 28576.96 138677.99 58915.27 1,000 Positive 4049.2 3755.92 * 2.848 5666.72 64935.85 32211.37 1,000 Positive 5083.88 7345.73 * 2.748 2389.52 60652.5 51602.24 0.369 Negative 5557.47 6279.55 * 2.848 6142.63 47288.71 29483.66 0.473 Negative 4925.67 4553.69 * 2.748 * 96.428 57586.34 45742.4 0.622 Negative 5305.72 4539.69 * 2.848 7728.42 55442.36 31864.41 0.929 Positive 3823.82 2290.35 * 2.848 * 1117.956 46990, 79 41,806.24 0.995 Positive 4768.32 5043.87 * 2.748 1575.6 67260.01 43436.37 0.235 Negative 9588.11 7393.75 * 2.713 3346.44 70368.35 70963.98 0.971 Positive 6390.61 8325.64 * 2.748 2618.97 66829.32 52036.35 0.121 Negative 4112.03 4571.56 16.88 * 137.094 75379.79 46781.19 1,000 Positive 3180.38 3379.28 23.64 7296.95 123056.96 45656.8 0.998 Positive 3854.81 4826.12 * 2.848 * 1117.956 57186.82 37735.41 0.125 Negative 7294.72 5412.72 * 2.713 15167.48 72224.53 48950.2 0.573 Negative 10462.12 6385.45 * 2 .713 * 137.094 71285.23 48371.26 1,000 Positive 4162.54 3153.15 * 2.848 * 1117,956 61330.52 37853.18 0.916 Positive 4356.77 5168.48 * 2.748 3221.16 64418.57 33628.83 0.650 Negative 3257 , 27 3383.43 * 2.748 7150.0 61693.67 53603.17 0.242 Negative 3948.11 3441.74 * 2.748 * 96.428 49354.15 37628.65 0.610 Negative 4787.01 6782.92 * 2.748 * 96.428 31548.44 34007 , 06 0.122 Negative 5835.4 4191.09 * 2.848 * 1117.956 104988.72 42763.13 0.998 Positive 6299.08 5020.5 * 2.713 2700.32 59700.89 48998.46 0.989 Positive 5581.91 4043.35 * 2.848 * 1117,956 25228.48 28032.17 0.863 Positive 7106.21 6212.93 * 2.713 * 137.094 65899.85 53695 0.883 Positive 3955.73 4211.36 * 2.848 * 1117.956 47497.17 36091.38 0.580 Negative 2963.31 2654.59 * 2.848 * 1117.956 51003,47 29368.86 0.686 Negative 5299.95 5462.46 * 2.748 * 96.428 26379.63 28845.21 0.850 Negative 4473.39 2735.08 * 2.848 * 1117,956 52741.3 26586.1 0.985 Positive 4772.06 6000.94 * 2.748 1844.56 73657.78 49611.26 0.324 Negative 4031.47 5145.34 * 2.748 * 96.428 66182.98 57681.17 0 , 304 Negative 7192.55 7500.24 * 2.748 1664.96 89568.46 46342,05 0.121 Negative 5132.07 4692.7 * 2.848 8214.76 48678.93 36482.01 0.856 Negative 3551.96 4361.44 * 2.713 * 137.094 76220.39 59454.59 0.995 Positive 8394.32 4986.89 66.04 50328.89 84994.34 40241.79 0.998 Positive 8909.99 4181.71 * 2.748 18748.47 86455.69 49568.08 0.965 Positive 3100, 34 6504.27 * 2.748 8281.02 66917.44 28090.43 1,000 Positive 5152.22 5426.52 * 2.848 * 1117,956 33276.04 28222.84 0.553 Negative 4107.77 2801.13 * 2.848 * 1117.956 46096.3 28260.99 0.998 Positive 5419.21 4780.34 * 2.848 10180.04 48033.78 39546.42 0.995 Positive 3994.64 3484.34 * 2.848 * 1117.956 38417.07 35506.36 0.842 Negative

9085.59 7118.81 * 2.713 * 137.094 41275.46 39726.91 0.956 Positive 5498.39 5387.37 * 2.748 6564.16 73233.03 35984.47 0.970 Positive 11201.59 8185.06 * 2.726 * 115.47 61797 , 2 46 298.94 0.298 Negative 3696.9 3816.81 232.7 2752.64 19076.77 30752.35 1,000 Positive 11087.68 12061.47 * 2.726 26004.47 109401.87 56916.61 0.155 Negative 10280.27 8378 , 3 * 2.726 9391.97 49838.78 48667.12 0.312 Negative 8326.2 8769.32 * 2.726 5767.06 43789.99 40538.14 0.997 Positive 5362.42 3035.58 * 2.848 * 1117.956 38110.19 27651,12 0.640 Negative 5906.71 8535.09 * 2.748 1777.08 49286.72 45015.27 0.498 Negative 3665.57 4238.43 * 2.848 * 1117.956 51060,98 23180.49 0.976 Positive 6138.75 4795.3 * 2.736 * 170, 39 67198.74 59263.4 0.596 Negative 7088.66 5270.79 * 2.726 * 115.47 78025.81 55020.36 0.629 Negative 7588.68 5564.94 * 2.726 * 115.47 36980.96 51413.75 0.21 Negative 6555 , 98 9152.04 * 2.748 9670.74 66052.28 42925.34 0.955 Positive 5740.04 4.306.88 * 2.736 3202.8 68907.31 52343.85 0.992 Positive 6094.56 5666.41 635.23 4421.42 19769 , 27 16 353.29 0.992 Positive 3897.52 4756.31 * 2.748 * 96.428 51785.32 42840.21 0.578 Negative 4415.7 4442.13 * 2.748 16369.24 100044.25 35605.51 1,000 Positive 3893.58 2958.9 * 2.848 * 1117.956 36306.21 23557.67 0.326 Negative 5406.65 7520.92 86.38 24668.88 154769.56 50778.7 0.911 Positive 5176.42 8295.34 * 2.848 7159.08 100506.51 38560.8 0.960 Positive 4703.51 4773.31 * 2.848 * 1117,956 85625.53 51694.63 0.855 Negative 9137.6 5648.96 * 2.726 * 115.47 17446.71 19444.67 0.965 Positive 7518.01 4598.54 * 2.726 * 115.47 25,991.63 34496.63 0.987 Positive 7282.64 6842.01 * 2.726 * 115.47 61515.64 49545.95 0.495 Negative 10149.01 7140.57 * 2.726 9520.31 46106.93 42772.21 0.980 Positive 5140.59 5866.17 * 2.748 6807.89 27766.36 30312.92 0.322 Negative 4991.33 4133.82 * 2.848 * 1117,956 66726.66 37617.68 0.569 Negative 6085.45 5899.78 * 2.748 5258.11 48408.21 37797.47 0.121 Negative 3901.34 4374.58 * 2.848 4778.85 112743.33 49843.22 0.829 Negative 3915.57 4124.87 * 2.748 * 96.428 53470.13 46899.56 0.300 Negative 4854.3 8 4792.58 * 2.748 3128.03 59607.84 45357.31 0.117 Negative 3789.48 3450.09 * 2.748 * 96.428 48741.49 46427.78 0.238 Negative 4649.03 7791.4 * 2.748 1397.82 40029.79 41819 , 76 0.312 Negative 5536.7 6371.17 209.47 1353.59 36084.7 30690.41 0.945 Positive 3426.08 3635 * 2.748 * 96.428 46161.84 37713.05 0.877 Positive 3854.24 4702.06 * 2.748 * 96.428 45546.4 36237.21 0.620 Negative 5513.71 5914.2 * 2.748 916.62 78093.92 43862.61 0.979 Positive 4434.14 6102.59 * 2.748 * 96.428 68897.32 39699.6 0.883 Positive 3464.2 8167, 53 * 2.749 * 125.26 82878.97 22343.54 0.953 Positive 3531.69 8056.77 * 2.749 20560.59 187551.75 49200.69 0.813 Negative 4546.85 9189.22 * 2.749 * 125.26 70636.69 29786 , 05 0.683 Negative 3406.43 8320.04 * 2.749 * 125.26 186334.44 27447.83 0.994 Positive 1969.7 5213.6 71.27 41644.82 148609.69 23429.66 0.991 Positive 5889.67 5789.34 22.4 * 125.26 120.270,39 36124.24 0.413 Negative 4713.79 5804.37 * 2.749 * 125.26 38538.07 30439.4 0.690 Negative 5966.24 7162.84 * 2.749 10149.85 83340.18 35685 , 23 0.666 N egative 3099.36 9407.61 * 2.749 10012.96 281022,53 52208.59 1,000 Positive 4103.71 7238.2 * 2.676 21360.63 184926.25 61314.71 0.774 Negative 5154.28 7453.54 * 2.749 14947.51 104164.95 33276.19 1,000 Positive 4227.22 5834.47 * 2.749 20485.46 121018.5 32839.09 0.711 Negative 3207.9 5136.62 * 2.749 24564.89 181346.16 44202.33 0.985 Positive 3560.64 6934 , 11 * 2.749 * 125.26 48114.18 23864 0.817 Negative 3262.26 6879.19 * 2.749 21085.89 65083.3 29622.77 0.697 Negative 4848.28 9627.74 * 2.749 * 125.26 70636.69 22126, 25 0.950 Positive 3112.12 5483.1 * 2.749 * 125.26 100410.46 24841.2 0.777 Negative 4815.46 6133.58 * 2.749 3195.35 165364.86 33713.54 1,000 Positive

4950.14 5921.19 * 2.749 3440.48 160777.27 40866.1 0.996 Positive 3488.87 5069.85 67.62 12804.83 240645.57 37807.54 0.980 Positive 3914.76 5062.91 * 2.761 2441.07 58,883.03 37145.81 0.956 Positive 5542.67 6038.4 * 2.761 5063.66 48265.51 26798.49 0.878 Positive 6302.99 7079.61 156.49 * 452.14 78417.57 35400.74 0.993 Positive 14896.72 3964 , 73 * 2,761 * 452.14 117754.41 52727.96 1,000 Positive 6022.07 7857.64 46.29 11235.66 77104.67 23815.05 0.999 Positive 61539.85 5970.01 * 2.761 86518.84 169343.47 30866.34 1,000 Positive 5148.54 5114.96 18.43 * 452.14 48417.17 25815.05 0.985 Positive 4386.85 5341.76 35.1 18716.56 268815.64 33280.16 0.993 Positive 3447.95 5552.96 * 2.761 * 452.14 142585.61 31439.68 0.999 Positive 4653.94 4328.72 * 2.761 * 452.14 21785.36 24305.1 0.900 Positive 5568.35 4318.63 * 2.761 4282.6 45559.34 23438, 28 0.901 Positive 5023.87 4084.35 * 2.761 * 452.14 33776.08 26987.81 0.855 Negative 3325.29 5461.22 17.6 * 452.14 97163.39 28542.88 0.994 Positive 5218.92 11787.82 ** 2003,144 13828,3 333 714.66 38978.77 1,000 Positive 5449.61 2599.26 17.19 * 452.14 104937.91 43750.43 0.999 Positive 6225.42 3891.92 * 2.761 3795.95 34904.18 23928.11 1,000 Positive 4746.3 7143 , 54 * 2.761 16205.81 55420.9 27101.43 0.380 Negative 3744.19 5076.78 20.49 * 452.14 51611.86 23099.32 0.989 Positive ** 30169.62 8640.21 * 2.761 86362.95 321136 , 51 31,439.68 1,000 Positive 4012.81 6364.89 * 2.761 4185.17 167102.26 31745.8 1,000 Positive 6412.98 6592.29 43.5 16908.03 181080.43 33972.74 1,000 Positive 3621.18 5146 , 24 * 2,761 8655.88 108240.14 40980.38 1,000 Positive 3630.64 7222.74 * 2.761 11235.66 222802.48 25437.24 0.998 Positive 4129.95 5773.1 * 2.761 2850.99 99233.83 32704, 03 0.972 Positive 4762.23 9279.05 17.19 * 452.14 83290.36 35903.91 0.971 Positive 6160.82 5602.5 * 2.761 * 452.14 64969.3 47272.6 0.695 Negative 3388.17 4766.61 * 2.761 * 452.14 348414.92 42085.16 0.988 Positive 4367.8 5313.73 * 2.761 2585.59 93177.98 42401.6 0.123 Negative 3124.33 5468.27 * 2.761 5234.88 85073.65 45746.03 0.991 Positive

3017.74 4338.82 * 2.761 * 452.14 70724.78 31669.25 0.880 Positive 7515.93 7203.86 * 2.761 * 452.14 73837.6 25437.24 0.989 Positive 5007.9 6655 * 2.761 * 452.14 74130, 78 31095.58 0.993 Positive 5318.19 8540.58 * 2.761 4331.33 184727.16 26041.85 0.996 Positive 3074.15 4993.69 * 2.761 2754.42 161476.25 26836.35 0.928 Positive 10519.51 4399.48 * 2.761 7026.99 53385.23 26836.35 0.982 Positive

3917.92 5278.75 * 2.761 * 452.14 14644.82 22158.34 0.824 Negative 6691.69 4544.99 * 2.761 * 452.14 38891.49 23588.97 0.932 Positive 5957.59 6933.54 * 2.761 20654.83 103703 , 48 22,685.19 0.999 Positive 4275.77 7630.16 * 2.761 * 452.14 111955.72 36718.36 0.552 Negative 11321.56 10294.76 * 2.726 20956.16 69689.26 48925.39 0.528 Negative 4290.63 4322 , 46 * 2.748 * 96.428 67615.48 75251.64 0.448 Negative 3847.03 3985.81 * 2.748 3104.77 56806.28 54345.27 0.117 Negative 5232.72 5236.1 * 2.757 3783.81 27928.1 28678.71 0.848 Negative 4670.45 6549.21 * 2.757 * 354.923 41217.83 37349.33 0.943 Positive 9076.52 6632.87 * 2.757 27025.29 50656.88 29851.77 1,000 Positive 6230.35 6640.49 * 2,757 9017.91 41188,02 32044 , 38 0.615 Negative 4752.71 6150.1 * 2.757 * 354.923 39587.38 32508.05 0.572 Negative 5904.1 6785.72 * 2.757 3759 22298.43 29327 0.116 Negative 3726.1 5372.48 * 2.757 16293.88 49728, 26 32786.35 0.997 Positive 6432.85 10035.02 * 2.757 15545.46 32009.88 30253.08 0.122 Negative 4894.23 6097.89 * 2.757 * 354.923 46453.9 8 29234.39 0.50 Negative 9113.86 5383.11 * 2.726 * 115.47 38954.96 31848.57 0.524 Negative 5852.51 6732.12 * 2.757 * 354.923 25277.8 26020.61 0.642 Negative 10345.94 7156.39 * 2.726 * 115.47 38396.94 28266.32 0.125 Negative 10555.02 6459.84 * 2.726 * 115.47 25418.79 26876.81 0.858 Negative

4911.96 5864.44 * 2.757 * 354.923 37688.72 31611.81 0.923 Positive 6402.11 5293.41 * 2.757 * 354.923 35563.9 39224.72 0.122 Negative 3239.86 4342.38 * 2.757 25638.22 52115.07 26546.71 1,000 Positive 4684.91 5534.98 * 2.757 * 104.283 78363.27 57094.5 0.947 Positive 5607.6 6606.22 * 2.757 5981.15 37222.45 37536.47 0.991 Positive 7945.47 5606.41 * 2.757 * 104.283 46554.03 39605.96 0.122 Negative

6017.52 4624.2 * 2.777 * 104.283 42239.66 46880.96 0.637 Negative 4988.91 6307.44 * 2.757 1938.02 26202.06 29944.38 0.761 Negative 6996.08 5684.55 * 2.757 14191.74 19780.28 27566 , 88 0.502 Negative 5125.47 4616.87 * 2.757 8554.04 41606.86 25246.03 0.991 Positive 4490.21 3986.3 * 2.757 * 104.283 67607.5 55804.44 0.983 Positive 5354.12 4584.47 * 2.736 * 170.39 29824.38 31361.14 0.999 Positive 4732.13 7532.1 * 2.757 * 354.923 44047.83 39444.12 0.588 Negative 8736.62 7731.27 * 2.757 3364.42 91532.89 54717.04 0.898 Positive 5024.48 6360 .11 * 2.757 * 354.923 39040.19 33436.1 0.852 Negative 7777.8 4891.65 * 2.757 11292.95 78388.37 44667.33 0.933 Positive 6534.46 7801.38 * 2.757 15835.25 66941.07 38129.65 0.208 Negative 5903.32 8009.98 * 2.757 17433.3 82490.53 39035.87 0.997 Positive 4250.76 3297.59 63.43 6693.53 31865.03 24780.66 0.982 Positive 8621.35 7741.92 * 2.757 722 , 02 65617.66 48945,17 0.978 Positive 6153.89 7992.98 * 2.757 3013.3 23791.47 27814.03 0.975 Positive 7476.28 7891.62 * 2.757 * 104.283 75886.86 4 4379.43 0.932 Positive 6409.21 4570.36 * 2.757 * 104.283 36112.14 34552.87 0.950 Positive 7950.89 4133.39 * 2.757 * 104.283 43159.42 46655.84 0.50 Negative 5397.2 5250.41 * 2.757 * 354.923 18561.18 27505.08 0.421 Negative 5334.31 5264.74 * 2.757 * 354.923 26608.92 27937.58 0.813 Negative 6263.42 3697.16 * 2.736 * 170.39 37303.4 44805.16 0.909 Positive 5149.28 6488.54 * 2.757 * 354.923 30559.43 34644.34 0.421 Negative 3198.18 3892.85 * 2.777 * 354.923 54050.07 34148.38 0.966 Positive 5715.13 4006.45 * 2.736 * 170.39 30686.98 40618, 29 0.644 Negative 5955.76 7469.11 * 2.757 * 354.923 39775.84 27381.48 0.959 Positive 4075.14 5080.19 * 2.757 3652.77 63042.99 37202.05 0.991 Positive 5655.85 8201.95 * 2.757 10285, 21 66663.31 43672.64 0.630 Negative 3723.26 3322.92 * 2.757 12981 13524.94 19194.84 0.571 Negative 5583.29 5317.78 * 2.757 * 104.283 45388.14 37581.1 1,000 Positive 4873.55 5358.08 * 2.757 * 354.923 54209.58 39256.05 0.950 Positive 5119.52 7753.68 * 2.757 * 354.923 41968.43 36601.59 0.837 Negative 4123.69 526 8.32 * 2.757 2439.25 33259.65 27906.69 0.125 Negative 5190.98 6360.11 * 2.757 8383.02 40440.61 32724.5 0.951 Positive 3626.93 6142.64 * 2.757 4254.56 66605.6 30129 , 59 0.898 Positive 6791.03 6606.22 * 2.757 11622.43 38342.59 33219.46 0.568 Negative 6403.97 5841.77 * 2.757 * 104.283 22242.9 30019.56 0.866 Positive 5276.15 6046 * 2.757 * 104.283 * 19.531 40303.44 0.950 Positive 4399.56 5478.84 * 2.761 8655.88 55636.3 28960.96 0.966 Positive 4960 5517.64 * 2.761 10688.76 41590.05 26912.07 0.847 Negative 4813.23 9463.94 * 2.761 7248, 67 98,402.59 31324.94 0.379 Negative 5864.17 5609.59 * 2.761 * 452.14 50367.97 37068.05 0.828 Negative 5295.76 6784.61 * 2.761 15529.28 206018.45 29722.09 0.999 Positive 3763, 13 4986.78 * 2.761 * 452.14 120915.01 30942.74 0.955 Positive 4765.42 4609.57 * 2.761 * 452.14 50812.1 35362.06 0.640 Negative 5494.52 6430.7 * 2.761 2441.07 46422 , 25 27859.57 0.412 Negative 5170.93 11155.13 * 2.761 157461.07 223753.85 27707.85 1,000 Positive 6685.2 7877.01 * 2.761 4185.17 61459.31 41216.76 0.342 Negative 4857.88 5170.6 * 2.761 * 452.14 42404.64 28163.14 0.399 Negative 3766.29 8692.18 * 2.761 9646.38 138264.63 28656.87 0.986 Positive 5677.58 6544.44 * 2.761 3553, 11 44 261.11 28163,14 0.121 Negative 3024 3622.46 * 2.761 3189.53 43991.71 19939.77 0.834 Negative

2611.24 3655.16 * 2.761 * 452.14 60909.9 20428.47 0.372 Negative 3061.61 5503.52 * 2.761 * 452.14 89296.04 26798.49 0.966 Positive 4209.17 4609.57 * 2.761 * 452 , 14 31279.86 35091.47 0.974 Positive 7156.77 5609.59 21.31 * 452.14 24484.77 24078.88 0.351 Negative 5065.41 4555.18 * 2.761 * 452.14 34168.71 33433.95 0.811 Negative 4361.45 5194.98 * 2.761 * 452.14 41102.75 37418.12 0.698 Negative 3331.58 6610.72 * 2.751 * 452.14 272147.02 37340.29 0.998 Positive 3741.04 48585.09 * 2.761 * 452.14 128786.36 26457.87 0.999 Positive 5030.26 7053.33 * 2.761 * 452.14 168948.83 34705.29 0.979 Positive 4431.32 5620.22 * 2.761 * 452.14 86725.78 20578.84 0.974 Positive 3577.06 6758.63 27.03 * 452.14 107124.78 20842.02 0.999 Positive 3981.17 7714.75 * 2.761 90964.46 152021.73 32972.78 1,000 Positive 4950.42 4708.45 * 2.761 * 452.14 49582.64 34049.78 0.980 Positive 4552.11 2996.81 * 2.761 * 452.14 34003.15 28694.87 0.125 Negative 4079.28 3845.7 * 2.761 * 452.14 69121.15 32320.44 0.117 Negative 3847.58 8337.41 * 2.757 19408.04 261866 , 04 34,306.25 0.999 Positive 2523.8 3659.73 * 2.757 * 424.639 52845.25 41205.64 0.291 Negative 5139.97 6642.27 * 2.757 20530.15 116033.39 37774.11 0.988 Positive 3580.19 4657.33 * 2.757 * 424.639 85710.11 34027.34 0.992 Positive 4206.5 8364.46 * 2.757 6494.01 187892.36 41149.21 0.999 Positive 3850.57 4324.11 16.75 * 424.639 29910.13 26748.24 0.539 Negative 4664.13 3326.32 * 2.757 * 424.639 33288.07 32578.58 0.591 Negative 2402.51 3174.4 * 2.757 * 424.639 78800.85 36653.44 0.974 Positive 7721.97 5073.15 * 2.757 2911.87 41578.08 32022.03 0.897 Positive 2885.4 3227.02 * 2.757 * 424.639 64281.56 33358.36 0.813 Negative 6078.13 5001.82 * 2.757 * 424.639 100338.39 33637.03 1,000 Positive 3799.79 6770.6 18.4 * 424.639 148054.08 39684.14 0.998 Positive 3859.55 3591.66 * 2.757 * 424.639 60816.99 39234.14 0.548 Negative 3093.81 4124.96 * 2.757 * 424.639 46342.56 24088.37 0.948 Positive 1573.35 3008 , 23 * 2.757 * 424.639 116281.1 33358.36 0.986 Positive 3907.48 4650.01 * 2.757 * 424.639 60189.9 37101.47 0.681 Negative 2676 , 9 3949.5 * 2.757 * 424.639 41730.16 34976.07 0.677 Negative 5228.58 6262.65 * 2.757 13694.25 87658.09 46425.37 0.964 Positive 3509.5 4657.33 * 2.757 * 424.639 27909.04 24864 , 13 0.998 Positive 3019.28 5137.24 * 2.757 * 424.639 63166.02 29077.35 0.965 Positive 4103.34 5084.44 * 2.757 * 424.639 59883.4 36205.73 0.932 Positive 3674.86 4804.56 * 2.757 * 424.639 55636.33 29632.38 0.774 Negative 3050.77 3759.06 * 2.757 * 424.639 46602.8 37549.82 0.931 Positive 2945.09 3598.45 * 2.757 * 424.639 48309.84 28799.92 0.999 Positive 2187.49 3838.37 * 2.757 * 424.639 68840.66 32745.62 0.998 Positive 5013.89 6992.11 * 2.757 * 424.639 80931.32 56592.17 0.923 Positive 3229.37 2860.39 * 2.757 * 424.639 51120.46 35646.51 0.977 Positive 2601, 54 3296.44 * 2.757 * 424.639 41199.27 30854.33 0.125 Negative 2862.73 4863.84 * 2.757 24536.39 219430.38 35478.84 0.998 Positive 3168.67 5415.41 * 2.757 * 424.639 61379.2 28023, 36 0.910 Positive 5847.2 9500.87 * 2.757 98685.33 186760.08 42505.27 1,000 Positive 3371.82 5212.98 * 2.757 * 424.639 68 371.22 34641.08 0.998 Positive 6610.45 7008.96 * 2.783 6260.09 89105.79 37909.81 0.986 Positive 3162.9 6014 * 2.757 10476.46 32573.8 20872.13 0.961 Positive 2945.09 5254.78 * 2.757 8979.39 136089.6 35311.21 1,000 Positive 4602.18 5757.1 * 2.757 * 424.639 80825.3 31410.2 0.927 Positive 2763.9 3322.99 * 2.757 10453.79 57212.81 34306.25 0.923 Positive 2794.89 3217.14 * 2.757 * 424.639 56994.84 29632.38 0.762 Negative 2548.74 3289.81 * 2.757 * 424.639 41935.96 33804.28 0.714 Negative 2257.86 3223.73 * 2.757 * 424.639 46505.1 37157.49 0.998 Positive 3521.26 7089.06 * 2.777 * 424.639 106747.4 37886.3 0.998 Positive 1857.58 3177.68 * 2.757 * 424.639 28797.3 23811.29 0.993 Positive

8254.55 9542.7 * 2.783 13458.99 86402.84 45818.43 0.991 Positive 2037.31 3043.87 * 2.757 2198.37 54395.08 33525.55 1,000 Positive 2187.49 3362.93 * 2.757 * 424.639 31135, 65 32 801.3 0.877 Positive 3418.57 2325.14 * 2.757 * 424.639 34298.17 30798.75 0.888 Positive 4540.4 6416.79 85.52 25323.39 53480.8 29576.87 1,000 Positive 6539.63 9888.55 * 2,783 11700,98 73883,6 33019,51 0,998 Positive 4807,19 4934,55 68,1 * 424,639 43028,03 35422,96 0,838 Negative 4518,37 5830,49 * 2,783 12165,66 108834,76 30601,26 0,999 Positive 6900,51 6932,06 * 2,783 7562,41 67708,34 42462,87 0,925 Positive 5592,35 6424,94 * 2,757 * 424,639 51068,33 33414,09 1,000 Positive 3348,48 4723,41 * 2,757 * 424,639 49530, 86 30354.29 0.432 Negative 9908.9 11860.03 18.05 39043.28 109233.64 25002.1 0.999 Positive 5653.75 7025.48 * 2.783 17252.59 231237.93 33643.13 1,000 Positive 7126.48 10936, 28 * 2,783 18840,98 110547,43 39756,53 0,999 Positive 4594,65 6103,33 * 2,783 2720,26 41964,78 26941,94 0,857 Negative 5974,15 7907,06 * 2,783 * 103,34 15 4275.12 48256.53 0.881 Positive 7449.83 9957.06 * 2.783 * 103.34 113328.07 33900.02 0.932 Positive 4932.02 6443.94 * 2.753 * 103.34 107512.85 32212.97 0.854 Negative 5798, 64 6085.83 * 2.757 8706.79 39348.5 38222.96 0.207 Negative 4061.47 6713.78 18.72 * 103.34 150394.08 33459.68 1,000 Positive 4823.72 3847.2 37.83 * 103, 34 106597.95 52110.03 0.997 Positive 3357.23 2815.78 * 2.757 * 424.639 34049.67 26859.09 0.310 Negative 4423.07 6219.81 * 2.776 1698.06 101943.65 31919.81 0.955 Positive 3497.74 4775 17.57 * 424.639 94403.13 45570.33 0.989 Positive 2457.51 2121.37 * 2.757 * 424.639 37693.46 28189.73 0.614 Negative 4356 7481.92 22.6 40798.25 176049.91 33804.28 1,000 Positive 2908.11 2527.21 * 2.757 * 424.639 26426.96 25695.29 0.641 Negative 3269.95 5647.62 16.75 * 424.639 87339.55 31132.23 1,000 Positive 5550.63 3376.27 * 2.757 * 424.639 42226.6 30798.75 0.831 Negative 4255.21 6033.92 * 2.757 * 424.639 42726.45 32188.96 0.326 Negative 4788.49 5998.08 * 2.757 * 424.639 59303.79 33525.55 0.382 Negative 3430.28 4613.44 * 2.757 17284.93 95712.71 40359.83 0.996 Positive 6184.19 4539.04 * 2.757 * 104.283 58132.32 45403.91 0.999 Positive 4688.06 5279.07 * 2.757 * 354.923 39892.14 37786,12 0.877 Positive 6996.08 5772.47 * 2.757 * 354.923 61853.63 44277.04 0.117 Negative 6359.1 10922.75 * 2.757 3709.38 34902.31 33714.73 0.943 Positive 5943.6 8882.74 * 2.757 13683.47 68,788.77 40,637.59 0.626 Negative

5717.17 10010.58 * 2.757 3184.11 119224.18 59825.16 0.991 Positive 7487.01 5953.33 113.33 * 104.283 57913.72 37612.7 0.559 Negative 7358.31 6144.07 * 2.757 * 104.283 41231.36 45403 , 91 0.977 Positive 5408.92 4755.09 * 2.757 * 104.283 25469.2 30019.56 0.116 Negative 4793.93 2973.07 * 2.757 * 354.923 51934.14 34768.4 0.758 Negative 5289.53 7074.94 * 2.848 * 1117.956 49732.1 30633.63 0.778 Negative 4791.21 5632.99 * 2.848 * 1117.956 44986.59 42045.15 0.589 Negative 6405.18 5699.18 * 2.757 * 354.923 46618.16 33033.81 0.806 Negative 7184.28 6160.53 * 2.848 2721.84 83393.95 39704.43 0.239 Negative 13227.69 6718.79 * 2.736 29871.03 106789.35 36567 0.984 Positive 3312.3 9831.16 * 2.757 36439.24 73802.53 28709.58 1,000 Positive 5136 , 1 3956.79 * 2.848 1628.72 35682.95 32558.68 0.908 Positive 4407.46 6247.37 * 2.757 16872.85 59598.71 33931.52 0.583 Negative 5779.78 7312.26 * 2.757 * 354.923 59846.29 37006.46 0.914 Positive 4887.24 6141.02 * 2.748 11545.26 126320.42 57394.84 0.955 Positive 5964.89 3705.66 * 2.757 13804.5 2 55549.75 37162.28 0.998 Positive 3309.51 5072.26 * 2.757 2289.11 44220.77 43196.39 0.958 Positive 7474.47 4370.11 * 2.736 966.48 69957.95 44287.69 0.378 Negative 4407.46 4126.05 * 2.757 * 354.923 46716.9 33962.49 0.948 Positive 4971.14 5336.5 315.94 2088.62 15201.96 21974.51 0.974 Positive

6824.85 6303.74 * 2.736 * 170.39 43081.64 42970.59 0.947 Positive 6300.82 9674.5 * 2.736 2039.94 52347.12 43723.21 0.997 Positive 3745.98 3566.08 64.74 * 354.923 36945.96 36259.29 0.986 Positive 4713.71 4076.78 * 2.736 5225.98 66848.94 54518.53 0.874 Positive 7043.37 5733.9 * 2.736 * 170.39 73591.88 49041.89 0.997 Positive 7499.48 10762.19 * 2.736 16626.62 104794.67 57124.97 0.656 Negative 4491.96 4203.46 * 2.757 * 354.923 28081.05 33962.49 0.395 Negative 7887.23 5808.56 22.95 1650.68 35549.82 41277 , 04 0.993 Positive 7398.05 6283.61 * 2.783 * 103.34 64847.54 50783.77 0.995 Positive 5509.69 7520.35 * 2.736 1877.87 88389.65 66053.15 0.842 Negative 7712.07 5044.09 * 2.736 * 170.39 67283.78 53241.56 0.977 Positive 13520.56 6477.18 * 2.757 * 354.923 41742.17 31055.84 0.979 Positive 5454.19 6847.11 * 2.757 * 354.923 81371.08 47545.05 0.354 Negative 4239 , 18 3780.9 * 2.757 4724.32 60438.4 44787.03 0.342 Negative 8253.98 8331.26 * 2.757 13368.66 43867.61 36321.51 0.956 Positive 14876.75 6258.62 * 2.757 * 354.923 5586 7.9 30500.06 0.987 Positive 5783.63 5368.59 * 2.736 4745.32 118386.61 54991.89 0.975 Positive 5949.68 6732.12 * 2.757 * 354.923 31802.17 27721.35 0.812 Negative 9266.09 10665, 45 * 2,783 * 103.34 50792.85 43467.21 0.829 Negative 8222.02 4433.68 * 2.783 1163.62 32598.04 4.1297,03 0.654 Negative 7489.23 4896.82 * 2.848 6649.45 28456.75 31594, 8 0.598 Negative 6939.96 4812.02 31.86 * 1117.956 50071.23 36091.38 0.201 Negative 4647.82 5024.9 * 2.848 1174.65 34953.2 24917.96 0.993 Positive 7107.1 7386.32 * 2.783 * 103, 34 51628.4 51541.11 0.230 Negative 8952.16 7827.05 * 2.783 4728.71 67563.77 44622.76 0.636 Negative 6900.51 7130.36 * 2.753 * 103.34 39310.55 40163.53 0.315 Negative 5999, 81 6508.4 * 2.783 * 103.34 61639.53 53021.97 0.465 Negative 3971.29 4326.78 * 2.848 * 1117.956 36047.07 24539.71 0.469 Negative 6280.62 4570.86 * 2.848 * 1117.956 49310.65 37931 , 73 0.756 Negative 10019.82 8574.15 * 2.736 9595.45 69059.62 57219.89 0.938 Positive 9184.31 9572.42 * 2.736 13085.13 63721.64 55323.37 0.997 Positive 4247.97 5164.05 * 2.736 28584.7 130344.6 61073.8 0.958 Positive 4454.05 6097.89 * 2.757 * 354.923 41855.19 35358.03 0.944 Positive 6849.81 5038.88 70.44 6249.63 34091.62 34394 , 46 0.991 Positive 10964.2 9220.52 * 2.736 47518.58 127494.91 60310.99 0.995 Positive 7862.2 6227.87 * 2.736 * 170.39 63216.32 52910.74 0.826 Negative 5021.52 5200.37 * 2.757 * 354.923 21991.56 26237.29 0.117 Negative 7570 5089.34 * 2.848 23127.15 113283.5 43242.8 0.953 Positive 6269.49 5550.8 * 2.783 * 103.34 69528.34 26100.3 0.993 Positive 6410, 94 6288.94 * 2.783 * 103.34 179872.1 37799.17 0.988 Positive 7650.64 7872.74 * 2.783 2684.53 64074.76 32982.84 0.900 Positive 9037.12 8218.3 * 2.783 6194.33 59967, 5 43095.01 0.973 Positive 5148.83 6336.92 * 2.783 3634.36 96724.48 42165.65 0.982 Positive 5621.74 4023.29 * 2.783 * 103.34 53937.88 36546.44 0.975 Positive 3316.45 6133, 87 31.26 2741.52 151370.48 33191.2 0.999 Positive 6978.89 3060.1 * 2.757 * 424.639 32289.72 26526.54 0.125 Negative 4448.02 6584.61 * 2.757 * 424.639 112638.4 28078.81 0.996 Positive 3272.86 4856.42 * 2.757 * 424.639 53211.55 30632.06 0.543 Negative 3491.87 4584.25 * 2.757 * 424.639 17245.27 24531.67 0.117 Negative 6919.87 6860.88 * 2,783 5532.88 425936, 58 54,432 0.987 Positive 3645.22 4047.48 * 2.757 * 424.639 42842.3 31910.76 0.117 Negative 7770 10965.16 * 2.757 80176.88 110873.85 32133.31 0.997 Positive 5270.11 5284.91 * 2.783 3703.64 218313.77 41942.84 0.995 Positive 2982.14 3893.82 * 2.757 * 424.639 120430.38 37157.49 0.690 Negative 7683.05 8322.97 * 2.783 2360.66 89652.1 44175.14 0.824 Negative 4938.39 5674, 65 * 2,783 * 103.34 89364.82 29466.56 0.993 Positive 5884.37 6779.04 * 2.783 23347.22 226219.03 29795.94 1,000 Positive

2632.19 2806.25 * 2.757 * 424.639 31657.19 24088.37 0.125 Negative 5756.21 7042.01 * 2.753 600.89 49457.43 45182.86 0.996 Positive 6025.48 7907.06 * 2.783 * 103.34 82878 , 26 46,305.02 0.735 Negative 5756.21 5997.98 * 2.783 * 103.34 81001.49 44548.13 1,000 Positive 7320.41 6481.51 * 2.773 * 103.34 51645.2 36325.59 0.970 Positive 3527.15 3776.26 * 2.757 6264.48 65297.82 41600.83 0.994 Positive 5666.55 7274.67 * 2.783 5132.34 40135.46 28076.14 0.819 Negative 9252.99 10315.09 * 2.783 4694.92 42768.97 31663 , 34 0.841 Negative 8952.16 9548.84 * 2.783 11047.73 70798.41 33459.68 0.984 Positive 3022.14 3841.83 * 2.757 * 424.639 143611.96 39740.41 0.999 Positive 3316.45 4435.55 39.27 * 424.639 65865.27 34585.26 0.998 Positive 9397.07 9322.92 40.03 11172.41 53739.23 31883.17 0.994 Positive 4732.45 4863.84 * 2.757 * 424.639 33240.26 26415.7 0.999 Positive 9547, 82 11400.68 * 2.783 13397.64 76934.24 35736.94 1,000 Positive 6823.11 7616.92 * 2.783 4048.14 91453.87 42054.24 0.946 Positive 7546.97 7741.63 * 2.783 1995.33 4592 1.21 29649.54 0.965 Positive 6533.19 6113.89 * 2.783 3215.77 121842.83 41386.21 1,000 Positive 6713.53 4128.72 * 2.783 * 103.34 42655.79 31040.66 0.680 Negative 6479.01 9657.93 * 2.757 5661.3 76837.22 49194.9 0.985 Positive 9410.17 5535.37 * 2.783 * 103.34 52015.71 45519.23 0.302 Negative 4991.87 7351.39 * 2.757 * 354.923 86996.49 37006 , 46 0.978 Positive 6713.58 4053.37 * 2.848 * 1117.956 77611.32 47679.25 0.121 Negative 8677.98666.66 * 2.783 5566.13 88185.57 58307.64 0.688 Negative 3792.87 4567.39 * 2.848 * 1117,956 65956.96 40773.24 0.117 Negative 5261.22 4766.29 * 2.848 * 1117,956 40014.05 34961.32 0.771 Negative 4631.93 4258.77 * 2.848 1679.8 54294.24 34572.58 0.320 Negative 8712.03 7960 , 96 * 2.757 * 354.923 47180.01 33281.35 0.377 Negative 6788.92 4903.91 * 2.848 * 1117.956 65909.13 47354.14 0.836 Negative 7277.27 4388.27 * 2.848 976.61 63535.66 48983.68 0.571 Negative 4719.44 3178.05 * 2.848 * 1117.956 25917.68 28833.62 0.595 Negative 6902.77 9002.44 * 2.757 * 354.923 61544.54 44563.8 0.955 Positive 56 71.68 4616 * 2.848 * 1117.956 38749.19 34494.89 1,000 Positive 2958.02 4679.24 * 2.757 * 354.923 46865.34 41141.37 0.239 Negative 4826.34 9493.94 * 2.757 15835.25 32332.39 24439, 05 0.997 Positive 4937.74 4216.5 * 2.692 * 404.102 38377.04 4.802.57 0.293 Negative 4354.99 4036.67 * 2.848 2248.5 56871.94 43362.85 0.987 Positive 4954.37 5177.78 * 2.692 * 404.102 22957.71 25435.43 0.385 Negative 6784.43 5402.1 * 2.783 * 103.34 33934.98 35810.5 0.632 Negative 5206.27 4743.23 * 2.783 * 103.34 49645.69 30271.78 0.661 Negative 3972, 79 4099.91 * 2.783 * 103.34 60907.69 43727.92 0.601 Negative 6732.86 6029.53 * 2.783 3215.77 77919.44 45108.15 0.213 Negative 4759.29 3010.98 * 2.848 * 1117.956 41992.31 32790.43 1,000 Positive 2580.3 3757.01 * 2.692 * 404.102 56037.14 30929.41 0.386 Negative 4632.8 4580.5 * 2.783 2324.41 67388.58 44622.76 0.997 Positive 4734.6 2494.96 33, 47 * 103.34 36594.47 33606.44 0.336 Negative 4137.94 3866.46 * 2.692 14428.52 50179.81 38313.23 0.990 Positive 6036.83 6038.51 * 2.848 8645.62 65706.24 4280 3.07 1,000 Positive 6040.96 4184.34 * 2.848 1730.99 47162.16 32829.07 0.979 Positive 4721.87 2807.91 * 2.783 * 103.34 57693.18 40681.93 0.776 Negative 8631.06 5219.02 * 2.848 * 1117.956 43399.6 43763.38 0.123 Negative 6567.42 7489.81 * 2.848 3875.63 71297.09 37617.68 0.121 Negative 3943.08 4742.01 * 2.692 * 404.102 41577.45 37617.38 0.998 Positive 7048.97 5742, 04 * 2,783 * 103.34 59520.26 45781.02 0.511 Negative 9515.04 5463.5 * 2.783 * 103.34 62089.69 47242.2 0.925 Positive 7527.54 5376.57 * 2.783 * 103.34 56317.15 52299, 85 0.956 Positive 4568.43 4629.91 * 2.848 * 1117.956 39949.41 39664.92 0.117 Negative 6256.64 6567.65 * 2.783 * 103.34 30294.24 40496.74 0.122 Negative

5390.81 5150.44 * 2.848 * 1117,956 46586.74 40456.14 0.954 Positive 9048.79 11948.97 * 2.848 5778.45 112592.65 74230.07 0.175 Negative 4783.23 3433.27 * 2.848 * 1117.956 69791.26 51323.29 0.831 Negative 5124.02 4539.69 * 2.848 5834.37 48227.5 29981.53 0.876 Positive 4404.01 3518.16 22.16 * 103.34 56361.73 37430.5 0.856 Negative 7158.79 5458, 38 * 2.783 4355.7 71784.03 64611.98 0.609 Negative 6346.63 5924.54 * 2.783 * 103.34 56067.94 46305.02 0.603 Negative 6230.93 5067.83 * 2.848 2040.33 57746.73 37696, 16 0.125 Negative 6255.77 4629.91 * 2.848 661.2 41271.15 4.1130.39 0.125 Negative 5756.21 5478.88 * 2.783 6685.94 106986.42 61251.17 0.994 Positive 4386.52 2565.91 * 2.848 * 1117,956 27267.21 31594.8 0.826 Negative 3171.53 3437.08 * 2.692 * 404.102 34239.76 32710.22 0.122 Negative 2859.37 3336.47 * 2.692 * 404.102 39438.69 33947.65 0.876 Positive 3157.72 4091, 43 * 2.795 * 434.712 90474.94 24068.27 0.991 Positive 4374.55 6320.41 * 2.795 24861.44 78288.73 36159.18 0.976 Positive 3091.24 4745.55 * 2.795 * 434.712 61 365.88 25811.53 0.876 Positive 2815.41 3999.32 * 2.795 * 434.712 116206.78 50712.55 0.841 Negative 3341.06 3698.03 * 2.795 2245.35 109578.08 33515.98 Positive 3890.35 8790, 47 * 2.795 14389.69 ** 4142.527 32857.68 0.998 Positive 3586.33 4931.53 * 2.795 * 434.712 75298.92 29059.15 0.991 Positive 4041.29 7198.11 27.38 17931.46 162100.91 19774 , 67 0.999 Positive

6217.58 5767.45 * 2.748 * 445.15 61484.25 34794.3 0.116 Negative 4737.1 2862.72 * 2.748 * 445.15 44071.57 40731.15 0.356 Negative 6511.85 5434.04 * 2.748 3255.7 59424 , 33 50260.97 0.480 Negative 6518.71 11789.03 * 2.748 30869.55 17036.56 15026.32 0.992 Positive 6928.31 4985.97 * 2.748 * 445.15 73261.19 49839.04 0.21 Negative 4907.28 4830 , 5 * 2.748 13007.46 195779.93 36870.8 0.997 Positive 6164.76 4815.55 * 2.848 * 1117.956 93774,29 22577.56 0.910 Positive 4564.06 5646.94 * 2.748 8359.12 181136.53 41917.7 0.999 Positive 2873.66 2886.55 * 2.795 2408.89 80288.73 29842.31 0.392 Negative 3306.25 16149.58 * 2.795 5607.98 ** 4142.527 40421.65 0.995 Positive 4364.15 6171.67 90.48 17399 , 29 110832.49 43241.4 0.993 Positive 6057.36 4848.51 * 2.748 * 445.15 119641.66 49073.78 0.947 Positive 6210.76 6345.88 * 2.748 11911.63 98490.64 49647.5 0.986 Positive 5124 , 85 5311.82 * 2.748 * 445.15 64024.49 40361.36 0.527 Negative 5904.35 5337.68 * 2.748 7932.42 92925.6 51608.48 0.252 Negative 6587.36 6187.23 * 2.748 * 445.15 77331 , 37 38,703.01 0.331 Negative 8492.12 5835.56 * 2.748 * 445.15 92284.63 38042.2 0.999 Positive 6402.19 6295.45 * 2.748 17651.88 66070.64 29130.04 4.992 Positive 5877.19 6299 , 32 * 2.748 * 445.15 52861.96 31322.54 0.340 Negative 6852.38 7104.89 * 2.748 * 445.15 138275.77 42960.09 0.675 Negative 5877.19 4744.29 * 2.748 * 445.15 82159, 57 30,818.54 0.835 Negative 6108.45 4848.51 * 2.748 * 445.15 77929.84 34612.73 0.779 Negative 6945.58 6454.92 * 2.748 * 445.15 88785.36 41657.72 0.602 Negative 5558.93 5646 , 94 * 2.748 6457.38 119389.29 43969.05 0.999 Positive 7385.92 5934.34 * 2.748 * 445.15 66719.22 35230.44 0.547 Negative 4960.77 5926.72 * 2.748 * 445.15 74454.9 34 394.97 0.123 Negative 9.337.94 4837.7 * 2.748 * 445.15 84.339,04 38886.82 0.987 Positive 13456.09 5622.25 * 2.736 4681.19 40061.83 38311.25 0.976 Positive 9430.09 8823.72 * 2.713 3788.53 44225.79 39822.66 0.999 Positive 5646.4 4929.86 * 2.684 * 445.855 60571.48 35864.25 0.561 Negative 16542.35 6641.92 * 2.736 17352.71 171946.12 69427.43 0.972 Positive 8851.84 7621.96 * 2.736 * 170.39 49293.94 45699.08 0.864 Positive 6887.27 6065.93 * 2.736 2783.61 37239.37 40288.85 0.993 Positive 7862.2 6983.76 * 2.736 3073.9 45084.86 47110.97 0.967 Positive 9228.25 8585.79 * 2.736 7047.9 104357.39 86841.15 0.154 Negative 10195.93 6341.74 * 2.736 3106.13 55842.56 53714.33 0.997 Positive

7418.22 5712.6 * 2.736 * 170.39 28164.39 43064.67 0.651 Negative 2780.79 4602.09 * 2.848 * 1117.956 441478.79 31017.78 1,000 Positive 2142.45 3890.59 * 2.848 2881.01 143415.84 26,358.24 0.971 Positive 5346.21 3328.63 * 2.848 5555.15 259815.65 33951.59 0.999 Positive 4041.4 3334.95 * 2.848 * 1117.956 167694.03 30940.92 0.987 Positive 5439.51 6042.31 * 2.848 9511.95 177716.64 30134.86 0.887 Positive 3715.65 2241.89 * 2.848 * 1117.956 148819.79 25485.92 0.835 Negative 6773.09 6733.92 * 2.748 2788.54 73523.2 46981.59 0.328 Negative 4647, 2 3399.39 * 2.748 18737.53 142706.42 41657.72 0.997 Positive 5877.19 5263.88 * 2.748 * 445.15 79333.58 55188.09 0.458 Negative 4920.65 6357.54 * 2.748 * 445.15 69265 , 5 41397.97 0.944 Positive 5269.21 4551.76 * 2.748 * 445.15 80046.47 49073.78 0.458 Negative 6105.04 6564.52 * 2.748 8958.15 56277.55 45132.24 0.125 Negative 6002.91 7720 , 73 * 2.748 * 445.15 65483.81 55384.24 0.306 Negative 5091.33 4658.47 * 2.748 4670.5 74353.54 55619.85 0.395 Negative 4169.86 5252.83 * 2.748 * 445.15 4 8126.04 47208.97 0.343 Negative 6326.91 5055.07 * 2.748 4263.34 62117.26 47778.33 0.304 Negative 4507.6 8374.92 * 2.748 14123.67 73924.48 27983.1 0.998 Positive 5911.14 5000.49 * 2.748 * 445.15 75258.62 58586.06 0.719 Negative 4853.84 5422.9 * 2.748 * 445.15 98686.29 51840.26 0.682 Negative 5014.3 6718.11 * 2.748 * 445.15 72629.23 40398 , 32 0.955 Positive 5734.78 4228.41 * 2.748 * 445.15 97959.56 45998.63 0.999 Positive 5047.78 3382.84 * 2.748 * 445.15 88582.82 51299.8 0.527 Negative 4627.23 5460.06 * 2.748 * 445.15 62788.57 45207.46 0.810 Negative 7118.56 6837 * 2.748 * 445.15 83320.24 41917.7 0.363 Negative 4113.74 4580.16 * 2.748 * 445.15 67238.44 46111.85 0.541 Negative 8619.04 9546.9 * 2.782 5997.52 76809.87 72452.91 0.260 Negative 7382.92 7896.55 * 2.782 2865.38 55109.08 47813.87 0.186 Negative 6571.18 8355.38 * 2.782 1233.01 44360 , 32 42867,39 0,235 Negative 5251,27 7226,5 * 2,782 * 137,6 53239,59 46924,19 0,125 Negative 5191,54 7320,91 * 2,782 10500,61 89932,52 54069,72 0,117 Negative 9400,36 6719 .99 46 , 72 12959.58 99553.87 57335.19 0.175 Negative 9744.43 8261.72 * 2.782 15067.6 54226.33 43657.61 0.121 Negative 7530.26 6176.1 * 2.782 * 137.6 78748.34 48767.11 0.296 Negative 7703.82 12037.97 * 2.782 17623.48 60285.86 46525.99 0.171 Negative 7960.26 9079.63 * 2.782 6764.15 61864.8 39175.48 0.435 Negative 9709.1 7837.19 * 2.782 5933, 63 42636.15 42078.45 0.339 Negative 7656.06 9073.92 * 2.782 7115.49 68764.19 52884.51 0.121 Negative 8645.29 12595.6 * 2.782 29012.26 83273.1 53227.18 0.307 Negative 6407.7 6699.58 * 2.782 1649.39 61393.82 50247.77 0.315 Negative 6588.4 10269.57 * 2.782 10788.01 78940.67 62650.83 0.125 Negative 7339.61 10658.01 * 2.782 816.32 58766 55353.19 0.121 Negative 6450.7 4638.82 * 2.782 * 137.6 56120.87 42897.76 0.326 Negative 6072.71 7950.65 * 2.782 4911.25 72688.41 54851.74 0.779 Negative 6519.54 8399.6 * 2.782 1521 , 3 74400,63 47875,3 0,585 Negative 7209,79 7352,48 * 2,782 1873,5 51467,77 36826,85 0,593 Negative 6985,07 8751,01 * 2,782 2481,52 61061,01 44540,57 0,125 Negative 6928, 95 7896.55 * 2.782 10756.08 59625.85 55290.47 0.196 Negative 7313.64 8102.89 * 2.782 13183.14 48025.92 43961.88 0.116 Negative 8304.47 10997.09 * 2.782 7913.93 46644.86 51114.21 0.117 Negative 10378.02 15666.23 * 2.782 27987.28 51840.61 32058.81 0.239 Negative 7960.26 12044.46 * 2.782 7083.55 62504.87 43505.54 0.125 Negative 5832.67 9005.55 * 2.782 6764.15 73466.84 43080.01 0.122 Negative 5055.09 7090.89 * 2.782 10085.47 97986.09 62715.06 0.293 Negative 7071.46 9119.62 * 2.782 * 137.6 53711.08 45761.29 0.122 Negative 6094.16 4851.02 * 2.782 * 137.6 56401.46 43323.13 0.494 Negative 4378.62 3162.61 * 2.782 * 137.6 63656.15 39597.91 0.117 Negative

4552.82 7054.53 * 2.782 7594.56 75645.04 52791.11 0.125 Negative 3205.06 2918.7 * 2.782 * 137.6 45046.12 36646.51 0.835 Negative 6898.74 6776.22 * 2.782 10851.88 54826.71 38271.21 0.116 Negative 7590.98 8846.82 * 2.782 * 137.6 71779.99 51300.14 0.486 Negative 6653 8666.83 * 2.782 * 137.6 52779.75 46342.34 0.603 Negative 7929.81 16990 , 15 * 2.782 28083.36 93257.19 79499.74 0.861 Negative 5473.32 8756.64 * 2.782 2929.35 73411.7 60315.78 0.117 Negative 10729.62 11709.41 * 2.782 22546.58 95396.54 65685, 33 0.639 Negative 5926.93 6562.36 * 2.782 2065.54 29028.75 50185.96 0.298 Negative 5944.07 6791.58 * 2.782 2673.47 61864.8 53663.79 0.220 Negative 9365.13 8722.92 * 2,782 5965 , 58 64897.4 48767.11 0.123 Negative 8304.47 10353.99 * 2.782 4847.34 86168.7 41563.26 0.547 Negative 9810.72 9338.01 * 2.782 4943.21 53869.12 48951.88 0.121 Negative 5247 4869 , 59 * 2.782 * 137.6 63506.33 42776.3 0.125 Negative 8138.78 9917.21 * 2.782 8233.29 37520.15 41290.72 0.345 Negative 6218.63 6096.78 * 2.782 5678.06 53522.01 38271.210.125 Negative 5635.78 5603.28 * 2.782 * 137.6 44870.51 48151.85 0.123 Negative 7218.44 11760.66 * 2.782 8616.51 86587.12 48151.85 0.122 Negative 8365.57 11992.61 * 2.782 17591 , 52 90806.28 60443.25 0.125 Negative 4586.83 3474.32 105.15 * 137.6 50393.57 40473.91 0.705 Negative 6184.28 8943.05 * 2,782 6285.02 59459.83 41866.25 0.229 Negative 7196.82 9153.96 * 2.782 4272.11 81495.04 45272.59 0.116 Negative 7538.93 8555.08 * 2.782 4240.15 64639.42 55729.78 0.117 Negative 5781.28 5334.95 * 2.782 1585.35 72994 .23 51672.3 0.510 Negative

6317.43 10848.52 * 2.782 13087.33 70800.27 40776.29 0.117 Negative 5824.11 5117.31 * 2.782 * 137.6 70526.22 57240.53 0.168 Negative 6399.1 10209.47 * 2.782 5997.52 55068.65 41139 , 37 0.122 Negative 7010.98 8141.12 * 2.782 4336.03 63740.6 55133.71 0.125 Negative 7595.31 9964.67 * 2.782 1008.69 99081.52 63454.77 0.747 Negative 6519.54 8903.38 * 2.782 * 137.6 54979.82 39205.64 0.122 Negative 5029.52 4606.74 * 2.782 11362.82 166991.62 51734.36 0.329 Negative 5319.56 5998.06 * 2.782 3920.52 54979.82 45303.12 0.117 Negative 7352.6 10719.3 * 2.782 2225.56 42889.49 37097.45 0.122 Negative 5725.64 7735.05 * 2.782 2257.55 64145.74 48890.28 0.117 Negative 6012.66 7247.44 * 2.782 * 137.6 57075.53 44114.1 0.125 Negative 6132.78 10185.47 * 2.782 * 137.6 80933.74 49691.84 0.125 Negative 5396.42 7405.19 * 2.782 5102.97 65378.11 45272.59 0.117 Negative 6523.84 7983.18 * 2.782 4527.78 50423.34 33196.64 0.193 Negative 5481.87 5958.72 * 2.782 * 137.6 68529.69 46372.94 0.317 Negative 9034.05 15318.11 * 2.71 1341.98 91999 02 637 01.84 0.117 Negative 7118.7 9055.22 * 2.71 9938.03 87167.58 73829.35 0.123 Negative 7184.8 8876.25 * 2.71 * 120.05 59946.73 55269.62 0.122 Negative 7392, 73 8335.77 * 2.71 * 120.05 66478.56 60366.21 0.121 Negative 4713.37 5430.29 * 2.71 3125.09 89224.09 67191.59 0.125 Negative 5010.64 4203.15 * 2, 71 * 120.05 72708 59734.48 0.123 Negative 6892.33 11090.44 * 2.71 9255.65 83204.35 72227.53 0.601 Negative 5597.72 6993.72 * 2.71 * 120.05 88945.54 72330 , 51 0.116 Negative 6290.45 7694.35 * 2.71 5213.69 104355.45 77808.18 0.179 Negative 6337.38 6155.76 * 2.71 * 120.05 107908.11 56508.36 0.187 Negative 16343.71 9139.2 * 2.71 * 120.05 107736 72073.16 0.245 Negative 4857.28 7108.98 * 2.71 * 120.05 142161.54 73621.99 0.156 Negative 5728.52 7136.52 * 2.71 624 , 49 98081.57 61635.37 0.186 Negative 5880.43 8563.39 * 2.665 * 158.78 82954.95 51315.27 0.125 Negative 7663.48 9255.73 * 2.665 7660.14 95401.72 47796.66 0.699 Negative 9672 , 11 6293.22 * 2.71 1879.53 118390.37 76648.76 0.597 Negative 4546.43 7441.62 * 2.71 2159.54 75578 , 3 51830,02 0.631 Negative 6628.69 8545.78 * 2.71 * 120.05 78967.8 47481.87 0.322 Negative

7875.95 7790.56 * 2.71 * 120.05 45558.05 53189.73 0.121 Negative 8780.62 5338.78 * 2.71 * 120.05 78551.16 64147.31 0.845 Negative 6159.13 9983.12 * 2.71 11295.01 88862.24 60123.01 0.122 Negative 5187.44 7419.29 * 2.71 1593.43 68670.47 62174.64 0.121 Negative 6056.04 6592.25 * 2.71 * 120.05 79528.28 69516.64 0.419 Negative 5504.37 9157.23 * 2.71 13550.56 75393.02 72227.53 0.125 Negative 5672.45 9834.22 * 2.71 4274.77 96066.54 75286.61 0.113 Negative 6798.11 6474.29 * 2.71 * 120.05 63503.26 53377.96 0.123 Negative 7586.76 8481.41 * 2.71 * 120.05 106638.26 65240.19 0.770 Negative 7653.08 7059.51 * 2.71 * 120.05 68507.47 69516.64 0.116 Negative 5485.71 7291.32 * 2.71 * 120.05 98211.87 54416.27 0.122 Negative 6567.56 5511.97 * 2.71 * 120 , 05 71,106.6 66589.24 0.308 Negative 8304.63 10003.59 * 2.665 12609.89 48124.9 34102.28 0.328 Negative 6966.58 8618.48 * 2.665 3554.76 37079.58 26685.93 0.113 Negative 5734, 89 7153.65 39.29 3156.51 68135.1 48741.75 0.755 Negative 5176.26 6550.2 * 2.665 7531.3 34043.52 29911.37 0.63 0 Negative 4021.77 7200.33 * 2.665 1676.12 23337.65 25676.41 0.968 Positive 6084.15 9908.56 * 2.71 * 120.05 76703.45 62076.49 0.117 Negative 8344.32 7236.7 451 , 18 11879,59 27178,66 32474,59 1,000 Positive 5435,98 5781,03 * 2,665 * 158.78 20283,22 25265,49 0,485 Negative 8295,81 9684,37 * 2,665 14415,31 48093,93 29723,38 0.192 Negative 5423.19 5913.15 * 2.665 * 158.78 50993.3 33609.59 0.216 Negative 6788.7 9661.65 * 2.71 11936.56 79037.54 56365.05 0.4400 Negative 6412.5 9283.85 * 2.71 5676.91 128489.4 89760.7 0.758 Negative 6827.9 8344.27 * 2.665 5393.34 55269.63 42800.38 0.123 Negative 4810.84 4241.63 * 2.71 * 120.05 59089.51 68,705.02 0.121 Negative 8446.63 8041.48 * 2.71 6908.41 167365.83 92063.21 0.861 Negative 5289.9 4610.79 * 2.71 * 120.05 94561.22 74504.69 0.123 Negative 5261, 34 6937.04 * 2.665 * 158.78 21672.35 30663.97 0.122 Negative 4072.27 6459.53 19.1 2689.35 51710.8 29084.66 0.538 Negative 6741.34 8148.8 * 2.665 8495.92 52089 , 13 30965.3 0.507 Negative 5812.67 5471.09 248.35 * 120.05 33421.05 438 95.24 0.523 Negative 6177.88 8598.58 * 2.71 * 120.05 72090.36 77491.35 0.125 Negative 6262.3 4976.62 * 2.71 * 120.05 59540.66 43985.3 0.116 Negative 7847 , 48 7136.52 * 2.71 5534.76 73690.09 63158.67 0.517 Negative 7373.81 12549.88 * 2.71 8225.99 88145.39 86379.62 0.377 Negative 8437.1 9465.92 * 2, 71 5070.4 83367.66 58139.32 0.117 Negative 4548.88 3995.36 * 2.656 * 158.78 17366 25489.6 0.122 Negative 6356.15 9115.17 60.45 3686.41 115776.17 61635.37 0.184 Negative 3756.95 5639.98 * 2.665 3089.95 37701.23 25975.39 0.564 Negative 4586.93 5918.05 * 2.665 * 158.78 31352.15 34747.36 0.125 Negative 5914.7 5350.93 * 2.665 * 158, 78 20627.87 27284.8 0.303 Negative 5410.4 5727.42 * 2.665 * 158.78 38534.98 30174.67 0.122 Negative 6393.71 7762.22 * 2.71 * 120.05 83216.89 70127.39 0.117 Negative 3811.55 5136.57 * 2.665 * 158.78 35002.43 32361.24 0.294 Negative 5478.62 5264.94 * 2.665 * 158.78 18282 22878.13 0.125 Negative 4588.15 4159.95 * 2.71 * 120.05 81087.68 66689.51 0.305 Negative 4671.52 3540.73 * 2.665 * 158.78 31328.21 30,588.67 0.377 Negative 4908.38 6635.3 * 2.71 3981.77 78,103.35 81916.42 0.404 Negative 4367.29 4630.34 * 2.665 * 158.78 72136.31 56728.75 0.863 Negative 6689.85 7796 , 23 * 2.71 * 120.05 55784.53 57082.55 0.591 Negative 5397.09 3049.18 * 2.71 * 120.05 60644.23 88868.74 0.390 Negative 7667.3 5338.78 * 2.71 * 120.05 81536.23 92176.15 0.411 Negative 5579.05 5187.12 * 2.71 * 120.05 56574.59 56556.15 0.354 Negative 4324.16 7637.94 51.65 * 120.05 98653.76 64296 0.860 Negative 5611.73 8935.76 * 2.71 * 120.05 63424.88 38559.56 0.603 Negative 5572.51 5408.44 * 2.665 * 158.78 22634 25041.43 0.680 Negative

6187.26 6721.66 * 2.71 * 120.05 47241.6 44707.15 0.553 Negative 7733.5 8673.7 * 2.665 3554.76 74665.62 41214.88 0.327 Negative 5411.08 7413.71 37.5 3834.41 46990.65 45159.55 0.221 Negative 4249.2 5298.34 * 2.665 * 158.78 35815.28 27247.36 0.851 Negative

3417.18 3052.07 36.81 * 158.78 44424.93 35241.29 0.125 Negative 5231.55 6364.21 * 2.665 * 158.78 47339.76 41871.44 0.855 Negative 6749.99 7325.28 * 2.665 * 158, 78 19854.81 25938.01 0.193 Negative 10043.23 8951.76 * 2.665 5913.99 39386.33 29798.57 0.121 Negative 6009.2 7436.03 * 2.71 * 120.05 68106.87 53754.94 0.125 Negative 5541.7 7224.85 * 2.71 1593.43 78046.2 54889.92 0.387 Negative 6450.07 2675.5 * 2.71 * 120.05 90537.54 62027.43 0.473 Negative 10070.28 18449.62 * 2,665 26,269.35 67001.99 33193.11 0.736 Negative 6775.95 6803.96 * 2.665 * 158.78 23805.77 30400.44 0.401 Negative 5726.34 6834.6 * 2.665 * 158.78 16556.15 23623.62 0.117 Negative 7686.25 9594.19 * 2.71 * 120.05 94976.33 71662.04 0.117 Negative 5224.69 3926.31 * 2.71 * 120.05 70686.16 44752.35 0.121 Negative 7052.64 15272 , 41 * 2.71 24461.77 88806.77 69923.61 0.184 Negative 7255.65 6624.53 * 2.71 * 120.05 73595.28 54463.59 0.399 Negative 4972.37 5255.4 * 2.665 * 158, 78 32 865.08 33723.24 0.116 Negative

8817.73 8159.61 * 2.665 27617.18 51661.62 22282.04 0.886 Positive 5811.91 4249 * 2.665 * 158.78 14328.09 26012.77 0.338 Negative 5435.981212.75 * 2.665 * 158.78 27935.89 26723, 34 0.121 Negative 8623.06 9109.17 * 2.71 * 120.05 105320.72 105748.64 0.388 Negative 7667.3 7767.89 * 2.71 * 120.05 74937.96 69415.01 0.21 Negative 6248.23 3935.79 * 2.71 * 120.05 100404.26 78072.57 0.156 Negative 6638.1 4410.82 * 2.71 * 120.05 75501.93 43355.67 0.600 Negative 5929.62 5440.48 203.29 * 120.05 85027.64 46021.77 0.935 Positive 7364.36 7870.1 * 2.71 * 120.05 75045.96 55032.23 0.117 Negative 8702.43 10321.12 20.27 8880.77 21811.54 24966 , 76 0.511 Negative 7323.01 8979.74 * 2.665 * 158.78 50977.09 27771.77 0.209 Negative 6577.11 4376.91 * 2.665 * 158.78 9202.55 23996.54 0.721 Negative 6508.06 6702.12 45.96 * 158.78 9856.63 18596.75 0.122 Negative 7759.77 6580.5 * 2.665 * 158.78 78010.59 37413.69 0.125 Negative 5136.24 5156.92 * 2.71 * 120.05 64020 58476.68 0.220 Negative 5215.37 5707.22 26.07 * 120.05 52422.88 55745.19 0.703 N egative 6295.14 5146.86 * 2.71 * 120.05 39442.17 45476.8 0.638 Negative 6694.55 10258.49 * 2.71 5213.69 71998.32 54652.96 0.827 Negative 6374.93 6139.96 * 2.71 * 120.05 88613.07 70586.62 0.333 Negative 11899.13 6657.7 * 2.723 13306.64 211375.29 78772.52 0.999 Positive 17290.8 10257.73 * 2.723 4411.95 138413.88 99290 , 06 0.711 Negative 11165.96 7934.4 * 2.736 3235.02 57082.94 47393.44 0.117 Negative 9875.21 4945.36 * 2.736 * 170.39 46370.67 46216.7 0.875 Positive 8769.32 5107.82 * 2.773 * 103.34 63114.74 49047.05 0.122 Negative 9279.18 6627.07 * 2.753 * 103.34 50221.35 64847.68 0.125 Negative 8854.19 5305.24 * 2.753 * 103.34 46993.25 53631, 1 0.116 Negative 8001.02 5914.07 22.5 1471.54 45811.63 49084.73 0.610 Negative 8488.93 5581.69 * 2.783 * 103.34 64887.38 62849.34 0.335 Negative 9109.05 4355.8 * 2.783 * 103.34 37197.14 47542.5 0.121 Negative 5287.13 5670.1 * 2.749 * 420.896 28853.59 43990.7 0.192 Negative 7905.9 6836.99 * 2.749 * 420.896 27490.85 31071.44 0.300 Negative 8796 , 2 5987.08 * 2.749 * 420.896 38884.3 7 32198.76 0.116 Negative 5543.45 4595.34 * 2.65 * 485.573 27711.55 26055.19 0.117 Negative 4022.56 4185.57 * 2.65 * 485.573 33232.33 25681.51 0.599 Negative 5114.24 4271 , 71 * 2.65 12263.87 32032.69 33375.58 0.117 Negative 8992.09 7025.95 * 2.65 3078.72 34040.9 28589.53 0.116 Negative 6031.26 4454.44 18.24 * 485.573 28321 , 9 25262.71 0.660 Negative 4492.32 4524.71 * 2.65 2463.27 32857.37 25611.59 0.122 Negative

8207.27 5062.27 27.59 * 420.896 35294.99 34394.78 0.341 Negative 11973.8 12846.67 * 2.749 * 420.896 56574.3 35873.49 0.843 Negative 7290.06 7407.84 * 2.749 * 420.896 51749.85 42,371.73 0.382 Negative 10398.94 8770.67 * 2.749 * 420.896 33012.17 31165.35 0.558 Negative 6586.17 4709.27 * 2.749 * 420.896 32561.62 36251.71 0.125 Negative 12182.72 7921.44 * 2.749 * 420.896 37104.38 40147.76 0.117 Negative 5711.54 5789.72 * 2.749 * 420.896 42084.5 51208.01 0.320 Negative 5108.39 4510.04 * 2.749 2792.38 41792.45 40275.13 0.116 Negative 5350.77 4848 , 58 * 2,749 * 420,896 38529,77 36346,31 0,230 Negative 3206,22 4413,92 * 2,757 * 424,639 50747,73 43865,06 0,125 Negative 4546,57 5577,62 * 2,757 * 424,639 17791,93 37493,75 0,612 Negative 5077.93 4396.39 * 2.748 8459.22 62588.5 54561.53 0.677 Negative 6814.45 4308.7 * 2.748 * 445.15 61836.26 51299.8 0.125 Negative 4418.02 3130.22 * 2.748 * 445, 15 41257 35812.79 0.319 Negative 9312.79 4037.91 * 2.748 * 445.15 58947.44 55816.38 0.303 Negative 6490.8 7640.62 * 2.665 * 158.78 57548, 11 69972.44 0.117 Negative 5355.01 4212.58 * 2.665 * 158.78 42802.6 52430.31 0.329 Negative 6598.7 4943.03 * 2.665 2387.34 44571.18 52789.56 0.121 Negative 4553.11 3558, 39 * 2,665 * 158.78 54058.13 58188.79 0.125 Negative 3895.6 3766.88 * 2.665 * 158.78 49140 56931.11 0.125 Negative 3962.88 3624.71 * 2.665 * 158.78 52785.91 58473, 52 0.122 Negative 8286.99 7189.95 * 2.665 * 158.78 65551.33 50878.3 0.334 Negative 4654.6 7019.32 * 2.665 2151.38 53845.72 50243.77 0.125 Negative 4701.14 4422.77 * 2.665 * 158.78 50420.31 53669.47 0.122 Negative 4460.16 5037.23 * 2.665 * 158.78 79108.61 54833.2 0.121 Negative 5274.11 5776.15 * 2.665 1982.2 72870.49 58188.79 0.125 Negative 3899.8 3321.25 * 2.665 * 158.78 57981.15 67693.79 0.338 Negative 4582.7 5514.27 * 2.665 * 158.78 60633.32 62365.29 0.123 Negative 4021.77 4616.45 * 2.665 * 158.78 15746.58 27659.36 0.316 Negative 5155 4695.32 * 2.665 * 158.78 82679.7 57620.14 0.121 Negative 6564.16 4376.91 * 2.665 * 158.78 42419.35 46110.08 0.633 Negative 5031 .8 5264.94 * 2.665 2252.63 53137.21 54230.72 0.322 Negative 7680.98 18570.31 * 2.665 * 158.78 32633 36612.03 0.297 Negative 3927.05 3664.05 * 2.65 * 485.573 27488.58 24845.58 0.357 Negative 6493.62 5022 , 25 * 2.65 8306.5 38706.02 27777.46 0.125 Negative 5264.94 4104.26 * 2.65 * 485.573 23087.06 29432.24 0.117 Negative 6769.18 9387.97 * 2.65 * 485.573 54013 , 96 35405.87 0.117 Negative 3549.2 4854.45 * 2.65 * 485.573 25983.51 21678.89 0.316 Negative 6239.17 5679.84 * 2.65 * 485.573 38943.51 33325.34 0.317 Negative 6951.72 6292.08 * 2.65 4103.65 39517.17 35868.3 0.121 Negative 3362.84 3909.5 * 2.65 * 485.573 27446.06 26995.34 0.320 Negative

5817.49 5949.05 * 2.65 * 485.573 34029.38 28397.87 0.125 Negative 5707.55 5045.08 * 2.65 * 485.573 33486.5 28829.6 0.125 Negative 5747.81 4463.2 * 2.65 * 485.573 35802.01 26031.8 0.334 Negative 6231.72 3705.31 * 2.65 * 485.573 46904.46 38317.83 0.311 Negative 8729.96 8222.93 * 2.65 * 485.573 41047.72 29625.84 0.309 Negative 6205.66 5866 , 84 * 2.65 * 485.573 36885.35 30721.7 0.489 Negative 3230.8 3492.28 * 2.65 * 485.573 22731.57 29094.33 0.125 Negative 3767.66 4314.99 * 2.65 * 485.573 27394, 16 22196.41 0.125 Negative 4660.39 3846.63 * 2.65 * 485.573 28958.87 24938.13 0.316 Negative 5452.71 5452.74 * 2.65 * 485.573 49064.31 24961.28 0.125 Negative 3386.04 4297 , 66 * 2.65 * 485.573 25682.84 21454.65 0.117 Negative 5942.61 4867.98 * 2.65 * 485.573 25433.73 29698.53 0.322 Negative 4780.14 5192.02 * 2.65 * 485.573 26232, 48 25216.28 0.125 Negative 3865.87 2643.91 * 2.65 * 485.573 22343.99 25634.89 0.125 Negative 5333.39 5499.78 * 2.65 * 485.573 28629.02 29698.53 0.323 Negative 4249.33 3383 , 16 * 2.65 * 485.573 23074.73 22399, 59 0.125 Negative

6433.56 3467.94 * 2.65 * 485.573 27864.25 26289.43 0.125 Negative 7215.97 5443.35 * 2.65 * 485.573 34868.95 28397.87 0.302 Negative 4558.71 5785.05 * 2.65 * 485.573 34868.95 29239.01 0.233 Negative 6735.07 7465.7 * 2.65 * 485.573 38320.13 30697.22 0.310 Negative 4727.24 4410.71 * 2.65 5886.76 32740.7 28326.1 0.121 Negative 5423.74 5252.14 * 2.65 * 485.573 39685.64 34461.84 0.122 Negative 5510.75 4419.44 * 2.65 * 485.573 28225.01 25798.14 0.121 Negative 4632.29 4972.16 24.17 * 485.573 34621.96 26524.2 0.122 Negative 6486.11 4332.34 * 2.65 7202.05 30990.05 29916.92 0.565 Negative 4653.36 4155.55 * 2.65 6153.76 36816.66 29577.41 0.121 Negative 6456.07 4215.65 * 2.65 * 485.573 23684.57 28757.52 0.320 Negative 9571.2 9795.52 * 2.65 5185.2 62141.3 25961.65 0.117 Negative 6403.57 7428.64 * 2.65 * 485.573 31703.02 24984.44 0.117 Negative 7509.55 5886.14 * 2.65 * 485.573 38587.86 27468.64 0.125 Negative 4506.47 4967.66 * 2.7 * 129.5 29637.23 26816.75 0.121 Negative 4238.85 4045.38 * 2.7 756.14 42501.56 30644.27 0.121 Ne gative 2975.3 2720.32 * 2.7 * 129.5 44985.54 24463.88 0.116 Negative 4070.41 4404.87 69.44 * 129.5 27628.05 22219.23 0.123 Negative 4544.71 5058.79 * 2.65 * 485.573 30780.15 23741.3 0.117 Negative 5060.64 5978.17 * 2.65 * 485.573 49854.18 30501.59 0.125 Negative 4249.33 4746.65 * 2.65 * 485.573 35464.19 27801 , 25 0.117 Negative 4544.71 5924.83 * 2.65 * 485.573 26602.74 25961.65 0.125 Negative 5000.01 4926.77 38.78 * 485.573 23781.08 25262.71 0.125 Negative 6456.07 3763.3 * 2.65 6105.18 40093.13 27872.66 0.121 Negative 7790.09 5660.79 * 2.65 * 485.573 28152.54 27967.96 0.117 Negative 5322.57 5026.82 * 2.65 4944.09 26671.79 25193.07 0.355 Negative 3395.99 439.83 * 2.65 * 485.573 31654.65 24245.99 0.121 Negative 3376.09 3775.77 * 2.65 * 485.573 28894.61 26948.13 0.575 Negative 3821.79 4463, 2 * 2.65 11010.69 37649.8 30355.11 0.122 Negative 5128.56 3215.43 * 2.65 * 485.573 23814.72 24706.91 0.121 Negative 7359.53 7022.13 * 2.7 * 129.5 32900.01 32395.9 0.320 Negative 4589.47 4397.24 * 2.7 1693.49 35318 26564.6 0.121 N egative 4995.98 4313.55 * 2.7 * 129.5 34193.74 39669.25 0.258 Negative 7564.08 5080.95 * 2.7 * 129.5 36286.09 24175.71 0.532 Negative 5610.85 5567, 06 * 2.7 * 129.5 33588.35 35796.36 0.123 Negative 4232.49 3668.96 * 2.7 2124.66 33380.99 29132.56 0.121 Negative 4509.66 4196.1 * 2.7 2459, 64 51557.53 33325.92 0.321 Negative 6282.06 4711.69 * 2.7 * 129.5 57722.22 32970.77 0.309 Negative 4439.49 3951.65 * 2.7 * 129.5 37926.05 32087, 5 0.123 Negative 5478.5 4180.99 * 2.7 1189.47 52079.03 25187.51 0.727 Negative 7901.64 5646.96 * 2.7 2674.85 50967.73 38040.58 0.121 Negative 2641.66 4151, 27 * 2.65 * 485.573 29699.57 27089.83 0.123 Negative 5883.66 4653 * 2.65 3316.3 87946.64 40773.3 0.248 Negative 3044.21 2708.01 * 2.65 * 485.573 28506.8 26312 , 88 0.121 Negative 4611.25 2640.15 * 2.65 * 485.573 30665.27 27944.12 0.374 Negative 4176.93 3721.85 24.77 11035.68 31794.57 22151.31 0.121 Negative 4607.74 4263.07 * 2.65 * 485.573 24936.98 757.47 0.584 Negative NA NA * 2.694 NA 46420.32 40948.48 0.297 Negative NA NA * 2.694 NA 43,203.85 53386.89 0.117 Negative NA NA * 2.694 NA 37636.62 56809.65 0.341 Negative NA NA * 2.694 NA 47517.45 50526.85 0.324 Negative NA NA * 2.694 NA 45599.22 50970.91 0.604 Negative NA NA * 2.694 NA 56541.49 47854.59 0.116 Negative 5308.05 5340.98 28.72 * 131.8 44073.03 33807.09 0.117 Negative 4352.76 3546.11 * 2.724 * 131.8 37618.49 35452.35 0.376 Negative 4160 , 25 3261.41 * 2.724 * 131.8 45759.95 43198.22 0.514 Negative 3608.61 3390.32 * 2.724 * 131.8 46264.74 29075.84 0.416 Negative

3302.11 4340.15 * 2.7 * 129.5 21431.26 24980.29 0.117 Negative 5365.68 4500.34 * 2.7 14022.66 45635.51 32572.49 0.116 Negative 4016.45 4534.81 * 2.7 3988.5 32731.5 36364.94 0.117 Negative 5824.31 3389.64 * 2.7 * 129.5 41345.5 25270.5 0.122 Negative 6174.72 6745.61 * 2.7 5754.06 29727 , 22 26,271.11 0.125 Negative 5288.4 4397.24 * 2.7 * 129.5 25180.42 26061.93 0.242 Negative 4678.93 3367.73 * 2.7 * 129.5 24910.24 29972.4 0.573 Negative 3743.95 4071.69 * 2.7 * 129.5 26418.8 20236.84 0.833 Negative 4819.7 5014.48 * 2.7 * 129.5 34742.17 35298.2 0.122 Negative 7004.37 4427, 75 * 2.7 1885.2 40660.93 30167.07 0.117 Negative 5069.8 7212.28 * 2.7 10886.97 45496.52 41391.03 0.192 Negative 4060.88 3120.91 * 2.7 * 129, 5 33,856.78 25519.83 0.409 Negative 4806.9 7855.7 * 2.7 5062.26 37758.7 29605.58 0.125 Negative 6330.9 6172.29 * 2.7 1717.46 15198.77 28447.91 0.121 Negative

5417.24 5515.25 * 2.7 * 129.5 34442.52 28149.63 0.125 Negative 9941.87 5662.97 20.72 5205.4 39495.3 24381.47 0.414 Negative 4111.7 4665.43 * 2.7 11891.45 28926.03 25915.73 0.553 Negative 5927.99 5747.18 * 2.7 * 129.5 22172.51 31538.75 0.121 Negative 3339.9 4271.81 * 2.7 1021.14 38603.17 28298 , 68 0.117 Negative 4404.43 4086.73 * 2.7 * 129.5 39272 33237.03 0.125 Negative 5986.36 5479.44 * 2.7 2196.47 35600.02 25063.13 0.576 Negative 6526.52 7851, 36 * 2.7 11221.71 41257.42 40044.57 0.117 Negative 4439.49 4439.2 * 2.7 * 129.5 29196.14 30731.24 0.362 Negative 4755.69 6058.26 * 2.7 * 129 , 5 31855.96 30253.69 0.121 Negative 3892.77 3850.84 * 2.7 * 129.5 33743.19 24959.59 0.116 Negative 4509.66 4321.15 * 2.7 * 129.5 41900.58 24835 , 46 0.125 Negative 5623.77 5139.75 * 2.7 * 129.5 37439.5 28533.27 0.121 Negative 5146.89 5088.79 * 2.7 * 129.5 27193.69 29476.39 0.125 Negative 3308, 41 4,147.01 * 2.7 * 129.5 32650.44 27852.12 0.121 Negative 4079.93 5249.86 * 2.7 * 129.5 33982.76 34667.15 0.123 Negative 3308.41 3635.71 19, 11 * 12 9.5 30170.16 27196.02 0.331 Negative 6233.25 5928.54 * 2.7 3988.5 38884.41 29584.04 0.356 Negative 5630.23 5487.39 * 2.7 5801.77 30132.57 26943, 04 0.122 Negative 6330.26 5308.45 * 2.738 * 378.8 33908.25 26199.36 0.125 Negative 4370.87 3970.77 * 2.738 * 378.8 21286.58 20201.15 0.255 Negative 3962.03 3220.5 * 2.738 * 378.8 14390.11 20034.78 0.121 Negative 5152.88 3455.21 * 2.738 * 378.8 36955.44 28910.22 0.122 Negative 6143.27 5560.7 * 2.738 * 378.8 38549.33 41273, 53 0.117 Negative 5634.79 4683.34 * 2.738 * 378.8 22921.32 31710.49 0.123 Negative 4867.23 4554.37 * 2.738 71023.13 50976.82 30640.76 0.489 Negative 7891.5 7947.81 * 2.738 * 378.8 30177.21 41348.62 0.849 Negative 5047.68 6413.22 * 2.738 * 378.8 37615.36 30731.39 0.325 Negative 5900.22 5907.49 * 2.738 11976.6 49726.68 33602.32 0.122 Negative 6256.74 5369.2 * 2.738 3589.17 39993.91 38483.39 0.188 Negative 6103.26 6601.77 * 2.738 * 378.8 37615.36 31208.47 0.117 Negative 7448.82 6900.9 * 2.738 * 378 .8 29722.3 36265.1 0.125 Negative 3430.22 277.24 * 2.738 * 3 78.8 20646.49 25158.21 0.125 Negative 5222 3473.66 * 2.738 * 378.8 25987.6 27227.74 0.205 Negative 4742.82 4860.55 * 2.738 10583.32 48048.3 40376.57 0.122 Negative 4716, 65 4702.96 * 2.738 9302.7 29690.41 30776.74 0.125 Negative 6457.43 5211.64 * 2.738 9990.61 19420.56 23080.71 0.125 Negative 5936.79 6610.38 * 2.738 * 378.8 31622, 63 34 396.56 0.346 Negative 4615.35 4157.06 * 2.738 5295.78 34208.01 25244.63 0.343 Negative 4130.47 4531.01 * 2.738 15260.16 38488.6 29424.33 0.125 Negative 6836.89 6112.51 * 2.738 6428.34 29866.17 34162.35 0.117 Negative 5571.89 3403.63 * 2.738 * 378.8 41537.38 30301.52 0.397 Negative 5518.97 2923.68 * 2.738 2164.69 33753.33 23570.24 Negative 0.317

5990.02 7706.19 * 2.738 3385.53 38610.15 26395.59 0.125 Negative 5963.4 6175.68 * 2.738 17852.19 40085.12 26964.32 0.593 Negative 4667.62 4080.82 * 2.738 * 378.8 30,107.26 37,127.59 0,299 Negative 6,601,59 4507,68 * 2,738 * 378,8 31027,52 29963,33 0,116 Negative 7530,35 5832,68 * 2,738 30,650.82 47818,63 31985,29 0,327 Negative 4244,07 1606.12 * 2.738 * 378.8 29600.2 25482.6 0.121 Negative 6210 4403.01 * 2.738 3634.56 28217.97 33975.35 0.553 Negative 5093.68 2898.54 * 2.738 * 378.8 30263.5 20346.88 0.570 Negative 5757.43 5519.81 * 2.738 * 378.8 33765.22 30776.74 0.125 Negative 3797.22 3140.44 * 2.738 * 378.8 36325.89 32214.83 0.123 Negative 5037.82 4523.23 * 2.738 3748.22 40043 26898.55 0.122 Negative 4972.16 4387.55 * 2.738 * 378.8 42131.97 39561.03 0.125 Negative 6433.99 4287.35 * 2.738 * 378.8 36370.51 42658.86 0.441 Negative 4795 , 17 4663.75 * 2.738 * 378.8 29563.13 23186.97 0.325 Negative 5757.43 4777.66 * 2.738 * 378.8 25711.5 31756.24 0.125 Negative 5143.01 4356.67 * 2.738 * 378, 8 26 381.74 33 602.32 0.383 Negat ive 7939.34 4821.04 * 2.738 * 378.8 34316.53 31962.37 0.121 Negative 6467.48 5626.31 * 2.738 * 378.8 28679.59 29648.6 0.618 Negative 4919.68 4179.99 * 2.738 * 378.8 23910.32 28088.03 0.304 Negative 4422.96 4757.98 * 2.738 2208.27 32216.32 27933.15 0.156 Negative 5714.32 5470.84 * 2.738 * 378.8 36600.83 26635.87 0.125 Negative 6712.42 3955.64 * 2.738 * 378.8 26184.1 36026.75 0.311 Negative 5638.1 4352.81 * 2.738 * 378.8 20462.86 23847.75 0.117 Negative 5143.01 4892.22 * 2.738 * 378 , 8 24,408.87 27866.83 0.117 Negative 5146.3 5131.31 * 2.738 * 378.8 39763.33 29401.93 0.121 Negative 5380.23 5264.02 * 2.738 * 378.8 45339.13 31710.49 0.319 Negative 5916.84 4546.58 * 2.738 6452.12 31471.53 31322.36 0.122 Negative 4007.34 3484.74 19.78 * 378.8 45700.65 28487.27 0.125 Negative 5096.97 5650.96 * 2.738 * 378 , 8 32675.28 32837 0.125 Negative 5179.2 4738.31 * 2.738 * 378.8 24849.96 29850.82 0.123 Negative 6448.85 5392.04 * 2.754 5251.05 39997.27 29659.99 0.305 Negative 5564.81 4300.33 * 2.754 * 167.88 39526.34 50424.65 0.337 Ne gative 6265.65 4419.13 * 2.754 * 167.88 26216.75 29021.43 0.301 Negative 4796.76 6635.55 * 2.754 15057.16 77551.22 31855.51 0.366 Negative 4382.22 3471.78 * 2.754 2757, 03 35446.16 34693.31 0.122 Negative 3463.7 5280.14 * 2.754 7388.82 62144.23 53421.5 0.489 Negative 4774.53 5429.44 * 2.754 1266.55 24452.44 21616.3 0.303 Negative 4442.14 4476.9 * 2.754 * 167.88 31110.57 26887.92 0.388 Negative 6304.85 6765.17 * 2.754 * 167.88 64254.33 39771.45 0.403 Negative 5590.6 3964.66 * 2.754 * 167.88 36645 , 71 36268.17 0.590 Negative 5080.28 4214.25 * 2.754 6165.77 26216.75 38435.59 0.123 Negative 5077.09 5280.14 * 2.754 4931.7 101272.23 63275.8 0.481 Negative 6465.23 5817.79 * 2.754 * 167.88 42913.32 33352.78 0.191 Negative 4790.41 4408.31 * 2.754 4907.18 42758.34 32650.64 0.117 Negative 5342.85 3929.18 * 2.754 * 167.88 44000.05 36441.41 0.117 Negative 4605.56 5340.83 * 2.738 1969.49 31088.24 32100 0.320 Negative 5245.05 3872.62 * 2.738 * 378.8 22456.72 27008.18 0.113 Negative 8702.04 5807.81 * 2.738 5248.98 28483,94 36 719.43 0.121 Negative 6934.59 4931.87 * 2.738 * 378.8 31365.58 30053.41 0.307 Negative 5175.14 9224.83 * 2.65 * 485.573 32796.21 32949.3 0.116 Negative 5078.49 7181, 76 * 2.65 * 485.573 37503.23 28709.49 0.125 Negative 4401.77 3363.06 * 2.65 * 485.573 40291.7 31508.18 0.311 Negative 3954.29 3863.37 * 2.65 * 485.573 30566.38 24545.36 0.125 Negative 3835.34 5058.79 * 2.65 * 485.573 31956.74 27255.39 0.123 Negative 4176.93 5270.69 * 2.65 * 485.573 32076.17 29263.15 0.841 Negative 4135.66 4129, 88 * 2.65 * 485.573 37439.69 28781.54 0.125 Negative 5387.56 5275.33 * 2.65 * 485.573 31832.34 32724.34 0.125 Negative

4132.23 3738.41 * 2.65 * 485.573 28540.97 26242.54 0.390 Negative 5243.36 5331.13 * 2.65 * 485.573 34469.94 26406.75 0.219 Negative 4290.81 6591.91 * 2.65 * 485.573 28109.13 22966.05 0.116 Negative 3669.91 3387.18 * 2.65 * 485.573 37211.87 23787.08 0.249 Negative 6644.31 7641.43 * 2.65 6421.24 29502.74 30893.22 0.113 Negative 3710.3 4791.47 * 2.65 * 485.573 30478.14 23147.95 0.345 Negative 6290.15 5949.17 * 2.738 * 378.8 19527.35 26177.58 0.121 Negative 5169.33 6158.81 * 2.738 * 378.8 60018.18 33393.06 0.123 Negative 5294.48 5211.64 * 2.738 * 378.8 48970.74 35480.58 0.125 Negative 5274.7 4742.24 26.33 * 378.8 38146.28 30459.7 0.553 Negative 7394.52 6074.7 * 2.738 3070.73 35270.52 37440.79 0.121 Negative 7103.35 7027.9 * 2.738 * 378.8 47539.59 30640.76 0.307 Negative 5990.02 6879.08 * 2.738 * 378.8 30032.08 32929.48 0.219 Negative 6840.25 4430.1 * 2.738 * 378.8 34510.23 30278.95 0.122 Negative 4357.86 2866.26 * 2.738 * 378.8 32767.77 28043.76 0.125 Negative 4854.12 4655.92 * 2.738 2981.26 21981.32 30912.89 0.125 N egative 5813.85 6277.15 * 2.738 1991.11 35494.18 33346.61 0.125 Negative 4348.1 3910.32 235.76 * 378.8 15782.25 22593.03 0.247 Negative 5057.53 5438.26 * 2.738 * 378.8 32336.24 23890.5 0.117 Negative 7618.77 6696.68 * 2.738 * 378.8 21798.91 24125.87 0.122 Negative 5654.66 5949.17 * 2.738 * 378.8 31321.06 34466.92 0.117 Negative 3777.86 3140.44 * 2.738 11079.31 14359.11 21728.07 0.117 Negative 6016.65 5409.79 * 2.738 * 378.8 19809.76 25028.69 0.125 Negative 5198.95 5667.42 * 2.738 * 378 .8 26858.74 25460.95 0.117 Negative 3705.99 5606.98 * 2.7 * 129.5 28812.42 26501.64 0.317 Negative 4538.38 3070.43 * 2.7 * 129.5 25244.87 21133 , 53 0.816 Negative 4461.81 5245.92 * 2.7 * 129.5 27242.75 23949.81 0.117 Negative 6739.8 9733.94 * 2.705 * 129.752 70041.55 43382.75 0.125 Negative 4167.94 7750.3 * 2.705 2696.88 44087.6 36685.5 0.575 Negative 6228.34 9416.94 * 2.705 14842.63 59322.24 39404.91 0.357 Negative 4996.8 6359.54 * 2.705 4469.41 33955.77 28782.38 0.117 Negative 5353.63 5737.42 * 2.705 * 129.752 47766.65 39203.85 0 , 125 Negative 6,109.88 6866.22 * 2.705 * 129.752 56636.82 34824.2 0.338 Negative 3809.17 4556.84 * 2.705 * 129.752 49861.9 37641.63 0.116 Negative 4253.9 4628.1 * 2.705 6822.81 52837.27 33761.41 0.818 Negative 6572.91 8481.16 * 2.705 7206 46837.87 37601.72 0.125 Negative 6850.38 7600.14 * 2.705 8410.58 67152.48 39767.27 0.325 Negative 6826.89 8347.06 * 2.705 3522.53 58540.72 39043.13 0.123 Negative 4682.05 4585.3 * 2.705 3922.76 45698.18 33879.27 0.297 Negative 7059.04 7866.1 * 2.705 11806.33 50378.6 33761.41 0.116 Negative 4908.3 5916.9 * 2.705 11560.2 70514.52 44656.28 0.355 Negative

5617.67 7060.21 * 2.705 1469.63 57441.77 43423.72 0.413 Negative 2775.3 4478.9 * 2.705 1970.58 64549.6 40210.94 0.117 Negative 3361.64 6130.54 * 2.705 1198.1 62078.01 43,710.68 0.125 Negative 5514.35 6186.45 * 2.705 3008.86 41134.1 33957.87 0.308 Negative 5168.28 8894.89 * 2.705 * 129.752 56045.41 45316.55 0.193 Negative 6175.64 8942.56 * 2.705 5043.29 33757.26 34508.82 0.125 Negative 8363.2 7009.37 * 2.705 1635.01 64906.62 41994.54 0.123 Negative 4408.18 5430.64 * 2.705 1342.25 37049.32 41222.59 0.117 Negative 6453 , 25 8738.53 * 2.705 4043.63 36434.03 34351.28 0.117 Negative 4939.87 4366.37 * 2.705 1198.1 26997.65 30289.52 0.457 Negative 4457.75 5086.1 * 2.705 * 129.752 56841.15 38,722.03 0.550 Negative 3652.92 1578.35 * 2.705 * 129.752 41538.74 36208.89 0.731 Negative 3821.24 2957.1 * 2.705 * 129.752 38056.71 35693.64 0.125 Negative 4063.98 4692.57 * 2.705 * 129.752 29916.02 30599.71 0.328 Negative 5695.39 6186.45 * 2.705 3582.29 55785.64 40049.51 0.300 Negative 3779.03 4757.35 24.57 * 129.752 42888.75 3716 3.08 0.514 Negative

7234.95 4411.97 * 2.705 * 129.752 35762.76 31805.15 0.121 Negative 4585.24 4275.65 * 2.705 7784.51 22346.09 25715.36 0.185 Negative 3200.07 2503.5 * 2.705 * 129.752 27757.91 24155.14 0.329 Negative 5047.5 4574.62 * 2.705 * 129.752 26250.35 28859.47 0.238 Negative 3349.84 5027.06 * 2.705 * 129.752 42570.63 35535.33 0.117 Negative 4070.08 3474.19 * 2.705 1635 , 01 34482.91 26288.06 0.305 Negative 5248.03 5683.24 * 2.705 * 129.752 37712.93 30793.77 0.443 Negative 5727.83 5407.9 * 2.705 7935.2 47645.17 28975.15 0.15 Negative 7881.2 11153.2 * 2.705 29093.23 49188.27 39646.42 0.125 Negative 3323.32 4272.17 * 2.705 * 129.752 35768.46 32234.24 0.314 Negative 3391.17 4990.29 * 2.705 * 129.752 41096.33 33761.41 0.116 Negative 5549.83 6559.31 * 2.705 3562.36 51651.09 31727.22 0.314 Negative 4269.29 3337.99 21.76 * 129.752 39594.68 31026.82 0.600 Negative 6073.76 3851.68 * 2.705 7612, 65 65658.23 37721.49 0.550 Negative 5379.29 8630.42 * 2.705 * 129.752 39987.71 34351.28 0.117 Negative 6589.57 6616.89 * 2.705 2716.28 30242 , 16 31688.26 0.296 Negative 306.28 2040.64 * 2.705 * 129.752 55568.77 44285.77 0.301 Negative 4278.52 3225.65 * 2.705 1989.4 44408.42 37961.22 0.814 Negative 7729.81 6262.67 * 2.705 2235.56 62809.31 39686.69 0.421 Negative 3620.01 4185.57 * 2.705 * 129.752 40483.1 34390.66 0.116 Negative 4776.09 3578.99 * 2.705 * 129.752 43941.21 33486.61 0.409 Negative 4924 , 64 3675.91 * 2.8 * 449.13 46040.64 37443.35 0.295 Negative 9758.24 5438.02 * 2.8 * 449.13 43075.52 47232.1 0.490 Negative 7606.39 6191.87 * 2.8 8144.59 58071.58 46794.54 0.229 Negative 6053.81 7160.62 * 2.8 3435.05 58738.03 55237.72 0.125 Negative 5623.86 3983.83 * 2.8 * 449.13 37223 , 66 39833.43 0.125 Negative 5946.98 4942.58 * 2.8 7156.13 48744.38 45011.57 0.297 Negative 6391.77 5352.17 * 2.8 55011.73 95941.71 56460.38 0.425 Negative 4871 , 82 7299.08 * 2.8 * 449.13 43948.29 38352.69 0.123 Negative 4957.67 5289.92 * 2.8 * 449.13 53328.31 36803.03 0.617 Negative 5943.64 6308.94 * 2.8 * 449.13 54516.13 47131.12 0.213 Negative 4056.47 3429.43 26.18 * 449.13 24 651.93 27771.66 0.338 Negative

6017.07 3096.29 * 2.8 * 449.13 40947.05 40640.66 0.117 Negative 5770.3 3814.73 23.7 * 449.13 32932.57 35115.96 0.451 Negative 6157.41 7060.4 * 2, 8 * 449.13 49026.67 44877.05 0.338 Negative 5890.27 7097.94 * 2.8 9170.96 52821.25 48073.88 0.246 Negative 5245.48 5967.34 * 2.8 * 449.13 45019, 15 38 352.69 0.125 Negative 6341.5 6063.32 * 2.8 11589.32 54287.31 44574.41 0.731 Negative 6949.88 8137.33 * 2.8 * 449.13 59798.27 49725.2 0.542 Negative 7240 , 33 8995.08 * 2.8 7067.85 55401.78 44238.16 0.123 Negative 5278.61 5387.26 * 2.8 * 449.13 63606.67 48006.52 0.415 Negative 4848.72 4083.62 * 2 , 8 2245.65 39707.97 45415.15 0.307 Negative 5471.01 4135.52 * 2.8 * 449.13 45748.5 44103.67 0.117 Negative 5278.61 3433.04 * 2.8 * 449.13 45412 , 04 42826.11 0.435 Negative 4838.82 3862.41 * 2.8 10320 41121.46 35892.41 0.305 Negative 4848.72 4535.8 * 2.8 * 449.13 54547.77 41582.18 0.693 Negative 5159, 38 7916.54 * 2.8 * 449.13 62207.46 46828.2 0.465 Negative 5783.62 7736.15 * 2.8 7740.1 60203.83 46962.82 0.518 Negative 6646.88 7719.03 * 2.8 * 449.13 50194.09 46794.54 0.847 Negative 5756.98 6881.81 * 2.8 * 449.13 39509.28 40472.51 0.472 Negative 4384.48 6051.3 * 2.8 * 449, 13 68685.23 55102 0.976 Positive 6936.39 7744.71 * 2.8 * 449.13 49189.73 44439.9 0.399 Negative 5963.66 6341.33 * 2.8 * 449.13 39021.79 42389.07 0.485 Negative 4391.05 4615.12 * 2.8 * 449.13 40255.51 39900.71 0.121 Negative 4739.88 4396.66 * 2.8 * 449.13 38305.05 38150.67 0.305 Negative 5106.43 4554, 66 * 2.8 * 449.13 35887.25 39799.79 0.116 Negative 4138.38 4797.36 * 2.8 * 449.13 40923.04 43532.11 0.207 Negative

5893.61 4381.66 * 2.8 * 449.13 46207.41 36162.31 0.616 Negative 5643.82 6931.53 * 2.8 2435.29 47192.86 41414.07 0.295 Negative 4089.23 5231.7 * 2.8 * 449.13 34378.61 33492.93 0.303 Negative 3997.54 5138.82 * 2.8 * 449.13 35908.96 39059.56 0.125 Negative 5520.83 5096.36 * 2.8 * 449, 13 36741.16 31627.88 0.195 Negative 3879.77 4157.8 * 2.8 * 449.13 64483.85 37645.48 0.554 Negative 4934.55 4288.14 * 2.8 * 449.13 42688.4 38420, 03 0.333 Negative 6980.23 5598.8 * 2.8 * 449.13 41217.94 39463.36 0.121 Negative 4710.21 5371.66 * 2.8 11561.91 58532.04 4 53949.39 0.543 Negative 5803.61 8281.22 * 2.8 11 753.57 58 781.05 44 305.41 0.117 Negative

6217.62 10248.24 * 2.8 * 449.13 44095.27 38420.03 0.256 Negative 5703.71 4747.87 * 2.8 3227.92 64889.25 45448.79 0.242 Negative 5218.98 5752.7 * 2, 8 6623.51 60434.49 60381.88 0.125 Negative 4079.4 3577.75 * 2.8 * 449.13 70527.65 49657.76 0.197 Negative 7091.61 8999.58 * 2.8 * 449.13 67095, 68 45314.25 0.334 Negative 4532.41 4804.98 * 2.8 * 449.13 56863.81 39833.43 0.117 Negative 5850.26 7438.29 * 2.8 * 449.13 57952.89 49185.76 0.117 Negative 5149.45 7345.4 * 2.8 * 449.13 50071.03 53136.81 0.382 Negative 6902.68 5406.77 * 2.8 * 449.13 46751.68 46693.58 0.719 Negative 5351.55 5061.68 * 2.8 3158.13 32717.46 35048.4 0.50 Negative 5690.4 3840.39 * 2.8 * 449.13 61749.34 58605.51 0.666 Negative 5770.3 4310.55 * 2.8 * 449.13 47295 , 23 49860.1 0.123 Negative 5278.61 5528.09 * 2.8 * 449.13 59509.3 48612.86 0.188 Negative 6267.83 6268.51 * 2.8 * 449.13 54992.91 54796.73 0.320 Negative 3784.99 2904.92 * 2.8 * 449.13 34199.62 31424.02 0.122 Negative 6633.44 5092.51 * 2.8 * 449.13 56797.55 54220.44 0.117 Negative 6144.04 6471, 3 * 2.8 * 449.13 87278.13 52392.63 0.396 Negative 3713.14 4404.16 * 2.8 2547.09 69693.94 53746.17 0.321 Negative 5444.45 6317.03 * 2.8 5161.05 53467, 5 40,405.25 0.125 Negative 6057.15 6931.53 * 2.8 5717.8 13708.42 35284.81 0.238 Negative 4059.75 3778.13 * 2.8 * 449.13 57221.45 41884.77 0.116 Negative 7687.94 6993.8 * 2.8 * 449.13 59282.78 46794.54 0.338 Negative 4440.34 4105.85 * 2.8 2397.71 50042.12 39497 0.318 Negative 6090.56 6683.94 29.9 * 449, 13 59912.56 35959.89 0.242 Negative 5583.97 6828.07 * 2.8 * 449.13 50535.91 42691.63 0.125 Negative 5544.08 5776.45 * 2.8 * 449.13 51471.61 43162, 3 0.331 Negative 5613.89 6585.57 * 2.8 * 449.13 18009.93 34135.84 0.125 Negative 4627.86 3060.74 * 2.8 * 449.13 17876.89 22191.13 0.121 Negative 6003.72 3570.49 * 2.8 * 449.13 57674.2 46458.03 0.381 Negative 5235.54 5469.32 * 2.8 * 449.13 36268.65 36162.31 0.121 Negative 6109.11 6347.2 * 2.754 3738 , 98 35593.42 33304.26 0.301 Negative 4619.15 5421.96 * 2.754 6016.88 58751.77 34913.79 0.121 Negative 5700.36 4895.75 * 2.754 * 167.8 8 40915.25 31207.97 0.538 Negative 5855.71 6169.55 * 2.754 * 167.88 39396.48 40202.48 0.125 Negative 6154.72 6455.93 * 2.754 * 167.88 58001.95 37211.03 0.117 Negative 3908 , 43 4892.08 * 2.754 1221.15 43068.88 31927.63 0.125 Negative 6011.51 5362.15 * 2.754 * 167.88 71733.35 36367.14 0.125 Negative 4149.49 5508.16 * 2.754 2757.03 108126 , 08 51544.7 0.815 Negative 5281.9 5268.97 * 2.754 * 167.88 80081.63 44635.16 0.121 Negative 6507.86 5787.43 * 2.754 * 167.88 71006.81 39341.67 0.116 Negative 4209.15 5302.48 * 2.754 * 167.88 55802.83 34033.77 0.200 Negative 5797.4 7548.69 * 2.754 4833.66 48704.21 44243.26 0.301 Negative 5464.97 6522.17 * 2.754 10813.61 61467.71 30801.63 0.117 Negative 5397.45 3703.12 * 2.754 950.82 46207.55 28879.88 0.480 Negative 4869.89 6331.7 * 2.754 4150.66 68589.9 33037.63 0.122 Negative 5868.68 3773.57 * 2.754 * 167.88 62622.36 30944.92 0.121 Negative 5237.03 4139.15 * 2.754 * 167.88 43033.48 34889.28 0.117 Negative

6102.6 5168.7 * 2.754 * 167.88 68100.3 41759.43 0.125 Negative 5761.8 6702.24 * 2.754 * 167.88 39885.59 32144.18 0.192 Negative 5105.84 6836.15 * 2.754 * 167 , 88 63548.29 34791.26 0.116 Negative 5632.54 6181.1 * 2.754 2638.49 108653.51 54006.02 0.187 Negative 6157.98 4567.42 * 2.754 * 167.88 50183.46 30849.38 0.21 Negative 5022 , 82 5624.8 * 2.754 * 167.88 72126.73 39974.13 0.123 Negative 5067.5 4285.97 * 2.754 13029.14 83712.49 31759.41 0.117 Negative 4344.41 3555.7 * 2.754 * 167.88 62068.6 47861.92 0.122 Negative 5160.19 6413.16 * 2.754 6888.13 137372.99 42866.83 0.180 Negative 4733.26 6405.39 * 2.754 * 167.88 85391.57 37235.92 0.335 Negative 4866.7 6370.46 * 2.754 10097.5 73069.53 43022.01 0.125 Negative 4505.29 5105.75 * 2.754 1794.65 95320.88 40075.58 0.125 Negative 3920.92 3629.32 * 2.754 * 167.88 42248.16 32529.9 0.310 Negative 3808.58 4253.67 * 2.754 * 167.88 104092.24 61679.41 0.187 Negative 5833.03 5309.93 * 2.754 * 167.88 46590.68 32626.48 0.121 Negative 4410.59 6012, 21 * 2.754 5670.3 51921.97 39467.95 0.123 Negative 5109.04 5302.48 * 2.754 * 167.88 110729.87 46504.41 0.175 Negative 5716.53 6662.99 * 2.754 * 167.88 68491.61 31423.52 0.122 Negative 4077.37 4625.51 * 2.754 1085.52 48126.22 36169.26 0.222 Negative 7028.68 5745.73 * 2.754 5472.78 46032.46 28903.46 0.117 Negative 6396.44 6211.92 * 2.754 * 167.88 45782.48 30777.76 0.125 Negative 3603.24 3925.63 * 2.754 * 167.88 47618.8 32747.3 0.125 Negative 6583.37 7744.25 * 2.754 * 167.88 45175.03 39341.67 0.306 Negative 6226.47 6765.17 * 2.754 * 167 , 88 48,302.02 40711.16 0.747 Negative 6422.64 6655.15 * 2.754 * 167.88 33083.29 23112.15 0.255 Negative 5115.43 5187.24 * 2.754 * 167.88 55557.46 48908.57 0.311 Negative 3895.94 3608.26 * 2.754 * 167.88 28916.03 21480.96 0.317 Negative 4259.43 2753.81 26.53 * 167.88 45004.11 38561.11 0.568 Negative 4284.6 6204.21 * 2.754 * 167.88 37200.78 30944.92 0.121 Negative 3507.07 2569.94 513.39 2662.18 14818.49 21684.01 0.720 Negative 4297.18 4253.67 * 2.754 * 167.88 38836.35 36045.71 0.522 Negative 5323.59 8988.51 * 2.754 * 167 , 88 37,950.84 28338.42 0.320 Negative 7608.03 5246.65 * 2.754 8193.85 53839.23 29375.87 0.324 Negative 5535.8 6592.48 * 2.754 4442.66 62371.84 34399.83 0.300 Negative 5564, 81 6050.5 * 2.754 * 167.88 70660.07 43073.78 0.333 Negative 4695.2 3957.56 * 2.754 * 167.88 52977.06 23271.57 0.334 Negative 5474.63 6561.21 * 2.754 * 167.88 61660.04 33328.52 0.121 Negative 5481.06 5958.7 * 2.754 * 167.88 42296.81 35134.59 0.116 Negative 6416.09 7621.85 * 2.754 * 167.88 50200.18 40787.61 0.308 Negative 6711, 64 6954.87 * 2.754 * 167.88 44752.7 41323.88 0.471 Negative 6455.4 6420.93 * 2.754 * 167.88 43625.1 27822.23 0.304 Negative 4511.61 4419.13 * 2.754 * 167.88 47919.36 38184.87 0.313 Negative 3696.47 3918.54 * 2.754 * 167.88 34418.77 27447.85 0.309 Negative 4445.29 2333 24.84 * 167.88 35170.76 34009.4 0.577 Negative 8761.33 8251.08 * 2.744 18269.3 111291,24 63076.26 0.528 Negative 4657.93 9226.31 * 2.744 5587.21 81946.17 47000.98 0.214 Negative 4977.42 6591.18 * 2.744 * 431.385 82281.32 41854, 96 0.713 Negative 5049.27 6101, 59 * 2.744 10079.0 76732.02 43769.9 0.338 Negative 9355.7 5245.25 * 2.744 4232.96 91565.36 54042.65 0.117 Negative 8690.81 8896.64 * 2.744 23503.31 70935.25 40000.31 0.308 Negative 4094.93 4305.81 * 2.744 12160.23 60502.11 35508.57 0.314 Negative 6489.38 7607.32 * 2.744 16413.81 100570.05 51740.79 0.576 Negative 7603.64 8720.7 * 2.744 5713, 62 70657.36 45279.82 0.566 Negative 4303.41 5902.44 * 2.744 * 431.385 67542.72 40414.8 0.323 Negative 4499.71 5642.61 * 2.744 6890.85 78115.99 46686.62 0.123 Negative 4737.43 7417.04 * 2.744 9323.67 90516.59 41626.71 0.305 Negative 7722.27 11524.48 * 2.744 13383.29 102090.77 41893.03 0.178 Negative

9984.46 11030.63 * 2.744 13277.73 76969.85 39812.25 0.121 Negative 7460.52 4415.16 * 2.744 17.351,05 73563.81 42656.43 0.187 Negative 6299.62 8238.52 * 2.744 27694.61 70733 , 02 42809.54 0.125 Negative 8770.96 10261.88 * 2.744 17905.99 70556.64 52853.18 0.353 Negative 4189.2 6903.41 * 2.744 * 431.385 62274.2 39286.84 0.349 Negative 5315.38 7910.35 * 2.744 * 431.385 67638.27 43577.36 0.121 Negative 6167.74 7721.48 * 2.744 17052.59 115290.47 43769.9 0.444 Negative 5947.17 10389.9 * 2.744 10372.92 96439.79 51956.96 0.113 Negative 6239.6 7057.36 * 2.744 2245.54 77788.35 53467.27 0.125 Negative 5464.01 6154.54 * 2.744 * 431.385 89330.54 57541.33 0.116 Negative 4925.8 4670.97 * 2.744 * 431.385 73233.67 43924.1 0.599 Negative 4328.55 3555.69 * 2.744 3119 46182.14 52322.96 0.624 Negative 4508.16 6746.69 * 2.744 2201.27 81251.12 46804.43 0.123 Negative 5781.31 8546.11 * 2.744 * 431.385 83142.57 41436.74 0.123 Negative 5890.8 9200.12 * 2.744 6701.9 95444.24 52894.04 0.735 Negative 4988.9 2052.63 67.9 * 431.385 76592.56 41398.78 0.322 Negative 4612.62 5274.11 * 2.744 * 431.385 61895.7 40452.54 0.599 Negative 5295.04 6026.19 * 2.744 2156.92 96667.19 45786.37 0.236 Negative 3724.68 6086.49 * 2.744 * 431.385 88872.15 54744.26 0.125 Negative 5155.98 5087.34 * 2.744 * 431.385 68118.44 50904.5 0.125 Negative 5312.47 4483.89 20.6 6533.87 114827.7 47829.19 0.155 Negative 6407, 92 6483.04 * 2.744 16052.32 110506.58 54331.16 0.187 Negative 4387.34 6295.2 * 2.744 * 431.385 86839.83 46647.37 0.564 Negative 7206.76 7332.56 * 2.744 * 431.385 112389.35 49179, 57 0.183 Negative 5578.19 5742.4 * 2.744 * 431.385 97066.95 43461.95 0.175 Negative 6850.8 7400.92 * 2.744 7520.31 67221.43 35362.12 0.122 Negative 5816.77 7364.7 * 2.744 4784, 67 70569.22 43461.95 0.121 Negative 6149.8 6754.5 * 2.744 13510 65377.25 42350.63 0.117 Negative 6383.82 4984.05 24.89 11066.19 65549.75 43039.5 0.125 Negative 6450.13 8021 , 96 * 2.744 13488.88 63647.83 39887.45 0.125 Negative 3781.85 4494.23 * 2.744 4742.32 56362.1 33614.43 0.116 Negative 8029.92 9756.4 * 2.7 44 14334.52 105984.32 43500.41 0.154 Negative 3444.03 3989.08 108.72 12476.24 51536.87 42694.69 0.344 Negative 5057.91 7522.09 * 2.744 7184.66 67998.01 46725.88 0.121 Negative 7059.03 7252.42 * 2.744 5481.81 101478.61 47671.09 0.182 Negative 6054.28 11007.13 * 2.744 11823.37 73762.78 35875.24 0.323 Negative 5286.33 8797.78 * 2.744 11802.32 78058.88 44310.26 0.117 Negative 5810.86 8905.25 * 2.744 2377.94 88618.86 38427.33 0.116 Negative 6668.09 6490.75 * 2.744 * 431.385 59256.53 39586.87 0.303 Negative 3970.79 3876, 17 24.89 * 431.385 50652.36 37646.45 0.810 Negative 5115.56 5328.36 * 2.744 * 431.385 51342.87 40830.37 0.116 Negative 5819.73 8571.58 * 2.744 8988.11 85140.1 48145.87 0.122 Negative 5089.6 4825.02 * 2.744 2815.49 61659.14 42312.45 0.332 Negative 4070.05 3584.81 44.16 2156.92 48536.63 42312.45 0.116 Negative 4902.89 4580.63 * 2.744 6932, 84 51079.53 36832.32 0.554 Negative 5516.65 8180.01 * 2.744 * 431.385 78030.35 45396.57 0.122 Negative 3872 5328.36 * 2.744 3549.55 54521.16 34960.04 0.373 N egative 4578.69 5492.11 * 2.744 * 431.385 64522.74 50139.94 0.117 Negative 10511.61 13075.14 * 2.744 43396.61 167205.9 50340.77 0.297 Negative 4505.35 7284.44 * 2.744 * 431.385 64387, 23 46 451.27 0.237 Negative 4164.21 6321.92 * 2.744 14249.88 64005.09 38688.44 0.116 Negative 5504.94 7093.05 * 2.744 7436.41 70720.4 41626.71 0.125 Negative 5121.33 3843.13 * 2.744 * 431.385 50930.91 42236.11 0.117 Negative 9997.65 15839.35 * 2.744 7981.67 97680.54 48344.12 0.266 Negative 3811.85 4674.46 * 2.744 * 431.385 59823.81 36795.41 0.332 Negative 4418 , 19 2675.86 * 2.744 * 431.385 50366.72 37017 0.125 Negative 5179.11 9685.04 * 2.744 * 431.385 123762,98 60133.48 0.428 Negative

5790.17 5205.64 * 2.744 * 431.385 82802.82 44426.29 0.245 Negative 6426 9649.44 * 2.744 10456.92 98786,98 50743.23 0.178 Negative 5513.72 5274.11 * 2.744 7310.55 69781.97 43692 , 86 0.117 Negative 5251.51 6738.89 * 2.744 * 431.385 58634.35 44078.45 0.319 Negative 5049.27 5928.62 * 2.744 5291.96 86154.57 44774.9 0.125 Negative 3379.85 4435.75 * 2.744 2553 , 6 65,480.68 43461.95 0.123 Negative 4062.58 6816.34 * 2.724 3933.01 69336.57 47933.01 0.240 Negative 4503.27 5369.52 76.44 * 131.8 64521.68 34244.8 0.117 Negative 3965.06 5158.44 * 2.724 9870.38 76176.84 34354.35 0.198 Negative 3913.1 4254.75 * 2.724 * 131.8 57652.5 38923.54 0.125 Negative 5367.87 6296.72 * 2.724 1183.86 72,167.99 30,511.48 0.121 Negative 5484.38 5402.19 * 2.724 8362.52 62316.27 39742.83 0.405 Negative 6534.34 5777.69 * 2.724 * 131.8 57290 34390.87 0.122 Negative 3382.63 5570, 5 * 2.724 1390.36 65291.95 42442.97 0.122 Negative 3547.21 3815.99 * 2.724 * 131.8 66972 48396.11 0.117 Negative 6473.19 7188.16 * 2.724 14608.67 58986.987878.77 0.125 N egative 3569.82 2778.69 * 2.724 * 131.8 80397.48 39221.16 0.236 Negative 6225.94 6348.08 * 2.724 * 131.8 135912.88 48898.8 0.436 Negative 4950.45 7685.52 * 2.724 2382 , 1 149120.26 53313.05 0.670 Negative 4999.98 4089.56 * 2.724 * 131.8 71120.75 44867.9 0.121 Negative 6633.01 7322.62 * 2.724 * 131.8 71414.62 44107.56 0.313 Negative 4323.36 4989.58 * 2.724 3334.94 70873.36 45898.09 0.338 Negative 5825.36 6356.66 * 2.724 5421.3 152659.24 46548.96 0.345 Negative 5357.9 7685.52 * 2.724 * 131.8 70038.59 43652.47 0.354 Negative 5692.58 4700.13 37.96 16954.07 71757.16 35388.8 0.240 Negative 3511.15 5289.24 39.36 14100.85 20842.68 26470.8 0.248 Negative 6010, 29 7727.83 * 2.543 9849.78 67272.37 36908.4 0.221 Negative 6272.91 7741.21 * 2.543 6397.68 56201.46 42734.01 0.116 Negative 6336.04 6293.4 * 2.543 5078.83 68578.91 33623.77 0.234 Negative 6318.5 4077.37 * 2.543 * 167 60566.62 34229.28 0.123 Negative 5870.51 5850.55 31.67 13050.5 58010.8 48644.6 0.331 Negative 6206.31 5916.91 * 2.543 1505.42 63270,84 34200.4 0, 125 Negative 5237 7076.46 * 2.543 * 167 45300.03 37437.68 0.125 Negative 5968.33 6016.77 * 2.543 7707 75769.73 43068.79 0.322 Negative 5365.46 7745.67 * 2.543 * 167 118027.72 65078, 76 0.176 Negative 5629.85 6632.77 * 2.543 13857.17 62469.38 38619.93 0.125 Negative 4628.28 4510.59 * 2.543 6025.04 4.48481,02 40409.21 0.125 Negative 4766.29 4291.15 * 2.543 1197 , 08 48761.71 32476.51 0.306 Negative 6944.84 8109.61 * 2.543 10101.22 71575.63 33106.52 0.117 Negative 6020.78 8584.66 * 2.543 18774.75 61487.66 33451.17 0.125 Negative 5671, 66 6963.73 * 2.543 1638.58 59899.7 36293.04 4.21 Negative 4966.75 5871.27 * 2.543 1438.01 52631.91 32390.79 0.596 Negative 4759.38 4224.89 * 2.543 835 46019.15 38234, 77 0.121 Negative 4628.28 5586.81 * 2.543 7343.43 49688.85 33451.17 0.322 Negative 4328.81 5431.48 * 2.543 131014.81 64253.94 39631.49 0.116 Negative 4918.32 6924.83 * 2.543 3184 , 77 52 507.36 27 910.06 0.121 Negative 6546.76 9191.2 * 2.543 4443.23 53758.83 30036.58 0.125 Negative 4291.02 5083.34 * 2.543 3879.24 43947.28 32248.01 0.125 Negative 5001.36 5268.98 * 2.543 * 167 48720.75 28690.32 0.117 Negative 3531.59 4775.53 * 2.543 1769.79 43015.07 27743.34 0.125 Negative 6045.26 7185.33 * 2.543 7629.27 79699.04 41915.13 0.117 Negative 5431.49 7115.6 * 2.543 7965.4 60713.7 32305.11 0.111 Negative 4807.73 6761.21 * 2.543 18177.03 92867.36 55820.48 0.125 Negative 3889 , 98 5586.81 * 2.543 * 167 53440.58 29193.87 0.125 Negative 3388.65 4310.67 * 2.543 * 167 41729.87 35592.55 0.122 Negative 3934.45 3692.94 * 2.543 * 167 64130.02 39571, 82 0.805 Negative 4061.15 5123.59 * 2.543 2918.14 51305.66 28885.96 0.125 Negative

5129.5 7215.89 * 2.543 * 167 79848.73 34982,04 0.116 Negative 4174.29 3122.35 * 2.543 * 167 56461.37 31507.57 0.121 Negative 5351.56 6125.39 * 2.543 17578.18 85787.8 39512.16 0.125 Negative 4098.85 6829.97 * 2.543 14392.82 119560.1 45802.46 0.710 Negative 3954.98 4345.84 * 2.543 * 167 59395.06 36351.55 0.123 Negative 7745.76 6238.68 * 2.543 6609.33 51644.62 27715.57 0.587 Negative 7227.62 6310.26 * 2.543 9344.8 51531.36 31820.43 0.125 Negative 7163.93 9807.66 * 2.543 12584.14 75532.91 35127.2 0.117 Negative 4346 3803.87 * 2.543 6820.09 86655.02 35301.55 0.125 Negative 6715.67 8644.67 * 2.543 9597.65 60787.4 38323.58 0.253 Negative 4373.5 5862.98 * 2.543 * 167 43895.78 30883, 63 0.117 Negative 6385.17 4926.99 * 2.543 1126.58 65394.31 36322.29 0.309 Negative 3545.22 3317.55 * 2.543 3155.33 65555.98 949.77 0.123 Negative 4266.97 4295.05 * 2.543 * 167 54272 , 51 32076.85 0.121 Negative 5001.36 6050.14 * 2.543 * 167 54448.06 36585.79 0.305 Negative 5292.53 8209.37 * 2.543 7862.16 90393.73 35971.62 0.122 Negative 7033.12 5204.26 * 2.543 13417.67 101520.37 37998.2 0.184 Negative 4421.65 5995.93 * 2.543 * 167 62917.96 37880.04 0.515 Negative 6108.25 7303.43 * 2.543 2090.51 70207.78 54249.75 0.125 Negative 3266.32 4416.32 * 2.543 8402.43 56499.99 35011.07 0.122 Negative 3879.72 3919.1 * 2.543 * 167 47461.11 27632.29 0.123 Negative 5501.05 6104.47 * 2.543 3360.49 60556.13 38708.94 0.125 Negative 6824.9 9314.25 * 2.543 4831.59 79549.74 42248.26 0.121 Negative 4776.64 3605.28 * 2.543 3678.9 59497.63 32619.49 0.125 Negative 5327.25 4151 , 03 * 2.543 5595.27 59549 32305.11 0.116 Negative 5396.73 5784.36 * 2.543 * 167 51314.32 24498.68 0.117 Negative 3535 3506.51 * 2.543 * 167 49214.66 31252.03 0.125 Negative 4044.01 6041.79 * 2.543 4331.35 56819.82 33250.04 0.125 Negative 3900.24 6242.88 * 2.543 8094.23 63670.75 39661.34 0.191 Negative 6592.48 9725.56 * 2.543 6238.34 80663.76 34808 , 03 0.117 Negative 3524.78 4158.8 * 2.543 * 167 56693.57 28104.78 0.234 Negative 7387.04 8984.29 * 2.543 10452.16 61253.16 41220.71 0.325 Negative 5035.97 4632.84 * 2.543 13710.8 63237.65 34779.04 0.803 Negative 4359.75 5941.84 * 2.543 * 167 55780.87 33594.99 0.116 Negative 5542.8 7687.74 * 2.543 9446.03 74663, 86 36410.08 0.125 Negative 6487.01 5123.59 * 2.543 3447.79 77946.77 44015.89 0.116 Negative 4876.84 8077.95 * 2.543 13270.9 89227.33 43434.77 0.317 Negative 7653.29 5767.84 * 2.543 9471.32 68947.6 43282.18 0.116 Negative 5139.9 5071.28 * 2.543 * 167 59951.49 44169.16 0.324 Negative 4050.87 3317.55 * 2.543 1370.01 59816.96 40469.19 0.129 Negative 3449, 88 3141.06 * 2.543 * 167 59579.84 40979.86 0.125 Negative 3948.14 4318.48 * 2.543 * 167 67033.91 40559.19 0.121 Negative 4297.89 4252.15 * 2.543 * 167 63771.21 37055.26 0.123 Negative 7379.45 9245.94 * 2.763 3884.72 117838.24 75866.17 0.982 Positive 7522.16 4934.24 * 2.763 * 458.411 43625.67 46574.46 0.560 Negative 12690.17 5359.08 43.19 16845, 56 102719.65 43100.85 0.994 Positive 12663.94 8360.96 * 2.763 36017.95 158735.57 66317.62 0.982 Positive 11402.59 5711.64 * 2.763 3546.55 102655.7 54948.15 0.423 Negative 10783.2 7791.78 * 2.763 3425.81 52298.58 43848.53 0.993 Positive 7124.39 6546.82 * 2.763 3208.54 45810.79 39642.51 1,000 Positive 18292.01 9027.41 * 2.763 14193, 62 136829.88 61947.69 0.977 Positive 14110.61 9107.48 * 2.763 9261.82 96511.57 49554.25 1,000 Positive 12585.3 8907.77 * 2.763 44329.75 130176.2 65057.27 0.473 Negative 10772.91 8360.96 * 2.763 3112 93688.49 45346.3 0.605 Negative 9334.6 3794.64 60.23 5335.58 51202.37 57358.58 0.822 Negative 7840.93 12007.32 * 2.656 5454.63 46989.06 39837, 77 0.221 Negative 13284.85 7506.2 * 2.656 9857.64 98102.24 76787.32 0.679 Negative

10009.96 7762.57 * 2.656 27977.9 139580.71 36534.68 0.832 Negative 8688.23 5769.6 * 2.656 6547.37 58818.44 55151.19 0.872 Positive 11174.15 7820.61 * 2.656 * 142.74 39209.21 39737.91 0.904 Positive 11210.67 10898.97 * 2.656 10993.75 132733.41 51821.17 1,000 Positive 8673.84 6298.56 22.45 5601.8 72102.49 46159.78 0.989 Positive 30981.02 8421.51 * 2.656 16435.8 148183.33 51970.19 1.000 Positive 7354.21 5339.37 * 2.656 5528.27 82533.18 77145.36 0.995 Positive 6269.76 6219.7 * 2.656 4332.28 62979.85 56245 , 84 0.457 Negative 8285.67 10240.32 * 2.656 * 142.74 64724.98 51970.19 0.125 Negative 12060.37 11894.51 * 2.656 16235.01 104486.8 56693.87 0.804 Negative 3926.45 3882.45 * 2.782 * 128.728 72805.99 36214.77 0.980 Positive 5136.61 4981.82 * 2.782 5844.69 97443.46 43503.33 0.994 Positive 9834.63 6470.88 * 2.782 11277.98 132065.47 50216.81 0.993 Positive 7872 , 55 6578.36 * 2.782 21223.71 110695.5 51107.43 0.999 Positive 6012.04 4 266.7 * 2.782 2151.02 88586.2 47763.06 0.911 Positive 3825.99 3976.91 * 2.782 6025.1 4 85619.07 36976.71 1,000 Positive 7633.5 8100.55 * 2.759 * 1116.987 59035.07 57129.3 0.125 Negative 16683.06 7635.8 * 2.759 90950.02 267653.48 76660.04 0.999 Positive 11654, 23 8848.09 * 2.759 70423.98 8585.88 60154.26 0.794 Negative 11792.72 6736.45 * 2.759 7519.85 184235.47 85539.09 0.989 Positive 31250.7 9805.27 * 2.759 82632.91 292919.34 84030.45 0.997 Positive 6935.42 8490.8 * 2.759 17227.03 137336.29 64755.66 0.968 Positive 14073.44 9234.17 * 2.759 2059.14 97883.95 93722.51 0.884 Positive 19988.65 10154.14 * 2.759 13594.5 120783.31 64321.89 1,000 Positive 9024.39 9315.88 17.18 26288.07 280016.32 65945.04 0.999 Positive 24348.92 9084.02 * 2.759 11977.87 150908.39 62141.51 0.994 Positive 8708 , 44 5529.72 * 2.759 27348.28 60844.62 68286.81 0.117 Negative 8712.75 8848.09 * 2.759 5687.75 98262.17 71504.29 0.988 Positive 7059.94 9770.47 * 2.759 * 1116.987 94557 , 82 96693.57 0.910 Positive 21075.63 9253.01 17.18 17165.72 231616.4 79474.93 0.999 Positive 8064.36 11379.33 * 2.759 3320.88 98191.45 73474, 21 0.978 Positive 7754.58 6193.12 38.31 5746.53 79976.83 68014.71 0.969 Positive 8861.71 5667.83 * 2.759 7817.09 64057.52 62859.1 0.402 Negative 16936.58 15482.2 * 2.759 22686, 89 91 452.24 85 986.95 0.857 Negative 9036.43 8573.99 * 2.818 14704,14 122175.47 84979.81 0.693 Negative 9921.06 8705.52 633.22 24190.4 63136.44 59127.52 0.987 Positive 8132, 48 10111.69 * 2.818 13417.1 81475.11 71313.82 0.50 Negative 9772.39 8101.8 * 2.818 8233.27 119004.56 66380.2 0.998 Positive 22318.29 9473.1 * 2.818 16810.12 127480.46 72379, 14 1,000 Positive 8544.5 9137.16 22.61 7445.49 109999.93 69821.18 0.824 Negative 11143.21 7974.36 * 2.818 8409.33 166612.52 73090.75 0.929 Positive 9881.52 10095.08 * 2.818 21227.44 273711.63 81470.22 0.947 Positive 19929 9009.28 * 2.818 42348.01 305270.66 80083.85 0.993 Positive 8503.08 9766.8 * 2.818 6524.2 161026.06 78700.88 0.406 Negative 5763.93 8154.52 * 2.818 58200.47 569512.69 55577.12 0.987 Positive 13116.48 9303.29 * 2.818 76872.74 488282.85 65515.16 0.986 Positive 5795.75 37 89.44 * 2.818 1000.76 55464.18 50736.62 0.122 Negative 19001.13 11175.61 56.99 5372.83 82928.93 84030.45 0.997 Positive 9268.75 14252.71 * 2.818 5451.65 63146.39 81026.2 0.284 Negative 10018.07 7466.07 * 2.818 14634.33 149244.95 86771.7 0.957 Positive 6574.86 5670.41 * 2.818 7511.66 86263.27 68667.89 0.877 Positive 16365.17 7127.99 * 2.818 56994.31 266498.88 101574.58 0.999 Positive 12973.54 20178.17 * 2.818 20617.11 110682.2 62919.29 0.701 Negative 13671.92 5331.79 * 2.665 11434.47 160501.95 53949.97 0.905 Positive 8737, 89 8795.65 * 2.665 4280.53 65145.56 62365.29 0.690 Negative 7257.3 6513.56 * 2.748 5939.83 95017.85 53663.85 0.877 Positive 18135.27 7392.35 * 2.748 40093.01 180301 61068.51 1,000 Positive

8124.65 7749.69 * 2.748 6791.03 90538.09 52304.5 0.244 Negative 11467.58 5874.98 * 2.815 10400.97 104452.38 50767.63 1,000 Positive 5543.03 5897.2 * 2.815 3799.24 214347 , 67 52619.83 1,000 Positive 2503.74 3298 * 2.815 1085.39 120566.94 42593.58 1,000 Positive 3454.61 4864.67 * 2.815 * 119.88 46618.62 49379.09 1,000 Positive 4696.29 5148.07 * 2.815 2749.97 86252.24 59523.16 0.998 Positive 3076.55 2704.3 * 2.815 * 119.88 62201.83 53380.3 1,000 Positive 6401.27 4892.83 * 2.815 * 119.88 93960.97 52961, 71 1,000 Positive 3709.23 4146.58 * 2.815 * 119.88 80397.85 49940.99 0.999 Positive 4841.3 6400.14 * 2.815 * 119.88 141399.93 54181.64 1,000 Positive 5686.04 7594.04 * 2.815 * 119.88 159645.24 68454.19 1,000 Positive 4903.94 3592.66 * 2.748 10047.93 100579.12 37932.2 0.836 Negative 3027.71 3651.43 * 2.815 * 119.88 106210.73 59640.79 0.998 Positive 5118.72 4419.63 * 2.815 3893.12 258509.16 45520.29 1,000 Positive 3712.5 5676.06 * 2.815 * 119.88 128320.69 55177.89 1,000 Positive 3725.57 3924.19 * 2.815 3424 66 122568, 86 57805.48 1,000 Positive 5059.2 6552.76 * 2.815 * 119.88 108708.09 64561.78 0.983 Positive 3961 6426.77 * 2.815 6312.94 132961.12 61413.17 1,000 Positive 2803.1 3101.24 * 2.815 * 119.88 111405.73 54909.22 1,000 Positive 2685.96 4578.4 * 2.815 * 119.88 74864.18 43030.4 0.992 Positive 5752.61 6113.34 * 2.815 5144.78 93127.27 53990.58 1,000 Positive 4930.68 3572.56 * 2.748 * 445.15 103763.1 49303.1 1,000 Positive 1751.11 2061.7 * 2.815 * 119.88 120245.16 42266.46 1,000 Positive 4229.64 4316.79 * 2.815 * 119.88 106778.98 58975.11 0.971 Positive 4897.38 2685.45 * 2.815 11844.9 125850.52 44345.44 0.994 Positive 4482.44 4031.7 * 2.815 951.77 206008.83 50918.26 0.999 Positive 3464.39 3285.04 * 2.815 * 119.88 66505.98 49192.1 0.999 Positive 4538.33 6210.99 * 2.815 * 119.88 185943.89 67107.77 1,000 Positive 3112.38 3951.01 * 2.815 * 119 , 88 104089.92 47071.02 1,000 Positive 3634.11 2919.2 * 2.815 * 119.88 73829.5 48036.13 1,000 Positive 3366.55 2941.46 * 2.815 3987.09 129377.67 45079.06 1,000 Positive 3732 , 1 3136,57 * 2 .815 * 119.88 117266.32 38945.68 0.871 Positive 4669.95 4183.9 * 2.815 * 119.88 10 1007.89 41649.69 1,000 Positive 3281.8 3392.26 * 2.815 * 119.88 173510.42 46183 , 65 1,000 Positive 1384.4 2610.25 * 2.815 * 119.88 114792.87 46552.99 1,000 Positive 3197.07 2361.74 * 2.815 * 119.88 47419.27 35770.3 0.999 Positive 4360.89 7425.41 * 2.815 5596.62 183636.9 50918.26 1,000 Positive 4272.28 5756.86 * 2.815 * 119.88 116448.8 70842.56 0.941 Positive 5390.31 8538.58 * 2.815 * 119.88 167799.61 50128, 6 0.920 Positive 7951.03 12966.19 * 2.815 * 119.88 123443.76 54066.98 0.990 Positive 2396.36 1901.41 * 2.815 * 119.88 104070.89 39844.02 0.999 Positive 3079.81 5312.9 * 2.815 6864.33 110579.24 42921.13 1,000 Positive 3967.55 4045.18 * 2.815 12877.1 88464.04 47219.24 1,000 Positive

Table 5. Agreement between mutations identified in the plasma with those identified in primary tumors.

Concentration Mutation Frequency Fragments Mutation Type AJCC Volume DNA in plasma identified in Pont. mutant allele / mL identified in PatientID # SampleID # Tumor Plasma stage (mL) (ng / mL) plasma * Omega (%) plasma tumor tissue CRC 468 CRC 468 PLS 1 Colorectal III 5 12.99 TP53 p.E286G, c.857A> G 11.42 0.39 15.6 Presente CRC 470 CRC 470 PLS 1 Colorectal II 7.5 2.79 APC p.E1306 *, c.3916G> T 5.93 0.36 3.1 Present CRC 473 CRC 473 PLS 1 Colorectal II 7.5 6.30 KRAS p.G13D, c.38G> A 3.40 3.68 71.4 Present CRC 476 CRC 476 PLS 1 Colorectal II 5 8.45 TP53 p.R249G , c.745A> G 20.05 7.25 188.8 Present CRC 480 CRC 480 PLS 1 Colorectal III 5 13.30 CTNNB1 p.T41A, c.121A> G 93.61 0.76 31.0 Present CRC 481 CRC 481 PLS 1 Colorectal II 5 13.85 PIK3CA p.H1047R, c.3140A> G 3.18 0.19 8.0 Present CRC 489 CRC 489 PLS 1 Colorectal II 7.5 5.77 KRAS p.Q61K, c .181C> A 4.06 0.17 3.0 Present CRC 504 CRC 504 PLS 1 Colorectal III 7.5 14.14 APC p.E1309fs, c.3927delAAAGA 17.49 1.67 72.6 Present CRC 506 CRC 506 PLS 1 Colorectal III 7.5 6.87 APC p.I1311fs, c.3931insA 4.40 0.19 4.0 Present

CRC 516 CRC 516 PLS 1 Colorectal II 7.5 13.15 CTNNB1 p.T41A, c.121A> G 4.69 0.03 1.4 Present CRC 518 CRC 518 PLS 1 Colorectal III 7.5 10.91 TP53 p .S215fs, c.644insGTG 87.57 8.48 284.9 Present CRC 520 CRC 520 PLS 1 Colorectal III 7.5 8.21 KRAS p.G12V, c.35G> T 4.98 0.48 12.1 Present CRC 521 CRC 521 PLS 1 Colorectal III 7.5 7.5 5.2 KRAS p.A146V, c.437C> T 3.64 0.45 7.3 Present CRC 524 CRC 524 PLS 1 Colorectal II 7.5 7.8 KRAS p .G12V, c.35G> T 4.09 0.36 8.7 Present CRC 527 CRC 527 PLS 1 Colorectal III 7.5 4.72 KRAS p.A146T, c.436G> A 3.10 2.30 33, 4 Present INDI 009 INDI 009 PLS 1 Lung II 7.5 2.59 TP53 p.C176F, c.527G> T 5.51 1.06 8.5 Present INDI 014 INDI 014 PLS 1 Lung II 7.5 1.61 TP53 p.E298 *, c.892G> T 5.89 1.36 6.7 Present INDI 023 INDI 023 PLS 1 Lung II 7.5 7.78 TP53 p.G266V, c.797G> T 7.61 0, 90 21.7 Present INDI 035 INDI 035 PLS 1 Colorectal II 7.5 3.07 KRAS p.G12D, c.35G> A 3.13 0.57 5.4 Present INDI 070 INDI 070 PLS 1 Breast III 7.5 16.58 PTEN p.C136R, c.406T> C 15.54 0.75 38.2 P current INDI 074 INDI 074 PLS 1 Pancreas II 7.5 7.45 KRAS p.G12V, c.35G> T 3.43 0.25 5.7 Present INDI 075 INDI 075 PLS 1 Ovary III 7.5 2.60 TP53 p.Y220C, c.659A> G 7.16 8.47 67.8 Present INDI 077 INDI 077 PLS 1 Liver II 7.5 62.35 TP53 p.K132R, c.395A> G 98.06 4.49 862 , 8 Not detected INDI 081 INDI 081 PLS 1 Colorectal II 7.5 25.31 TP53 p.W53 *, c.158G> A 30.50 0.23 18.1 Present INDI 097 INDI 097 PLS 1 Colorectal III 7.5 16.40 BRAF p.V600E, c.1799T> A 4.82 0.28 14.2 Present INDI 108 INDI 108 PLS 1 Colorectal II 7.5 2.95 TP53 p.R267W, c.799C> T 4.91 16.76 152.3 Present INDI 109 INDI 109 PLS 1 Colorectal III 7.5 21.96 TP53 p.R248Q, c.743G> A 3.44 4.87 329.4 Present INDI 116 INDI 116 PLS 1 Stomach I 7 , 5 39.39 TP53 p.S260Y, c.779C> A 144.55 40.92 4,964.8 Present INDI 119 INDI 119 PLS 1 Esophagus I 7.5 53.59 TP53 p.H179R, c.536A> G 39 , 32 2.17 359.0 Present INDI 124 INDI 124 PLS 1 Colorectal II 7.5 20.63 KRAS p.G12D, c.35G> A 4.80 0.54 34.5 Not detected INDI 125 INDI 125 PLS 1 Esophagus II 7.5 2 3.56 TP53 p.L194H, c.581T> A 5.32 0.72 52.6 Present INDI 126 INDI 126 PLS 1 Stomach III 7.5 25.59 TP53 p.K132R, c.395A> G 7.89 0.63 49.9 Present INDI 128 INDI 128 PLS 1 Colorectal III 7.5 6.68 CTNNB1 p.S45F, c.134C> T 4.62 0.43 8.9 Present INDI 144 INDI 144 PLS 1 Colorectal II 7 , 5 54,08 KRAS p.G12D, c.35G> A 4.12 0.24 40.6 Present INDI 150 INDI 150 PLS 1 Colorectal II 7.5 78.23 TP53 G.7578176C> T (Junction site) 189 , 18 2.43 585.4 Present INDI 151 INDI 151 PLS 1 Stomach II 7.5 37.78 TP53 p.Y220C, c.659A> G 3.51 0.46 53.6 Present INDI 156 INDI 156 PLS 1 Colorectal III 7.5 35.66 TP53 p.P190fs, c.570delCCT 323.25 6.15 675.5 Present INDI 171 INDI 171 PLS 1 Stomach III 7.5 91.76 TP53 p.G199fs, c.597insA 3.89 0.12 33.6 Present INDI 172 INDI 172 PLS 1 Stomach II 7.5 70.98 TP53 p.R248Q, c.743G> A 4.20 3.23 706.9 Present INDI 182 INDI 182 PLS 1 Liver III 7 , 5 50.25 TP53 p.A159P, c.475G> C 4.64 0.11 17.0 Present INDI 184 INDI 184 PLS 1 Esophagus II 7.5 110.40 TP53 p.H193R, c.578A> G 3.10 0.04 14 , 7 Present INDI 185 INDI 185 PLS 1 Stomach II 7.5 44.97 TP53 p.S183fs, c.549insA 41.06 1.40 194.4 Present INDI 198 INDI 198 PLS 1 Stomach III 7.5 10.36 TP53 p.V274F, c.820G> T 12.36 1.67 53.3 Present INDI 210 INDI 210 PLS 1 Stomach III 7.5 6.85 TP53 p.C135 *, c.405C> A 4.85 1.05 22.2 Present INDI 212 INDI 212 PLS 1 Colorectal III 7.5 8.5.94 BRAF p.V600E, c.1799T> A 33.45 2.63 72.4 Present INDI 219 INDI 219 PLS 1 Liver III 7.5 51 , 72 KRAS p.G12C, c.34G> T 7.72 0.57 90.9 Present INDI 221 INDI 221 PLS 1 Colorectal III 7.5 4.60 NRAS p.G12D, c.35G> A 26.32 5 , 19 73.6 Not detected INDI 222 INDI 222 PLS 1 Colorectal II 7.5 29.31 TP53 p.G266E, c.797G> A 62.15 7.25 654.1 Present INDI 224 INDI 224 PLS 1 Lung III 7 , 5 48.72 TP53 p.H179R, c.536A> G 4.74 0.20 30.3 Not detected INDI 231 INDI 231 PLS 1 Lung III 7.5 2.04 KRAS p.G12D, c.35G> A 24.34 9.38 59.0 Present INDI 233 INDI 233 PLS 1 Colorectal I 7.5 6.54 TP53 p.G154V, c.461G> T 43.39 7.70 155.0 Not detected INDI 243 INDI 243 PLS 1 Correct l I 7.5 6.33 APC p.E1309fs, c.3927delAAAGA 39.68 2.96 57.7 Present INDI 244 INDI 244 PLS 1 Colorectal III 7.5 4.06 KRAS p.G12C, c.34G> T 5.17 0.65 8.2 Present INDI 245 INDI 245 PLS 1 Colorectal III 7.5 4.36 KRAS p.Q61K, c.181C> A 7.71 1.19 16.0 Present INDI 255 INDI 255 PLS 1 Colorectal II 7.5 3.60 KRAS p.G13V, c.38G> T 7.72 0.57 6.4 Present INDI 256 INDI 256 PLS 1 Colorectal III 7.5 5.06 KRAS p.G12S, c.34G > A 9.64 3.97 61.8 Present INDI 259 INDI 259 PLS 1 Colorectal II 7.5 11.59 TP53 p.P151H, c.452C> A 9.52 0.92 33.0 Not detected INDI 268 INDI 268 PLS 1 Colorectal III 7.5 6.66 NRAS p.Q61R, c.182A> G 7.27 3.34 68.5 Present INDI 284 INDI 284 PLS 1 Colorectal II 7.5 17.11 APC p.E1309fs, c. 3927delAAAGA 4.74 0.12 6.4 Present INDI 285 INDI 285 PLS 1 Lung II 7.5 4.88 TP53 p.E339 *, c.1015G> T 5.83 1.29 19.4 Present INDI 287 INDI 287 PLS 1 Breast III 7.5 2.11 TP53 p.S94 *, c.281C> G 9.17 1.43 9.3 Present INDI 288 INDI 288 PLS 1 Lung III 7.5 5.12 TP53 p.T125T, c.375G> T (Exon End) 16.13 8.37 132.1 Present INDI 301 INDI 301 PLS 1 Breast II 7.5 9.18 TP53 p.S241fs, c.723delTCC 3.25 0.06 1.8 Not detected INDI 305 INDI 305 PLS 1 Colorectal II 7.5 20, 30 TP53 p.S241fs, c.723delTCC 240.28 8.37 523.1 Not detected INDI 306 INDI 306 PLS 1 Colorectal III 7.5 3.55 APC p.E1309fs, c.3927delAAAGA 123.62 36.29 396, 8 Present INDI 310 INDI 310 PLS 1 Colorectal II 7.5 13.05 PIK3CA p.E545A, c.1634A> C 4.41 0.70 28.2 Present INDI 318 INDI 318 PLS 1 Stomach III 7.5 5.16 TP53 p.K305 *, c.913A> T 21.62 1.21 19.3 Not detected INDI 327 INDI 327 PLS 1 Colorectal II 7.5 2.83 TP53 p.I195N, c.584T> A 7.58 0 , 52 4.6 Present INDI 329 INDI 329 PLS 1 Colorectal III 7.5 14.29 BRAF p.V600E, c.1799T> A 89.47 10.04 442.0 Present INDI 331 INDI 331 PLS 1A Lung III 7, 5 2.53 KRAS p.G12C, c.34G> T 56.58 10.88 84.8 Present INDI 340 INDI 340 PLS 1 Colorectal III 7.5 3.43 CTNNB1 p.S37F, c.110C> T 5, 67 0.39 4.2 Present INDI 343 INDI 343 PLS 1 Colorectal III 7.5 0.91 TP53 p.G244V, c.731G> T 3.44 0.34 1.0 Present INDI 353 INDI 353 PLS 1A Lung II 7.5 2.27 TP53 p.K132N, c.396G> T 17.68 0.24 1.6 Present INDI 365 INDI 365 PLS 1 Colorectal II 7.5 3.06 APC p.E1309fs, c.3927delAAAGA 5.91 0.60 5.6 Present INDI 367 INDI 367 PLS 1 Liver I 7.5 13.22 CTNNB1 p.I35S, c.104T> G 8.39 0.28 11.6 Present

INDI 368 INDI 368 PLS 1 Breast II 7.5 3.09 TP53 p.E224 *, c.670G> T 8.24 1.42 13.5 Not detected INDI 371 INDI 371 PLS 1A Lung III 7.5 1.47 KRAS p.G12V, c.35G> T 20.54 7.71 34.8 Present INDI 377 INDI 377 PLS 1 Colorectal II 7.5 14.70 BRAF p.V600E, c.1799T> A 3.35 0.05 2.2 Present INDI 380 INDI 380 PLS 1 Colorectal II 7.5 3.57 TP53 p.N288fs, c.862delA 4.95 0.32 3.5 Present INDI 394 INDI 394 PLS 1A Lung II 7.5 6.92 PIK3CA p.E542K, c.1624G> A 3.31 0.10 2.1 Present INDI 395 INDI 395 PLS 1A Lung I 7.5 4.23 TP53 p.T125T, c.375G> T (Exon End) 3, 12 0.27 3.5 Present INDI 397 INDI 397 PLS 1 Colorectal III 7.5 7.43 PIK3CA p.H1047Q, c.3141T> G 5.54 0.33 7.6 Present INDI 439 INDI 439 PLS 1 Colorectal III 7.5 12.99 BRAF p.F595L, c.1785T> G 14.83 0.91 36.2 Present INDI 445 INDI 445 PLS 1 Breast II 7.5 5.43 TP53 p.E171fs, c.513delGACGGAG 11, 87 0.69 11.6 Present INDI 447 INDI 447 PLS 1 Breast III 7.5 26.17 PIK3CA p.G1049R, c.3145G> C 209.33 21.72 1,751.3 Present INDI 457 INDI 457 PLS 1 Esophagus II 7.5 3 5.05 TP53 p.E221 *, c.661G> T 58.98 1.39 150.0 Present INDI 459 INDI 459 PLS 1 Breast II 7.5 41.57 PIK3CA p.H1047R, c.3140A> G 399, 50 60.44 7,738.7 Present INDI 461 INDI 461 PLS 1 Colorectal III 7.5 8.60 TP53 p.V172F, c.514G> T 4.56 0.40 10.6 Present INDI 462 INDI 462 PLS 1 Breast III 7.5 82.66 TP53 G.7578176C> A (Junction site) 165.23 62.32 15.866.1 Present INDI 463 INDI 463 PLS 1 Colorectal II 7.5 11.05 TP53 p.R273C, c.817C> T 3.59 2.43 82.6 Present INDI 464 INDI 464 PLS 1 Liver III 7.5 57.74 TP53 p.K132 *, c.394A> T 12.33 0.32 56.3 Present INDI 466 INDI 466 PLS 1 Colorectal II 7.5 18.81 TP53 p.T155fs, c.463insA 5.88 0.19 11.1 Present INDI 470 INDI 470 PLS 1 Colorectal III 7.5 8.40 TP53 p.Q100 *, c.298C > T 3.75 0.28 7.3 Present INDI 494 INDI 494 PLS 1 Lung III 7.5 4.62 TP53 p.G244A, c.731G> C 3.64 0.14 2.0 Present INDI 505 INDI 505 PLS 1 Lung II 7.5 34.64 TP53 p.R267P, c.800G> C 4.44 0.08 8.3 Present INDI 511 INDI 511 PLS 1 Colorectal III 7.5 4.19 TP53 p.S183fs, c .549insA 3.80 0.18 2.3 Present INDI 514 INDI 514 PLS 1 Stomach I 7.5 6.42 CTNNB1 p.T41A, c.121A> G 5.54 0.04 0.8 Not detected INDI 515 INDI 515 PLS 1 Colorectal III 7.5 15.27 TP53 p.K132N, c.396G> T 4.98 0.04 2.0 Not detected INDI 518 INDI 518 PLS 1 Colorectal III 7.5 9.71 TP53 p.N131fs, c.392delCAA 6.18 0.18 5 , 2 Present INDI 525 INDI 525 PLS 1 Colorectal I 7.5 31.87 TP53 p.C277F, c.830G> T 3.01 0.03 3.1 Not detected INDI 539 INDI 539 PLS 1 Colorectal II 7.5 8 , 83 PTEN p.D92A, c.275A> C 8.98 0.49 13.4 Present INDI 544 INDI 544 PLS 1 Colorectal III 7.5 2.48 TP53 p.G334R, c.1000G> C 185.06 52 , 81 403.4 Present INDI 555 INDI 555 PLS 1 Colorectal II 7.5 1.44 APC p.E1309fs, c.3927delAAAGA 8.85 0.78 3.5 Present INDI 558 INDI 558 PLS 1 Colorectal I 7.5 8 , 45 TP53 p.H368Y, c.1102C> T 3.95 0.11 2.8 Not detected INDI 567 INDI 567 PLS 1 Colorectal II 7.5 3.64 PIK3CA p.E545K, c.1633G> A 5.37 0.89 10.0 Present INDI 581 INDI 581 PLS 1 Colorectal II 7.5 6.91 TP53 G.7579311C> A (Junction site) 4.23 1.40 29.8 Present INDI 585 INDI 585 PLS 1 Esophagus III 7.5 14.36 PIK3CA p.E545K, c.1633G> A 5.64 0.25 10.9 Not detected INDI 601 INDI 601 PLS 1 Breast III 7.5 15.18 TP53 p. R213 *, c.637C> T 5.23 9.25 432.4 Present INDI 602 INDI 602 PLS 1 Colorectal II 7.5 3.35 TP53 p.E204 *, c.610G> T 3.05 0.33 3 , 4 Present INDI 614 INDI 614 PLS 1 Colorectal II 7.5 21.63 NRAS p.G12D, c.35G> A 3.65 0.12 7.8 Present INDI 623 INDI 623 PLS 1 Breast III 7.5 6, 71 TP53 p.R273P, c.818G> C 17.70 2.09 43.1 Present INDI 625 INDI 625 PLS 1 Colorectal II 7.5 2.73 KRAS p.G12S, c.34G> A 3.06 0.38 3 , 2 Present INDI 626 INDI 626 PLS 1 Colorectal II 7.5 11.58 KRAS p.G12D, c.35G> A 4.47 0.43 15.4 Present INDI 636 INDI 636 PLS 1 Liver III 7.5 27, 51 CTNNB1 p.T41A, c.121A> G 310.96 4.60 390.0 Present INDI 639 INDI 639 PLS 1 Liver II 7.5 18.63 TP53 p.R158L, c.473G> T 4.31 0, 18 10.2 Present INDI 645 INDI 645 PLS 1 Liver III 7.5 27.37 TP53 p.R249S, c.747G> T 16.21 8.37 706.0 Present INDI 648 INDI 648 PLS 1 Colorectal III 7.5 1.21 KRAS p.G12V, c.35G> T 28.57 2 4.26 90.6 Present INDI 663 INDI 663 PLS 1 Esophagus II 7.5 29.39 TP53 p.Q331 *, c.991C> T 100.09 1.39 125.9 Present INDI 665 INDI 665 PLS 1 Esophagus III 7.5 14.81 FGFR2 p.P253R, c.758C> G 51.21 0.35 16.1 Present INDI 670 INDI 670 PLS 1 Colorectal III 7.5 2.88 TP53 p.G117fs, c.350insGGAC 83, 06 14.28 126.9 Present INDI 678 INDI 678 PLS 1 Colorectal III 7.5 2.03 TP53 p.E224fs, c.670insG 13.00 1.10 6.9 Present INDI 687 INDI 687 PLS 1 Lung III 7, 5 11.57 KRAS p.G12V, c.35G> T 56.40 5.16 183.9 Present INDI 693 INDI 693 PLS 1 Lung III 7.5 8.08 TP53 p.H193Y, c.577C> T 92, 56 22.33 555.6 Present INDI 696 INDI 696 PLS 1 Lung II 7.5 1.71 HRAS p.G13V, c.38G> T 3.20 0.33 1.7 Present INDI 701 INDI 701 PLS 1 Colorectal II 7.5 1.54 BRAF p.V600E, c.1799T> A 3.67 0.17 0.8 Present INDI 702 INDI 702 PLS 1 Lung II 7.5 22.55 TP53 p.E298 *, c.892G> T 4.22 0.14 9.6 Present INDI 708 INDI 708 PLS 1 Colorectal III 7.5 7.20 BRAF p.L597R, c.1790T> G 6.15 0.34 7.4 Not detected INDI 710 INDI 710 PLS 1 Colorectal III 7.5 4.72 PIK3CA p.Q546R, c.1637A> G 3.28 0.16 2.4 Present INDI 711 INDI 711 PLS 1 Breast III 7.5 2.28 PIK3CA p.E545K, c.1633G> A 4.38 0.42 2.9 Present INDI 723 INDI 723 PLS 1 Ovary III 7.5 2.80 TP53 p.S215I, c.644G> T 3.13 0.69 6.0 Present INDI 751 INDI 751 PLS 1 Colorectal III 2 1.70 TP53 p.E336 *, c.1006G> T 3.10 0.88 4.6 Not detected INDI 753 INDI 753 PLS 1 Esophagus III 7.5 8.37 TP53 p.Y220C, c. 659A> G 4.61 3.46 89.1 Present INDI 764 INDI 764 PLS 1 Colorectal II 7.5 1.44 TP53 p.E51fs, c.151delG 8.55 1.78 7.9 Present INDI 769 INDI 769 PLS 1 Colorectal II 7.5 1.51 TP53 p.I251V, c.751A> G 50.33 50.29 234.0 Present INDI 770 INDI 770 PLS 1 Colorectal II 7.5 5.95 APC p.E1306 *, c .3916G> T 4.97 0.16 3.0 Present INDI 771 INDI 771 PLS 1 Liver II 7.5 23.39 CTNNB1 p.S45A, c.133T> G 3.36 0.02 1.7 Present INDI 777 INDI 777 PLS 1 Colorectal II 7.5 23.48 TP53 p.D42fs, c.124insGA 3.27 0.02 1.3 Not detected INDI 778 INDI 778 PLS 1 Liver II 7.5 16.38 CTNNB1 p.I35S, c.104T> G 4.39 0.08 4.2 Present and INDI 779 INDI 779 PLS 1 Colorectal II 7.5 1.88 TP53 p.E11Q, c.31G> C 99.20 50.22 291.5 Present INDI 782 INDI 782 PLS 1 Colorectal III 7.5 15.27 BRAF p.V600E, c.1799T> A 36.75 2.88 135.7 Present INDI 786 INDI 786 PLS 1 Liver I 7.5 3.23 TP53 p.C135F, c.404G> T 7.05 1.16 11 , 6 Present

INDI 798 INDI 798 PLS 1 Ovary II 7.5 22.29 TP53 p.Y163C, c.488A> G 52.92 7.31 502.2 Present INDI 800 INDI 800 PLS 1 Liver II 7.5 3.60 CTNNB1 p .T41A, c.121A> G 5.78 0.13 1.4 Present INDI 802 INDI 802 PLS 1 Pancreas II 7.5 15.09 CDKN2A p.E88 *, c.262G> T 3.80 0.09 4 , 0 Present INDI 812 INDI 812 PLS 1 Ovary II 7.5 3.11 CTNNB1 p.T41A, c.121A> G 77.58 3.18 30.5 Present INDI 818 INDI 818 PLS 1 Ovary III 7.5 6, 53 TP53 p.G154fs, c.460delG 41.01 2.83 56.9 Present INDI 819 INDI 819 PLS 1 Breast III 7.5 1.52 AKT1 p.E17K, c.49G> A 8.34 6.06 28 , 4 Present INDI 838 INDI 838 PLS 1 Colorectal III 7.5 4.20 APC p.E1306 *, c.3916G> T 3.44 0.11 1.5 Present INDI 850 INDI 850 PLS 1 Esophagus II 7.5 8 , 01 TP53 p.H193P, c.578A> C 124.89 21.37 527.0 Present INDI 859 INDI 859 PLS 1 Esophagus II 7.5 15.56 TP53 p.Y234N, c.700T> A 7.28 0 , 59 28.3 Present INDI 872 INDI 872 PLS 1 Mama III 7.5 5.17 PIK3CA p.H1047L, c.3140A> T 13.66 0.89 14.2 Present INDI 884 INDI 884 PLS 1 Mama II 7, 5 17.62 TP53 p.Y220C, c.659A> G 12.84 11.7 7 638.8 Present INDI 905 INDI 905 PLS 1 Esophagus II 7.5 19.99 TP53 p.K132R, c.395A> G 8.45 0.61 37.4 Not detected INDI 928 INDI 928 PLS 1 Stomach II 7, 5 6.23 TP53 p.F338fs, c.1014delC 4.08 0.16 3.0 Present INDI 932 INDI 932 PLS 1 Ovary III 7.5 5.40 TP53 p.L252fs, c.754delTCC 69.94 10.81 179.8 Present

[1240] * Coordinates refer to the hg19 release of the human reference genome (Genome Reference Consortium GRCh37, Feb 2009), Table 6. Results of cancer type localization for the 617 cancer patients identified by CancerSEEK. More Second Type Breast Colorectal RF RF LiverRF LungRF OvarianRF Pancreas GI Upper RF Probable Most Probable PadienteID # SampleID # Tumor Probabilid, Probabilid, ProbabilidProbabilidProbabilid Probabilid, Probabilid, Prediction prediction CRC 456 CRC 456 PLS 1 Colorectal 0.08 0.034 0.088 0.088 0.088 0.088 0.088 Colorectal Liver CRC 457 CRC 457 PLS 1 Colorectal 0.136 0.574 0.032 0.084 0.062 0.016 0.096 Colorectal Breast CRC 463 CRC 463 PLS 1 Colorectal 0.29 0.382 0.008 0.122 0.124 0.016 0.058 Colorectal Breast CRC 467 CRC 467 PLS 1 Colorectal 0.098 0.586 0.032 0.016 0.054 0.114 Colorectal upper GI CRC 468 CRC 468 PLS 1 Colorectal 0.102 0.448 0.09 0.194 0.058 0.028 0.08 Colorectal Pulm CRC 470 CRC 470 PLS 1 Colorectal 0.424 0.338 0.02 0.068 0.074 0.03 0.046 Colorectal CRC 471 CRC 471 PLS 1 Colorectal 0.264 0.34 0.03 0.186 0.076 0.048 0.056 Colorectal Breast CRC 473 CRC 473 PLS 1 Colorectal 0.056 0.526 0.102 0.094 0.016 0.026 0.18 Colorectal upper GI CRC 476 CRC 476 PLS 1 C olorectal 0.046 0.646 0.03 0.134 0.044 0.034 0.066 Colorectal Pulm CRC 477 CRC 477 PLS 1 Colorectal 0.106 0.424 0.058 0.186 0.118 0.03 0.078 Colorectal Pulm CRC 479 CRC 479 PLS 1 Colorectal 0.092 0.46 0.092 0.154 0.01 0.066 0.126 Colorectal CRC 480 CRC 480 PLS 1 Colorectal 0.052 0.66 0.066 0.104 0.018 0.01 0.09 Colorectal Pulm CRC 481 CRC 481 PLS 1 Colorectal 0.138 0.50 0.052 0.078 0.056 0.074 0.096 Colorectal Breast CRC 484 CRC 484 PLS 1 Colorectal 0.09 0, 59 0.028 0.134 0.014 0.064 0.08 Colorectal Pulm CRC 486 CRC 486 PLS 1 Colorectal 0.282 0.396 0.024 0.13 0.094 0.01 0.064 Colorectal Breast CRC 488 CRC 488 PLS 1 Colorectal 0.042 0.624 0.032 0.164 0.058 0.022 0.058 Colorectal Pulm CRC 489 CRC 489 PLS 1 Colorectal 0.18 0.45 0.028 0.096 0.078 0.102 0.066 Colorectal Breast CRC 492 CRC 492 PLS 1 Colorectal 0.07 0.26 0.04 0.292 0.198 0.028 0.112 Colorectal Lung CRC 497 CRC 497 PLS 1 Colorectal 0.032 0.45 0.172 0.042 0.036 0.118 0.15 Colorectal Liver CRC 498 CRC 498 PLS 1 Colorectal 0.08 4 0.432 0.02 0.164 0.052 0.114 0.134 Colorectal Pulm CRC 503 CRC 503 PLS 1 Colorectal 0.044 0.404 0.042 0.3 0.03 0.042 0.138 Colorectal Pulm CRC 504 CRC 504 PLS 1 Colorectal 0.038 0.568 0.052 0.04 0.094 0.052 0.156 Colorectal upper GI CRC 512 CRC 512 PLS 1 Colorectal 0.28 0.372 0.062 0.184 0.044 0.022 0.036 Colorectal Breast CRC 513 CRC 513 PLS 1 Colorectal 0.052 0.326 0.16 0.092 0.016 0.18 0.174 Colorectal Pancreas CRC 514 CRC 514 PLS 1 Colorectal 0.048 0.402 0.094 0.282 0.024 0.064 0.086 Colorectal Pulm CRC 518 CRC 518 PLS 1 Colorectal 0.092 0.47 0.068 0.108 0.08 0.024 0.158 Colorectal upper GI CRC 520 CRC 520 PLS 1 Colorectal 0.144 0.278 0.032 0.314 0.158 0.014 0.06 Colorectal Lung CRC 522 CRC 522 PLS 1 Colorectal 0.088 0.54 0.018 0.188 0.02 0.042 0.104 Colorectal Pulm CRC 524 CRC 524 PLS 1 Colorectal 0.054 0.484 0.08 0.15 0.014 0.028 0.19 Colorectal upper GI CRC 525 CRC 525 PLS 1 Colorectal 0.086 0.338 0.068 0.316 0.036 0.032 0.124 Colorectal Pulm CRC 527 CRC 527 PLS 1 Col rectal 0.14 0.318 0.044 0.244 0.018 0.098 0.158 Colorectal Pulm CRC 530 CRC 530 PLS 1 Colorectal 0.038 0.354 0.016 0.47 0.012 0.038 0.072 Colorectal Lung

INDI 001 INDI 001 PLS 1 Lung 0.088 0.388 0.018 0.16 0.256 0.066 0.034 Colorectal Ovary INDI 003 INDI 003 PLS 1 Lung 0.028 0.218 0.07 0.242 0.238 0.122 0.062 Lung Ovary INDI 004 INDI 004 PLS 1 Lung 0.036 0.388 0.054 0.192 0.046 0.306 Colorectal Pancreas INDI 009 INDI 009 PLS 1 Lung 0.066 0.204 0.03 0.566 0.046 0.016 0.072 Colorectal Lung INDI 010 INDI 010 PLS 1 Lung 0.022 0.352 0.03 0.182 0.088 0.106 0.22 Colorectal GI upper INDI 012 INDI 012 PLS 1 Lung 0.006 0.166 0.026 0.676 0.058 0.018 0.05 Colorectal Lung INDI 014 INDI 014 PLS 1 Lung 0.092 0.444 0.06 0.24 0.022 0.066 0.036 Colorectal Lung INDI 016 INDI 016 PLS 1 Lung 0.074 0.25 0.006 0.418 0.142 0.094 0.016 Colorectal Lobe INDI 022 INDI 022 PLS 1 Lung 0.166 0.254 0.028 0.438 0.094 0.008 0.012 Colorectal Lung INDI 023 INDI 023 PLS 1 Lung 0.076 0.3 0.014 0.47 0.02 0.038 0.082 Colorectal Lung INDI 025 INDI 025 PLS 1 Lung 0.092 0.326 0.024 0.308 0.212 0.032 0.006 Colorectal Pulm INDI 027 INDI 027 PLS 1 Colorectal 0.056 0.476 0.036 0.21 0.168 0.018 0.036 Colorectal Pulm INDI 030 INDI 030 PLS 1 Colorectal 0.132 0.424 0.026 0.048 0.11 0.226 0.034 Colorectal Pancreas INDI 034 INDI 034 PLS 1 Colorectal 0.044 0.43 0.032 0.116 0.144 0.162 Pancreas INDI 039 INDI 039 PLS 1 Colorectal 0.136 0.496 0.056 0.094 0.11 0.044 0.064 Colorectal Breast INDI 043 INDI 043 PLS 1 Colorectal 0.08 0.578 0.04 0.116 0.086 0.02 0.08 Colorectal Pulm INDI 044 INDI 044 PLS 1 Colorectal 0.29 0.318 0.054 0.034 0.068 0.002 0.234 Colorectal Breast INDI 045 INDI 045 PLS 1 Colorectal 0.09 0.532 0.082 0.028 0.01 0.008 0.25 Colorectal upper GI INDI 046 INDI 046 PLS 1 Colorectal 0.03 0.336 0.072 0.048 0.004 0.014 0.496 Upper GI Colorectal INDI 047 INDI 047 PLS 1 Colorectal 0.052 0.438 0.04 0.076 0.006 0.01 0.378 Colorectal upper GI INDI 051 INDI 051 PLS 1 Breast 0.046 0.322 0.036 0.104 0.412 0.06 0.02 Colorectal Ovary INDI 052 INDI 052 PLS 1 Breast 0.082 0.416 0.032 0.262 0.136 0.06 0.012 Color rectal Pulm INDI 059 INDI 059 PLS 1 Breast 0.466 0.17 0.058 0.152 0.07 0.054 0.03 Colorectal Breast INDI 060 INDI 060 PLS 1 Breast 0.528 0.262 0.02 0.088 0.016 0.016 0.07 Colorectal Breast INDI 061 INDI 061 PLS 1 Breast 0.596 0.268 0.016 0.046 0.014 0.01 0.05 Breast Colorectal INDI 064 INDI 064 PLS 1 Breast 0.564 0.256 0.054 0.046 0.034 0.008 0.038 Colorectal Breast INDI 067 INDI 067 PLS 1 Breast 0.568 0.212 0.03 0.066 0.02 0.01 0.094 Colorectal Breast INDI 069 INDI 069 PLS 1 Breast 0.43 0.356 0.068 0.064 0.006 0.016 0.06 Colorectal Breast INDI 070 INDI 070 PLS 1 Breast 0.33 0.198 0.164 0.116 0.076 0.04 0.076 Colorectal Breast INDI 071 INDI 071 PLS 1 Breast 0.376 0.418 0.04 0.038 0.028 0.02 0.08 Colorectal Breast INDI 072 INDI 072 PLS 1 Breast 0.508 0.26 0.016 0.146 0.024 0.01 0.036 Colorectal Breast INDI 074 INDI 074 PLS 1 Pancreas 0.038 0.3 0.042 0.132 0.052 0.306 0 , 13 Colorectal Pancreas INDI 075 INDI 075 PLS 1 Ovary 0.214 0.298 0.012 0.166 0.222 0.066 0.022 Colorectal Ovary INDI 076 INDI 076 PLS 1 Esophagus 0.074 0.34 4 0.152 0.052 0.002 0.02 0.356 Upper GI Colorectal INDI 077 INDI 077 PLS 1 Liver 0.054 0.194 0.33 0.056 0.028 0.054 0.284 Liver Upper GI INDI 078 INDI 078 PLS 1 Liver 0.058 0.428 0.168 0.064 0.002 0.014 0.266 Colorectal GI Upper INDI 079 INDI 079 PLS 1 Liver 0.012 0.272 0.262 0.014 0.01 0.02 0.41 Upper GI Colorectal INDI 080 INDI 080 PLS 1 Liver 0.062 0.416 0.064 0.252 0.046 0.118 0.042 Colorectal Pulm INDI 081 INDI 081 PLS 1 Colorectal 0.02 0.32 0.092 0.404 0.038 0.066 0.06 Colorectal Lung INDI 083 INDI 083 PLS 1 Stomach 0.01 0.268 0.112 0.022 0 0.006 0.582 Upper GI Colorectal INDI 084 INDI 084 PLS 1 Stomach 0.072 0.342 0.268 0.06 0.044 0.008 0.206 Colorectal Liver INDI 085 INDI 085 PLS 1 Stomach 0.036 0.49 0.036 0.178 0.022 0.036 0.202 Colorectal upper GI INDI 086 INDI 086 PLS 1 Stomach 0.036 0.352 0.038 0.016 0 0.054 0.504 upper GI Colorectal INDI 087 INDI 087 PLS 1 Stomach 0.308 0.334 0.034 0.114 0.038 0.02 0.152 Colorectal breast INDI 089 INDI 089 P LS 1 Colorectal 0.102 0.606 0.11 0.034 0.024 0.012 0.112 Colorectal upper GI INDI 090 INDI 090 PLS 1 Colorectal 0.072 0.446 0.08 0.064 0.01 0.062 0.266 Colorectal GI higher INDI 093 INDI 093 PLS 1 Colorectal 0.044 0.446 0.054 0.188 0.038 0.048 0.222 Colorectal GI superior INDI 094 INDI 094 PLS 1 Colorectal 0.016 0.566 0.05 0.02 0.02 0.046 0.282 Colorectal GI superior INDI 097 INDI 097 PLS 1 Colorectal 0.104 0.404 0.096 0.102 0.042 0.178 0.074 Colorectal Pancreas INDI 100 IND16 100 PLS 1 Colorectal 0.57 0.086 0.044 0.024 0.062 0.098 Colorectal Breast INDI 102 INDI 102 PLS 1 Colorectal 0.062 0.458 0.072 0.132 0.018 0.052 0.206 Colorectal upper GI INDI 104 INDI 104 PLS 1 Stomach 0.024 0.326 0.422 0.088 0.01 0.026 0.104 Colorectal Liver INDI 107 INDI 107 PLS 1 Colorectal 0.02 0.584 0.068 0.058 0.008 0.01 0.252 Colorectal upper GI INDI 108 INDI 108 PLS 1 Colorectal 0.226 0.536 0.04 0.068 0.038 0.008 0.084 Colorectal Breast INDI 109 INDI 109 PLS 1 Colorectal 0.104 0.29 0.27 0, 0 28 0.026 0.012 0.27 Colorectal Liver INDI 110 INDI 110 PLS 1 Stomach 0.016 0.574 0.046 0.056 0.006 0.008 0.294 Colorectal upper GI INDI 111 INDI 111 PLS 1 Stomach 0.046 0.402 0.12 0.094 0.01 0 0.328 Colorectal GI upper

INDI 112 INDI 112 PLS 1 Esophagus 0.038 0.266 0.116 0.054 0.036 0.022 0.468 Upper GI Colorectal INDI 113 INDI 113 PLS 1 Colorectal 0.022 0.442 0.056 0.058 0.022 0.048 0.352 Colorectal upper GI INDI 116 INDI 116 PLS 1 Stomach 0.026 0.326 0.096 0.114 0.018 0.018 0.374I Colorectal INDI 119 INDI 119 PLS 1 Esophagus 0.076 0.196 0.09 0.05 0.23 0.03 0.328 upper GI Ovary INDI 120 INDI 120 PLS 1 Stomach 0.09 0.346 0.04 0.014 0.02 0.012 0.478 upper GI Colorectal INDI 121 INDI 121 PLS 1 Stomach 0.042 0.512 0.058 0.106 0.008 0.01 0.264 Colorectal upper GI INDI 122 INDI 122 PLS 1 Colorectal 0.126 0.228 0.374 0.034 0.042 0.01 0.186 Liver Colorectal INDI 123 INDI 123 PLS 1 Colorectal 0.106 0.54 0.056 0.018 0.026 0.036 0.218 Colorectal upper GI INDI 124 INDI 124 PLS 1 Colorectal 0.014 0.31 0.042 0.01 0.002 0.012 0.61 Upper GI Colorectal INDI 125 INDI 125 PLS 1 Esophagus 0.018 0.3 0.086 0.018 0.006 0.012 0.56 Upper GI Colorectal INDI 126 INDI 126 PLS 1 Stomach 0.01 0, 256 0.062 0.018 0.006 0.004 0.644 upper GI Colorectal INDI 127 INDI 127 PLS 1 Colorectal 0.01 0.34 0.04 0.018 0.002 0.002 0.588 upper GI Colorectal INDI 128 INDI 128 PLS 1 Colorectal 0.134 0.424 0.102 0.084 0.058 0.084 0.114 Colorectal Breast INDI 129 INDI 129 PLS 1 Liver 0.144 0.296 0.366 0.02 0.014 0.026 0.154 Liver Colorectal INDI 130 INDI 130 PLS 1 Stomach 0.012 0.298 0.07 0.03 0.01 0.01 0.57 Upper GI Colorectal INDI 131 INDI 131 PLS 1 Stomach 0.024 0.286 0.104 0.03 0.018 0.016 0.522 Upper GI Colorectal INDI 132 INDI 132 PLS 1 Esophagus 0.014 0.4 0.032 0.03 0.004 0.004 0.516 Upper GI Colorectal INDI 133 INDI 133 PLS 1 Stomach 0.01 0.152 0.128 0.014 0.004 0.016 0.676 GI Upper Colorectal INDI 134 INDI 134 PLS 1 Stomach 0.016 0.31 0.032 0.004 0.002 0.008 0.628 Upper GI Colorectal INDI 135 INDI 135 PLS 1 Stomach 0.018 0.252 0.114 0.034 0.024 0.02 0.538 Upper GI Colorectal INDI 136 INDI 136 PLS 1 Colorectal 0.08 0.424 0.06 0.018 0.012 0.006 0.4 Colorectal GI s uperior INDI 137 INDI 137 PLS 1 Stomach 0.012 0.272 0.206 0.052 0.004 0.016 0.438 Upper GI Colorectal INDI 139 INDI 139 PLS 1 Stomach 0.098 0.384 0.066 0.036 0.034 0.038 0.344 Colorectal GI upper INDI 140 INDI 140 PLS 1 Stomach 0.014 0.288 0.102 0.008 0.004 0.004 0, 57 Upper GI Colorectal INDI 141 INDI 141 PLS 1 Stomach 0.02 0.386 0.076 0.034 0.008 0.014 0.462 Upper GI Colorectal INDI 143 INDI 143 PLS 1 Colorectal 0.006 0.232 0.272 0.046 0.01 0.004 0.43 Upper GI Liver INDI 144 INDI 144 PLS 1 Colorectal 0.05 0.384 0.064 0.046 0.028 0.008 0.42 upper GI Colorectal INDI 145 INDI 145 PLS 1 Colorectal 0.064 0.466 0.128 0.07 0.028 0.036 0.208 Colorectal upper GI INDI 146 INDI 146 PLS 1 Colorectal 0.084 0.472 0.102 0.114 0.016 0.016 0.166 Colorectal GI upper INDI 147 INDI 147 PLS 1 Stomach 0.264 0.42 0.064 0.024 0.016 0.038 0.174 Colorectal Breast INDI 148 INDI 148 PLS 1 Colorectal 0.016 0.296 0.106 0.01 0 0.032 0.54 upper GI Colorectal INDI 150 INDI 150 PLS 1 Col rectal 0.11 0.274 0.074 0.014 0.03 0.02 0.478 upper GI Colorectal INDI 151 INDI 151 PLS 1 Stomach 0.084 0.438 0.13 0.018 0.006 0.016 0.308 Colorectal upper GI INDI 152 INDI 152 PLS 1 Colorectal 0.048 0.412 0.16 0.032 0.024 0.012 0.312 Colorectal upper GI INDI 156 INDI 156 PLS 1 Colorectal 0.03 0.288 0.144 0.018 0.008 0.03 0.482 upper GI Colorectal INDI 158 INDI 158 PLS 1 Colorectal 0.084 0.568 0.094 0.044 0.042 0.02 0.148 Colorectal GI upper INDI 160 INDI 160 PLS 1 Colorectal 0.012 0.702 0.022 0.05 0.002 0.014 0.198 Colorectal upper GI INDI 161 INDI 161 PLS 1 Stomach 0.03 0.616 0.038 0.05 0.006 0.006 0.254 Colorectal GI upper INDI 162 INDI 162 PLS 1 Stomach 0.08 0.482 0.146 0.062 0.018 0 , 03 0.182 Colorectal upper GI INDI 163 INDI 163 PLS 1 Colorectal 0.054 0.43 0.118 0.112 0.038 0.022 0.176 Colorectal upper GI INDI 165 INDI 165 PLS 1 Colorectal 0.018 0.592 0.034 0.092 0.012 0.018 0.234 Colorectal GI upper INDI 167 INDI 167 PLS 1 Stomach 0.022 0.38 0.0 62 0.008 0.016 0.004 0.508 Upper GI Colorectal INDI 168 INDI 168 PLS 1 Stomach 0.008 0.33 0.086 0.006 0.02 0.146 0.404 Upper GI Colorectal INDI 169 INDI 169 PLS 1 Colorectal 0.202 0.468 0.042 0.018 0.034 0.008 0.228 Colorectal GI upper INDI 170 INDI 170 PLS 1 Colorectal 0.068 0.422 0.05 0.01 0.006 0.006 0.438 Upper GI Colorectal INDI 171 INDI 171 PLS 1 Stomach 0.02 0.346 0.166 0.012 0.014 0.052 0.39 Upper GI Colorectal INDI 172 INDI 172 PLS 1 Stomach 0.138 0.282 0.166 0.008 0 , 03 0.136 0.25 Colorectal upper GI INDI 173 INDI 173 PLS 1 Stomach 0.012 0.376 0.064 0.008 0.014 0.006 0.52 upper GI Colorectal INDI 175 INDI 175 PLS 1 Colorectal 0.012 0.374 0.072 0.014 0.026 0.046 0.456 Upper GI Colorectal INDI 176 INDI 176 PLS 1 Stomach 0.018 0.344 0.056 0.02 0.012 0.016 0.534 Upper GI Colorectal INDI 177 INDI 177 PLS 1 Liver 0.024 0.212 0.554 0.01 0.006 0.016 0.178 Colorectal Liver INDI 178 INDI 178 PLS 1 Colorectal 0.028 0.408 0.088 0.022 0.008 0.036 0.41 GI s uperior Colorectal INDI 179 INDI 179 PLS 1 Colorectal 0.03 0.33 0.144 0.024 0.002 0.018 0.452 upper GI Colorectal INDI 180 INDI 180 PLS 1 Colorectal 0.062 0.462 0.108 0.082 0.086 0.018 0.182 Colorectal upper GI INDI 182 INDI 182 PLS 1 Liver 0.01 0.206 0.332 0.012 0.004 0.008 0.428 Upper GI Liver INDI 183 INDI 183 PLS 1 Stomach 0.08 0.50 0.052 0.034 0.022 0.016 0.288 Colorectal upper GI INDI 184 INDI 184 PLS 1 Esophagus 0.014 0.24 0.048 0.012 0.022 0.008 0.656 Upper GI Colorectal

INDI 185 INDI 185 PLS 1 Stomach 0.028 0.362 0.054 0.036 0.076 0.03 0.414 Upper GI Colorectal INDI 186 INDI 186 PLS 1 Stomach 0.008 0.284 0.202 0 0.006 0.02 0.48 Upper GI Colorectal INDI 187 INDI 187 PLS 1 Stomach 0.12 0.566 0.042 0.038 0.044 0.002 0.188 Colorectal upper GI INDI 188 INDI 188 PLS 1 Stomach 0.056 0.368 0.09 0.02 0.048 0.02 0.398 upper GI Colorectal INDI 189 INDI 189 PLS 1 Esophagus 0.038 0.448 0.09 0.02 0.04 0.004 0.36 Colorectal upper GI INDI 190 INDI 190 PLS 1 Liver 0.016 0.368 0.108 0.016 0.008 0.018 0.466 upper GI Colorectal INDI 191 INDI 191 PLS 1 Colorectal 0.078 0.494 0.09 0.014 0.016 0.012 0.296 Colorectal GI upper INDI 192 INDI 192 PLS 1 Colorectal 0.028 0.366 0.126 0.028 0.026 0.176 0.25 Colorectal upper GI INDI 194 INDI 194 PLS 1 Colorectal 0.172 0.412 0.084 0.048 0.062 0.046 0.176 Colorectal GI upper INDI 195 INDI 195 PLS 1 Colorectal 0.208 0.458 0.05 0.018 0.008 0.012 0.246 Colorectal GI upper INDI 19 INDI 196 PLS 1 Correct l 0.006 0.268 0.112 0.006 0 0 0.608 upper GI Colorectal INDI 197 INDI 197 PLS 1 Colorectal 0.072 0.402 0.128 0.022 0.028 0.038 0.31 Colorectal upper GI INDI 198 INDI 198 PLS 1 Stomach 0.026 0.174 0.55 0.014 0.01 0.028 0.198 Liver GI upper INDI 199 INDI 199 PLS 1 Stomach 0.014 0.422 0.082 0.008 0.002 0.016 0.456 upper GI Colorectal INDI 200 INDI 200 PLS 1 Breast 0.32 0.384 0.076 0.01 0.018 0.028 0.164 Colorectal Breast INDI 202 INDI 202 PLS 1 Colorectal 0.042 0.482 0.056 0.052 0.002 0.018 0.348 Colorectal upper GI INDI 203 INDI 203 PLS 1 Stomach 0.032 0.428 0.068 0.028 0.01 0.09 0.344 Colorectal GI upper INDI 205 INDI 205 PLS 1 Liver 0.026 0.28 0.242 0.034 0.02 0.236 0.162 Colorectal Liver INDI 206 INDI 206 PLS 1 Stomach 0.018 0.316 0.224 0.014 0.01 0.016 0.402 upper GI Colorectal INDI 207 INDI 207 PLS 1 Colorectal 0.158 0.622 0.02 0.034 0.032 0.016 0.118 Colorectal Breast INDI 208 INDI 208 PLS 1 Liver 0.036 0.11 0.634 0.014 0.014 0.082 0, 11 INDI Colorectal Liver 209 INDI 209 PLS 1 Colorectal 0.112 0.332 0.248 0.024 0.042 0.06 0.182 Colorectal Liver INDI 210 INDI 210 PLS 1 Stomach 0.076 0.446 0.078 0.13 0.016 0.044 0.21 Colorectal upper GI INDI 211 INDI 211 PLS 1 Colorectal 0.01 0.476 0.074 0.036 0.022 0.008 0.374 Colorectal upper GI INDI 212 INDI 212 PLS 1 Colorectal 0.066 0.61 0.08 0.054 0.042 0.004 0.144 Colorectal GI upper INDI 213 INDI 213 PLS 1 Colorectal 0.234 0.538 0.038 0.064 0.024 0.022 0.08 Colorectal Breast INDI 214 INDI 214 PLS 1 Stomach 0.06 0.6 0.08 0.084 0.006 0.044 0.126 Colorectal upper GI INDI 215 INDI 215 PLS 1 Liver 0.292 0.26 0.122 0.126 0.102 0.024 0.064 Colorectal breast INDI 216 INDI 216 PLS 1 Colorectal 0.098 0.662 0.034 0.092 0.006 0.036 0.072 Colorectal Breast INDI 218 INDI 218 PLS 1 Colorectal 0.054 0.564 0.06 0.15 0.034 0.028 0.11 Colorectal Pulm INDI 219 INDI 219 PLS 1 Liver 0.036 0.492 0.124 0.122 0.008 0.026 0.152 Colorectal Pulm INDI 220 PLS 1 Lung 0, 12 0.206 0.004 0.378 0.246 0.014 0.032 Lung Ovary INDI 221 INDI 221 PLS 1 Colorectal 0.034 0.17 0.032 0.606 0.098 0.01 0.05 Colorectal Lung INDI 222 INDI 222 PLS 1 Colorectal 0.038 0.434 0.026 0.204 0.134 0.084 0.08 Colorectal Pulm INDI 224 INDI 224 PLS 1 Lung 0 , 04 0.316 0.076 0.244 0.116 0.018 0.21 Colorectal Pulm INDI 225 INDI 225 PLS 1 Colorectal 0.052 0.252 0.056 0.044 0.028 0.478 0.09 Colorectal Pancreas INDI 227 INDI 227 PLS 1 Lung 0.032 0.446 0.018 0.35 0.03 0.018 0.106 Colorectal Pulm INDI 229 INDI 229 PLS 1 Colorectal 0.04 0.388 0.056 0.188 0.054 0.05 0.224 Colorectal GI higher INDI 230 INDI 230 PLS 1 Colorectal 0.11 0.238 0.032 0.47 0.026 0.052 0.072 Colorectal lung INDI 231 INDI 231 PLS 1 Lung 0, 07 0.246 0.036 0.372 0.234 0.016 0.026 Colorectal Lung INDI 233 INDI 233 PLS 1 Colorectal 0.082 0.302 0.022 0.276 0.174 0.06 0.084 Colorectal Pulm INDI 238 INDI 238 PLS 1 Liver 0.172 0.144 0.048 0.074 0.068 0.312 0.08 Pancreas Breast INDI 239 INDI 239 PLS 1 Lung 0.092 0.356 0.156 0.146 0.036 0.136 0.078 Colorectal Liver INDI 243 INDI 243 PLS 1 Colorectal 0.06 0.648 0.022 0.088 0.048 0.108 0.026 Colorectal Pancreas INDI 244 INDI 244 PLS 1 Colorectal 0.02 0.69 0.036 0.082 0.032 0.112 0.028 Colorectal Pancreas INDI 245 PLS.468 Colorectal 1 0.11 0.126 0.176 0.02 Colorectal Pancreas INDI 247 INDI 247 PLS 1 Lung 0.244 0.358 0.06 0.156 0.06 0.076 0.046 Colorectal Breast INDI 250 INDI 250 PLS 1 Colorectal 0.036 0.53 0.024 0.244 0.02 0.084 0.062 Colorectal Pulm INDI 251 INDI 251 PLS 1 Colorectal 0.032 0.78 0.02 0.072 0.006 0.056 0.034 Colorectal Pulm INDI 252 INDI 252 PLS 1 Lung 0.348 0.188 0.07 0.178 0.082 0.048 0.086 Colorectal Breast INDI 255 INDI 255 PLS 1 Colorectal 0.292 0.324 0.018 0.128 0.168 0.062 0.008 Colorectal Breast INDI 256 INDI 256 PLS 1 Colorectal 0.178 0.394 0.062 0.09 0.098 0.15 0.028 Colorectal Breast INDI 257 INDI 257 PLS 1 Lung 0.078 0.412 0.082 0.134 0.024 0.148 0.126 Colorectal Pancreas INDI 259 INDI 259 PLS 1 0.016 0.536 0.1886 0, 026 0.084 0.082 Colorectal lung INDI 266 INDI 266 PLS 1 Lung 0.072 0.154 0.028 0.622 0.024 0.016 0.084 Colorectal lung INDI 268 INDI 268 PLS 1 Colorectal 0.13 0.556 0.012 0.12 0.03 0.13 0.022 Colorectal breast INDI 273 INDI 273 PLS 1 Lung 0.302 0.212 0.038 0.23 0.07 0.054 0.094 Breast Pulm

INDI 276 INDI 276 PLS 1 Liver 0.044 0.13 0.474 0.064 0.016 0.184 0.088 Liver Pancreas INDI 278 INDI 278 PLS 1 Ovary 0.142 0.19 0.01 0.034 0.464 0.148 0.012 Colorectal Ovary INDI 279 INDS 279 PLS 1 Colorectal 0.164 0.426 0.006 0.166 0.166 0.086 0.016 Colorectal Breast INDI 284 INDI 284 PLS 1 Colorectal 0.088 0.5 0.054 0.214 0.04 0.05 0.054 Colorectal Pulm INDI 285 INDI 285 PLS 1 Lung 0.234 0.2 0.038 0.288 0.174 0.042 0.024 Breast Lung INDI 287 INDI 287 PLS 1 Breast 0.488 0.286 0.034 0.166 0.044 0.084 0.038 Colorectal Breast INDI 288 INDI 288 PLS 1 Lung 0.15 0.2 0.092 0.046 0.286 0.146 0.08 Colorectal Ovary INDI 290 INDI 290 PLS 1 Colorectal 0.07 0.564 0.018 0.11 0.036 0.168 0.054 Colorectal Pancreas INDI 293 INDI 293 PLS 1 Colorectal 0.166 0.48 0.018 0.19 0.042 0.086 0.018 Colorectal Pulm INDI 295 INDI 295 PLS 1 Colorectal 0.22 0.592 0.048 0.044 0.028 0.012 0.056 Colorectal Breast INDI 296 INDI 296 PLS 1 Colorectal 0.078 0.72 , 09 0.056 0.012 0.01 0.052 Colorectal Liver INDI 297 INDI 297 PLS 1 Breast 0.322 0.358 0.022 0.14 0.102 0.006 0.05 Colorectal Breast INDI 298 INDI 298 PLS 1 Colorectal 0.168 0.714 0.02 0.03 0.016 0.006 0.046 Colorectal Breast INDI 299 INDI 299 PLS 1 Colorectal 0.034 0.57 0.088 0 , 02 0.016 0.054 0.218 Colorectal upper GI INDI 300 INDI 300 PLS 1 Breast 0.644 0.238 0.02 0.048 0.012 0.008 0.03 Colorectal breast INDI 301 INDI 301 PLS 1 Breast 0.55 0.188 0.03 0.108 0.042 0.014 0.068 Colorectal breast INDI 302 INDI 302 PLS 1 Colorectal 0.058 0.588 0.174 0.042 0.002 0.012 0.124 Colorectal Liver INDI 303 INDI 303 PLS 1 Colorectal 0.152 0.472 0.094 0.144 0.004 0.05 0.084 Colorectal Breast INDI 304 INDI 304 PLS 1 Colorectal 0.024 0.286 0.246 0.02 GI upper 305 INDI 305 PLS 1 Colorectal 0.106 0.406 0.158 0.058 0.016 0.054 0.202 Colorectal upper GI INDI 306 INDI 306 PLS 1 Colorectal 0.038 0.552 0.084 0.09 0.038 0.036 0.162 Colorectal GI upper INDI 307 INDI 307 PLS 1 Colorectal 0.066 0.04 0.384 0.104 29 Colorectal Upper GI INDI 308 INDI 308 PLS 1 Liver 0.04 0.238 0.358 0.042 0.008 0.016 0.298 Liver Upper GI INDI 309 INDI 309 PLS 1 Colorectal 0.064 0.474 0.078 0.018 0.002 0.018 0.346 Colorectal Upper GI INDI 310 INDI 310 PLS 1 Colorectal 0.01 0.222 0.172 0.024 0.004 0.002 0.566 upper GI Colorectal INDI 311 INDI 311 PLS 1 Colorectal 0.144 0.426 0.034 0.016 0.006 0.014 0.36 Colorectal upper GI INDI 313 INDI 313 PLS 1 Esophagus 0.042 0.288 0.218 0.048 0.008 0.038 0.348 Upper GI Colorectal INDI 314 INDI 314 INDI 314 INDI 314 0.204 0.444 0.056 0.034 0.018 0.004 0.22 Colorectal upper GI INDI 318 INDI 318 PLS 1 Stomach 0.048 0.318 0.082 0.206 0.082 0.156 0.108 Colorectal Pulm INDI 320 INDI 320 PLS 1 Colorectal 0.036 0.45 0.086 0.056 0.014 0.228 0.13 Colorectal Pancreas IND2 322 INDI 322 PLS 1 Colorectal 0.054 0.482 0.044 0.102 0.016 0.23 0.072 Colorectal pancreas INDI 324 INDI 324 PLS 1 Lung 0.218 0.226 0.01 0.406 0.062 0.03 0.048 Colorectal lung INDI 325 INDI 325 PLS 1 Colorectal al 0.21 0.47 0.04 0.084 0.03 0.138 0.028 Colorectal breast INDI 326 INDI 326 PLS 1 Lung 0.04 0.278 0.048 0.422 0.022 0.072 0.118 Colorectal lung INDI 327 INDI 327 PLS 1 Colorectal 0.214 0.494 0.034 0.138 0.056 0.042 0.022 Colorectal Breast INDI 328 INDI 328 PLS 1 Colorectal 0.114 0.48 0.026 0.094 0.124 0.104 0.058 Colorectal Ovary INDI 329 INDI 329 PLS 1 Colorectal 0.082 0.244 0.124 0.018 0.026 0.404 0.092 Colorectal Pancreas INDI 331 INDI 331 PLS 0.044 0.036 0.323 0.0032 Colorectal Pulm INDI 335 INDI 335 PLS 1A Lung 0.18 0.47 0.004 0.146 0.112 0.064 0.024 Colorectal Breast INDI 336 INDI 336 PLS 1A Lung 0.028 0.232 0.026 0.56 0.024 0.024 0.106 Colorectal Lung INDI 338 INDI 338 PLS 1 Colorectal 0.096 0, 31 0.036 0.052 0.096 0.39 0.02 Colorectal Pancreas INDI 340 INDI 340 PLS 1 Colorectal 0.028 0.45 0.084 0.092 0.074 0.164 0.108 Colorectal Pancreas INDI 341 INDI 341 PLS 1 Liver 0.126 0.202 0.13 0.056 0.068 0.266 0.52 Colorectal Pancreas INDI 344 INDI 344 Colorectal PLS 1 0.078 0.204 0.052 0.064 0.026 0.532 0.044 Colorectal pancreas INDI 346 INDI 346 PLS 1A Lung 0.034 0.162 0.018 0.7 0.03 0.024 0.032 Colorectal lung INDI 353 INDI 353 PLS 1A Lung 0.102 0.312 0.012 0.328 0.166 0.044 0.026 INDI 354 PLS 1 Colorectal 0.142 0.448 0.052 0.15 0.104 0.062 0.042 Colorectal Pulm INDI 355 INDI 355 PLS 1A Lung 0.074 0.412 0.016 0.37 0.014 0.056 0.058 Colorectal Pulm INDI 356 PLS 1 Colorectal 0.222 0.386 0.056 0.12 0.084 0.096 0.036 Colorectal Breast INDI 360 INDI 360 PLS 1A Lung 0.23 0.23 0.004 0.392 0.06 0.016 0.068 Colorectal Breast INDI 365 INDI 365 PLS 1 Colorectal 0.088 0.438 0.08 0.126 0.08 0.114 0.074 Colorectal Pulm INDI 366 INDI 366 PLS 1 Pancreas 0.052 0.282 0.054 0.06 0.078 0.448 0.026 Colorectal pancreas INDI 367 INDI 367 PLS 1 Liver 0.112 0.24 0.106 0.122 0.038 0.32 0.062 Colorectal pancreas INDI 368 INDI 368 PLS 1 Breast 0.044 0.376 0.09 0.138 0.018 0.2 0.134 Colorectal Pancreas INDI 371 INDI 371 PL S 1A Lung 0.046 0.364 0.022 0.254 0.106 0.084 0.124 Colorectal Pulm INDI 372 INDI 372 PLS 1A Lung 0.104 0.37 0.026 0.356 0.064 0.05 0.03 Colorectal Pulm INDI 374 INDI 374 PLS 1 Colorectal 0.062 0.542 0.044 0.078 0.016 0.164 0.094 Colorectal Pancreas

INDI 375 INDI 375 PLS 1 Liver 0.106 0.212 0.136 0.212 0.178 0.05 0.106 Colorectal Pulm INDI 377 INDI 377 PLS 1 Colorectal 0.168 0.37 0.034 0.166 0.114 0.068 0.08 Colorectal Breast INDI 380 INDI 380 PLS 1 Colorectal 0.062 0.618 0.036 0.088 0.016 0.124 0.056 Colorectal Pancreas INDI 383 INDI 383 PLS 1A Lung 0.054 0.422 0.018 0.314 0.046 0.046 0.1 Colorectal Pulm INDI 387 INDI 387 PLS 1 Liver 0.124 0.302 0.066 0.246 0.02 0.116 0.126 Colorectal Pulm INDI 388 INDI 388 PLS 0.502 0.02 0.168 0.064 0.138 0.032 Colorectal Pulm INDI 389 INDI 389 PLS 1A Lung 0.262 0.222 0.046 0.146 0.136 0.136 0.052 Colorectal Breast INDI 391 INDI 391 PLS 1 Liver 0.114 0.456 0.152 0.1 0.04 0.02 0.118 Colorectal Liver INDI 393 INDI 393 PLS 1 Liver 0.034 0.174 0.488 0.058 0.018 0.106 0.116 Liver Colorectal INDI 394 INDI 394 PLS 1A Lung 0.042 0.386 0.056 0.284 0.032 0.09 0.11 Colorectal Pulm INDI 396 INDI 396 PLS 1 Colorectal 0.118 0.328 0.038 0.28 0.012 0.124 Colorectal Pulm INDI 397 IN DI 397 PLS 1 Colorectal 0.062 0.53 0.046 0.152 0.02 0.078 0.112 Colorectal Pulm INDI 400 INDI 400 PLS 1 Colorectal 0.114 0.446 0.024 0.098 0.048 0.256 0.014 Colorectal Pancreas INDI 412 INDI 412 PLS 1 Breast 0.328 0.248 0.084 0.12 0.038 0, 09 0.092 Colorectal Breast INDI 413 INDI 413 PLS 1 Lung 0.206 0.162 0.01 0.234 0.342 0.016 0.03 Ovary Pulm INDI 415 INDI 415 PLS 1 Lung 0.02 0.136 0.314 0.318 0.056 0.028 0.128 Lung Liver INDI 417 INDI 417 PLS 1 Lung 0.068 0.422 0.056 0.344 0.016 0.03 0.064 Colorectal Pulm INDI 418 INDI 418 PLS 1 Colorectal 0.09 0.662 0.032 0.054 0.014 0.022 0.126 Colorectal upper GI INDI 422 INDI 422 PLS 1 Colorectal 0.038 0.636 0.042 0.054 0.026 0.032 0.172 Colorectal GI upper INDI 423 PLS 1 Colorectal 0.04 0.514 0.08 0.066 0.01 0.016 0.274 Colorectal upper GI INDI 427 INDI 427 PLS 1 Colorectal 0.06 0.526 0.086 0.046 0.058 0.094 0.13 Colorectal GI upper INDI 429 INDI 429 PLS 1 Colorectal 0.062 0.606 0.084 0.054 0.044 0.028 0.122 Colorectal Upper GI INDI 433 INDI 433 PLS 1 Colorectal 0.096 0.356 0.122 0.122 0.118 0.094 0.072 Colorectal Pulm INDI 437 INDI 437 PLS 1 Colorectal 0.09 0.566 0.042 0.07 0.014 0.08 0.138 Colorectal upper GI INDI 439 INDI 439 PLS 1 Colorectal 0, 05 0.454 0.124 0.02 0.014 0.072 0.266 Colorectal upper GI INDI 440 INDI 440 PLS 1 Esophagus 0.012 0.294 0.152 0.012 0.008 0.02 0.502 Upper GI Colorectal INDI 441 INDI 441 PLS 1 Esophagus 0.016 0.208 0.124 0.006 0 0.002 0.644 Upper Colorectal INDI 442 INDI 442 PLS 1 Breast 0.452 0.294 0.026 0.098 0.042 0.016 0.072 Colorectal Breast INDI 443 INDI 443 PLS 1 Breast 0.438 0.256 0.036 0.14 0.026 0.028 0.076 Colorectal Breast INDI 444 INDI 444 PLS 1 Colorectal 0.096 0.304 0.176 0.054 0.008 0.106 0.256 Colorectal GI 445 INDI 445 PLS 1 Breast 0.25 0.198 0.054 0.104 0.116 0.246 0.032 Breast Pancreas INDI 446 INDI 446 PLS 1 Esophagus 0.054 0.366 0.184 0.04 0.02 0.062 0.274 Colorectal upper GI INDI 447 INDI 447 PLS 1 Breast 0.394 0.098 0.122 0.054 0.126 0.146 0.06 Breast Pancreas INDI 449 INDI 449 PLS 1 Breast 0.576 0.256 0.026 0.042 0.008 0.006 0.086 Colorectal Breast INDI 450 INDI 450 PLS 1 Esophagus 0.042 0.338 0.238 0.02 0.002 0.026 0.334 Colorectal upper GI INDI 452 INDI 452 PLS 1 Colorectal 0.008 0 , 3 0.18 0.014 0.004 0.02 0.474 upper GI Colorectal INDI 453 INDI 453 PLS 1 Breast 0.338 0.342 0.048 0.156 0.04 0.022 0.054 Colorectal Breast INDI 454 INDI 454 PLS 1 Breast 0.612 0.254 0.018 0.062 0.01 0.026 0.018 Colorectal Breast INDI 456 INDI 456 PLS 1 Colorectal 0.188 0.464 0.056 0.026 0.036 0.066 0.164 Colorectal Breast INDI 457 INDI 457 PLS 1 Esophagus 0.02 0.274 0.268 0.016 0.024 0.184 0.236 Colorectal Liver INDI 458 INDI 458 PLS 1 Breast 0.23 0.272 0.11 0.038 0 , 06 0.162 0.128 Colorectal Breast INDI 459 INDI 459 PLS 1 Breast 0.302 0.252 0.13 0.094 0.046 0.032 0.144 Colorectal Breast INDI 461 INDI 461 PLS 1 Colorectal 0.06 0.558 0.084 0.076 0.002 0.014 0.206 Colorectal upper GI INDI 462 INDI 462 PLS 1 Breast 0.316 0.136 0.102 0.03 0.084 0.246 0.086 Breast Pancreas INDI 463 INDI 463 PLS 1 Colorectal 0.104 0.64 0.056 0.048 0.046 0.022 0.084 Colorectal Breast INDI 464 INDI 464 PLS 1 Liver 0.126 0.128 0.204 0.038 0.118 0.066 0.26 Upper GI Liver INDI 465 INDI 465 PLS 1 Esophagus 0.016 0.294 0.162 0.014 0.02 0.018 0.476 upper GI Colorectal INDI 466 INDI 466 PLS 1 Colorectal 0.272 0.428 0.086 0.058 0.038 0.024 0.094 Colorectal Breast INDI 467 INDI 467 PLS 1 Colorectal 0.238 0.488 0.088 0.026 0.018 0.03 0.112 Colorectal Breast INDI 468 INDI 468 PLS 1 Colorectal 1 0.57 0.048 0.036 0.054 0.032 0.102 Colorectal Breast INDI 470 INDI 470 PLS 1 Colorectal 0.254 0.454 0.066 0.076 0.038 0.018 0.094 Colorectal Breast INDI 472 INDI 472 PLS 1 Colorectal 0.21 0.48 0.038 0.106 0.078 0.014 0.074 Colorectal Breast INDI 473 INDI 473 PLS 1 Breast 0.288 0.372 0.056 0.104 0.082 0.038 0.06 Colorectal Breast INDI 474 INDI 474 PLS 1 Breast 0.346 0.356 0.072 0.032 0.036 0.084 0.074 Colorectal Breast INDI 475 INDI 475 PLS 1 Liver 0.042 0.472 0.108 0.02 0.01 0.042 0.306 Co lorectal upper GI INDI 476 INDI 476 PLS 1 Esophagus 0.048 0.338 0.204 0.038 0.012 0.028 0.332 Colorectal upper GI INDI 477 INDI 477 PLS 1 Colorectal 0.054 0.386 0.062 0.118 0.012 0.01 0.358 Colorectal GI

INDI 478 INDI 478 PLS 1 Breast 0.498 0.214 0.042 0.072 0.014 0.124 0.036 Colorectal Breast INDI 480 INDI 480 PLS 1 Breast 0.48 0.304 0.024 0.086 0.014 0.02 0.072 Colorectal Breast INDI 482 INDI 482 PLS 1 Liver 0.184 0.24 0.39 0.028 0.016 0.016 0.126 Liver Colorectal INDI 489 INDI 489 PLS 1 Breast 0.36 0.2 0.012 0.312 0.06 0.018 0.038 Breast Pulm INDI 490 INDI 490 PLS 1 Breast 0.288 0.622 0.11 0.122 0.122 0.12 0.036 Colorectal Breast INDI 494 INDI 494 PLS 1 Lung 0.13 0.238 0.104 0.326 0.03 0.036 0.136 Colorectal Lung INDI 503 INDI 503 PLS 1 Lung 0.056 0.21 0.064 0.444 0.006 0.008 0.212 Upper GI lung INDI 505 INDI 505 PLS 1 Lung 0.046 0.332 0.178 0.19 0.016 0.01 0.228 Colorectal upper GI INDI 506 INDI 506 PLS 1 Colorectal 0.072 0.404 0.026 0.236 0.178 0.068 0.016 Colorectal Pulm INDI 507 INDI 507 PLS 1 Lung 0.116 0.16 0.118 0.39 0.006 0.044 0.17 Lung GI upper INDI 514 INDI 514 PLS 1 Stomach 0.076 0.244 0.062 0.52 0.016 0.02 0.082 Colorectal Lung INDI 515 INDI 515 PLS 1 Colorr etal 0.054 0.452 0.044 0.322 0.024 0.08 0.024 Colorectal Pulm INDI 516 INDI 516 PLS 1 Pancreas 0.058 0.36 0.024 0.3 0.066 0.15 0.042 Colorectal Pulm INDI 518 INDI 518 PLS 1 Colorectal 0.072 0.492 0.07 0.194 0.016 0.136 0 , 02 Colorectal Pulm INDI 521 INDI 521 PLS 1 Colorectal 0.092 0.492 0.036 0.204 0.066 0.088 0.022 Colorectal Pulm INDI 523 INDI 523 PLS 1 Colorectal 0.056 0.354 0.032 0.138 0.04 0.34 0.04 Colorectal Pancreas INDI 524 PLS 1 Lung 0.076 0.446 0.08 0.108 0.02 0.07 0.2 Colorectal GI higher INDI 525 INDI 525 PLS 1 Colorectal 0.066 0.472 0.06 0.244 0.022 0.122 0.014 Colorectal Pulm INDI 526 INDI 526 PLS 1 Esophagus 0.102 0.176 0.018 0.504 0.024 0.078 0.098 Lung Colorectal INDI 527 INDI 527 PLS 1 Colorectal 0.12 0.448 0.012 0.24 0.106 0.062 0.012 Colorectal Pulm INDI 528 INDI 528 PLS 1 Colorectal 0.062 0.582 0.026 0.156 0.03 0.05 0.094 Colorectal Pulm INDI 533 INDI 533 PLS 1 Pancreas 0.058 0.396 0.02 0.118 0.072 0.288 0.048 Colorectal pancreas INDI 535 INDI 535 PLS 1 Lung 0.02 0.332 0.022 0.43 0.012 0.008 0.176 Lung Colorectal INDI 538 INDI 538 PLS 1 Lung 0.116 0.206 0.008 0.444 0.012 0.032 0.142 Lung Colorectal INDI 539 INDI 539 PLS 1 Colorectal 0.108 0.47 0.028 0.164 0.096 0.08 0.054 Colorectal Pulm INDI 544 INDI 544 PLS 1 Colorectal 0.048 0.4 0.008 0.434 0.02 0.022 0.068 Colorectal lung INDI 545 INDI 545 PLS 1 Lung 0.396 0.126 0.008 0.388 0.048 0.01 0.024 Breast Pulm INDI 548 INDI 548 PLS 1 Breast 0.37 0.214 0.028 0.222 0.072 0.02 0.074 Breast Pulm INDI 549 INDI 549 PLS 1 Lung 0.332 0.366 0.022 0.122 0.092 0.01 0.056 Colorectal Breast INDI 551 INDI 551 PLS 1 Colorectal 0.048 0.54 0.022 0.248 0.048 0.062 0.032 Colorectal Pulm INDI 555 INDI 555 PLS 1 Colorectal 0.022 0.598 0.028 0.232 0.022 0.024 0.074 Colorectal Pulm INDI 558 INDI 558 PLS 1 Colorectal 0.106 0.526 0.106 0.122 0.008 0.042 0.09 Colorectal Pulm INDI 562 INDI 562 PLS 1 Colorectal 0.118 0.50 0.182 0.034 0.008 0.036 0.114 Colorectal F4 1 Colorectal 0.0 6 0.626 0.046 0.074 0.01 0.01 0.174 Colorectal upper GI INDI 567 INDI 567 PLS 1 Colorectal 0.078 0.398 0.156 0.05 0.004 0.064 0.25 Colorectal GI upper INDI 570 INDI 570 PLS 1 Colorectal 0.042 0.46 0.122 0.04 0.008 0.072 0.256 Colorectal upper GI INDI 574 INDI 574 PLS 1 Colorectal 0.248 0.4 0.032 0.186 0.064 0.01 0.06 Colorectal Breast INDI 575 INDI 575 PLS 1 Breast 0.526 0.202 0.044 0.138 0.014 0.02 0.056 Colorectal Breast INDI 581 INDI 581 PLS 1 Colorectal 0.114 0.324 0.106 0.054 0.062 0.036 0.304 Colorectal upper GI INDI 582 INDI 582 PLS 1 Colorectal 0.028 0.478 0.1 0.008 0.008 0.01 0.368 Colorectal GI higher INDI 583 PLS 1 Colorectal 0.156 0.55 0.036 0.03 0.052 0.02 0.156 Colorectal Breast INDI 584 INDI 584 PLS 1 Colorectal 0.034 0.412 0.09 0.056 0.014 0.024 0.37 Colorectal upper GI INDI 585 INDI 585 PLS 1 Esophagus 0.022 0.322 0.068 0.028 0.01 0.024 0.526 upper GI Colorectal INDI 586 INDI 586 PLS 1 Liver 0.088 0.264 0.258 0.064 0.066 0.082 0.178 Colorectal Liver o INDI 587 INDI 587 PLS 1 Colorectal 0.044 0.412 0.076 0.088 0.008 0.036 0.336 Colorectal upper GI INDI 591 INDI 591 PLS 1 Colorectal 0.012 0.504 0.138 0.004 0.01 0.03 0.302 Colorectal upper GI INDI 593 INDI 593 PLS 1 Breast 0.190 0.322 0.248 0.026 0.004 0.064 0.13 Colorectal Liver INDI 594 INDI 594 PLS 1 Colorectal 0.43 0.312 0.018 0.098 0.03 0.006 0.106 Colorectal Breast INDI 595 INDI 595 PLS 1 Esophagus 0.032 0.234 0.154 0.014 0.014 0.022 0.53 Upper GI Colorectal INDI 598 INDI 598 PLS 1 Colorectal 0.088 0.534 0.102 0.078 0.006 0.024 0.168 Colorectal upper GI INDI 599 INDI 599 PLS 1 Colorectal 0.642 0.178 0.012 0.074 0.02 0.032 0.042 Colorectal INDI 601 INDI 601 PLS 1 Breast 0.32 0.282 0.106 0.08 0.046 0.052 0.114 Breast Colorectal INDI 602 INDI 602 PLS 1 Colorectal 0.118 0.516 0.128 0.036 0.044 0.018 0.12 Colorectal Liver INDI 604 INDI 604 PLS 1 Breast 0.272 0.526 0.028 0.048 0.032 0.018 0.076 Colorectal Breast INDI 605 INDI 605 PLS 1 Colorectal 0.098 0.50 0.02 0.08 0.012 0.026 , 0 1 0.272 Colorectal upper GI INDI 608 INDI 608 PLS 1 Colorectal 0.126 0.624 0.034 0.08 0.098 0.012 0.026 Colorectal breast INDI 609 INDI 609 PLS 1 Colorectal 0.092 0.464 0.128 0.102 0.024 0.024 0.166 Colorectal upper GI

INDI 610 INDI 610 PLS 1 Colorectal 0.252 0.524 0.056 0.05 0.048 0.012 0.058 Colorectal Breast INDI 611 INDI 611 PLS 1 Colorectal 0.038 0.606 0.076 0.068 0.004 0.018 0.19 Colorectal upper GI INDI 613 INDI 613 PLS 1 Breast 0.444 0.386 0.026 0.052 0, 03 0.002 0.058 Colorectal Breast INDI 614 INDI 614 PLS 1 Colorectal 0.202 0.538 0.084 0.054 0.018 0.006 0.098 Colorectal Breast INDI 615 INDI 615 PLS 1 Colorectal 0.07 0.562 0.058 0.04 0.008 0.006 0.256 Colorectal GI upper INDI 617 INDI 617 PLS 1 Colorectal 0.51 0.08 0.132 0.038 0.062 0.124 Colorectal Pulm INDI 618 INDI 618 PLS 1 Colorectal 0.058 0.47 0.04 0.114 0.024 0.054 0.24 Colorectal upper GI INDI 620 INDI 620 PLS 1 Colorectal 0.03 0.44 0.102 0.034 0.006 0.022 0.366 Colorectal GI upper INDI 621 INDI 621 PLS 1 Colorectal 0.062 0.55 0.072 0.062 0.002 0.022 0.23 Colorectal GI upper INDI 622 INDI 622 PLS 1 Liver 0.022 0.19 0.528 0.012 0.01 0.032 0.206 Liver GI upper INDI 623 INDI 623 PLS 1 Breast 0.2 0.33 0.028 0.114 0.25 0.0 22 0.056 Colorectal Ovary INDI 624 INDI 624 PLS 1 Colorectal 0.098 0.67 0.02 0.094 0.05 0.026 0.042 Colorectal Breast INDI 625 INDI 625 PLS 1 Colorectal 0.32 0.474 0.04 0.102 0.012 0.012 0.04 Colorectal Breast INDI 626 INDI 626 PLS 1 Colorectal 0.072 0.406 0.118 0.044 0.026 0.14 0.194 Colorectal upper GI INDI 627 INDI 627 PLS 1 Breast 0.39 0.392 0.05 0.062 0.06 0.01 0.036 Colorectal Breast INDI 628 INDI 628 PLS 1 Colorectal 0.184 0.548 0.054 0.104 0.01 0 0.1 Colorectal Breast INDI 631 INDI 631 PLS 1 Colorectal 0.128 0.612 0.04 0.064 0.026 0.006 0.124 Colorectal Breast INDI 632 INDI 632 PLS 1 Colorectal 0.152 0.382 0.16 0.052 0.034 0.104 0.116 Colorectal Liver INDI 633 INDI 633 PLS 1 Colorectal 0.262 0.486 0.036 0.094 0.018 0.012 0.092 Colorectal Breast INDI 635 INDI 635 PLS 1 Colorectal 0.032 0.684 0.034 0.058 0.03 0.122 0.04 Colorectal Pancreas INDI 636 INDI 636 PLS 1 Liver 0.072 0.486 0.068 0.166 0.024 0.018 0.17 Colon Upper GI INDI 637 INDI 637 PLS 1 Breast 0.404 0.214 0.038 0.192 0.0 42 0.002 0.108 Colorectal Breast INDI 638 INDI 638 PLS 1 Breast 0.412 0.232 0.022 0.236 0.042 0.006 0.05 Breast Pulm INDI 639 INDI 639 PLS 1 Liver 0.08 0.172 0.422 0.044 0.014 0.06 0.208 Liver GI upper INDI 640 INDI 640 PLS 1 Colorectal 0.024 0.738 0.038 0.034 0.022 0.054 0.09 Colorectal GI superior INDI 643 INDI 643 PLS 1 Colorectal 0.032 0.748 0.026 0.05 0.07 0.008 0.066 Colorectal Ovary INDI 644 INDI 644 PLS 1 Colorectal 0.022 0.678 0.032 0.11 0.024 0.078 0.056 Colorectal Pulm INDI 645 INDI 645 PLS 1 Liver 0.052 0.206 0.428 0.03 0.008 0.046 0.23 Liver upper GI INDI 647 INDI 647 PLS 1 Colorectal 0.094 0.404 0.074 0.016 0.024 0.282 0.102 Colorectal pancreas INDI 648 INDI 648 PLS 1 Colorectal 0.058 0.408 0.064 0 06 0.072 0.12 0.218 Colorectal superior GI INDI 649 INDI 649 PLS 1 Colorectal 0.02 0.65 0.048 0.068 0.032 0.102 0.08 Colorectal pancreas INDI 651 INDI 651 PLS 1 Colorectal 0.016 0.678 0.02 0.056 0.054 0.082 0.094 Colorectal superior GI INDI 654 INDI 654 PLS 1 Colorectal 0, 024 0.618 0.05 0.012 0.026 0.108 0.162 Colorectal upper GI INDI 657 INDI 657 PLS 1 Colorectal 0.08 0.424 0.048 0.06 0.118 0.078 0.192 Colorectal GI upper INDI 658 INDI 658 PLS 1 Colorectal 0.1 0.38 0.036 0.074 0.296 0.048 0.066 Colorectal Ovary INDI 660 INDI 660 PLS 1 Colorectal 0.052 0.71 0.008 0.048 0.096 0.008 0.078 Colorectal Ovary INDI 661 INDI 661 PLS 1 Liver 0.166 0.414 0.086 0.026 0.052 0.028 0.228 Colorectal GI upper INDI 663 INDI 663 PLS 0.014 0.04 0.226 0.016 0.348 Upper GI liver INDI 665 INDI 665 PLS 1 Esophagus 0.054 0.432 0.106 0.018 0.006 0.04 0.344 Colorectal upper GI INDI 669 INDI 669 PLS 1 Liver 0.01 0.132 0.522 0.048 0.008 0.016 0.264 Upper GI liver INDI 670 INDI 670 PLS 1 Colorectal 0.042 0.39 0.034 0.222 0.032 0.206 0.074 Colorectal Pulm INDI 671 INDI 671 PLS 1 Lung 0.312 0.11 0.024 0.432 0.052 0.014 0.056 Lung Breast INDI 673 INDI 673 PLS 1 Breast 0.382 0.134 0.06 0.222 0.074 0.048 0.08 Breast Pulm INDI 675 INDI 675 PLS 1 Colorectal 0.03 0.43 0.036 0.128 0.132 0.182 0.062 Colorectal Pancreas INDI 678 INDI 678 PLS 1 Colorectal 0.046 0.572 0.018 0.246 0.014 0.062 0.042 Colorectal Pulm INDI 686 INDI 686 PLS 1 Pancreas 0.116 0.222 0.076 0.062 0.036 0.416 687I Pancreas PLS 1 Lung 0.044 0.342 0.172 0.146 0.048 0.05 0.198 Colorectal GI higher INDI 688 INDI 688 PLS 1 Colorectal 0.272 0.19 0.006 0.378 0.1 0.044 0.01 Lung Breast INDI 691 INDI 691 PLS 1 Lung 0.442 0.33 0.048 0.038 0.04 0.044 0.058 Colorectal Breast INDI 692 INDI 692 PLS 1 Colorectal 0.104 0.306 0.08 0.068 0.074 0.306 0.062 Colorectal Colorectal INDI 693 INDI 693 PLS 1 Lung 0.088 0.292 0.15 0.202 0.038 0.04 0.19 Colorectal Pulm INDI 694 INDI 694 PLS 1 Lung 0.222 0.50 0.056 0.098 0.048 0.024 0.048 Colorectal Breast INDI 696 INDI 696 PLS 1 Lung 0.122 0.294 0.108 0.268 0.02 0.05 0.148 Colorectal Pulm INDI 697 PLS 1 Breast 0.232 0.244 0.026 0.346 0.074 0.044 0.054 Breast Lung INDI 698 INDI 698 PLS 1 Ovary 0.242 0.13 0.01 0.258 0.262 0.078 0.02 Ovary Pulm INDI 699 INDI 699 PLS 1 Pancreas 0.142 0.52 0.006 0.138 0.05 0.13 0.014 Colorectal Breast INDI 700 INDI 700 PLS 1 Lung 0.08 0.178 0.026 0 .5 0.016 0.05 0.15 Colorectal Lung

INDI 701 INDI 701 PLS 1 Colorectal 0.058 0.448 0.016 0.32 0.08 0.06 0.018 Colorectal Pulm INDI 702 INDI 702 PLS 1 Lung 0.046 0.50 0.06 0.266 0.064 0.084 0.048 Colorectal Pulm INDI 706 INDI 706 PLS 1 Lung 0.326 0.286 0.002 0.086 0.076 0.21 0.014 Colorectal Breast INDI 708 INDI 708 PLS 1 Colorectal 0.018 0.722 0.026 0.134 0.006 0.072 0.022 Colorectal Pulm INDI 710 INDI 710 PLS 1 Colorectal 0.144 0.462 0.02 0.128 0.1 0.136 0.01 Colorectal Breast INDI 711 INDI 711 PLS 1 Breast 0.394 0.274 0.022 0.11 0.064 0.102 0.034 Breast Colorectal INDI 714 INDI 714 PLS 1 Breast 0.272 0.366 0.044 0.164 0.038 0.1 0.046 Colorectal Breast INDI 715 INDI 715 PLS 1 Breast 0.536 0.156 0.032 0.062 0.056 0.124 0.034 Colorectal INDI 716 INDI 716 PLS 1 Colorectal 0.044 0.47 0.07 0.078 0.054 0.194 0.09 Colorectal Pancreas INDI 723 INDI 723 PLS 1 Ovary 0.082 0.206 0.018 0.202 0.408 0.04 0.044 Colorectal Ovary INDI 734 PLS 1 Pancreas 0.09 0.24 0.012 0.076 0.142 0.426 0.014 Colorectal pancreas INDI 735 INDI 73 5 PLS 1 Pancreas 0.056 0.394 0.06 0.156 0.03 0.258 0.046 Colorectal Pancreas INDI 736 INDI 736 PLS 1 Lung 0.048 0.43 0.076 0.238 0.05 0.076 0.082 Colorectal Pulm INDI 737 INDI 737 PLS 1 Stomach 0.038 0.248 0.068 0.42 0.042 0.032 0.152 Colorectal Lung INDI 740 INDI 740 PLS 1 Pancreas 0.126 0.324 0.02 0.156 0.28 0.08 0.014 Colorectal Ovary INDI 741 INDI 741 PLS 1 Pancreas 0.036 0.068 0.038 0.004 0.03 0.818 0.006 Colorectal Pancreas INDI 745 INDI 745 PLS 1 Pancreas 0.046 0.186 0.038 0.036 0.014 0.654 0.026 Pancreas Colorectal INDI 746 INDI 746 PLS 1 Colorectal 0.052 0.582 0.052 0.048 0.01 0.078 0.177 Colorectal upper GI INDI 747 INDI 747 PLS 1 Colorectal 0.046 0.636 0.034 0.042 0.049I Top Color 749 PLS 1 Colorectal 0.118 0.594 0.006 0.108 0.084 0.058 0.032 Colorectal Breast INDI 750 INDI 750 PLS 1 Colorectal 0.036 0.618 0.024 0.092 0.04 0.126 0.064 Colorectal Pancreas INDI 751 INDI 751 PLS 1 Colorectal 0.012 0.764 0.006 0.010 C6 olorectal Pulm INDI 752 INDI 752 PLS 1 Colorectal 0.036 0.654 0.02 0.13 0.044 0.038 0.078 Colorectal Pulm INDI 753 INDI 753 PLS 1 Esophagus 0.004 0.414 0.044 0.002 0 0.008 0.528 Upper GI Colorectal INDI 755 INDI 755 PLS 1 Esophagus 0.032 0.4 0.004 0.02 0.296 Colorectal upper GI INDI 758 INDI 758 PLS 1 Colorectal 0.038 0.614 0.046 0.054 0.068 0.112 0.068 Colorectal pancreas INDI 760 INDI 760 PLS 1 Liver 0.044 0.142 0.566 0.018 0 0.032 0.198 Liver GI upper INDI 761 INDI 761 PLS 1 Colorectal 0.048 0.054 0.07 0.06 0.1 Colorectal upper GI INDI 764 INDI 764 PLS 1 Colorectal 0.024 0.61 0.044 0.096 0.04 0.05 0.136 Colorectal GI upper INDI 765 INDI 765 PLS 1 Colorectal 0.018 0.352 0.288 0.022 0.018 0.126 0.176 Colorectal Liver INDI 767 INDI 767 PLS 1 Colorectal 0.048 0.624 0.028 0.106 0.016 0.108 0.07 Colorectal Pancreas INDI 769 INDI 769 PLS 1 Colorectal 0.036 0.616 0.052 0.124 0.06 0.03 0.082 Colorectal Pulm INDI 770 INDI 770 PLS 1 Colorectal 1 09 0.746 0 0.064 0.05 0.042 0.008 Colorectal Breast INDI 771 INDI 771 PLS 1 Liver 0.02 0.31 0.3 0.17 0.016 0.026 0.158 Colorectal Liver INDI 774 INDI 774 PLS 1 Colorectal 0.056 0.684 0.014 0.066 0.068 0.08 0.032 Colorectal Pancreas INDI 775 INDI 775 PLS 1 Liver 0.022 0.24 0.41 0.038 0.016 0.01 0.264 Liver GI upper INDI 776 INDI 776 PLS 1 Esophagus 0.046 0.424 0.084 0.092 0.014 0.034 0.306 Colorectal upper GI INDI 777 INDI 777 PLS 1 Colorectal 0.226 0 , 45 0.004 0.132 0.086 0.072 0.03 Colorectal Breast INDI 778 INDI 778 PLS 1 Liver 0.026 0.252 0.328 0.152 0.02 0.012 0.21 Colorectal Liver INDI 779 INDI 779 PLS 1 Colorectal 0.168 0.54 0.01 0.14 0.064 0.048 0.03 Colorectal Breast INDI 780 INDI 780 PLS 1 Colorectal 0.184 0.432 0.004 0.208 0.1 0.058 0.014 Colorectal Pulm INDI 781 INDI 781 PLS 1 Colorectal 0.036 0.728 0.008 0.12 0.022 0.054 0.032 Colorectal Pulm INDI 782 INDI 782 PLS 1 Colorectal 0.088 0.50 0.02 0.044 0.208 0.118 0.018 Colorectal Ovary INDI 784 INDI 784 PLS 1 Stomach 0.098 0.266 0.278 0.124 0.022 0.09 0.122 Colorectal Liver INDI 786 INDI 786 PLS 1 Liver 0.028 0.268 0.216 0.186 0.026 0.082 0.194 Colorectal Liver INDI 797 INDI 797 PLS 1 Stomach 0.064 0.268 0.066 0.402 0.022 0.07 0.108 Colorectal Lobe INDI 798 INDI 798 PLS 1 Ovary 0.15 0.332 0.024 0.226 0.14 0.064 0.064 Colorectal Pulm INDI 800 INDI 800 PLS 1 Liver 0.074 0.15 0.376 0.162 0.008 0.032 0.199 Liver GI higher INDI 802 INDI 802 PLS 1 Pancreas 0.09 0.32 0.016 0.292 0.084 0.156 0.042 Colorectal Pulm INDI 812 INDI 812 PLS 1 Ovary 0.106 0.308 0.024 0.228 0.202 0.104 0.028 Colorectal Pulm INDI 815 INDI 815 PLS 1 Colorectal 0.06 0.596 0.014 0.184 0.08 0.032 0.034 Colorectal Pulm INDI 817 INDI 817 PLS 1 Liver 0.096 0.31 0.11 0.01 0.026 0.246 Colorectal GI superior INDI 818 INDI 818 PLS 1 Ovary 0.048 0.186 0.006 0.07 0.61 0.072 0.008 Colorectal ovary INDI 819 INDI 819 PLS 1 Breast 0.304 0.282 0.004 0.284 0.07 0.026 0.03 Breast Pulm INDI 824 INDI 824 PLS 1 Esophagus 0.104 0.244 0.104 0.308 0.012 0.14 0.088 Colorectal Lung INDI 826 INDI 826 PLS 1 Colorectal 0.056 0.446 0.018 0.43 0.004 0.02 0.026 Colorectal Pulm INDI 827 INDI 827 PLS 1 Colorectal 0.132 0.438 0.008 0.194 0.126 0.082 0.02 Colorectal Pulm

INDI 830 INDI 830 PLS 0 Breast 0.2 0.302 0.008 0.342 0.102 0.036 0.01 Colorectal Lung INDI 833 INDI 833 PLS 1 Breast 0.098 0.478 0.012 0.258 0.068 0.052 0.034 Colorectal Pulm INDI 841 INDI 841 PLS 1 Colorectal 0.07 0.58 0.026 0.172 0.086 0.062 0.004 Colorectal Pulm INDI 844 INDI 844 PLS 1 Stomach 0.31 0.232 0.024 0.254 0.08 0.026 0.074 Breast Pulm INDI 846 INDI 846 PLS 1 Colorectal 0.028 0.416 0.034 0.06 0.008 0.006 0.448 Upper GI Colorectal INDI 847 INDI 847 PLS 1 Liver 0.09 0.226 0.464 0.012 0.012 0.022 0.174 Liver Colorectal INDI 848 INDI 848 PLS 1 Colorectal 0.106 0.516 0.026 0.062 0.01 0.048 0.232 Colorectal upper GI INDI 850 INDI 850 PLS 1 Esophagus 0.07 0.388 0.098 0.11 0.048 0.038 0.238 Colorectal upper GI INDI 853 INDI 853 PLS 1 Esophagus 0.026 0.282 0.238 0.024 0.01 0.032 0.388 Upper GI Colorectal INDI 854 INDI 854 PLS 1 Colorectal 0.006 0.294 0.108 0.002 0.002 0.002 0.586 Upper GI Colorectal INDI 855 INDI 855 0.32 PL12 1 Colorectal 0.008 0.018 0.3 52 Upper GI Liver INDI 859 INDI 859 PLS 1 Esophagus 0.088 0.50 0.066 0.146 0.042 0.046 0.104 Colorectal Pulm INDI 861 INDI 861 PLS 1 Liver 0.014 0.388 0.244 0.006 0.006 0.032 0.34 Colorectal upper GI INDI 862 INDI 862 PLS 1 Pancreas 0.06 0.436 , 05 0.028 0.068 0.246 Colorectal upper GI INDI 864 INDI 864 PLS 1 Liver 0.014 0.234 0.408 0.018 0.01 0.014 0.302 Liver upper GI INDI 867 INDI 867 PLS 1 Esophagus 0.016 0.274 0.11 0.012 0.006 0.03 0.552 upper Colorectal INDI 868 INDI 868 PLS 1 Liver 0.012 0.146 0.526 0.028 0.01 0.022 0.256 Liver upper GI INDI 870 INDI 870 PLS 1 Breast 0.316 0.28 0.07 0.102 0.084 0.082 0.066 Colorectal Breast INDI 872 INDI 872 PLS 1 Breast 0.192 0.418 0.092 0.124 0.084 0.066 0.024 Colorectal Breast INDI 877 INDI 877 PLS 1 Breast 0.416 0.308 0.042 0.07 0.024 0.088 0.052 Colorectal Breast INDI 878 INDI 878 PLS 1 Breast 0.394 0.306 0.092 0.07 0.042 0.07 0.026 Colorectal Breast INDI 884 INDI 884 PLS 1 Breast 0.172 0.322 0.06 0.088 0.23 0.038 0.06 Colorret al Ovary INDI 887 INDI 887 PLS 1 Breast 0.372 0.214 0.042 0.062 0.022 0.194 0.094 Colorectal Breast INDI 888 INDI 888 PLS 1 Pancreas 0.054 0.256 0.006 0.112 0.024 0.53 0.018 Colorectal Pancreas INDI 889 INDI 889 PLS 1 Colorectal 0.124 0.578 0.02 0, 1 0.036 0.13 0.012 Colorectal Pancreas INDI 893 INDI 893 PLS 1 Pancreas 0.032 0.292 0.054 0.022 0.042 0.522 0.036 Colorectal Pancreas INDI 896 INDI 896 PLS 1 Lung 0.044 0.488 0.062 0.186 0.046 0.118 0.046 Colorectal Pulm INDI 897 0.288 INDI 897 PLS , 01 0.244 0.068 0.06 0.012 Colorectal Breast INDI 898 INDI 898 PLS 1 Breast 0.12 0.512 0.002 0.152 0.042 0.166 0.006 Colorectal Pancreas INDI 902 INDI 902 PLS 1 Breast 0.206 0.262 0.016 0.106 0.154 0.248 0.008 Colorectal Pancreas INDI 905 INDI 905 PLS 1 Esophagus 0.008 0.36 0.074 0.054 0.032 0.034 0.438 Upper GI Colorectal INDI 907 INDI 907 PLS 1 Esophagus 0.018 0.382 0.04 0.154 0.018 0.014 0.374 Colorectal upper GI INDI 908 INDI 908 PLS 1 Esophagus 0.004 0.238 0.09 0.038 0.018 0.034 0.578 G Upper I Colorectal INDI 909 INDI 909 PLS 1 Esophagus 0.108 0.47 0.018 0.128 0.128 0.044 0.104 Colorectal Pulm INDI 911 INDI 911 PLS 1 Esophagus 0.022 0.562 0.08 0.104 0.018 0.09 0.124 Colorectal GI upper INDI 917 INDI 917 PLS 1 Liver 0.056 0.328 0.228 0.188 0.034 0.046 0.12 Colorectal Liver INDI 920 INDI 920 PLS 1 Ovary 0.27 0.148 0.01 0.29 0.18 0.046 0.056 Lung Breast INDI 928 INDI 928 PLS 1 Stomach 0.038 0.343 0.39 0.102 0.016 0.036 0.084 Colorectal Liver INDI 931 INDI 931 PLS 1 Ovary 0.172 0.122 0.012 0.326 0.308 0.02 0.02 Lung Ovary INDI 932 INDI 932 PLS 1 Ovary 0.104 0.272 0.034 0.196 0.274 0.09 0.03 Colorectal Ovary LCR 812 LCR 812 PLS1 Pancreas 0.138 0.124 0.102 0.016 0.028 0.548 0.044 Pancreas Breast PANC 669 PANC 669 PLS 1 Pancreas 0.048 0.208 0.04 0.026 0.022 0.606 0.05 Colorectal pancreas PANC 671 PANC 671 PLS 1 Pancreas 0.078 0.186 0.07 0.018 0.01 0.554 0.084 Colorectal pancreas PANC 672 672 PLS 1 Pancreas 0.052 0.08 0.072 0.024 0.004 0.732 0.036 Pancreas Co lorretal PANC 674 PANC 674 PLS 1 Pancreas 0.11 0.148 0.048 0.042 0.008 0.596 0.048 Pancreas Colorectal PANC 675 PANC 675 PLS 1 Pancreas 0.344 0.198 0.06 0.064 0.054 0.188 0.092 Colorectal Breast PANC 676 PANC 676 PLS 1 Pancreas 0.016 0.06 0.098 0.036 0.004 Colorectal Pancreas PANC 677 PANC 677 PLS 1 Pancreas 0.05 0.066 0.02 0.016 0.008 0.82 0.02 Colorectal Pancreas PANC 760 PANC 760 PLS 1 Pancreas 0.112 0.324 0.074 0.192 0.046 0.118 0.134 Colorectal Pulm PANC 761 PANC 761 PLS 1 Pan 0.104 0.244 0.016 0.168 0.034 0.356 0.098 Colorectal pancreas PANC 762 PANC 762 PLS 1 pancreas 0.106 0.22 0.038 0.036 0.042 0.534 0.024 Colorectal pancreas PANC 764 PANC 764 PLS 1 pancreas 0.08 0.228 0.096 0.092 0.03 0.388 PANC pancreas 765 765 PLS 1 Pancreas 0.104 0.118 0.052 0.018 0.024 0.652 0.032 Pancreas Colorectal PANC 766 PANC 766 PLS 1 Pancreas 0.056 0.37 0.088 0.06 0.044 0.29 0.092 Colorectal Pancreas PANCA 1001 PANCA 1001 PLS 1 Pancreas 0.058 0.318 0.104 0.094 0.034 0.236 0.156 Colorectal Pancreas PANCA 1004 PANCA 1004 PLS 1 Pancreas 0.07 0.2 0.072 0.116 0.034 0.412 0.096 Colorectal Pancreas PANCA 1005 PANCA 1005 PLS 1 Pancreas 0.024 0.182 0.042 0.056 0.04 0.606 0.05 Colorectal Pancreas

PANCA 1006 PANCA 1006 PLS 1 Pancreas 0.084 0.098 0.108 0.018 0.04 0.62 0.032 Pancreas Liver PANCA 1008 PANCA 1008 PLS 1 Pancreas 0.104 0.3 0.118 0.222 0.026 0.164 0.066 Colorectal Pulm PANCA 1009 PANCA 1009 PLS 1 Pancreas 0.084 0.026 0.074 0.102 0.248 0.09 Colorectal Pancreas PANCA 1011 PANCA 1011 PLS 1 Pancreas 0.018 0.028 0.014 0 0 0.93 0.01 Colorectal Pancreas PANCA 1013 PANCA 1013 PLS 1 Pancreas 0.028 0.076 0.036 0.014 0.02 0.8 0.026 Colorectal Pancreas PANCA 1014 PANCA 1014 PLS 1 Pancreas 0.034 0.3 0.118 0.014 0.034 0.446 0.054 Colorectal Pancreas PANCA 1015 PANCA 1015 PLS 1 Pancreas 0.044 0.192 0.084 0.02 0.016 0.6 0.044 Colorectal Pancreas PANCA 1016 PANCA 1016 PLS 1 Pancreas 0.128 0.174 0.068 0.04 0.05 PANCA 1018 PANCA 1018 PLS 1 Pancreas 0.042 0.05 0.012 0.008 0.06 0.818 0.01 Pancreas Ovary PANCA 1020 PANCA 1020 PLS 1 Pancreas 0.092 0.204 0.184 0.022 0.03 0.384 0.084 Colorectal pancreas PANCA 1022 PANCA 1022 PLS 1 Pancreas 0.036 0.078 0.014 0.004 0.008 0.85 0.01 Colorectal Pancreas PANCA 1025 PANCA 1025 PLS 1 Pancreas 0.058 0.08 0.038 0.042 0.078 0.694 0.01 Colorectal Pancreas PANCA 1028 PANCA 1028 PLS 1 Pancreas 0.09 0.18 0.062 0.042 0.05 0.514 0.062 Colorectal Pancreas PANCA 1030 PANCA 1030 PLS 1 Pancreas 0.008 0.052 0.036 0.01 0.012 0.862 0.02 Colorectal Pancreas PANCA 1031 PANCA 1031 PLS 1 Pancreas 0.064 0.068 0.014 0.018 0.022 0.808 0.006 Colorectal Pancreas PANCA 0.134 PANCA 1034 PLS1012 1 0.084 0.058 0.398 0.032 Colorectal pancreas PANCA 1040 PANCA 1040 PLS 1 Pancreas 0.068 0.176 0.188 0.048 0.026 0.434 0.06 Pancreas Liver PANCA 1043 PANCA 1043 PLS 1 Pancreas 0.01 0.072 0.022 0.002 0.01 0.872 0.012 Colorectal pancreas PANCA 1044 PANCA 1044 PANCA 1044 PANCA 1044 PANCA 1044 PANCA 1044 PANCA 1044 PANCA 1 Pancreas 0.024 0.06 0.006 0.006 0.018 0.88 0.006 Pancreas Colorectal PANCA 1048 PANCA 1048 PLS 1 Pancreas 0.008 0.026 0.002 0.008 0.022 0.934 0 Pancreas Colorectal PANCA 1050 PANCA 1050 PLS 1 Pancreas 0.064 0.082 0.032 0.024 0.024 0.754 0.02 Pancreas Colorectal PANCA 1051 PANCA 1051 PLS 1 Pancreas 0.006 0.05 0.006 0 0.006 0.902 0.03 Colorectal Pancreas PANCA 1053 PANCA 1053 PLS 1 Pancreas 0.066 0.25 0.08 0.052 0.046 0.398 0.108 Colorectal Pancreas PANCA 1054 PANCA 1054 PLS 1 Pancreas 0 , 03 0.036 0.024 0 0.008 0.9 0.002 Colorectal Pancreas PANCA 1056 PANCA 1056 PLS 1 Pancreas 0.092 0.186 0.082 0.032 0.03 0.5 0.078 Colorectal Pancreas PANCA 1058 PANCA 1058 PLS 1 Pancreas 0.046 0.108 0.014 0.012 0.022 0.786 0.012 Colorectal PANCA 10 PANCA 1059 PLS 1 Pancreas 0.046 0.166 0.054 0.062 0.024 0.596 0.052 Pancreas Colorectal PANCA 1061 PANCA 1061 PLS 1 Pancreas 0.022 0.064 0.04 0.006 0.016 0.838 0.014 Colorectal Pancreas PANCA 1149 PANCA 1149 PLS 1 Pancreas 0.142 0.168 0.042 0.016 0.016 0.016 0.016 0.016 0.016 PANCA 1153 PLS 1 Pancreas 0.088 0.288 0.05 0.086 0.018 0.336 0.134 Colorectal Pancreas PANCA 1155 PANCA 1155 PLS 1 Pancreas 0.122 0.304 0.094 0.022 0.064 0.348 0.046 Colorectal Pancreas PAP 938 PAP 938 PLS 1 Ovary 0.122 0.144 0.052 0.02 0.438 0.186 0.038 Ovary Pancreas PAP 939 PAP 939 PLS 1 Ovary 0.034 0.09 0.032 0.078 0.702 0.038 0.026 Colorectal Ovary PAP 940 PAP 940 PLS 1 Ovary 0.026 0.046 0.008 0.024 0.85 0.024 0.022 Colorectal Ovary PAP 941 PAP 941 PLS 1 Ovary 0.112 0.234 0.016 0.172 0.402 0.016 0.048 Colorectal Ovary PAP 944 PAP 944 PLS 1 Ovary 0.03 0.13 0.104 0.078 0.5 0.09 0.068 Colorectal Ovary PAP 945 PAP 945 PLS 1 Ovary 0.042 0.076 0 , 01 0.056 0.796 0.006 0.014 Colorectal Ovary PAP 946 PAP 946 PLS 1 Ovary 0.06 0.098 0.01 0.04 0.626 0.154 0.012 Ovarian Pancreas PAP 947 PAP 947 PLS 1 Ovary 0.048 0.334 0.02 0.142 0.356 0.054 0.046 Colorectal Ovary PAP 949 PAP 949 PLS 1 Ovary 0.074 0.374 0.112 0.084 0.174 0.074 0.108 Colorectal Ovary PAP 950 PAP 950 PLS 1 Ovary 0.026 0.104 0.032 0.028 0.71 0.076 0.024 Colorectal Ovary PAP 953 PAP 953 PLS 1 Ovary 0.04 0.37 0.03 0.038 0 , 42 0.06 0.042 Colorectal Ovary PAP 954 PAP 954 PLS 1 Ovary 0.02 0.096 0.024 0.066 0.746 0 , 022 0.026 Colorectal Ovary PAP 955 PAP 955 PLS 1 Ovary 0.032 0.08 0.002 0.07 0.798 0.012 0.006 Colorectal Ovary PAP 956 PAP 956 PLS 1 Ovary 0.078 0.146 0.042 0.09 0.59 0.028 0.026 Colorectal Ovary PAP 957 PAP 957 PLS 1 Ovary 0.12 0.256 0.022 0.108 0.41 0.04 0.044 Colorectal Ovary PAP 959 PAP 959 PLS 1 Ovary 0.05 0.172 0.026 0.068 0.604 0.05 0.03 Colorectal Ovary PAP 961 PAP 961 PLS 1 Ovary 0.05 0 , 17 0.024 0.086 0.568 0.058 0.044 Colorectal Ovary PAP 962 PAP 962 PLS 1 Ovary 0.046 0.21 0.136 0.25 0.238 0.026 0.094 Lung Ovary PAP 974 PAP 974 PLS 1 Ovary 0.102 0.274 0.014 0.084 0.462 0.042 0.022 Colorectal Ovary PAPA 1330 PAPA 1330 PLS 1 Ovary 0.116 0.184 0.076 0.062 0.472 0.016 0.074 Colorectal ovary PAPA 1331 PAPA 1331 PLS 1 Ovary 0.022 0.092 0.01 0.058 0.79 0.012 0.016 Colorectal ovary PAPA 1332 PAPA 1332 PLS 1 Ovary 0.114 0.378 0.014 0.16 0.268 0.026 0.04 Colorectal Ovary PAPA 1333 PAPA 1333 PLS 1 Ovary 0.038 0.074 0.06 0.04 0.698 0.056 0.034 Colorectal ovary PAPA 1334 PAPA 1334 PLS 1 Ovary 0.024 0.14 0.016 0.068 0.656 0.048 0.048 Colorectal Ovary PAPA 1335 PAPA 1335 PLS 1 Ovary 0.086 0.286 0.058 0.248 0.208 0.01 0.104 Pulmonary Color PAPA 1336 PAPA 1336 PLS 1 Ovary 0.054 0.3 0.024 0.092 0.416 0.066 0.048 Colorectal Ovary

PAPA 1339 PAPA 1339 PLS 1 Ovary 0.032 0.076 0.04 0.016 0.74 0.05 0.046 Colorectal Ovary PAPA 1342 PAPA 1342 PLS 1 Ovary 0.028 0.064 0.012 0.108 0.724 0.048 0.016 Ovary Pulm PAPA 1343 PAPA 1343 PLS 1 Ovary 0.012 0.054 0.004 0, 03 0.878 0.002 0.02 Colorectal Ovary PAPA 1344 PAPA 1344 PLS 1 Ovary 0.12 0.18 0.014 0.072 0.56 0.016 0.038 Colorectal Ovary PAPA 1345 PAPA 1345 PLS 1 Ovary 0.046 0.134 0.022 0.062 0.706 0.01 0.02 Colorectal Ovary PAPA 1346 PAPA 1346 PLS 1 Ovary 0.034 0.042 0.004 0.064 0.824 0.02 0.012 Ovary Pulm PAPA 1347 PAPA 1347 PLS 1 Ovary 0.044 0.19 0.044 0.058 0.576 0.034 0.054 Colorectal Ovary PAPA 1348 PAPA 1348 PLS 1 Ovary 0.066 0.138 0.014 0.096 0.624 0.038 0.024 Colorectal Ovary PAPA 1349 PAPA 1349 PLS 1 Ovary 0.042 0.052 0.016 0.054 0.752 0.056 0.028 Ovarian Pancreas PAPA 1350 PAPA 1350 PLS 1 Ovary 0.174 0.266 0.026 0.208 0.276 0.042 0.028 Colorectal Ovary PAPA 1353 PAPA 1353 PLS 1 Ovary 0.14 0.234 0.01 0.112 0.312 0.116 0.026 Colorectal Ovary PAPA 1354 PAPA 1354 PLS 1 Ovary 0.186 0.342 0.036 0.154 0.2 0.058 0.024 Colorectal Ovary PAPA 1355 PAPA 1355 PLS 1 Ovary 0.084 0.278 0.032 0.174 0.36 0.026 0.046 Colorectal Ovary PAPA 1356 PAPA 1356 PLS 1 Ovary 0.05 0.112 0.066 0.028 0, 64 0.04 0.064 Colorectal Ovary PAPA 1357 PAPA 1357 PLS 1 Ovary 0.034 0.058 0.022 0.04 0.8 0.02 0.026 Colorectal Ovary

Table 7. General demography and histopathology of patients with pancreatic cancer Pancreatic cancer Pancreatic cancer Pancreatic cancer Pancreatic cancer Pancreatic cancer Cancer with detectable with undetectable with detectable with undetectable with detectable with pancreatic undetectable KRAS KRAS CA19-9 CA19-9 HGF HGF (n = 221) (N = 66) (N = 155) p-value (N = 109) (N = 112) p-value (N = 23) (N = 198) Variable N (%) N (%) N ( %) N (%) N (%) N (%) N (%) Age, mean 1 SD 66.9 1 10.5 66.2 1 10.8 67.3 1 10.3 0.473 65.9 1 10 , 24 67.9 1 10.6 0.136 68.1 1 9.4 66.8 1 10.6 Gener, 0.07 0.721 Male 121 (54.8) 30 (24.8) 91 (75.2) 61 (50.4) 60 (49.6) 10 (8.3) 111 (91.7)? Female 100 (45.2) 36 (36.0) 64 (64.0) 48 (48.0) 52 (52.0) 13 (13.0) 87 (87.0) Race 0.889 0.598? White 188 (85.1) 55 (29.3) 133 (70.7) 95 (50.5) 93 (49 , 5) 16 (8.5) 172 (91.5) African-American 4 (1.8) 1 (25.0) 3 (75.0) 2 (50.0) 2 (50.0) 4 (100.0)? Hispanic 1 (0.5) 1 (100.0) 1 (100.0) 1 (100.0)? Asian 12 (5.4) 5 (41.7) 7 ( 58.3) 7 (58.3) 5 (41.7) 3 (25.0) 9 (75.0)? Other or unknown, 16 (7.2) 2 (40.0) 3 (60.0) ) 4 (25.0) 12 (75.0) 3 (18.7) 13 (81.3) Classic symptoms PDAC 0.194 0.69? Absent 45 (20.4) 49 (27.8) 127 (72, 2) 21 (46.7) 24 (53.3) 1 (2.2) 44 (97.8)? Present 176 (79.6) 17 (37.8) 28 (62.2) 88 (50, 0) 88 (50.0) 22 (12.5) 154 (87.5) Abdominal pain 0.361 0.207? Absent 127 (57.5) 41 (32.3) 86 (67.7) 58 (45.7) 69 (54.3) 12 (9.5) 115 (90.5) Present 94 (42.5) 25 (26.6) 69 (73.4) 51 (54.3) 43 (45.7) 11 (11.7) 83 (88.3) Jaundice 0.528 0.841? Absent 110 (49.8) 35 (31.8) 75 (68.2) 55 (50.0) 55 (50.0) 10 (9 , 1) 100 (90.9)? Present 111 (50.2) 31 (27.9) 80 (72.1) 54 (48.7) 57 (51.3) 13 (11.7) 98 (88 , 3) Tumor size (cm), median (IQR) 3.0 (2.43.8) 3.5 (3.04.5) 2.7 (2.23.5) 0.002 3.4 (2 , 54.5) 2.8 (2.13.5) <0.001 2.7 (2.04.0) 3.0 (2.43.8) Tumor size 0.004 0.028 ?? 1.00 cm 2 (0 , 9) 2 (100.0) 2 (100.0) 2 (100.0)? 1.011.50 cm 22 (10.0) 5 (22.7) 17 (77.3) 6 (27.3) 16 (72.7) 5 (22.7) 17 (77.3)? 1.512.00 cm 12 (5.4) 2 ( 16.7) 10 (83.3) 4 (33.3) 8 (66.7) 1 (8.3) 11 (91.7)? 2.012.50 cm 47 (21.3) 4 (8.5 ) 43 (91.5) 20 (42.5) 27 (57.5) 4 (8.5) 43 (91.5)? 2.513.00 cm 38 (17.2) 12 (31.6) 26 (68, 4) 17 (44.7) 21 (55.3) 4 (10.5) 34 (89.5)? 3.013.50 cm 36 (16.3) 15 (41.7) 21 (58.3) 21 (58.3) 15 (41.7) 2 (5.6) 34 (94.4)? 3.514.00 cm 22 (10.0) 10 (45.5) 12 (54.6) 13 (59, 1) 9 (40.9) 2 (9.1) 20 (90.9)?> 4.00 cm 42 (19.0) 18 (42.9) 24 (57.1) 28 (66.7) 14 (33.3) 5 (11.9) 37 (88.1) AJCC T stage 0.003 0.001? T1 22 (10.0) 5 (22.7) 17 (77.3) 4 (18.2) 18 (81.8) 3 (13.6) 19 (86.4)? T2 46 (20.8) 5 (10.9) 41 (89.1) 18 (39.1) 28 (60.9) 4 (8.7) 42 (91.3)? T3 153 (69.2) 56 (36.6) 97 (63.4) 87 (56.9) 66 (43.1) 16 (10.5) 137 (89.5) AJCC N stage 0.004 0.009? N0 51 (23.1) 7 (13.7) 44 (86.3) 17 (33.3) 34 (66.7) 6 (11.8) 45 ( 88.2)? N1 170 (76.9) 59 (34.7) 111 (65.3) 92 (54.1) 78 (45.9) 17 (10.0) 153 (90.0) AJCC M stage? M0 221 (100.0) 66 (29.9) 155 (70.2) 109 (49.3) 112 (50.7) 23 (10.4) 198 (89.6)

? M1 AJCC stage 0.013 0.035? AI 12 (5.4) 3 (25.0) 9 (75.0) 2 (16.7) 10 (83.3) 3 (25.0) 9 (75.0) ? IB 17 (7.7) 17 (100.0) 7 (41.2) 10 (58.8) 1 (5.9) 16 (94.1)? IIA 22 (10.0) 4 (18, 2) 18 (81.8) 8 (36.4) 14 (63.6) 2 (9.1) 20 (90.9)? IIB 170 (76.9) 59 (34.7) 111 (65, 3) 92 (54.1) 78 (45.9) 17 (10.0) 153 (90.0) Degree of tumor differentiation 0.118 0.666 - Well / Moderately differentiated 141 (63.8) 37 (26.2) 104 (79.8) 68 (48.2) 73 (51.8) 14 (9.9) 127 (90.1)? Bad differentiation 80 (36.2) 29 (36.3) 51 (63.7) 41 (51.3) 39 (48.7) 9 (11.3) 71 (88.7) Perineural invasion 0.086 0.044? Absent 26 (11.8) 4 (15.4) 22 (84.6) 8 ( 30.8) 18 (69.3) 3 (11.5) 23 (88.5)? Present 195 (88.2) 62 (31.8) 133 (68.2) 101 (51.8) 94 ( 48.2) 20 (10.3) 175 (89.7) Lymphovascular invasion 0.078 <0.001? Missing 76 (34.4) 17 (22.4) 59 (77.6) 25 (32.9) 51 (67 , 1) 8 (10.5) 68 (89.5)? Present 145 (65.6) 49 (33.8) 96 (66.2) 84 (57.9) 61 (42.1) 15 (10 , 3) 130 (89.7) Num, of nodules harvested, median (19 (1527) 20 (1728) 19 (1427) 0.047 19 (1 626) 20 (1429) 0.973 20 (1728) 19 (1527) Num, harvested nodules 0.355 0.544 ?? 20 nodules 111 (50.2) 30 (27.0) 81 (73.0) 57 (51.3) 54 (48.7) 9 (8.1) 102 (91.9)?> 20 nodules 110 (49.8) 36 (32.7) 74 (67.3) 52 (47.3) 58 (52.7 ) 14 (12.7) 96 (87.3) Num, positive nodules, median (I2 (15) 4.0 (2.07.0) 1 (0.04.0) <0.001 3 (16) 2 (04) 0.038 2 (05) 2 (15) Positive nodules 0.004 0.009? Absent 51 (23.1) 7 (13.7) 44 (86.3) 6 (11.8) 45 (88.2)? Present 170 (76.9) 17 (10.0) 153 (90.0)

Pancreatic cancer Pancreatic cancer Pancreatic cancer Pancreatic cancer Pancreatic cancer Pancreatic cancer with detectable with undetectable with detectable with undetectable with detectable with undetectable CEA CEA OPN OPN assay combination test p-value (N = 14) (N = 207) p-value (N = 207) = 15) (N = 206) p-value (N = 141) (N = 80) p-value N (%) N (%) N (%) N (%) N (%) N (%) 0.579 68 , 6 1 12.0 66.8 1 10.4 0.539 70.3 1 11.4 66.7 1 10.4 0.204 66.5 1 10.1 67.7 1 11.09 0.43 0.251 0.139 0.515 0.238 5 (4.1) 116 (95.9) 7 (5.8) 114 (94.2) 73 (60.3) 48 (39.7) 9 (9.0) 91 (91.0) 8 ( 8.0) 92 (92.0) 68 (68.0) 32 (32.0) 0.011 0.083 0.44 0.24 12 (6.4) 176 (93.6) 13 (6.9) 175 ( 93.1) 119 (63.3) 69 (36.7) 4 (100.0) 4 (100.0) 2 (50.0) 2 (50.0) 1 (100.0) 1 (100, 0) 1 (100.0) 12 (100.0) 12 (100.0) 11 (91.7) 1 (8.3) 2 (12.5) 14 (87.5) 2 (12.5) 14 (87.5) 8 (50.0) 8 (50.0) 0.044 0.56 0.971 0.354 2 (4.4) 43 (95.6) 12 (6.8) 164 (93.2) 114 ( 64.8) 62 (35.2) 12 (6.8) 164 (93.2) 3 (6.7) 42 (93.3) 27 (60.0) 18 (40.0) 0.58 8 0.594 0.837 0.771 9 (7.1) 118 (92.9) 9 (7.1) 118 (92.9) 80 (62.9) 47 (37.0) 5 (5.3) 89 (94, 7) 6 (6.4) 88 (93.6) 61 (64.9) 33 (35.1) 0.524 0.593 0.803 0.741 6 (5.5) 104 (94.5) 7 (6.4) 103 ( 93.6) 69 (62.7) 41 (37.3) 8 (7.2) 103 (92.8) 8 (7.2) 103 (92.8) 72 (64.9) 39 (35, 1) 0.619 4.0 (2.75.0) 3.0 (2.43.7) 0.343 2.5 (2.43.0) 3.0 (2.43.8) 0.312 3.2 (2 , 54.0) 2.5 (2.13.4) 0.002 0.648 0.313 0.266 0.013 2 (100.0) 2 (100.0) 2 (100.0) 2 (9.1) 20 (90.9) 3 (13.6) 19 (86.4) 11 (50.0) 11 (50.0) 12 (100.0) 12 (100.0) 6 (50.0) 6 (50.0) 1 ( 2.1) 46 (97.9) 5 (10.6) 42 (89.4) 24 (51.1) 23 (48.9) 2 (5.3) 36 (94.7) 4 (10, 5) 34 (89.5) 23 (60.5) 15 (39.5) 1 (2.8) 35 (97.2) 36 (100.0) 29 (80.6) 7 (19.4) 2 (9.1) 20 (90.9) 22 (100.0) 17 (77.3) 5 (22.7) 6 (14.3) 36 (85.7) 3 (7.1) 39 ( 92.9) 31 (73.8) 11 (26.2) 0.822 0.936 0.2 0.002 1 (4.6) 21 (95.5) 3 (13.6) 19 (86.4) 8 (36, 4) 14 (63.6) 3 (6.5) 43 (93.5) 1 (2.2) 45 (79.8) 25 (54.4) 21 (45.6) 10 (6.5) 143 (93.5) 11 (7.2) 142 (92.8) 108 (70.6) 45 (29.4) 0.717 0.144 0.733 0.002 1 (1.9) 50 (98.1) 4 (7.8) 47 (92.2) 23 (45.1) 28 (54.9) 13 (7.7) 157 (92.3) 11 (6.5) 159 (93.5) 118 (69.4) 52 (30.6)

14 (6.3) 207 (93.7) 15 (6.8) 206 (93.2) 141 (63.8) 80 (36.2)

0.364 0.36 0.052 0.012 1 (8.3) 11 (91.7) 3 (25.0) 9 (75.0) 4 (33.3) 8 (66.7) 17 (100.0) 17 ( 100.0) 8 (47.1) 9 (52.9) 22 (100.0) 1 (4.6) 21 (95.4) 11 (50.0) 11 (50.0) 13 (7, 7) 157 (92.4) 11 (6.5) 159 (93.5) 118 (69.4) 52 (30.6) 0.757 0.969 0.811 0.389 9 (6.4) 132 (93.6) 10 ( 7.1) 131 (92.9) 87 (61.7) 54 (38.3) 5 (6.3) 75 (93.8) 5 (6.3) 75 (93.8) 54 (67, 5) 26 (32.5) 0.841 0.579 0.845 0.119 1 (3.9) 25 (96.2) 2 (7.7) 24 (92.3) 13 (50.0) 13 (50.0) 13 ( 6.7) 182 (93.3) 13 (6.7) 182 (93.3) 128 (65.6) 67 (34.4) 0.967 0.291 0.929 0.005 3 (3.9) 73 (96.1) 5 (6.6) 71 (93.4) 39 (51.3) 37 (48.7) 11 (7.6) 134 (92.4) 10 (6.9) 135 (93.1) 102 ( 70.3) 43 (29.7) 0.173 22 (1826) 19 (1527) 0.345 26 (1928) 19 (1527) 0.197 20 (1627) 18 (1427) 0.044 0.261 0.262 0.059 0.285 5 (4.5) 106 ( 95.5) 4 (3.6) 107 (96.4) 67 (60.4) 44 (39.6) 9 (8.2) 101 (91.8) 11 (10.0) 99 (99, 0) 74 (67.3) 35 (32.7) 0.965 4 (27) 2 (15) 0.185 4 (06) 2 (15) 0.547 3 (16) 1 (03) <0.001 0.717 0.144 0.733 0.002 1 (1 , 9) 50 (98.1) 4 (7.8) 47 (92.2) 23 (45.1) 28 (54.9) 13 (7.7) 157 (92.3) 11 (6.5) 159 (93.5) 118 (69.4) 52 (30.6)

Table 8. Summary of KRAS plasma mutations and protein biomarkers identified in 221 PDAC patients Plasma DNA Mutation Mutation Mutant Mutant CA19-9 conc.

CEA conc.

HGF conc.

OPN conc.

Combination test Size identified concentration identified in frequency mean allele fragments / mL plasma plasma plasma plasma result AJCC Tumor Sample ID # (ng / mL) in plasma tumor tissue (%) plasma (U / mL) (pg / mL) (pg / mL) (pg / mL) (Positive / Negative) Stage (cm) PANC 304 5.56 Not detected Not applicable Not applicable Not applicable 19 4.367 193 44.946 Negative IB 6 PANC 305 204.02 Not detected Not applicable Not applicable Not applicable 504 1.800 406 104.131 Positive IIB 1.4 PANC 317 10.36 Not detected Not applicable Not applicable Not applicable 52 1.928 166 22.155 Negative IIB 2.5 PANC 333 58.51 Not detected Not applicable Not applicable Not applicable 1.184 3.369 380 98.827 Positive IIB 5 PANC 335 13.28 KRAS p.G12V, c.35G> T KRAS p.G12V, c.35G> T 1.306 53.4 562 1.911 188 33.482 Positive IIB 4 PANC 336 13.67 KRAS p.G12D, c.35G> A KRAS p.G12D, c.35G> A 0.926 39.0 850 1.451 287 84.651 Positive IIB 5 PANC 339 41.41 KRAS p.G12V, c.35G> T KRAS p.G12V, c.35G> T 0.186 23, 8 2.867 4.167 171 72.479 Positive IIB 3.5 PANC 345 27.27 Not detected Not applicable Not applicable Not applicable 30 1.394 182 55.337 Negative IIA 3 PANC 346 22.77 Not detected Not applicable Not applicable Not applicable 88 1.386 244 69.136 Negative IIB 2.5 PANC 347 26 , 33 Not detected Not applicable Not applicable Not applicable 24 950 171 52.809 Negative IIB 1.5 PANC 348 26.52 KRAS p.G12V, c.35G> T KRAS p.G12V, c.35G> T 0.195 15.9 82 782 166 41.685 Positive IIB 3.1 PANC 349 7.10 Not detected Not applicable Not applicable Not applicable 17 927 166 10.288 Negative IIB 2.5 PANC 350 25.02 KRAS p.G12V, c.35G> T KRAS p.G12V, c .35G> T 0.319 24.6 17 942 301 43.281 Positive IIB 4 PANC 352 19.44 KRAS p.G12V, c.35G> T KRAS p.G12V, c.35G> T 0.536 32.1 101 470 166 40.485 Positive IIB 3.5 PANC 353 50.05 KRAS p.G12V, c.35G> T KRAS p.G12V, c.35G> T 0.038 5.8 17 649 301 22.987 Positive IIA 3.5 PANC 355 16.22 Not detected Not applicable Not applicable Not applicable 104 491 166 29.925 Positive IIB 2.2 PANC 358 19.76 Not detected Not applicable el Not applicable Not applicable 153 1.988 182 42.774 Positive IIB 4.7 PANC 359 83.17 Not detected Not applicable Not applicable Not applicable 39 671 166 125.383 Negative IIB 2 PANC 386 16.51 Not detected Not applicable Not applicable Not applicable 21 3.132 166 68,102 Negative IB 3 PANC 387 11.11 KRAS p.G12R, c.34G> C KRAS p.G12R, c.34G> C 0.118 4.0 305 865 193 78.342 Positive IIB 3.5 PANC 389 38.42 Not detected No applicable Not applicable Not applicable 48 2,230 166 88,144 Negative IIB 4,5 PANC 390 19,48 Not detected Not applicable Not applicable Not applicable 136 1,153 166 44,946 Positive IIA 1,5 PANC 391 28,88 Not detected Not applicable Not applicable Not applicable 123 4.805 471 141.916 Positive IIB 4 PANC 394 49.66 Not detected Not applicable Not applicable Not applicable 19 942 166 27.094 Negative IIB 2 PANC 396 217.52 KRAS p.G12V, c.35G> T KRAS p.G12V, c.35G> T 0.031 20.7 115 4.830 1.161 171.589 Positive IA 1.2 PANC 400 30.05 Not detected Not applicable Not applicable Not applicable 51 7.891 166 61.285 Positive IIB 1.3 PANC 402 43.58 KRAS p.Q61H, c.183A> C KRAS p.Q61H, c.183A> C 0.043 5.8 284 1.779 254 45.704 Positive IIB 4.5 PANC 403 13.51 Not detected Not applicable Not applicable Not applicable 859 2.065 171 22.822 Positive IB 3.2 PANC 404 26.75 Not detected Not applicable Not applicable 37 519 229 72.106 Negative IIA 2.5 PANC 406 59.49 KRAS p.G12V, c .35G> T KRAS p.G12V, c.35G> T 0.050 9.1 319 797 224 98.908 Positive IIB 3

PANC 407 63.44 Not detected Not applicable Not applicable Not applicable 71 888 422 61.686 Negative IIB 1.5 PANC 409 10.58 Not detected Not applicable Not applicable 1.765 2.541 153 36.394 Positive IIB 5.5 PANC 464 13.00 No detected Not applicable Not applicable Not applicable 3.528 6.691 2.314 170.915 Positive IIB 2.5 PANC 465 8.22 Not detected Not applicable Not applicable 344 2.071 168 32.069 Positive IIB 2.4 PANC 467 6.76 KRAS p.G12R, c. 34G> C KRAS p.G12R, c.34G> C 0.761 15.9 1.059 5.191 220 8.328 Positive IIB 3.5 PANC 468 10.28 KRAS p.G12D, c.35G> A KRAS p.G12D, c.35G> A 0.261 8.3 1.399 13.410 609 25.473 Positive IIB 5 PANC 469 5.98 KRAS p.G12A, c.35G> C KRAS p.G12A, c.35G> C 0.125 2.3 40 2.517 466 38.844 Positive IIB 2 PANC 470 6.49 Not detected Not applicable Not applicable Not applicable 26 715 168 59.706 Negative IA 1.3 PANC 471 13.78 Not detected Not applicable Not applicable Not applicable 256 1.718 392 135.997 Positive IIB 2.5 PANC 485 2.64 Not detected No ap eligible Not applicable Not applicable 162 930 153 43.260 Positive IB 3.4 PANC 492 8.99 Not detected Not applicable Not applicable Not applicable 39 1.664 311 25.695 Negative IIB 2.5 PANC 496 11.54 Not detected Not applicable Not applicable Not applicable 1.039 3.693 961 78.977 Positive IIB 3.5 PANC 498 8.89 Not detected Not applicable Not applicable Not applicable 41 616 1.114 199.989 Positive IIB 2.5 PANC 502 13.60 Not detected Not applicable Not applicable Not applicable 94 5.542 1.204 116.386 Positive IB 2 , 8 PANC 506 2.71 Not detected Not applicable Not applicable Not applicable 205 2.195 153 33.292 Positive IIB 7.5 PANC 507 63.64 KRAS p.G12V, c.35G> T KRAS p.G12V, c.35G> T 0.018 3.5 32 2.964 1.994 172.022 Positive IA 1.2 PANC 508 26.47 KRAS p.G12V, c.35G> T KRAS p.G12V, c.35G> T 0.016 1.3 21 744 244 57.888 Positive IIB 1.5 PANC 509 8.60 Not detected Not applicable Not applicable Not applicable 311 2.804 311 38.455 Positive IIB 3.2 PANC 510 9.49 Not detected Not applicable Not applicable Not applicable 16 1.312 153 23.441 Negative IIB 2.9 PANC 511 8.94 Not detected Not applicable Not applicable Not applicable 240 1.642 1.267 60.977 Positive IIB 2.7 PANC 512 4.21 Not detected Not applicable Not applicable Not applicable 768 1.855 153 41.353 Positive IIB 1.9 PANC 513 6.34 Not detected Not applicable Not applicable Not applicable 19 1.193 244 54.510 Negative AI 2.1 PANC 514 7.59 Not detected Not applicable Not applicable 2.200 11.133 1.329 178.169 Positive IA 1.4 PANC 515 5 , 28 KRAS p.G12R, c.34G> C KRAS p.G12R, c.34G> C 0.235 3.8 2.472 12.039 871 164.018 Positive IIB 5 PANC 520 16.01 Not detected Not applicable Not applicable Not applicable 301 2.844 181 114.368 Positive IIB 2.5 PANC 529 6.94 Not detected Not applicable Not applicable Not applicable 60 1.071 395 66.301 Negative IIA 4.5 PANC 532 30.19 Not detected Not applicable Not applicable Not applicable 91 1.947 585 50.001 Negative IB 3.2 PANC 533 7.37 Not detected Not applicable Not applicable Not applicable 27 1.341 649 50.001 Negat ivo IIB 1.5 PANC 534 8.58 KRAS p.G12V, c.35G> T KRAS p.G12V, c.35G> T 0.317 8.4 21 1.061 153 40.392 Positive IIB 2.5 PANC 536 25.27 Not detected Not applicable Not applicable Not applicable 120 1.855 332 72.280 Positive IIB 2.3 PANC 538 15.46 Not detected Not applicable Not applicable Not applicable 146 952 711 84.887 Positive IIA 2.4 PANC 539 6.77 Not detected Not applicable Not applicable Not applicable 16 1.644 756 140.211 Negative IIB 1.5 PANC 540 8.16 Not detected Not applicable Not applicable Not applicable 20 1.158 170 35.144 Negative IIB 2.3 PANC 541 20.74 KRAS p.G12D, c.35G> A KRAS p.G12D , c.35G> A 0.073 4.7 205 2.906 323 25.058 Positive IIB 4.5 PANC 542 5.73 Not detected Not applicable Not applicable Not applicable 31 629 571 37.655 Negative IIB 3.5 PANC 543 9.63 KRAS p.G12V , c.35G> T KRAS p.G12V, c.35G> T 0.490 14.5 2.499 13.902 688 63.505 Positive IIB 4.8 PANC 544 3.82 Not detected Not applicable Not applicable Not applicable 259 932 161 41.347 Positive IB 2, 2 PANC 545 12.23 KRAS p.G12V, c.3 5G> T KRAS p.G12V, c.35G> T 0.189 7.1 16 9.420 350 38.806 Positive IIB 3.2 PANC 546 6.11 Not detected Not applicable Not applicable Not applicable 102 616 369 97.001 Positive IB 3.7 PANC 547 10.73 KRAS p.G12V, c.35G> T KRAS p.G12V, c.35G> T 0.033 1.1 88 689 547 99.692 Positive IIB 3 PANC 548 4.91 Not detected Not applicable Not applicable Not applicable 450 5.248 304 40.912 Positive IIB 3 PANC 549 21.33 Not detected Not applicable Not applicable Not applicable 3.807 911 879 81.002 Positive IIA 3.7 PANC 550 7.44 Not detected Not applicable Not applicable Not applicable 439 1.078 387 243.042 Positive IIA 2.5 PANC 552 10.00 KRAS p.Q61H, c.183A> T KRAS p.Q61H, c.183A> T 0.363 11.2 865 5.884 730 107.142 Positive IIB 2.8 PANC 671 49.52 KRAS p.G12V, c.35G> T Not available 0.035 5.4 146 10.180 2.199 62.382 Positive IIB 5 PANC 676 9.25 Not detected Not applicable Not applicable Not applicable 113 2.563 465 161.059 Positive IIB 2.5 PANC 677 25.79 Not detected Not applicable Not applicable Not applicable 316 4.840 439 154.906 Positive IIB 3 PANC 678 25.51 Not detected Not applicable Not applicable Not applicable 16 1.424 460 66.699 Negative IIB 2.3 PANC 681 5.31 KRAS p.G12R, c.34G> C KRAS p.G12R, c.34G > C 0.086 1.4 100 483 197 98.462 Positive IIB 4 PANC 682 12.50 KRAS p.G12V, c.35G> T KRAS p.G12V, c.35G> T 0.362 13.9 280 1.260 422 128.365 Positive IIB 3 PANC 683 26.55 Not detected Not applicable Not applicable Not applicable 73 2.128 833 121.584 Negative IIB 2.3 PANC 685 2.37 Not detected Not applicable Not applicable 16 453 161 99.923 Negative IIB 3.5 PANC 686 16.17 KRAS p .G12V, c.35G> T KRAS p.G12V, c.35G> T 0.192 9.6 1.015 3.401 1.170 50.532 Positive IIB 4 PANC 687 5.04 Not detected Not applicable Not applicable Not applicable 54 583 161 46.582 Negative IIB 4 PANC 689 11.07 Not detected Not applicable Not applicable Not applicable 39 1.267 341 40.125 Negative IIB 1.5 PANC 690 3.32 KRAS p.G12D, c.35G> A KRAS p.G12D, c.35G> A 0.152 1.6 3.059 2.50 161 38.276 Positive IIB 6 PANC 691 3.99 Does not detect tated Not applicable Not applicable Not applicable 16 982 631 32.496 Negative IB 2.2 PANC 692 9.83 KRAS p.G12D, c.35G> A KRAS p.G12D, c.35G> A 0.155 4.7 226 2.116 355 63.024 Positive IIB 4 PANC 693 22.56 Not detected Not applicable Not applicable Not applicable 16 978 456 71.529 Negative IIB 5 PANC 694 10.64 Not detected Not applicable Not applicable Not applicable 228 2.880 341 375.513 Positive IIB 6 PANC 695 10.44 KRAS p. G12V, c.35G> T KRAS p.G12V, c.35G> T 0.022 0.775 992 378 64.465 Positive IIB 2.6 PANC 696 44.54 KRAS p.G12V, c.35G> T KRAS p.G12V, c.35G> T 0.023 3.2 717 18.814 1.246 273.534 Positive IIB 5.1 PANC 697 0.90 Not detected Not applicable Not applicable Not applicable 522 915 224 107.832 Positive IIB 3.1 PANC 699 7.56 Not detected Not applicable Not applicable applicable Not applicable 174 1.606 161 43.853 Positive IIB 3.1 PANC 700 20.84 KRAS p.G12D, c.35G> A KRAS p.G12D, c.35G> A 0.048 3.1 21 453 395 49.027 Positive IIB 1.5 PANC 701 18.67 Not detected Not applicable Not applicable Not applicable 16 55 7 161 35.235 Negative AI 1.9

PANC 702 11.08 Not detected Not applicable Not applicable Not applicable 24 763 395 59.316 Negative IIB 3.1 PANC 703 9.18 Not detected Not applicable Not applicable 53 1.071 995 91.996 Positive IIB 1.4 PANC 704 18.37 No detected Not applicable Not applicable Not applicable 175 2.423 2.444 97.539 Positive IIB 2.5 PANC 705 3.61 KRAS p.G12D, c.35G> A KRAS p.G12D, c.35G> A 1.047 11.7 24 1.323 161 88.445 Positive IIB 4.8 PANC 706 5.23 KRAS p.G12D, c.35G> A KRAS p.G12D, c.35G> A 0.077 1.2 958 3.830 170 76.477 Positive IIB 3.5 PANC 707 6.10 Not detected No applicable Not applicable Not applicable 16 3.575 506 61.577 Negative IIB 1.5 PANC 708 3.04 KRAS p.G12D, c.35G> A KRAS p.G12D, c.35G> A 0.137 1.3 156 1.431 627 45.733 Positive IIB 3 .8 PANC 709 4.82 Not detected Not applicable Not applicable Not applicable 79 702 323 39.687 Negative IB 3 PANC 710 2.49 Not detected Not applicable Not applicable Not applicable 78 1.257 520 119.731 Negative IIB 0.8 PANC 711 3.20 No detected Not applicable No to applicable Not applicable 95 2,877 950 60,142 Positive IIB 3.8 PANC 712 9.06 Not detected Not applicable Not applicable Not applicable 29 823 798 57.233 Negative IIB 2.8 PANC 713 6.71 Not detected Not applicable Not applicable Not applicable 992 2.610 742 137.147 Positive IIB 4.5 PANC 714 7.35 Not detected Not applicable Not applicable Not applicable 702 1.364 436 130.904 Positive IIB 3.5 PANC 715 1.98 KRAS p.G12D, c.35G> A KRAS p.G12D, c. 35G> A 0.105 0.6 980 3.312 159 54.937 Positive IIB 3.8 PANC 716 2.78 Not detected Not applicable Not applicable Not applicable 16 789 337 39.212 Negative IA 1.5 PANC 718 13.95 Not detected Not applicable Not applicable No applicable 679 2.275 446 73.885 Positive IIB 3 PANC 719 21.28 KRAS p.G12V, c.35G> T KRAS p.G12V, c.35G> T 0.095 6.2 16 2.549 165 96.616 Positive IIB 5 PANC 720 204.08 KRAS p.G12D, c.35G> A KRAS p.G12D, c.35G> A 0.038 23.6 3.998 4.943 3.346 133.514 Positive IIB 13 PANC 721 5.67 Not detected Not applicable Not applicable Not applicable 113 2.377 159 50.47 2 Positive IB 2.5 PANC 722 16.61 Not detected Not applicable Not applicable Not applicable 453 1.027 659 50.874 Positive IIB 3 PANC 723 5.32 Not detected Not applicable Not applicable 60 1.208 165 44.355 Negative IB 3 PANC 724 22, 12 KRAS p.G12D, c.35G> A KRAS p.G12D, c.35G> A 0,681 46,4 2,137 1,120 621 81,074 Positive IIB 3 PANC 725 11,90 Not detected Not applicable Not applicable Not applicable 489 2,322 492 108,399 Positive IB 3.2 PANC 726 4.47 Not detected Not applicable Not applicable Not applicable 54 1.199 316 43.871 Negative IIB 0.6 PANC 728 8.52 Not detected Not applicable Not applicable Not applicable 322 2.377 305 47.738 Positive IIB 2.7 PANC 729 15.76 Not detected Not applicable Not applicable Not applicable 83 1.346 203 28.360 Negative IIB 4.9 PANC 730 7.34 KRAS p.G12D, c.35G> A KRAS p.G12D, c.35G> A 0.552 12.5 986 712 717 76,921 Positive IIB 3 PANC 731 6.87 Not detected Not applicable Not applicable Not applicable 62 1.803 295 35.198 Negative IIB 2.4 PANC 732 7.11 Does not detect do Not applicable Not applicable Not applicable 232 918 483 72.023 Positive IIB 2 PANC 734 9.96 Not detected Not applicable Not applicable Not applicable 16 1.840 203 65.339 Negative IIB 2.3 PANC 735 3.05 KRAS p.G12R, c.34G> C KRAS p.G12R, c.34G> C 0.191 1.8 16 5.061 159 53.287 Positive IIB 2.5 PANC 736 8.25 Not detected Not applicable Not applicable Not applicable 16 729 625 53.206 Negative IA 2.5 PANC 738 85, 58 Not detected Not applicable Not applicable Not applicable 219 2.294 1.639 103.509 Positive IIB 7 PANC 739 1.36 Not detected Not applicable Not applicable Not applicable 17 658 165 22.376 Negative IIB 2.5 PANC 742 11.51 Not detected Not applicable Not applicable No applicable 16 708 203 62,410 Negative IA 1.3 PANC 743 4,35 Not detected Not applicable Not applicable Not applicable 16 1,257 250 55,460 Negative IIB 3.7 PANC 744 18,95 Not detected Not applicable Not applicable Not applicable 55 623 1,052 53,689 Positive IIB 3.4 PANC 747 11.99 KRAS p.G12D, c.35G> A Not available 0.559 20.6 492 2.677 663 62.573 Positive IIB 1.5 PANC 748 5.88 Not detected Not applicable Not applicable Not applicable 17 1.794 407 14.425 Negative IA 2.1 PANC 750 31.50 Not detected Not applicable Not applicable Not applicable 369 953 1.353 57.798 Positive IIB 2 PANC 759 20.82 KRAS p.G12V, c.35G> T KRAS p.G12V, c.35G> T 0.025 1.6 102 5.464 178 59.616 Positive IIB 5 PANC 760 3.51 Not detected Not applicable Not applicable Not applicable 16 3.601 233 48,824 Negative IIB 3 PANC 761 3.59 Not detected Not applicable Not applicable Not applicable 72 1.208 203 44.396 Negative IIA 3 PANC 762 18.45 KRAS p.G12R, c.34G> C KRAS p.G12R, c.34G> C 0.052 3.0 2.159 15.443 412 51.759 Positive IIB 4.5 PANC 765 22.24 KRAS p.G12D, c.35G> A KRAS p.G12D, c.35G> A 0.062 4.2 983 12.251 2.968 237.322 Positive IIB 3 PANC 766 14.29 Not detected Not applicable Not applicable Not applicable 34 2.070 11.433 39.090 Positive IIB 2.5 PANC 767 3.05 Not detected Not applicable Not applicable 40 568 261 64.280 Negative IIB 2.5 PANC 768 3.16 N Not detected Not applicable Not applicable Not applicable 16 1,876 159 32,042 Negative IIB 5 PANC 769 5,07 Not detected Not applicable Not applicable Not applicable 41 729 284 50,231 Negative IIB 2,2 PANCA 1001 6,35 Not detected Not applicable Not applicable Not applicable 54 653 357 74.764 Negative IIA 8.1 PANCA 1006 196.38 Not detected Not applicable Not applicable Not applicable 468 1.316 543 336.611 Positive IIB 2.6 PANCA 1009 4.62 KRAS p.Q61H, c.183A> C Not available 1,002 14 , 2 521 3,941 188 70,449 Positive IIB 3,5 PANCA 1010 7.47 Not detected Not applicable Not applicable Not applicable 18 748 255 50,834 Negative IA 1.5 PANCA 1011 26,58 KRAS p.G12D, c.35G> A Not available 0.054 4.4 367 2.297 799 100.221 Positive IIB 3.5 PANCA 1012 5.43 Not detected Not applicable Not applicable Not applicable 106 1.382 471 51.979 Positive IIB 3.5 PANCA 1014 45.97 KRAS p.G12D, c.35G> A Not available 0.135 19.1 17 7.747 770 127.393 Positive IIB 2.7 PANCA 1018 18.46 Not detected Not applicable Not applicable No applicable 350 3,362 653 70,298 Positive IIB 2 PANCA 1020 15,47 Not detected Not applicable Not applicable Not applicable 44 1,741 808 83,017 Negative IIB 2,2 PANCA 1022 24,75 Not detected Not applicable Not applicable 57 1,208 388 74,679 Negative IIB 2 , 2 PANCA 1024 2.66 Not detected Not applicable Not applicable Not applicable 17 1.299 225 39.828 Negative IIB 3 PANCA 1030 23.08 Not detected Not applicable Not applicable Not applicable 42 467 425 97.065 Negative IIA 3.5 PANCA 1031 3.53 No detected Not applicable Not applicable Not applicable 885 5.128 270 65.798 Positive IIB 5.1 PANCA 1034 5.64 Not detected Not applicable Not applicable Not applicable 56 2.598 539 27.895 Negative IIB 2.5 PANCA 1036 3.95 Not detected Not applicable Not applicable No applicable 16 1.208 471 43.230 Negative IIA 2.1 PANCA 1039 12.31 Not detected Not applicable Not applicable Not applicable 221 1.636 304 93.935 Positive IIB 3.5 PANCA 1040 17.85 Not detected Not applicable Not applicable 38 1.76 0 407 39,719 Negative IIA 2

PANCA 1042 11.96 KRAS p.G12V, c.35G> T Not available 0.168 6.2 21 723 234 42.484 Positive IIB 9 PANCA 1043 4.31 Not detected Not applicable Not applicable Not applicable 172 3.383 363 78.154 Positive IIA 3.9 PANCA 1044 7.59 KRAS p.G12R, c.34G> C Not available 0.240 5.6 57 2.527 516 67.807 Positive IIB 3.5 PANCA 1047 3.55 Not detected Not applicable Not applicable Not applicable 68 1.471 263 25.107 Negative IIB 3 PANCA 1050 67.68 KRAS p.G12D, c.35G> A Not available 0.030 6.2 124 1.068 240 69.507 Positive IIB 2.4 PANCA 1051 48.18 Not detected Not applicable Not applicable Not applicable 149 1.688 643 124.142 Positive IIB 3 , 2 PANCA 1052 7.75 Not detected Not applicable Not applicable Not applicable 17 774 209 42.915 Negative IIA 3 PANCA 1053 37.23 Not detected Not applicable Not applicable Not applicable 1.488 1.945 287 209.138 Positive IIB 2.4 PANCA 1054 14.89 No detected Not applicable Not applicable Not applicable 225 1.212 643 171.694 Positive IIB 2.8 PANCA 1055 3.20 Not detected Not applicable Not applicable Not applicable 20 519 166 36.638 Negative IIB 1.5 PANCA 1056 15.66 Not detected Not applicable Not applicable Not applicable 77 1.702 527 79.215 Negative IIA 3.5 PANCA 1057 4.35 Not detected Not applicable Not applicable Not applicable 643 1.522 193 35.159 Positive IIB 3.4 PANCA 1058 13.87 Not detected Not applicable Not applicable Not applicable 75 4.573 255 131.223 Negative IIB 2 PANCA 1059 5.55 KRAS p.Q61H, c.183A> T Not available 0.139 2.4 511 1.856 225 39.537 Positive IIB 4.6 PANCA 1061 31.05 KRAS p.G12D, c.35G> A Not available 0.293 28.1 497 6.198 693 64.133 Positive IIB 4 PANCA 1063 4.77 Not detected Not applicable Not applicable Not applicable 317 2.309 166 23.728 Positive IIB 3.5 PANCA 1068 2.75 Not detected Not applicable Not applicable Not applicable 132 3.007 1.351 74.987 Positive IIA 1.5 PANCA 1072 2.97 Not detected Not applicable Not applicable Not applicable 346 4.152 351 43.140 Positive IIB 10, 7 PANCA 1075 3.28 Not detected Not applicable Not applicable Not applicable 53 1.290 437 69.424 Negative IIA 2.7 PANCA 1077 4.94 Not detected Not applicable Not applicable Not applicable Not applicable 149 706 437 38.236 Positive IIB 2.2 PANCA 1081 5.94 Not detected Not applicable Not applicable Not applicable 571 887 5.831 61.522 Positive IIA 4 , 5 PANCA 1082 16.29 KRAS p.G12V, c.35G> T Not available 0.069 3.5 37 672 325 33.631 Positive IIB 2.9 PANCA 1086 11.57 KRAS p.G12D, c.35G> A Not available 0.143 5.1 35 3.621 176 30.480 Positive IIA 3.2 PANCA 1093 25.35 KRAS p.G12V, c.35G> T Not available 0.032 2.5 766 1.019 413 27.440 Positive IIB 2.8 PANCA 1096 12.58 KRAS p. G12R, c.34G> C Not available 0.016 0.6 175 736 159 21.452 Positive IIA 2.3 PANCA 1098 15.31 KRAS p.G12C, c.34G> T Not available 0.164 7.7 17 4.174 193 33.631 Positive IIA 3 .5 PANCA 1099 29.75 Not detected Not applicable Not applicable Not applicable Not applicable 40 576 6.969 38.006 Positive IIB 2.6 PANCA 1101 7.29 Not detected Not applicable Not applicable Not applicable 144 1.434 351 55.228 Positive IIB 6.2 PANCA 1103 56, 56 Not detected o Not applicable Not applicable Not applicable 100 740 623 48.670 Positive IIB 7 PANCA 1104 15.77 KRAS p.G12D, c.35G> A KRAS p.G12D, c.35G> A 0.057 2.8 348 714 209 78.418 Positive IIB 3 , 2 PANCA 1105 11.49 KRAS p.G12D, c.35G> A KRAS p.G12D, c.35G> A 0.124 4.4 17 1.373 240 31.826 Positive IA 2 PANCA 1106 5.97 Not detected Not applicable Not applicable No applicable 23 531 225 39,106 Negative IB 2,4 PANCA 1107 19,46 KRAS p.G12D, c.35G> A KRAS p.G12D, c.35G> A 0,038 2,3 230 1,693 159 46,762 Positive IIB 6.5 PANCA 1108 19.29 Not detected Not applicable Not applicable Not applicable 128 1.683 255 41.923 Positive IIB 2.5 PANCA 1109 27.57 Not detected Not applicable Not applicable Not applicable 240 1.645 500 159.902 Positive IIB 2.6 PANCA 1112 31.13 Not detected No applicable Not applicable Not applicable 721 18.071 868 69.336 Positive IIB 4 PANCA 1114 8.49 Not detected Not applicable Not applicable Not applicable 118 1.217 338 29.314 Positive IB 2.4 PANCA 1116 11.96 Not detected Not applicable Not applicable Not applicable 928 17.570 338 52.155 Positive IIB 2.4 PANCA 1117 4.72 Not detected Not applicable Not applicable Not applicable 35 2.080 225 27.191 Negative IIB 1.8 PANCA 1120 14.63 Not detected Not applicable Not applicable Not applicable 18 959 217 99.822 Negative IB 3 PANCA 1121 5.03 KRAS p.G12D, c.35G> A KRAS p.G12D, c.35G> A 0.303 4.7 18 1.084 207 129.675 Positive IIB 3.7 PANCA 1122 15.72 Not detected Not applicable Not applicable Not applicable 33 600 226 67.353 Negative IIB 3.1 PANCA 1123 9.61 Not detected Not applicable Not applicable Not applicable 602 7.363 165 72.515 Positive IIB 3.7 PANCA 1124 60.56 Not detected Not applicable Not applicable Not applicable 55 1.887 327 49,108 Negative IIB 2.8 PANCA 1125 4.42 Not detected Not applicable Not applicable Not applicable 17 456 188 65.246 Negative IIB 4.5 PANCA 1126 7.41 KRAS p.G12R, c.34G> C KRAS p.G12R, c .34G> C 0.086 2.0 921 1.695 349 27.386 Positive IIB 6.3 PANCA 1128 5.70 Not detected Not applicable Not applicable Not applicable 44 791 279 49, 199 Negative IIB 3.9 PANCA 1130 35.65 KRAS p.G12R, c.34G> C KRAS p.G12R, c.34G> C 0.012 1.3 17 456 327 124.862 Positive IIB 3 PANCA 1131 9.85 Not detected No applicable Not applicable Not applicable 90 2.50 253 74.674 Negative IIB 3.8 PANCA 1132 8.62 KRAS p.G12V, c.35G> T Not available 0.031 0.8 17 697 165 99.541 Positive IIB 3.5 PANCA 1133 4.08 No detected Not applicable Not applicable Not applicable 76 668 167 47.645 Negative IIB 6 PANCA 1134 4.34 Not detected Not applicable Not applicable 230 456 262 81.905 Positive IIB 4.8 PANCA 1135 3.84 KRAS p.G12V, c.35G> T KRAS p.G12V, c.35G> T 0.070 0.8 113 8.486 188 66.142 Positive IIB 4 PANCA 1136 10.09 Not detected Not applicable Not applicable 295 964 167 99.588 Positive IIB 2.4 PANCA 1137 23.59 No detected Not applicable Not applicable Not applicable 31 673 342 110.202 Negative IIB 3 PANCA 1138 71.54 Not detected Not applicable Not applicable Not applicable 49 456 295 79.503 Negative IIB 6 PANCA 1140 1.72 Not detected N Not applicable Not applicable Not applicable 17 456 165 31.215 Negative IIB 4.4 PANCA 1141 7.23 Not detected Not applicable Not applicable Not applicable 91 766 207 67.846 Negative IB 2.4 PANCA 1142 18.82 Not detected Not applicable Not applicable Not applicable 92 619 311 123.668 Negative IIB 3.7

[1241] highlighted values represent protein concentrations below or below the detection limit of the assay; adjusted values as corresponding upper and lower detection limits

TNM Stay.

Age Gender T2N0M0 63 M T3N1M0 70 M T3N1M0 53 M T3N1M0 68 F T3N1M0 63 F T3N1M0 73 M T3N1M0 63 M T3N0M0 51 M T2N1M0 82 F T3N1M0 69 M T3N1M0 62 M T3N1M0 69 M T3N1M0 70 M T3N1M0 70 M T3N1M0 T3N1M0 58 M T3N1M0 65 M T2N0M0 44 M T3N1M0 68 M T2N1M0 72 F T3N0M0 62 M T2N1M0 69 M T1N1M0 40 F T1N0M0 74 M T2N1M0 69 F T3N1M0 86 F T2N0M0 63 F

T3N0M0 72 M T3N1M0 73 F T1N1M0 68 M T3N1M0 73 M T3N1M0 56 F T2N1M0 51 M T3N1M0 27 M T3N1M0 67 M T3N1M0 64 F T1N0M0 64 F T3N1M0 41 M T2N1M0 78 M T2N1M0 63 M T3N1M0 63 M T3N1M0 F T1N0M0 56 F T1N1M0 78 F T2N1M0 53 F T3N1M0 72 M T2N1M0 72 M T1N1M0 51 M T1N0M0 83 M T1N0M0 80 F T2N1M0 79 M T3N1M0 67 M T3N0M0 66 M T2N0M0 77 M T3N1M0 76 M

T3N1M0 77 F T3N1M0 65 M T3N0M0 70 M T1N1M0 79 M T2N1M0 64 F T3N1M0 70 F T3N1M0 59 F T3N1M0 71 F T2N0M0 59 F T2N1M0 74 F T2N0M0 58 M T3N1M0 57 M T2N1M0 69 M T3N0M1 M T3N1M0 65 F T3N1M0 73 M T3N1M0 48 M T2N1M0 61 F T3N1M0 66 M T2N1M0 72 M T3N1M0 70 F T2N1M0 82 F T3N1M0 81 F T3N1M0 44 M T2N0M0 67 M T3N1M0 80 M

T3N1M0 80 F T3N1M0 60 M T3N1M0 74 M T3N1M0 79 M T3N1M0 63 M T2N1M0 63 M T1N1M0 68 F T1N0M0 64 F T2N1M0 85 F T3N1M0 62 F T3N1M0 68 F T3N1M0 68 F T3N1M0 68 M T3N1M0 M T2N1M0 78 M T3N1M0 76 F T2N1M0 72 M T3N1M0 65 M T3N1M0 75 M T1N0M0 62 M T3N1M0 55 M T3N1M0 68 F T3N1M0 57 M T2N0M0 81 M T2N1M0 91 F T2N1M0 84 F T2N1M0 84 F T2N1M0 84 F T2N1M0 84 F

T2N0M0 76 M T1N1M0 72 M T3N1M0 65 F T3N1M0 73 M T3N1M0 58 F T3N1M0 48 M T3N1M0 61 M T3N1M0 84 F T2N1M0 82 M T1N0M0 64 F T3N1M0 70 M T3N1M0 79 F T1N0M0 66 F T3N1M0 71 F F T2N1M0 71 F F F T3N1M0 68 F T3N1M0 57 F T3N1M0 49 M T3N0M0 54 M T3N1M0 71 F T3N1M0 48 F T2N1M0 72 M T3N1M0 71 M T3N1M0 60 F T3N1M0 60 M T3N0M0 74 M T3N1M0 78 F T3N1M0 66 F T3N1M0 66 F

T1N0M0 86 M T3N1M0 72 M T3N1M0 69 F T3N1M0 65 M T1N1M0 53 F T3N1M0 56 F T3N1M0 70 M T3N1M0 58 F T3N0M0 69 M T3N1M0 82 F T3N1M0 63 F T3N0M0 63 F T3N1M0 63 M T3N0M0 63 M T3N0M0 63 M T3N0M0 63 M T3N0M0 F T3N1M0 48 M T3N1M0 68 F T3N1M0 62 M T3N0M0 78 M T3N1M0 78 F T3N1M0 75 F T3N1M0 60 M T3N0M0 64 M T3N1M0 71 M T3N1M0 68 F T3N1M0 55 M T3N1M0 67 M T3N1M0 62 M

T3N0M0 73 F T3N1M0 76 F T3N0M0 78 M T3N1M0 56 F T3N0M0 62 F T3N1M0 62 F T3N0M0 59 F T3N1M0 65 M T3N0M0 59 F T3N0M0 61 M T3N1M0 59 F T3N1M0 81 F T3N1M0 69 F T3N1M0 57 F T3N1M0 M T3N1M0 53 M T3N1M0 61 M T3N1M0 68 F T2N0M0 61 F T3N1M0 85 F T1N1M0 70 F T2N0M0 85 M T3N1M0 86 F T3N1M0 58 F T3N1M0 74 M T2N1M0 73 F T3N1M0 63 F T3N1M0 63 F T3N1M0 63 F T3N1M0 63 F

T3N1M0 70 M T3N1M0 66 M T3N1M0 81 M T3N1M0 78 M T3N1M0 72 M T3N1M0 67 M T3N1M0 41 F T3N1M0 57 F T3N1M0 74 M T3N1M0 72 F T3N1M0 63 F T2N0M0 74 F T3N1M0 60 M Biomarkers and M mutations summary of protein identified in 182 healthy patients Plasma DNA CA19-9 conc.

CEA conc.

HGF conc.

OPN conc. concentration Mutation identif, Freq. plasma allele Frag.Mutant plasma fragment plasma palsma plasma Result combin.

SampleID # (ng / mL) in mutant plasma (%) / mL plasma (U / mL) (pg / mL) (pg / mL) (pg / mL) (Positive / Negative) Age Gender LCR 588 7.34 Not detected Not applicable Not applicable 21 803 182 35.610 Negative 63 F LCR 589 7.48 Not detected Not applicable Not applicable 16 686 165 17.420 Negative 45 M LCR 590 8.50 Not detected Not applicable Not applicable 16 426 247 26.361 Negative 54 M LCR 591 10 , 50 Not detected Not applicable Not applicable 16 2,041 258 55,002 Negative 44 M LCR 592 5,04 Not detected Not applicable Not applicable 16 432 203 28,195 Negative 53 F LCR 593 11,14 Not detected Not applicable Not applicable 16 900 258 18,755 Negative 65 M LCR 594 4.84 Not detected Not applicable Not applicable 16 468 162 14.061 Negative 57 M LCR 595 3.73 Not detected Not applicable Not applicable 27 1.303 162 14.183 Negative 65 M LCR 597 4.77 Not detected Not applicable Not applicable 16 459 351 38.344 Negative 51 M LCR 599 3.41 Not detected Not applicable Not applicable 16 1.732 162 37.623 Negative 51 M LCR 6 00 4.65 Not detected Not applicable Not applicable 16 579 162 37.929 Negative 66 F LCR 601 3.19 Not detected Not applicable Not applicable 16 696 185 13.362 Negative 61 M LCR 602 9.94 KRAS p.G12C, c.34G> T 0.009 0.3 16 744 217 10.376 Positive 69 M LCR 603 2.92 Not detected Not applicable Not applicable 28 470 162 10.738 Negative 60 F LCR 604 7.73 Not detected Not applicable Not applicable 16 506 162 19.584 Negative 63 F LCR 605 5 , 75 Not detected Not applicable Not applicable 16 426 162 8,921 Negative 67 M LCR 606 6.06 Not detected Not applicable Not applicable 16 426 200 15.269 Negative 55 M LCR 607 8.85 Not detected Not applicable Not applicable 16 426 226 28.984 Negative 43 F LCR 608 3.74 Not detected Not applicable Not applicable 16 636 241 26.108 Negative 66 F LCR 610 2.05 Not detected Not applicable Not applicable 16 567 162 20.444 Negative 51 M LCR 611 2.71 Not detected Not applicable Not applicable 29 699 162 23.243 Negative 66 F LCR 612 4.86 Not detected Not applicable Not applicable 16 93 4 235 40.079 Negative 62 F LCR 613 2.46 Not detected Not applicable Not applicable 16 444 165 16.178 Negative 48 F LCR 614 5.18 Not detected Not applicable Not applicable 22 975 238 26.919 Negative 56 F

CSF 616 9.30 Not detected Not applicable Not applicable 16 2.127 167 35.610 Negative 81 F CSF 617 8.47 Not detected Not applicable Not applicable 16 754 270 22.878 Negative 61 F CSF 618 3.18 Not detected Not applicable Not applicable 16 1.248 162 20,592 Negative 60 F LCR 619 5.61 Not detected Not applicable Not applicable 16 426 162 9.660 Negative 55 F LCR 620 5.33 Not detected Not applicable Not applicable 16 549 247 22.200 Negative 59 F LCR 621 2.80 Not detected Not applicable Not applicable applicable 16 592 162 40.622 Negative 79 F LCR 622 16.35 Not detected Not applicable Not applicable 17 790 223 29.486 Negative 57 F LCR 623 5.34 Not detected Not applicable Not applicable 16 2.241 191 9.963 Negative 45 F LCR 624 3.81 No detected Not applicable Not applicable 16 1,131 165 19,490 Negative 71 F LCR 625 17.16 Not detected Not applicable Not applicable 16 441 302 18,452 Negative 76 M LCR 626 3.95 Not detected Not applicable Not applicable 16 1,335 162 18,604 Negative 60 F LCR 627 2, 76 Not detected Not after eligible Not applicable 16 426 170 29.790 Negative 69 F LCR 628 2.29 Not detected Not applicable Not applicable 16 777 165 7.349 Negative 54 F LCR 629 7.50 Not detected Not applicable Not applicable 16 744 794 24.299 Negative 54 M LCR 630 14, 32 Not detected Not applicable Not applicable 19 985 176 36.077 Negative 79 F LCR 631 3.87 Not detected Not applicable Not applicable 16 876 165 13.279 Negative 59 F LCR 632 7.22 Not detected Not applicable Not applicable 16 1.089 162 9.543 Negative 66 F CSF 633 10.48 Not detected Not applicable Not applicable 17 693 194 34.462 Negative 60 M LCR 634 5.81 Not detected Not applicable Not applicable 23 667 162 42.951 Negative 62 M CSF 635 4.11 Not detected Not applicable Not applicable 16 462 162 8,799 Negative 66 M LCR 636 4.08 Not detected Not applicable Not applicable 17 1.357 194 65.503 Negative 72 F LCR 637 2.56 Not detected Not applicable Not applicable 16 518 176 18.774 Negative 47 F LCR 638 3.27 Not detected Not applicable No applicable 16 1.040 229 31.61 6 Negative 78 F LCR 639 5.42 Not detected Not applicable Not applicable 16 715 206 8.578 Negative 71 M LCR 640 9.99 Not detected Not applicable Not applicable 16 2.041 235 38.236 Negative 73 M LCR 641 5.50 Not detected Not applicable No applicable 16 503 188 19.809 Negative 56 M LCR 642 6.58 Not detected Not applicable Not applicable 16 836 162 36.364 Negative 63 F LCR 643 2.70 Not detected Not applicable Not applicable 16 1.152 165 10.961 Negative 61 M LCR 644 4.70 No detected Not applicable Not applicable 16 783 162 17,150 Negative 61 F LCR 645 8.55 Not detected Not applicable Not applicable 16 524 182 8.329 Negative 62 F LCR 646 7.59 Not detected Not applicable Not applicable 16 426 188 29.987 Negative 77 M LCR 647 7.61 Not detected Not applicable Not applicable 16 426 162 34.606 Negative 76 F LCR 648 5.30 Not detected Not applicable Not applicable 16 579 162 32.743 Negative 55 M LCR 649 7.69 Not detected Not applicable Not applicable 16 1.274 162 11.315 Negative 61 F LCR 650 4.06 Does not detect Not applicable Not applicable 38 1.660 182 9.870 Negative 51 F LCR 652 4.71 Not detected Not applicable Not applicable 16 426 162 12.882 Negative 58 F LCR 653 21.77 Not detected Not applicable Not applicable 16 426 162 40.550 Negative 75 F LCR 654 3.38 Not detected Not applicable Not applicable 16 546 165 6.750 Negative 51 F LCR 655 3.34 Not detected Not applicable Not applicable 16 1.138 162 43.097 Negative 60 M LCR 658 1.80 Not detected Not applicable Not applicable 16 500 162 19.020 Negative 57 F LCR 659 4.23 Not detected Not applicable Not applicable 16 426 176 8.726 Negative 61 F LCR 660 1.72 Not detected Not applicable Not applicable 16 1.064 188 24.843 Negative 61 M LCR 661 1.04 Not detected Not applicable Not applicable 16 790 162 8.077 Negative 69 M LCR 662 3.35 Not detected Not applicable Not applicable 16 482 229 30.309 Negative 59 M LCR 663 4.40 Not detected Not applicable Not applicable 16 450 229 44.524 Negative 81 M LCR 665 8.49 Not detected No applicable Not applicable 16 467 2 85 32.269 Negative 68 M LCR 666 1.95 Not detected Not applicable Not applicable 18 1.926 229 57.623 Negative 72 M LCR 667 2.13 Not detected Not applicable Not applicable 16 719 266 39.455 Negative 50 F LCR 668 5.91 Not detected Not applicable Not applicable 16 975 242 25.239 Negative 80 F LCR 670 2.93 Not detected Not applicable Not applicable 16 520 229 46.196 Negative 63 F LCR 671 0.90 Not detected Not applicable Not applicable 16 1.878 185 30.792 Negative 52 F LCR 672 2.56 Not detected Not applicable Not applicable 31 1.583 248 18.261 Negative 66 M LCR 673 2.67 Not detected Not applicable Not applicable 24 1.048 235 8.727 Negative 58 F LCR 674 5.50 Not detected Not applicable Not applicable 16 1.038 223 33.914 Negative 63 F LCR 675 8.63 Not detected Not applicable Not applicable 16 884 313 29.085 Negative 55 M LCR 676 3.34 Not detected Not applicable Not applicable 16 2.128 282 35.973 Negative 63 M LCR 677 12.26 Not detected Not applicable Not applicable 16 1.104 232 55.870 Negative 73 M

CSF 678 3.13 Not detected Not applicable Not applicable 16 1.190 260 50.299 Negative 56 F LCR 679 5.80 Not detected Not applicable Not applicable 16 523 165 18.044 Negative 66 M CSF 680 4.18 Not detected Not applicable Not applicable 18 1.023 183 31.692 Negative 64 F LCR 681 5.83 Not detected Not applicable Not applicable 16 2.417 322 28.243 Negative 61 F LCR 682 6.17 Not detected Not applicable Not applicable 16 1.027 164 36.327 Negative 65 F LCR 683 6.17 Not detected Not applicable Not applicable applicable 16 460 178 24,109 Negative 82 M LCR 684 4.83 Not detected Not applicable Not applicable 16 460 235 29.522 Negative 56 F LCR 685 8.62 Not detected Not applicable Not applicable 41 6.031 254 87.258 Negative 63 F LCR 686 4.78 No detected Not applicable Not applicable 16 502 242 34.839 Negative 58 F LCR 687 1.96 Not detected Not applicable Not applicable 20 1.579 164 64.373 Negative 51 F LCR 688 7.42 Not detected Not applicable Not applicable 37 1.045 279 15.710 Negative 80 M LCR 689 1.99 Not detected No the applicable Not applicable 16 569 172 21,582 Negative 57 M LCR 690 6.21 Not detected Not applicable Not applicable 28 1,094 185 52,982 Negative 81 M LCR 691 3,99 Not detected Not applicable Not applicable 21 887 216 48,990 Negative 53 M LCR 692 3 , 42 Not detected Not applicable Not applicable 18 5,826 304 72,589 Negative 66 M LCR 693 2,57 Not detected Not applicable Not applicable 16 735 164 41,653 Negative 61 M LCR 694 1,54 Not detected Not applicable Not applicable 23 713 216 40,538 Negative 59 F LCR 697 12.02 Not detected Not applicable Not applicable 16 599 254 35.549 Negative 61 M LCR 698 6.49 Not detected Not applicable Not applicable 38 2.371 341 35.178 Negative 74 M LCR 699 0.90 Not detected Not applicable Not applicable 19 724 264 29,592 Negative 71 F LCR 700 3.76 Not detected Not applicable Not applicable 28 1.451 307 96.120 Negative 78 F LCR 701 1.86 Not detected Not applicable Not applicable 16 539 311 21.619 Negative 52 M LCR 702 3.95 Not detected Not applicable Not applicable 16 1,650 398 133.358 Negative 85 F LCR 703 0.65 Not detected Not applicable Not applicable 20 569 223 46.299 Negative 85 M LCR 704 7.32 Not detected Not applicable Not applicable 16 2.616 406 35.208 Negative 81 M LCR 705 5.14 Not detected Not applicable Not applicable 33 1.038 273 55.638 Negative 54 F LCR 706 10.62 Not detected Not applicable Not applicable 32 1.090 273 23.953 Negative 66 M LCR 707 3.90 Not detected Not applicable Not applicable 16 1.586 365 43.633 Negative 82 M LCR 709 2.36 Not detected Not applicable Not applicable 16 1.795 467 28.573 Negative 67 F LCR 710 7.20 Not detected Not applicable Not applicable 16 958 437 24.261 Negative 71 M LCR 711 2.26 Not detected Not applicable Not applicable 16 1.345 298 49.222 Negative 66 M LCR 712 11.51 Not detected Not applicable Not applicable 16 1.823 213 41.932 Negative 61 M LCR 713 9.78 Not detected Not applicable Not applicable 45 3.706 260 31.262 Negative 79 M LCR 714 1.93 Not detected Not applicable Not applicable 16 917 266 48.714 Negative 72 F LCR 715 0.50 Not detected Not applicable Not applicable 16 972 202 23.148 Negative 52 M LCR 716 5.21 Not detected Not applicable Not applicable 16 885 324 25.231 Negative 69 M LCR 717 1.27 Not detected Not applicable Not applicable 41 1.788 670 54.642 Negative 75 F LCR 718 3.12 Not detected Not applicable Not applicable 16 778 238 28.390 Negative 52 M LCR 719 3.19 Not detected Not applicable Not applicable 16 593 169 45.608 Negative 60 M LCR 720 3.36 Not detected Not applicable Not applicable 16 2.475 198 56.420 Negative 61 F LCR 721 5.91 Not detected Not applicable Not applicable 17 1.302 198 60.134 Negative 75 M LCR 722 5.16 Not detected Not applicable Not applicable 16 710 166 71.301 Negative 80 M LCR 723 5.22 Not detected Not applicable Not applicable 20 1.076 175 40.538 Negative 61 F LCR 725 2.85 Not detected Not applicable Not applicable 16 2.492 172 43.981 Negative 65 F LCR 726 1.45 Not detected Not applicable Not applicable 16 1.435 307 59.059 Negative 60 F LCR 727 1.80 Not detected No the applicable Not applicable 16 1.388 165 12.989 Negative 65 M LCR 728 1.82 Not detected Not applicable Not applicable 16 436 510 22.565 Negative 51 M LCR 729 1.61 Not detected Not applicable Not applicable 18 2.384 307 38.693 Negative 67 M LCR 730 2 , 75 Not detected Not applicable Not applicable 16 460 164 25,950 Negative 71 M LCR 731 1.45 Not detected Not applicable Not applicable 16 436 165 22.922 Negative 56 M LCR 732 0.76 Not detected Not applicable Not applicable 16 1.188 255 64.361 Negative 50 M LCR 733 3.81 Not detected Not applicable Not applicable 26 1.567 172 17.531 Negative 80 M LCR 734 3.43 Not detected Not applicable Not applicable 16 1.310 260 66.017 Negative 77 F LCR 736 2.97 Not detected Not applicable Not applicable 22 2.152 178 84.797 Negative 59 M LCR 737 2.65 Not detected Not applicable Not applicable 17 1.068 242 97.150 Negative 81 M LCR 738 7.94 Not detected Not applicable Not applicable 16 2.50 383 122.519 Negative 84 F LCR 739 1.95 Not detected Not applicable Not applicable 16 1.306 235 109.025 Negative 36 M

CRL 740 3.11 Not detected Not applicable Not applicable 27 1.863 172 82.561 Negative 81 M CRL 741 100.42 Not detected Not applicable Not applicable 16 1.027 297 121.739 Negative 65 M CRL 742 5.67 Not detected Not applicable Not applicable 32 615 316 60,066 Negative 50 M LCR 743 4.22 Not detected Not applicable Not applicable 26 1,792 488 147,000 Negative 77 M LCR 744 4.06 Not detected Not applicable Not applicable 47 3,195 201 43.356 Negative 68 M LCR 745 1.30 Not detected Not applicable No applicable 31 744 183 22.467 Negative 60 F LCR 746 1.53 Not detected Not applicable Not applicable 16 2.197 346 69.390 Negative 53 M LCR 747 1.78 Not detected Not applicable Not applicable 16 4.709 165 52.773 Negative 80 M LCR 749 1.32 No detected Not applicable Not applicable 19 1,062 337 60,685 Negative 70 F LCR 750 1.36 Not detected Not applicable Not applicable 16 2,957 183 30,706 Negative 78 M LCR 751 2.10 Not detected Not applicable Not applicable 16 1,049 220 18,994 Negative 76 M LCR 752 9.31 Does not hold ctado Not applicable Not applicable 16 965 183 157.671 Negative 88 M LCR 753 1.52 Not detected Not applicable Not applicable 16 717 349 61.187 Negative 80 M LCR 754 2.21 Not detected Not applicable Not applicable 16 649 211 31.323 Negative 40 M LCR 755 1.39 Not detected Not applicable Not applicable 21 1.060 251 19.701 Negative 55 F LCR 757 1.62 Not detected Not applicable Not applicable 59 2.893 430 90.072 Negative 75 F LCR 758 67.40 Not detected Not applicable Not applicable 52 2.628 266 25.239 Negative 73 M LCR 759 1.97 Not detected Not applicable Not applicable 19 2.415 556 113.487 Negative 55 M LCR 760 0.65 Not detected Not applicable Not applicable 16 436 193 77.203 Negative 55 F LCR 761 0.94 Not detected Not applicable Not applicable 16 1.490 246 13.138 Negative 62 M LCR 762 135.67 Not detected Not applicable Not applicable 16 538 300 27.765 Negative 73 M LCR 763 37.22 Not detected Not applicable Not applicable 16 460 188 26.953 Negative 60 M LCR 764 71.68 Not detected No applicable Not ap available 18 544 178 43.842 Negative 72 F LCR 765 64.69 Not detected Not applicable Not applicable 16 2.379 229 41.584 Negative 51 M LCR 766 6.85 Not detected Not applicable Not applicable 16 1.045 174 14.859 Negative 76 F LCR 768 4.67 No detected Not applicable Not applicable 25 5.365 430 33.531 Negative 56 M LCR 769 4.49 Not detected Not applicable Not applicable 16 1.256 229 20.133 Negative 51 M LCR 770 3.25 Not detected Not applicable Not applicable 16 2.093 255 16.594 Negative 34 M LCR 771 100.85 Not detected Not applicable Not applicable 43 1.262 291 37.877 Negative 70 M LCR 772 7.79 Not detected Not applicable Not applicable 16 1.682 198 28.353 Negative 73 M LCR 773 5.23 Not detected Not applicable Not applicable 22 461 182 19.383 Negative 44 M LCR 774 7.43 Not detected Not applicable Not applicable 16 460 210 18.985 Negative 67 F LCR 775 5.50 Not detected Not applicable 37 1.041 502 38.859 Negative 51 F LCR 776 2.82 Not detected Not applicable Not applicable 16 1.934 164 31, 262 Negative 50 M LCR 777 4.78 Not detected Not applicable Not applicable 17 7.378 341 32.736 Negative 86 F LCR 778 3.11 Not detected Not applicable Not applicable 27 2.055 332 23.600 Negative 57 M LCR 779 11.52 Not detected Not applicable Not applicable applicable 42 1.439 264 22.109 Negative 65 F LCR 780 5.49 Not detected Not applicable Not applicable 20 1.427 281 28.847 Negative 65 F LCR 781 3.34 Not detected Not applicable Not applicable 24 779 298 34.569 Negative 71 F LCR 782 3.10 No detected Not applicable Not applicable 48 1,083 164 56,076 Negative 56 F LCR 783 4.26 Not detected Not applicable Not applicable 29 460 185 6.296 Negative 68 M LCR 784 4.30 Not detected Not applicable Not applicable 16 5.715 229 26.211 Negative 62 F LCR 785 6.71 Not detected Not applicable Not applicable 16 2.530 322 93.629 Negative 55 M LCR 786 5.05 Not detected Not applicable Not applicable 16 2.921 235 51.194 Negative 75 F

[1242] Highlighted values represent protein concentrations below or below the assay's detection limit; values set as corresponding upper or lower detection limits

Table 10. Sample tumor specific mutations detected in PaCan patients Identified mutation Identified mutation Allele frequency SampleID # Type Cancer in plasma in medium tumor tissue mutant (%) INDI 006 PLS 1 Lung TP53 p.R280S, c.840A> T TP53 p .R280S, c.840A> T 11,230 INDI 008 PLS 1 Lung FBXW7 p.R479G, c.1435C> G FBXW7 p.R479G, c.1435C> G 1.705 INDI 008 PLS 1 Lung TP53 p.E258 *, c.772G> T TP53 p.E258 *, c.772G> T 1,429 INDI 009 PLS 1 Lung TP53 p.C176F, c.527G> T TP53 p.C176F, c.527G> T 1,061 INDI 014 PLS 1 Lung TP53 p.E298 *, c.892G> T TP53 p.E298 *, c.892G> T 1,360 INDI 021 PLS 1 Lung TP53 p.A88fs, c.263C> TP53 p.A88fs, c.263C> 5,431 INDI 023 PLS 1 Lung TP53 p.G266V , c.797G> T TP53 p.G266V, c.797G> T 0.904 INDI 027 PLS 1 CRC TP53 p.R175H, c.524G> A TP53 p.R175H, c.524G> A 2,486 INDI 034 PLS 1 CRC TP53 p .R175H, c.524G> A TP53 p.R175H, c.524G> A 0.130 INDI 070 PLS 1 PTEN p.C136R, c.406T> C PTEN p.C136R, c.406T> C 0.749 INDI 075 PLS 1 Ovarian TP53 p.Y220C, c.659A> G TP53 p.Y2 20C, c.659A> G 8,472 INDI 077 PLS 1 Liver TP53 p.K132R, c.395A> G TP53 p.K132R, c.395A> G 4,396 INDI 081 PLS 1 CRC TP53 p.W53 *, c.158G> A TP53 p.W53 *, c.158G> A 0,232 INDI 091 PLS 1 Gastric TP53 p.R248Q, c.743G> A TP53 p.R248Q, c.743G> A 7,420 INDI 109 PLS 1 CRC TP53 p.R248Q, c. 743G> A TP53 p.R248Q, c.743G> A 4,869 INDI 116 PLS 1 Gastric TP53 p.S260Y, c.779C> A TP53 p.S260Y, c.779C> A 40,923 INDI 119 PLS 1 Esophagus TP53 p.H179R, c.536A> G TP53 p.H179R, c.536A> G 2,175 INDI 119 PLS 1 Esophagus APC p.N1455fs, c.4364A> APC p.N1455fs, c.4364A> 1,078 INDI 124 PLS 1 CRC TP53 p.A63V, c.188C> T TP53 p.A63V, c.188C> T 46,229 INDI 126 PLS 1 Gastric TP53 p.K132R, c.395A> G TP53 p.K132R, c.395A> G 0.634 INDI 128 PLS 1 CRC CTNNB1 p. S45F, c.134C> T CTNNB1 p.S45F, c.134C> T 0.434 INDI 139 PLS 1 Gastric GNAS p.R201H, c.602G> A GNAS p.R201H, c.602G> A 19,853 INDI 150 PLS 1 CRC KRAS p.G12C, c.34G> T KRAS p.G12C, c.34G> T 3,448 INDI 150 PLS 1 CRC TP53 G.7578176C> T (splice site) TP53 G.7578176C> T (splice site) 2,429 INDI 154 PLS 1 DelG Intestine.

KRAS p.G12D, c.35G> A KRAS p.G12D, c.35G> A 0.975 INDI 156 PLS 1 CRC KRAS p.G12V, c.35G> T KRAS p.G12V, c.35G> T 7,212 INDI 156 PLS 1 CRC TP53 p.P190fs, c.570CCT> TP53 p.P190fs, c.570CCT> 6,150 INDI 157 PLS 1 CRC TP53 p.R282W, c.844C> T TP53 p.R282W, c.844C> T 54,847 INDI 172 PLS 1 Gastric TP53 p.R248Q, c.743G> A TP53 p.R248Q, c.743G> A 2,950 INDI 185 PLS 1 Gastric TP53 p.S183fs, c.549> A TP53 p.S183fs, c.549> A 1,404 INDI 190 PLS 1 Liver TP53 p.P151fs, c.451> C TP53 p.P151fs, c.451> C 0.273 INDI 198 PLS 1 Gastric TP53 p.V274F, c.820G> T TP53 p.V274F, c.820G> T 1,669 INDI 210 PLS 1 Gastric TP53 p.C135 *, c.405C> A TP53 p.C135 *, c.405C> A 1,050 INDI 211 PLS 1 CRC TP53 p.R175H, c.524G> A TP53 p.R175H, c .524G> A 0.253 INDI 212 PLS 1 CRC BRAF p.V600E, c.1799T> A BRAF p.V600E, c.1799T> A 2,627 INDI 217 PLS 1 CRC BRAF p.D594G, c.1781A> G BRAF p.D594G , c.1781A> G 1,237 INDI 219 PLS 1 Liver KRAS p.G12C, c.34G> T KRAS p.G12C, c.34G> T 0.395 INDI 222 PLS 1 CRC TP53 p.G266E, c.797G> A TP53 p .G266E, c.797G> A 7,385 IN DI 222 PLS 1 CRC KRAS p.G13D, c.38G> A KRAS p.G13D, c.38G> A 6,933 INDI 231 PLS 1 Lung KRAS p.G12D, c.35G> A KRAS p.G12D, c.35G> A 9,384 INDI 240 PLS 1 CRC TP53 p.E224E, c.672G> A (Exon End) TP53 p.E224E, c.672G> A (Exon End) 19,758 INDI 240 PLS 1 CRC NRAS p.Q61K, c.181C> A NRAS p.Q61K, c.181C> A 9,620 INDI 242 PLS 1 Lung TP53 p.R290fs, c.869G> TP53 p.R290fs, c.869G> 0.297 INDI 243 PLS 1 CRC APC p.E1309fs, c.3927AAAGA> APC p.E1309fs, c.3927AAAGA> 2,958 INDI 244 PLS 1 CRC KRAS p.G12C, c.34G> T KRAS p.G12C, c.34G> T 0.654 INDI 245 PLS 1 CRC KRAS p.Q61K, c.181C> A KRAS p.Q61K, c.181C> A 1,191 INDI 255 PLS 1 CRC PIK3CA p.H1047R, c.3140A> G PIK3CA p.H1047R, c.3140A> G 1,121 INDI 255 PLS 1 CRC KRAS p.G13V, c. 38G> T KRAS p.G13V, c.38G> T 0.574 INDI 256 PLS 1 CRC KRAS p.G12S, c.34G> A KRAS p.G12S, c.34G> A 3,966 INDI 256 PLS 1 CRC TP53 p.R175H, c.524G> A TP53 p.R175H, c.524G> A 2,050 INDI 263 PLS 1 CRC TP53 p.D281N, c.841G> A TP53 p.D281N, c.841G> A 50,972

INDI 263 PLS 1 CRC BRAF p.V600E, c.1799T> A BRAF p.V600E, c.1799T> A 31.738 INDI 268 PLS 1 CRC NRAS p.Q61R, c.182A> G NRAS p.Q61R, c.182A> G 3,337 INDI 284 PLS 1 CRC APC p.E1309fs, c.3927AAAGA> APC p.E1309fs, c.3927AAAGA> 0.122 INDI 285 PLS 1 Lung TP53 p.E339 *, c.1015G> T TP53 p.E339 *, c. 1015G> T 1,293 INDI 287 PLS 1 Breast TP53 p.S94 *, c.281C> G TP53 p.S94 *, c.281C> G 1,429 INDI 288 PLS 1 Lung TP53 p.T125T, c.375G> T (Exon End ) TP53 p.T125T, c.375G> T (Exon End) 8,374 INDI 306 PLS 1 CRC TP53 p.R175H, c.524G> A TP53 p.R175H, c.524G> A 46,107 INDI 306 PLS 1 CRC APC p. E1309fs, c.3927AAAGA> APC p.E1309fs, c.3927AAAGA> 36,318 INDI 310 PLS 1 CRC PIK3CA p.E545A, c.1634A> C PIK3CA p.E545A, c.1634A> C 0.694 INDI 329 PLS 1 CRC BRAF p. V600E, c.1799T> A BRAF p.V600E, c.1799T> A 10.039 INDI 329 PLS 1 CRC PIK3CA p.E542K, c.1624G> A PIK3CA p.E542K, c.1624G> A 5,058 INDI 331 PLS 1A Lung KRAS p.G12C, c.34G> T KRAS p.G12C, c.34G> T 10,876 INDI 340 PLS 1 CRC TP53 p.R175H, c.524G> A TP53 p.R175H, c.524G> A 1,319 INDI 345 PLS 1 CRC TP53 p.M160fs, c.479> TGGCC TP53 p.M160fs, c.479> TGGCC 38,114 INDI 345 PLS 1 CRC KRAS p.Q61K, c.181C> A KRAS p.Q61K, c.181C> A 23,235 INDI 349 PLS 1 CRC KRAS p.G12C, c.34G> T KRAS p.G12C, c.34G> T 0.977 INDI 349 PLS 1 CRC TP53 p.R248Q, c.743G> A TP53 p.R248Q, c.743G> A 0.925 INDI 353 PLS 1A Lung TP53 p.M133L, c.397A> T TP53 p.M133L, c.397A> T 0.235 INDI 365 PLS 1 CRC APC p.E1309fs, c.3927AAAGA> APC p.E1309fs, c.3927AAAGA > 0.596 INDI 367 PLS 1 Liver CTNNB1 p.I35S, c.104T> G CTNNB1 p.I35S, c.104T> G 0.284 INDI 370 PLS 1 CRC TP53 p.A161T, c.481G> A TP53 p.A161T, c. 481G> A 42,557 INDI 370 PLS 1 CRC KRAS p.A146V, c.437C> T KRAS p.A146V, c.437C> T 42,018 INDI 371 PLS 1A Lung KRAS p.G12V, c.35G> T KRAS p.G12V, c.35G> T 7,711 INDI 381 PLS 1A Lung PIK3CA p.H1047L, c.3140A> T PIK3CA p.H1047L, c.3140A> T 2,035 INDI 400 PLS 1 CRC TP53 p.R175H, c.524G> A TP53 p. R175H, c.524G> A 2,962 INDI 435 PLS 1 CRC KRAS p.G12C, c.34G> T KRAS p.G12C, c.34G> T 0.706 INDI 435 PLS 1 CRC TP53 p.Y163N, c.487T> A TP53 p.Y163 N, c.487T> A 0.554 INDI 439 PLS 1 CRC KRAS p.G12C, c.34G> T KRAS p.G12C, c.34G> T 0.931 INDI 439 PLS 1 CRC BRAF p.F595L, c.1785T> G BRAF p.F595L, c.1785T> G 0.901 INDI 445 PLS 1 Breast TP53 p.E171fs, c.513GACGGAG> TP53 p.E171fs, c.513GACGGAG> 0.695 INDI 447 PLS 1 Breast TP53 p.F113S, c.338T> C TP53 p.F113S, c.338T> C 23.948 INDI 447 PLS 1 Breast PIK3CA p.G1049R, c.3145G> C PIK3CA p.G1049R, c.3145G> C 21.723 INDI 457 PLS 1 Esophagus TP53 p.E221 *, c.661G > T TP53 p.E221 *, c.661G> T 1,375 INDI 457 PLS 1 Esophagus TP53 p.G266V, c.797G> T TP53 p.G266V, c.797G> T 1.104 INDI 459 PLS 1 Breast PIK3CA p.H1047R, c.3140A> G PIK3CA p.H1047R, c.3140A> G 60,444 INDI 459 PLS 1 Breast TP53 p.E285K, c.853G> A TP53 p.E285K, c.853G> A 58,047 INDI 461 PLS 1 CRC TP53 p. V172F, c.514G> T TP53 p.V172F, c.514G> T 0.308 INDI 462 PLS 1 Breast TP53 G.7578176C> A (splice site) TP53 G.7578176C> A (splice site) 65,428 INDI 464 PLS 1 Liver TP53 p.K132 *, c.394A> T TP53 p.K132 *, c.394A> T 0.297 INDI 466 PLS 1 CRC KRAS p.G12V, c.35G> T KRAS p.G12V, c.35G> T 0.2 65 INDI 509 PLS 1 Breast TP53 p.P278T, c.832C> A TP53 p.P278T, c.832C> A 38.691 INDI 518 PLS 1 CRC TP53 p.N131fs, c.392CAA> TP53 p.N131fs, c.392CAA> 0,175 INDI 536 PLS 1 CRC KRAS p.G13D, c.38G> A KRAS p.G13D, c.38G> A 57,820 INDI 536 PLS 1 CRC TP53 p.C135R, c.403T> C TP53 p.C135R, c.403T > C 57.647 INDI 539 PLS 1 CRC PTEN p.D92A, c.275A> C PTEN p.D92A, c.275A> C 0.492 INDI 539 PLS 1 CRC BRAF p.V600E, c.1799T> A BRAF p.V600E, c .1799T> A 0.396 INDI 542 PLS 1 CRC KRAS p.G12C, c.34G> T KRAS p.G12C, c.34G> T 36.797 INDI 543 PLS 1 Lung TP53 p.G105A, c.314G> C TP53 p.G105A , c.314G> C 2.993 INDI 544 PLS 1 CRC TP53 p.G334R, c.1000G> C TP53 p.G334R, c.1000G> C 52.811 INDI 552 PLS 1 Lung TP53 p.C238F, c.713G> T TP53 p .C238F, c.713G> T 6,014 INDI 555 PLS 1 CRC APC p.E1309fs, c.3927AAAGA> APC p.E1309fs, c.3927AAAGA> 0,779 INDI 567 PLS 1 CRC TP53 p.R175H, c.524G> A TP53 p .R175H, c.524G> A 2,632 INDI 567 PLS 1 CRC KRAS p.G12A, c.35G> C KRAS p.G12A, c.35G> C 2,594 INDI 567 PLS 1 CRC PIK3CA p.E545K, c.1633G> A PIK3CA p.E545 K, c.1633G> A 0.880 INDI 580 PLS 1 CRC KRAS p.G12D, c.35G> A KRAS p.G12D, c.35G> A 27.279 INDI 580 PLS 1 CRC TP53 p.M237I, c.711G> A TP53 p.M237I, c.711G> A 24,892 INDI 581 PLS 1 CRC KRAS p.G13D, c.38G> A KRAS p.G13D, c.38G> A 3.409

INDI 581 PLS 1 CRC TP53 G.7579311C> A (splice site) TP53 G.7579311C> A (splice site) 1.358 INDI 585 PLS 1 Esophagus TP53 p.E343 *, c.1027G> T TP53 p.E343 *, c. 1027G> T 0.519 INDI 601 PLS 1 Breast TP53 p.R213 *, c.637C> T TP53 p.R213 *, c.637C> T 9.270 INDI 618 PLS 1 CRC TP53 p.A63V, c.188C> T TP53 p. A63V, c.188C> T 47,323 INDI 623 PLS 1 Breast TP53 p.R273P, c.818G> C TP53 p.R273P, c.818G> C 2,140 INDI 628 PLS 1 CRC TP53 p.A63V, c.188C> T TP53 p.A63V, c.188C> T 47,184 INDI 636 PLS 1 Liver CTNNB1 p.T41A, c.121A> G CTNNB1 p.T41A, c.121A> G 3,966 INDI 645 PLS 1 Liver TP53 p.R249S, c.747G> T TP53 p.R249S, c.747G> T 7,169 INDI 648 PLS 1 CRC TP53 p.E258G, c.773A> G TP53 p.E258G, c.773A> G 28,968 INDI 648 PLS 1 CRC KRAS p.G12V, c. 35G> T KRAS p.G12V, c.35G> T 24,217 INDI 665 PLS 1 Esophagus FGFR2 p.P253R, c.758C> G FGFR2 p.P253R, c.758C> G 0.354 INDI 665 PLS 1 Esophagus TP53 p.C135F, c.404G> T TP53 p.C135F, c.404G> T 0.275 INDI 670 PLS 1 CRC TP53 p.G117fs, c.350> GGAC TP53 p.G117fs, c.350> GGAC 14,282 INDI 675 PLS 1 CRC BRAF p. I592 V, c.1774A> G BRAF p.I592V, c.1774A> G 56,661 INDI 678 PLS 1 CRC KRAS p.Q61H, c.183A> T KRAS p.Q61H, c.183A> T 1,164 INDI 678 PLS 1 CRC TP53 p.E224fs, c.670> G TP53 p.E224fs, c.670> G 1,096 INDI 687 PLS 1 Lung KRAS p.G12V, c.35G> T KRAS p.G12V, c.35G> T 5,151 INDI 687 PLS 1 Lung TP53 p.R273L, c.818G> T TP53 p.R273L, c.818G> T 0.519 INDI 693 PLS 1 Lung TP53 p.H193Y, c.577C> T TP53 p.H193Y, c.577C> T 22,235 INDI 701 PLS 1 CRC BRAF p.V600E, c.1799T> A BRAF p.V600E, c.1799T> A 0.170 INDI 702 PLS 1 Lung TP53 p.E298 *, c.892G> T TP53 p.E298 *, c.892G> T 0.138 INDI 708 PLS 1 CRC KRAS p.G12C, c.34G> T KRAS p.G12C, c.34G> T 0.257 INDI 710 PLS 1 CRC PIK3CA p.Q546R, c.1637A> G PIK3CA p.Q546R, c. 1637A> G 0.163 INDI 714 PLS 1 Breast TP53 p.R175H, c.524G> A TP53 p.R175H, c.524G> A 2,123 INDI 748 PLS 1 CRC TP53 p.S241fs, c.722C> TP53 p.S241fs, c .722C> 0.261 INDI 751 PLS 1 CRC PIK3CA p.N345K, c.1035T> A PIK3CA p.N345K, c.1035T> A 8,228 INDI 752 PLS 1 CRC TP53 p.R175H, c.524G> A TP53 p.R175H, c.524G> A 0.659 INDI 753 PLS 1 Esophagus TP53 p.Y220C, c.659A> G TP53 p.Y220C, c.659A> G 3,457 INDI 764 PLS 1 CRC TP53 p.E51fs, c.151G> TP53 p.E51fs, c.151G> 1,779 INDI 769 PLS 1 CRC TP53 p.I251V, c.751A> G TP53 p.I251V, c.751A> G 50,294 INDI 770 PLS 1 CRC TP53 p.E285K, c.853G> A TP53 p.E285K, c.853G> A 0.152 INDI 778 PLS 1 Liver CTNNB1 p.I35S, c.104T> G CTNNB1 p.I35S, c.104T> G 0.084 INDI 779 PLS 1 CRC TP53 p.E11Q, c.31G> C TP53 p.E11Q, c.31G> C 50.219 INDI 782 PLS 1 CRC BRAF p.V600E, c.1799T> A BRAF p.V600E, c.1799T> A 2,885 INDI 786 PLS 1 Liver TP53 p.C135F, c.404G> T TP53 p.C135F, c.404G> T 1,135 INDI 796 PLS 1 Liver PIK3CA p.H1047R, c.3140A> G PIK3CA p.H1047R, c.3140A> G 0.521 INDI 798 PLS 1 Ovarian TP53 p.Y163C, c.488A> G TP53 p.Y163C, c. 488A> G 7,314 INDI 804 PLS 1 CRC KRAS p.G12R, c.34G> C KRAS p.G12R, c.34G> C 30,128 INDI 804 PLS 1 CRC APC p.E1309fs, c.3927AAAGA> APC p.E1309fs, c .3927AAAGA> 23,715 INDI 812 PLS 1 Ovarian KRAS p.G12C, c.34G> T KRAS p.G12C, c.34G> T 3,472 INDI 812 PLS 1 Ovarian CTNNB1 p.T41A, c.121A> G CTNNB1 p. T41A, c.121A> G 3,184 INDI 813 PLS 1 Liver TP53 p.K381fs, c.1141A> TP53 p.K381fs, c.1141A> 0,763 INDI 815 PLS 1 CRC BRAF p.V600E, c.1799T> A BRAF p. V600E, c.1799T> A 0,224 INDI 817 PLS 1 Liver CTNNB1 p.I35S, c.104T> G CTNNB1 p.I35S, c.104T> G 0.099 INDI 818 PLS 1 Ovarian TP53 p.G154fs, c.460G> TP53 p .G154fs, c.460G> 2,827 INDI 819 PLS 1 Breast AKT1 p.E17K, c.49G> A AKT1 p.E17K, c.49G> A 6,061 INDI 819 PLS 1 Breast TP53 p.R175H, c.524G> A TP53 p.R175H, c.524G> A 4,100 INDI 827 PLS 1 CRC TP53 p.R175H, c.524G> A TP53 p.R175H, c.524G> A 0,370 INDI 834 PLS 1 Ovarian TP53 G.7578290C> A (splice site ) TP53 G.7578290C> A (splice site) 2.801 INDI 845 PLS 1 CRC TP53 p.E258 *, c.772G> T TP53 p.E258 *, c.772G> T 0.208 INDI 850 PLS 1 Esophagus TP53 p.H193P, c.578A> C TP53 p.H193P, c.578A> C 21,370 INDI 859 PLS 1 Esophagus TP53 p.Y234N, c.700T> A TP53 p.Y234N, c.700T> A 0.591 INDI 866 PLS 1 Mama TP53 p. R110H, c.329G> A TP53 p.R110H, c.329G> A 40,819 INDI 872 PLS 1 Breast PIK3CA p.H1047L, c.3140A> T PIK3CA p.H1047L, c.3140A> T 0.90 2 INDI 872 PLS 1 Breast TP53 p.H179Y, c.535C> T TP53 p.H179Y, c.535C> T 0.490 INDI 884 PLS 1 Breast TP53 p.Y220C, c.659A> G TP53 p.Y220C, c.659A > G 11,696 INDI 906 PLS 1 Pancreas TP53 p.F134fs, c.400> T TP53 p.F134fs, c.400> T 0.245

INDI 906 PLS 1 Pancreas KRAS p.Q61H, c.183A> T KRAS p.Q61H, c.183A> T 0.241 INDI 910 PLS 1 Esophagus TP53 p.H193L, c.578A> T TP53 p.H193L, c.578A> T 0.096 INDI 925 PLS 1 Gastric TP53 p.R196 *, c.586C> T TP53 p.R196 *, c.586C> T 17.732 INDI 932 PLS 1 Ovarian TP53 p.L252fs, c.754TCC> TP53 p.L252fs, c .754TCC> 10,810

Table 11. PlasmaSeqS with 5718 Combined White Blood Cell Summary.

Type MAF MAF MAF Experiment Patient Model Sample ampMatch Name Chrom Position MutType Base Based For Gene nt Start codon Start aaRef aaPlar in PT in WBCs 5578 CRC 458 CRC 458 PLS CRC TP53_0248_0261 cr 17 7577538 SBS CT TP53 743 CR 883 568 468 PLS CRC TP53_0272_0283 cr 17 7577105 SBS GC TP53 833 278 PR 5.573 1.613 7.638 5578 CRC 469 CRC 469 PLS CRC TP53_0248_0261 cr 17 7577538 SBS CT TP53 743 248 RQ 4.171 Not found 4.015 5578 CRC 470 CRC 470 CR34 1024 342 R * 0.859 72.662 Not found 5578 CRC 471 CRC 471 PLS CRC TP53_0196_0205 cr 17 7578263 SBS GA TP53 586 196 R * 1.842 67.010 Not found 5578 CRC 473 CRC 473 PLS CRC TP53_219_224 cr 17 7578190 SBS TC TP53 659 220 0.290 5578 CRC 476 CRC 476 PLS CRC TP53_0248_0261 cr 17 7577536 SBS TC TP53 745 249 RG 7.207 62.896 Not found 5578 CRC 477 CRC 477 PLS CRC TP53_113_125 cr 17 7579332 SBS CT TP53 355 119 AT 0.101 Not found Not found 5578 CRC 483 CRC 483 PLS CRC TP53_172A cr 17 7578394 SBS TC TP53 536 179 HR 0.622 Not found 1.018 5578 CRC 489 CRC 489 PLS CRC KRAS_0057_0064 cr 12 25380277 SBS GT KRAS 181 61 QK 0.169 23.333 Not found 5578 CRC 49.34 494 PLS CRC APC_1450_1458 cr 5 112175639 SBS CT APC 4348 1450 R * 0.473 27.418 Not found 5578 CRC 494 CRC 494 PLS CRC TP53_0248_0261 cr 17 7577538 SBS CT TP53 743 248 RQ 0.836 76.451 0.203 5553 CRC 494 CR27 490 CR2 490 CR27 CT TP53 818 273 RH 0.170 Not found Not found 5578 CRC 521 CRC 521 PLS CRC KRAS_144_147A cr 12 25378561 SBS GA KRAS 437 146 AV 0.440 48.106 Not found 5578 CRC 523 CRC 523 PLS CRC TP53_172A cr 17 7578394 SBS TC TP57 536 179 HR 0 found 0.856 5578 CRC 524 CRC 524 PLS CRC KRAS_0057_0064 cr 12 25380282 SBS GT KRAS 176 59 AE 0.155 Not found 0.754 5578 CRC 524 CRC 524 PLS CRC PIK3CA_1039_1050 cr 3 178952085 SBS AG PIK3CA 3140 1047 HR 0.18 2 40.747 Not found 5578 CRC 527 CRC 527 PLS CRC KRAS_144_147A cr 12 25378562 SBS CT KRAS 436 146 AT 2,200 32.549 Not found 5578 CRC 531 CRC 531 PLS CRC TP53_0227_0236 cr 17 7577580 SBS TC TP53 701 234 YC 557 Not found 456 CRC 456 PLS CRC CTNNB1_0039_0046 cr 3 41266121 SBS AG CTNNB1 118 40 TA 0.105 Not found Not found 5578 and 557 CRC 456 CRC 456 PLS CRC PIK3CA_0542_0551 cr 3 178936083 SBS AC PIK3CA 1625 542 Not found 55.108 CRC 456 PLS CRC TP53_026 cr 17 7579707 SBS TC TP53 89 30 NS 0.108 Not found Not found 5578 and 557 CRC 456 CRC 456 PLS CRC TP53_033_040 cr 17 7579574 SBS TC TP53 113 38 QR 0.118 Not found Not found 5578 and 557 CRC 456 CRC 456 PLS CRC TP53_113_125 cr 17 7579338 SBS CG TP53 349 117 GR 0.101 Not found Not found 5578 and 557 CRC 456 CRC 456 PLS CRC TP53_208_218 cr 17 7578221 SBS TC TP53 628 210 ND 0.101 Not found Not found 5578 and 557 CRC 456 CRC 456 PLS CRC TP53_219_224 cr 17 7578191 SBS AG TP53 658 220 YH 0.159 Not found Not found 5578 and 557 CRC 456 CRC 456 PLS CRC TP53_233_245 cr 17 7577551 SBS CT TP53 730 244 GS 0.103 Not found Not found 5578 and 557 CR7 CRC 456 PLS CRC TP53_345 cr 17 7573972 SBS TG TP53 1055 352 DA 0.103 Not found Not found 5578 and 557 CRC 456 CRC 456 PLS CRC TP53_345 cr 17 7573991 SBS CG TP53 1036 346 EQ 0.103 Not found Not found 5578 and 557 CRC 456 CRC 456 PLS CRC TP53_374_385 cr 17 7572985 SBS TG TP53 1124 375 QP 0.132 Not found Not found 5578 and 557 CRC 460 CRC 460 PLS CRC CDKN2A_7_88 cr 9 21971098 SBS CT CDKN2A 260 87 RQ 0.130 Not found Not found 5578 and 557 CRC 460 CRC 460 CR60 TP53_0196_0205 cr 17 7578255 SBS TG TP53 594 198 ED 0.101 Not found Not found 5578 and 557 CRC 460 CRC 460 PLS CRC TP53_233_245 cr 17 7577551 SBS CT TP53 730 244 GS 0.344 Not found Not found 5578 and 557 CRC 461 CRC 461 PLS CRC TP53_219_224 cr 17 7578191 SBS AG TP53 658 220 YH 0.117 Not found Not found 5578 and 557 CRC 474 CRC 474 PLS CRC TP53_113_125 cr 17 7579349 SBS AC TP53 338 113 FC 0.116 Not found Not found 5578 and 557 CRC 481 CRC 481 PLS CRC FB47 cr 4 153249366 Indel NULL T FBXW7 1412 471 E fs 0.171 15.621 Not found 5578 and 557 CRC 481 CRC 481 PLS CRC PIK3CA_1039_1050 cr 3 178952085 SBS AG PIK3CA 3140 1047 HR 0.267 32.437 Not found 5578 and 557 CRC 491 CRC 491 CRC 491 CR7 153249384 SBS CT FBXW7 1394 465 RH 0.153 24.088 Not found 5578 and 557 CRC 497 CRC 497 PLS CRC APC_1304_1310 cr 5 112175216 SBS GT APC 3925 1309 E * 0.316 29.560 Not found 5578 and 557 CRC 497 CRC 497 PLS CRC F4X7 CR7 FBXW7 1436 479 RQ 0.550 27.814 Not found 5578 and 557 CRC 504 CRC 504 PLS CRC APC_1304_1310 cr 5 112175218 Indel AAAGA NULL APC 3927 1309 E fs 1.619 64.283 Not found 5578 and 557 CRC 504 CRC 504 PLS CRC TP53_0272_ cr 17 7577121 SBS GA TP53 817 273 RC 0.922 60.499 Not found 5578 and 557 CRC 512 CRC 512 PLS CRC KRAS_012_ + 13_-4 cr 12 25398284 SBS CT KRAS 35 12 GD 0.211 38.645 Not found 5578 and 557 CRC 512 CRC 512 PLS CRC TP53_132 cr 17 7578536 SBS TG TP53 394 132 KQ 0.135 Not found Not found 5578 and 557 CRC 516 CRC 516 PLS CRC TP53_172A cr 17 7578395 SBS GC TP53 535 179 HD 0.183 Not found Not found 5578 and 557 CRC 517 CRC 517 PLS CRC TP53_233_245 cr 17 7577548 SBS CT TP53 733 245 GS 0.430 79.836 Not found 5578 and 557 CRC 518 CRC 518 PLS CRC TP53_208_218 cr 17 7578205 Indel NULL CAC TP53 644 215 S fs 8.816 75,000 Not found 5578 and 557 CRC 522 CRC 522 PLS CRC TP53_219_229 AG TP53 658 220 YH 0.132 Not found Not found 5578 and 557 CRC 526 CRC 526 PLS CRC AKT1_0016_0017 cr 14 105246551 SBS CT AKT1 49 17 EK 0.101 42.495 Not found 5579 CRC 457 CRC 457 PLS CRC TP53_188_195 cr 17 7578265 SBS AG TP TP 0.120 Not found found 0.563 5579 CRC 468 CRC 468 PLS CRC TP53_0280_0289 cr 17 7577081 SBS TC TP53 857 286 EG 0.358 74.714 Not found 5579 CRC 468 CRC 468 PLS CRC TP53_188_195 cr 17 7578268 SBS AC TP53 581 194 LR 0.377 CRS 470 CRC NRAS_0055_0062 cr 1 115256529 SBS TA NRAS 182 61 QL 0.369 55.290 Not found 5579 CRC 473 CRC 473 PLS CRC KRAS_012_ + 13_-4 cr 12 25398281 SBS CT KRAS 38 13 GD 3.840 51.923 Not found 5579 CRC 477 CRC 477 PLS CRC TP53 7577139 SBS GA TP53 799 267 RW 2,700 1.405 3.604 5579 CRC 480 CRC 480 PLS CRC CTNNB1_0039_0046 cr 3 41266124 SBS AG CTNNB1 121 41 TA 0.774 33.306 Not found 5579 CRC 480 CRC 480 PLS CRC KRAS_012_ + 13_-4 cr 12 25 12 GD 0.494 49.603 Not found 5579 CRC 480 CRC 480 PLS CRC TP53_167_177 cr 17 7578427 SBS TC TP53 503 168 HR 0.782 28.687 Not found 5579 CRC 489 CRC 489 PLS CRC TP53_233_245 cr 17 7577548 SBS CT TP53 733 245 GS 55.479 Not found 0.752 CRC 505 CRC 505 PLS CRC TP53_233_245 cr 17 7577568 SBS CT TP53 713 238 CY 0.360 Not found 0.610 5579 CRC 506 CRC 506 PLS CRC APC_1304_1310 cr 5 112175222 Indel NULL A APC 3931 1311 I fs 0.180 50.17 Not found CRC 5079 CTNNB1_0039_0046 cr 3 41266137 SBS CT CTNNB1 134 45 SF 0.109 73.257 Not found 5579 CRC 520 CRC 520 PLS CRC KRAS_012_ + 13_-4 cr 12 25398284 SBS CA KRAS 35 12 GV 0.471 76.048 Not found 5579 CRC 521 CRC 521 CR37 519 PLS SBS GA FBXW7 1099 367 R * 0.267 65,306 Not found 5579 CRC 521 CRC 521 PLS CRC PIK3CA_0542_0551 cr 3 178936091 SBS GA PIK3CA 1633 545 EK 0.119 29.290 Not found 5579 CRC 524 CRC 524 PLS CRC KRAS_012_ + 13_ 35 12 GV 0.358 41.673 Not found 5579 CRC 531 CRC 531 PLS CRC KRAS_012_ + 13_-4 cr 12 25398284 SBS CA KRAS 35 12 GV 0.144 37.804 Not found

5579 CRC 531 CRC 531 PLS CRC TP53_233_245 cr 17 7577580 SBS TC TP53 701 234 YC 0.246 Not found Not found 5578 CRC 482 CRC 482 PLS CRC TP53_172A cr 17 7578406 SBS CT TP53 524 175 RH 0.143 82.996 Not found 5578 and 557 CRC 456 PLS CRC TP53_053 cr 17 7579520 SBS TC TP53 167 56 EG 0.122 Not found Not found 5578 and 557 CRC 456 CRC 456 PLS CRC TP53_132_142 cr 17 7578535 SBS TG TP53 395 132 KT 0.159 Not found Not found 5578 and 557 CRC 456 CRC 456 PLS CRC TP53_151_163 cr 17 7578475 SBS GA TP53 455 152 PL 0.107 Not found 0.063 5578 and 557 CRC 474 CRC 474 PLS CRC TP53_033_040 cr 17 7579574 SBS TA TP53 113 38 QL 0.110 Not found Not found 5578 and 557 CRC 474 CRC 474 PLS CRC TP53_3 7573974 SBS CG TP53 1053 351 KN 0.111 Not found Not found 5578 and 557 CRC 491 CRC 491 PLS CRC TP53_0248_0261 cr 17 7577538 SBS CT TP53 743 248 RQ 0.186 59.012 0.059 5578 and 557 CRC 512 CRC 512 PLS CRC NRAS_0055_6255 NRAS 182 61 QP 0.103 Not found Not found 5578 and 557 CRC 512 CRC 512 PLS CRC TP53_374_385 cr 17 7572977 SBS AC TP53 1132 378 SA 0.111 Not found Not found 5578 and 557 CRC 526 CRC 526 PLS CRC TP53_0196_0205 cr 17 7578245 SBS GA 202 RC 0.108 Not found Not found 5579 CRC 470 CRC 470 PLS CRC APC_1304_1310 cr 5 112175207 SBS GT APC 3916 1306 E * 0.344 56.466 Not found 5579 CRC 482 CRC 482 PLS CRC TP53_167_177 cr 17 7578406 SBS CT TP53 524 175 RH 0.138 80.375 5578 CRC 464 CRC 464 PLS CRC PPP2R1A_176_187 cr 19 52715982 SBS CT PPP2R1A 547 183 RW 0.126 Not found Not found 5578 and 557 CRC 512 CRC 512 PLS CRC TP53_342 cr 17 7573997 SBS GC TP53 1030 344 LV 0.120 Not found 5555 512 CRC 512 PLS CRC TP53_342 cr 17 7574003 SBS GA TP53 1024 342 R * 0.154 53.715 Not found 5578 and 557 CRC 512 CRC 512 PLS CRC TP53_342 cr 17 7574005 SBS AG TP53 1022 341 FS 0.103 Not found Not found 5 578 CRC 465 CRC 465 PLS CRC TP53_0227_0236 cr 17 7577580 SBS TC TP53 701 234 YC 0.115 Not found Not found 5578 CRC 530 CRC 530 PLS CRC TP53_113_125 cr 17 7579316 Indel NULL GAC TP53 371 124 C fs 0.155 CR Not found 5579 465 PLS CRC TP53_188_195 cr 17 7578280 SBS GC TP53 569 190 PR 0.148 Not found Not found Table 12. PlasmaSeqS with 5502 Combined White Blood Cell Summary.

MAF MAF MAF Experiment Patient Model Type Sample ampMatchName Chrom Position MutType BaseDE Base For Gene nt Start codon Start aaRef aaVar in Plasma in WBCs in PT 4946 LCR 600 LCR 600 PLS1 Normal control TP53_151_163 cr 17 7578457 SBS CT TP53 473 158 RH 0.115 0.007 Not applicable LCR 627 PLS1 Normal control TP53_172A cr 17 7578406 SBS CT TP53 524 175 RH 0.180 0.161 Not applicable 4958 LCR 628 LCR 628 PLS1 Normal control TP53_151_163 cr 17 7578461 SBS CT TP53 469 157 VI 0.146 0.027 Not applicable 4959 LCR 634 LCR 634 PL1 Normal control TP53_0 cr 17 7577538 SBS CT TP53 743 248 RQ 0.110 0.015 Not applicable 4959 LCR 636 LCR 636 PLS1 Normal controlPIK3CA_1039_1050 cr 3 178952076 SBS AG PIK3CA 3131 1044 NS 0.200 Not found Not applicable 4959 LCR 636 LCR 636 PLS1 Normal control TP53_0272_03 844 282 RW 0.150 0.002 Not applicable 4965 LCR 667 LCR 667 PLS1 Normal control TP53_233_245 cr 17 7577547 SBS CT TP53 734 245 GD 0.128 0.03 7 Not applicable 4984 LCR 700 LCR 700 PLS1 Normal control TP53_113_125 cr 17 7579328 SBS TC TP53 359 120 KR 0.281 0.459 Not applicable 4984 LCR 701 LCR 701 PLS1 Normal control TP53_233_245 cr 17 7577586 SBS AG TP53 695 232 IT 0.895 LCR 70 Not applicable 4984 LCR 701 PLS1 Normal control TP53_172A cr 17 7578406 SBS CT TP53 524 175 RH 0.140 0.017 Not applicable 4971 LCR 721 LCR 721 PLS1 Normal control TP53_0248_0261 cr 17 7577539 SBS GA TP53 742 248 RW 0.177 0.137 Not applicable 4971 LCR 730 LCR 730 716 cr 17 7578406 SBS CT TP53 524 175 RH 0.160 0.026 Not applicable 4971 LCR 738 LCR 738 PLS1 Normal control TP53_0227_0236 cr 17 7577574 SBS TC TP53 707 236 YC 12.230 3.489 Not applicable 4971 LCR 738 LCR 738 PLS1 Normal control TP53_167_177 CG 17 754 517 173 VL 6,876 7.333 Not applicable 4971 LCR 738 LCR 738 PLS1 Normal control TP53_0272_0283 cr 17 7577111 SBS GT TP53 827 276 AD 5.810 5.512 Not applicable 4985 LCR 760 LCR 760 PLS1 No control extension TP53_0272_0283 cr 17 7577094 SBS GA TP53 844 282 RW 0.113 0.017 Not applicable 4977 LCR 764 LCR 764 PLS1 Normal control TP53_0272_0283 cr 17 7577120 SBS CT TP53 818 273 RH 0.176 0.008 Not applicable 4978 LCR 782 L0 726 PLS1 Normal control SBS CT TP53 743 248 RQ 0.110 Not found Not applicable 4866 PANC 317 PANC 317 PLS 2 PDAC TP53_0272_0283 cr 17 7577105 SBS GC TP53 833 278 PR 0.348 0.679 Not found 4866 PANC 317 PANC 317 PLS 2 PDAC TP53_368_to_374 cr 17 7572995 Ind K fs 0.100 0.039 Not found 4866 PANC 317 PANC 317 PLS1 PDAC TP53_0272_0283 cr 17 7577105 SBS GC TP53 833 278 PR 0.943 0.679 Not found 4868 PANC 355 PANC 355 PLS1 PDAC TP53_0248_0261 cr 17 7577517 SBS AC TP16 764 255 Found PANC 355 PANC 355 PLS1 PDAC TP53_208_218 cr 17 7578203 SBS CT TP53 646 216 VM 0.102 0.020 Not found 4869 PANC 387 PANC 387 PLS PDAC TP53_172A cr 17 7578406 SBS CT TP53 524 1 75 RH 0.153 0.008 39.5883777 4869 PANC 387 PANC 387 PLS PDAC KRAS_012_ + 13_-4 cr 12 25398285 SBS CG KRAS 34 12 GR 0.118 Not found 30.56 4869 PANC 389 PANC 389 PLS PDAC TP53_0248_0261 cr 17 7577517 SBS AC TP53 764 255 IS 0.101 Not found Not found 4870 PANC 400 PANC 400 PLS 1 PDAC TP53_0248_0261 cr 17 7577536 SBS TA TP53 745 249 RW 0.791 0.675 Not found 4870 PANC 400 PANC 400 PLS 1 PDAC TP53_0272_0283 cr 17 7577120 SBS CT TP53 818 273 RH 0.133 0.07070 4880 PANC 462 PANC 462 PLS1 PDAC KRAS_012_ + 13_-4 cr 12 25398285 SBS CG KRAS 34 12 GR 2,223 Not found 39.7744361 4880 PANC 462 PANC 462 PLS1 PDAC TP53_233_245 cr 17 7577556 SBS CA TP53 725 242 CF 1,741 Not found 59.259 4999 PANC 469 PANC 469 PLS1 PDAC KRAS_012_ + 13_-4 cr 12 25398284 SBS CG KRAS 35 12 GA 0.125 Not found 23.9110287 5007 PANC 485 PANC 485 PLS 1 PDAC TP53_208_218 cr 17 7578212 SBS GA TP53 637 213 R * 0.118 Not found Not found found 5019 PANC 509 PANC 509 PLS 1 PDAC TP53_0248_0261 cr 17 7577538 SBS CT TP53 743 248 RQ 0.146 0.227 Not found 5019 PANC 509 PANC 509 PLS 1 PDAC TP53_172A cr 17 7578393 SBS AT TP53 537 179 HQ 0.21 0.040 Not found 5019 PANC 513 PANC 513 PLS 1 PDAC TP53_19 TC TP53 659 220 YC 0.124 0.028 Not found 5098 PANC 552 PANC 552 PLS 1 PDAC KRAS_0057_0064 cr 12 25380275 SBS TA KRAS 183 61 QH 0.363 Not found 16.0069849 5098 PANC 552 PANC 552 PLS 1 PDAC GNAS_201 cr 20 57484421 SBS GA GNAS 602 201 RH 0.318 0.035 9.232715 5098 PANC 552 PANC 552 PLS 1 PDAC TP53_233_245 cr 17 7577568 SBS CT TP53 713 238 CY 0.317 0.002 12.9032258 5098 PANC 552 PANC 552 PLS 1 PDAC CDKN2A_7_88 cr 9 21971107 SBS TC CDKN2 0.1 10.5439893 5023 PANC 686 PANC 686 PLS 1 PDAC KRAS_012_ + 13_-4 cr 12 25398284 SBS CA KRAS 35 12 SGS 0.192 Not found 38.7512676 5023 PANC 691 PANC 691 PLS 1 PDAC TP53_0280_0289 cr 17 7577094 SBS GA TP53 844 282 0.273 Not Found 5023 PANC 691 PANC 691 PLS 2 PDAC TP53_167_177 cr 17 7578413 SBS CG TP53 517 173 VL 0.140 0.145 3.2258065 5023 PANC 691 PANC 691 PLS 3 PDAC TP53_167_177 cr 17 7578413 SBS CG TP53 517 173 VL 0.121 0.145 3.2258065 5024 PANC 692 692 PLS 1 PDAC KRAS_012_ + 13_-4 cr 12 25398284 SBS CT KRAS 35 12 GD 0.155 Not found 71.5549482 5024 PANC 692 PANC 692 PLS 1 PDAC TP53_233_245 cr 17 7577580 SBS TC TP53 701 234 YC 0.132 0.064 Not found 5024 PANC 692 PANC PLS 1 PDAC TP53_0272_0283 cr 17 7577124 SBS CT TP53 814 272 VM 0.101 Not found 27.7915633 5024 PANC 694 PANC 694 PLS1 PDAC TP53_172A cr 17 7578406 SBS CT TP53 524 175 RH 0.176 Not found Not found 5024 PANC 697 PDC 697 PL15 1 cr 17 7578457 SBS CT TP53 473 158 RH 0.135 0.019 Not found 5041 PANC 708 PANC 708 PLS 1 PDAC TP53_151_163 cr 17 7578478 SBS GT TP53 452 151 PH 0.169 Not found 19.930975 5041 PANC 708 PANC 708 PLS 1 PDAC TP53_0248_0261 cr 17 7577539 S 17 GA TP53 742 248 RW 0.154 0.043 Not found 5041 PANC 708 PANC 708 PLS 1 PDAC KRAS_012_ + 13_-4 cr 12 25398284 SBS CT KRAS 35 12 GD 0.137 Not found 16.9190826 5041 PANC 715 PANC 715 PLS 1 PDAC TP53_172A cr 17 7578406 SBS CT TP53 524 175 RH 0.274 0.255 Not found

5041 PANC 715 PANC 715 PLS 1 PDAC KRAS_012_ + 13_-4 cr 12 25398284 SBS CT KRAS 35 12 GD 0.105 Not found 35.971223 5042 PANC 717 PANC 717 PLS 1 PDAC TP53_151_163 cr 17 7578457 SBS CT TP53 473 158 RH 0.118 0.055 Not found 5042 PANC 717 PANC 717 PLS 1 PDAC KRAS_012_ + 13_-4 cr 12 25398284 SBS CT KRAS 35 12 GD 0.117 Not found 12.4012367 5040 PANC 725 PANC 725 PLS 1 PDAC TP53_298_306 cr 17 7577022 SBS GA TP53 916 306 R * 0.451 0.460 found 5048 PANC 759 PANC 759 PLS 1 PDAC TP53_172A cr 17 7578403 SBS CT TP53 527 176 CY 0.357 0.233 Not found 5048 PANC 759 PANC 759 PLS 1 PDAC TP53_132_142 cr 17 7578535 SBS TC TP53 395 132 KR 0.105 0.14 PAN Not found 5048 PANC 759 PLS 2 PDAC TP53_172A cr 17 7578403 SBS CT TP53 527 176 CY 0.723 0.233 Not found 5048 PANC 759 PANC 759 PLS 2 PDAC TP53_132_142 cr 17 7578535 SBS TC TP53 395 132 KR 0.148 0.144 Not found 5048 PANC 760 PANC 760 PL33 1 PDAC TP53_33 7577559 SBS GA TP53 722 241 SF 2.8 65 3,014 Not found 5048 PANC 760 PANC 760 PLS 1 PDAC TP53_0272_0283 cr 17 7577120 SBS CT TP53 818 273 RH 0.358 0.089 Not found 5048 PANC 760 PANC 760 PLS 2 PDAC TP53_233_245 cr 17 7577559 SBS GA TP53 722 241 SF 3.418 3.014 Not found 768 PANC 768 PLS 1 PDAC TP53_0272_0283 cr 17 7577120 SBS CT TP53 818 273 RH 0.186 0.167 Not found 5049 PANC 768 PANC 768 PLS 2 PDAC TP53_0272_0283 cr 17 7577120 SBS CT TP53 818 273 RH 0.234 0.167 Not found

Table 13. Characteristics of patient and tumor.

PatientID Age Race Type Cancer Diagnosis Histopathological Stage Sample Esc Pap Sample Esc Tao Sample Plasma PAP 001 45 NA Endometrium Well to Moderately differentiated endometrial adenocarcinoma I Positive Not tested Not tested PAP 002 53 NA Ovarian Moderately to very poorly different, serous adenocarcinoma III Positive Not tested Not tested PAP 003 53 NA Endometrium Moderately differentiated endometrium adenocarcinoma I Positive Not tested Not tested PAP 006 57 NA Ovarian Moderately to very poorly differs. ovarian serous papillary aIII Negative Not tested Not tested PAP 007 63 NA Ovarian Moderately to very poorly differs. serous ovarian papillary aIII Negative Not tested Not tested PAP 010 73 NA Endometrium Well differentiated endometrial adenocarcinoma I Positive Not tested Not tested PAP 011 58 NA Endometrium Well differentiated endometrial adenocarcinoma I Positive Not tested Not tested PAP 024 56 Caucasian Endometrial Endometrial carcinoma with scaling differentiation I Negative Not tested Not tested PAP 025 75 African Endometrium Serous carcinoma high grade I Positive Not tested Not tested PAP 026 86 Caucasian Endometrium Endometrial adenocarcinoma I Negative Not tested Not tested PAP 027 NA NA Ovarian NA NA Negative Not tested PAP 030 73 NA Endometrium Adenocarcinoma endometrium I Negative Not tested Not tested PAP 032 82 NA Endometrium Adenocarcinoma endometrium I Negative Not tested Not tested PAP 033 68 Caucasian Endometrium Endometrial adenocarcinoma with squamous differentiation I Positive Not tested Not tested PAP 034 55 Caucasian Endometrium Endometrial adenocarcinoma Iometric ivo Not tested Not tested PAP 035 50 Caucasian Endometrium Endometrial adenocarcinoma IV Positive Not tested Not tested PAP 036 57 Caucasian Ovarian Serous carcinoma low grade I Negative Not tested Not tested PAP 037 64 Asian Endometrium Papillary carcinoma of the endometrium I Positive Not tested PAP 038 46 Asian Ovarian NA I Negative Not tested Not tested PAP 039 72 Asian Ovarian Serous adenocarcinoma I Positive Not tested Not tested PAP 040 56 Asian Endometrium Endometrium adenocarcinoma I Negative Not tested Not tested PAP 041 65 Asian Endometrial Papillary carcinoma I Negative Not tested No tested PAP 042 45 Asian Ovarian Adenosquamous Cell Carcinoma III Positive Not tested Not tested PAP 058 NA NA Ovarian NA NA Negative Not tested Not tested PAP 059 NA NA Ovarian NA NA Positive Not tested Not tested PAP 060 NA NA Ovarian NA III Negative Not tested Not tested PAP 061 NA NA Ovarian NA III Negative Not tested No tested PAP 062 NA NA Ovarian NA III Positive Not tested Not tested PAP 063 NA NA Ovarian NA III Negative Not tested Not tested PAP 065 58 Asian Endometrial Endometrial adenocarcinoma, type Viloglandular I Positive Not tested Not tested PAP 066 70 Asian Endometrial Endometrial Cell Carcinoma III Positive Not tested Not tested PAP 067 61 Asian Endometrium Endometrial adenocarcinoma I Positive Not tested Not tested PAP 068 63 Asian Endometrium Endometrial adenocarcinoma with mucinous differentiation I Negative Not tested Not tested PAP 069 51 Asian Endometrium Endometrial adenocarcinoma I Positive Not tested Not tested PAP 070 49 Asian Endometrium Endometrial adenocarcinoma II Positive Not tested Not tested PAP 071 56 Caucasian Endometrium Endometrial carcinoma with mucinous differentiation I Negative Not tested Not tested PAP 072 64 NA Ovarian Serous adenocarcinoma IV Negative Not tested Not tested PAP 073 64 NA Ovarian Adenocarcinoma serous III Negative N Not tested Not tested PAP 074 73 NA Ovarian Serous Adenocarcinoma IV Negative Not tested Not tested PAP 075 54 NA Ovarian Serous Adenocarcinoma III Negative Not tested Not tested PAP 076 53 NA Ovarian Serous Adenocarcinoma III Negative Not tested Not tested PAP 078 53 NA Serous Adenocarcinoma Serous III Negative Not tested Not tested PAP 079 66 NA Ovarian Serous adenocarcinoma IV Positive Not tested Not tested PAP 080 94 NA Endometrium NA III Positive Not tested Not tested PAP 081 59 NA Ovarian NA NA Negative Not tested PAP 083 51 NA Endometrium NA I Negative Not tested Not tested PAP 084 66 NA Endometrium NA III Positive Not tested Not tested PAP 119 58 Asian Endometrium Endometrial adenocarcinoma, villoglandular pattern I Positive Not tested Not tested PAP 120 57 Asian Endometrium Endometrial adenocarcinoma, villoglandular pattern I Positive Not tested Not tested PAP 121 61 Asian Endometrium Endometrial adenocarcinoma, villoglandular pattern I Posit ivo Not tested Not tested PAP 122 54 Asian Endometrium Endometrial adenocarcinoma, mixed solid-villoglandular patternI Positive Not tested Not tested PAP 123 47 Asian Endometrial Endometrial adenocarcinoma, villoglandular pattern I Negative Not tested Not tested

PAP 125 62 Asian Endometrium Endometrial adenocarcinoma, villoglandular pattern I Negative Not tested Not tested PAP 126 53 Asian Endometrium Endometrial adenocarcinoma, villoglandular pattern I Negative not tested Not tested PAP 128 54 Asian Endometrium Endometrial adenocarcinoma, villoglandular pattern I Positive Not tested PAP 129 129 50 Asian Endometrial Endometrial adenocarcinoma, viloglandular pattern I Negative Not tested Not tested PAP 131 56 Asian Endometrial Adenocarcinoma, viloglandular pattern I Positive Not tested Not tested PAP 132 62 Asian Endometrial Endometrial adenocarcinoma, solid pattern I Positive Not tested PAP 168 39 Asian Ovarian Adenocarcinoma (probably serous high grade) IV Positive Not tested Not tested PAP 169 48 Asian Ovarian Carcinosarcoma IV Positive Not tested Not tested PAP 172 42 Asian Ovarian Adenocarcinoma (probably serous high grade) I Negative Not tested Not tested PAP 173 51 Asiatic o Ovarian Transp I cell carcinoma Positive Not tested Not tested PAP 174 42 Asian Ovarian Adenoacanthoma I Negative Not tested Not tested PAP 175 61 Asian Endometrium Adenosquamous cell carcinoma IV Positive Not tested Not tested PAP 176 57 Asian Endometrium Endometrial adenocarcinoma I Positive Not tested No tested PAP 177 59 Asian Endometrial Endometrial Adenocarcinoma I Positive Not tested Not tested PAP 178 53 Asian Endometrium Endometrial Adenocarcinoma I Positive Not tested Not tested PAP 179 67 Asian Endometrial Endometrial Adenocarcinoma I Positive Not tested Not tested PAP 180 52 Asian Endometrial Endometrial Adenocarcinoma I Positive Not tested Not tested PAP 181 78 Asian Endometrium Endometrial villoglandular adenocarcinoma I Positive Not tested Not tested PAP 182 56 Asian Ovarian adenosquamous cell carcinoma I Negative Not tested negative PAP 183 56 Asian Endometrium Endometrial adenocarcinoma viloglan dular III Negative Not tested Not tested PAP 184 25 Asian Ovarian Villaglandular endometrial adenocarcinoma I Positive Not tested Positive PAP 185 54 Asian Ovarian Papillary serous adenocarcinoma (probably serous high grade) I Negative Not tested Negative PAP 186 48 Asian Ovarian papillary transitional cell carcinoma II Positive Not tested Positive PAP 193 53 Asian Ovarian Papillary serous cystadenocarcinoma (probably high grade serous) III Negative Not tested Positive PAP 194 45 Asian Ovarian Endometrial adenocarcinoma, villoglandular type I Positive Not tested Not tested PAP 195 55 Asian ovarian Serous papillary adenocarcinoma (provav, serous high grade) III Positive Not tested Negative PAP 196 54 Asian Ovarian Mixed carcinoma (probably serous high grade) I Negative Not tested Positive PAP 197 54 Asian Ovarian Serous adenocarcinoma (probably serous high grade) I Negative Not tested Positive PAP 198 44 Asian Ovarian Carcinoma transp cell.

III Negative Not tested Positive PAP 199 51 Asian Ovarian Serous adenocarcinoma (probably serous high-grade) I Positive Not tested Positive PAP 200 56 Asian Endometrial Endometrial adenocarcinoma, type villil with mucinous I Positive Not tested Not tested PAP 201 59 Asian Endometrial endometrial adenocarcinoma, type macroglandular I Positive Not tested Not tested PAP 203 71 Asian Endometrium Endometrial adenocarcinoma, mixed type I Positive Not tested Not tested PAP 204 52 Asian Endometrium Endometrial adenocarcinoma, viloglandular type with scaly I Positive Not tested Not tested PAP 205 54 Asian Endometrial Endometrial adenocarcinoma with metaplasia squamous I Positive Not tested Not tested PAP 206 58 Asian Endometrium Endometrium Adenocarcinoma, Viloglandular type III Positive Not tested Not tested PAP 207 59 Asian Endometrium Endometrial adenocarcinoma, type Viloglandular I Positive Not tested Not tested PAP 208 61 NA Endometrium Adenocarcinoma and ndometric I Positive Not tested Not tested PAP 209 62 NA Endometrium Endometrial adenocarcinoma II Positive Not tested Not tested PAP 210 50 NA Ovarian Mucinous adenocarcinoma I Negative Not tested Positive PAP 212 61 NA Endometrium Endometrial adenocarcinoma NA Negative Not tested Not tested PAP 213 70 NA Endometrium Endometrial adenocarcinoma II Positive Not tested Not tested PAP 214 56 NA Endometrium Endometrial adenocarcinoma I Positive Not tested Not tested PAP 215 62 NA Endometrium Serous adenocarcinoma I Positive Not tested Not tested PAP 216 58 NA Endometrium Endometrial adenocarcinoma I Positive Not tested PAP 217 64 NA Endometrium Adenocarcinoma endometrium I Negative Not tested Not tested PAP 218 80 NA Endometrium Adenocarcinoma serous IV Positive Not tested Not tested PAP 219 64 NA Endometrium Endometrial adenocarcinoma III Positive Not tested Not tested PAP 220 70 NA Endometrium Adenocarcinoma trans cell III Positive Not tested Not tested PAP 221 57 NA E Endometrial adenocarcinoma III Positive Not tested Not tested PAP 222 68 NA Endometrial endometrial adenocarcinoma I Positive Not tested Not tested PAP 224 71 NA Endometrium Endometrial adenocarcinoma III Positive Not tested Not tested PAP 225 61 NA Endometrial adenocarcinoma I Positive Not tested PAP6 22 69 NA Endometrium Endometrial adenocarcinoma I Negative Not tested Not tested PAP 227 63 NA Endometrium Endometrial adenocarcinoma III Positive Not tested Not tested PAP 228 75 NA Endometrium Endometrial adenocarcinoma II Positive Not tested Not tested PAP 229 54 NA Endometrium Endometrial adenocarcinoma I Positive Not tested Not tested Not tested PAP 230 79 NA Ovarian Low-grade serous carcinoma III Positive Not tested negative PAP 231 82 NA Endometrium Serous adenocarcinoma I Positive Not tested Not tested PAP 232 77 NA Endometrium Endometrial adenocarcinoma III Positive Not tested Not tested PAP 233 54 NA Endometrial endometrial adenocarcinoma III Positive N Not tested Not tested PAP 234 49 NA Endometrium Endometrial adenocarcinoma I Positive Not tested Not tested PAP 235 61 NA Endometrium NA NA Positive Not tested Not tested PAP 236 64 NA Endometrium NA NA Positive Not tested Not tested PAP 237 64 NA Endometrium NA NA Positive No tested Not tested PAP 238 48 NA Endometrium NA NA Positive Not tested Not tested

PAP 239 68 NA Endometrium NA NA Positive Not tested Not tested PAP 240 57 NA Endometrium NA NA Positive Not tested Not tested PAP 241 82 NA Ovarian NA NA Negative not tested negative PAP 242 65 NA Endometrium NA NA Positive Not tested PAP 243 51 NA Endometrium NA NA Positive Not tested Not tested PAP 244 63 NA Endometrium NA NA Positive Not tested Not tested PAP 245 63 NA Endometrium NA NA Positive Not tested Not tested PAP 246 63 NA Endometrium NA NA Positive Not tested PAP 247 58 NA Endometrium NA NA Positive Not tested Not tested PAP 248 59 Asian Ovarian Serous papillary carcinoma III Negative Not tested negative PAP 250 53 Asian Ovarian Endometrial adenocarcinoma, viloglandular type IV Positive Not tested Positive PAP 251 79 Asian Ovarian Endometrial adenocarcinoma, solid type I Negative Not tested Positive PAP 252 42 Asian Ovarian Mucinous Cystadenocarcinoma I Negative Not Tested Negative PAP 253 60 Asian Ovarian Cystadenocarcinoma papillary serous oma (probably serous high grade) III Negative Not tested negative PAP 254 46 Asian Ovarian Papillary serous cystadenocarcinoma I Positive Not tested negative PAP 255 62 Asian Ovarian Mucinous adenocarcinoma III Positive Not tested Positive PAP 257 45 Asian Ovarian Papillary serous papillary I Negative Non tested Negative PAP 258 56 Asian Ovarian Mucinous Adenocarcinoma I Negative Not tested Negative PAP 259 57 Asian Ovarian Transenal Adenocarcinoma I Negative Not tested Negative PAP 261 53 Asian Ovarian Mucinous Adenocarcinoma I Negative Not tested Positive PAP 262 49 Asian Ovarian Serous Adenocarcinoma (probably serous high serous adenocarcinoma) grade) III Positive Not tested Positive PAP 263 55 Asian Ovarian Trans cell carcinoma I Negative Not tested negative PAP 264 56 Asian Ovarian Serous adenocarcinoma I Positive Not tested negative PAP 265 46 Asian Endometrium Endometrial adenocarcinoma, type II Villaggio II Positive No tested Not tested PAP 266 51 Asian Endometrium Mucinous adenocarcinoma I Positive Not tested Not tested PAP 267 46 Asian Endometrium Endometrial adenocarcinoma, viloglandular type I Positive Not tested Not tested PAP 268 70 Asian Endometrium Carcino-Sarcoma (Malignant Mixed Mullerian Tumor) I Positive Not tested Not tested PAP 269 55 Asian Endometrial Carcino-Sarcoma (Malignant Mixed Tumor Mullerian) I Positive Not tested Not tested PAP 270 56 Asian Endometrial Endometrial adenocarcinoma, viloglandular type I Positive Not tested Not tested PAP 271 53 Asian Endometrial Endometrial adenocarcinoma, Positive viloglandular type I Positive Not tested Not tested PAP 272 54 Asian Endometrium Endometrial stromal sarcoma II Positive Not tested Not tested PAP 273 57 Asian Endometrium Endometrial adenocarcinoma, villoglandular type I Positive Not tested Not tested PAP 274 59 Asian Endometrium Mucinous adenocarcinoma I Positive Not tested PAP 275 37 37 Asian Endometri o Endometrial adenocarcinoma, viloglandular type I Positive Not tested Not tested PAP 276 65 Asian Endometrium Endometrial adenocarcinoma, viloglandular type I Positive Not tested Not tested PAP 277 61 Asian Endometrial Endometrial adenocarcinoma, type V villoglandular I Positive Not tested Adopted PAP 524 64 NA Ovarian serous (probably serous high grade) III Positive Not tested Not tested PAP 525 65 NA Ovarian Serous adenocarcinoma III Negative Not tested Not tested PAP 526 69 NA Ovarian Serous adenocarcinoma (probably serous high grade) IV Negative Not tested Not tested PAP 527 58 NA Ovarian Serous adenocarcinoma (probably serous high grade) III Negative Not tested Not tested PAP 528 68 NA Ovarian Serous adenocarcinoma (probably serous high grade) III Positive Not tested Not tested PAP 529 70 NA Ovarian Serous adenocarcinoma (probably serous high grade) III Negative Not tested Not tested PAP 530 68 NA Ovarian Adenocarcinoma serous (probably serous high grade) III Positive Not tested Not tested PAP 531 62 NA Serous Adenocarcinoma serous (probably serous high grade) II Positive Not tested Not tested PAP 532 51 NA Ovarian Serous adenocarcinoma (probably serous high grade) III Negative Not tested No tested PAP 533 48 NA Ovarian Serous adenocarcinoma (probably serous high grade) III Negative Not tested Not tested PAP 535 65 NA Ovarian Serous adenocarcinoma (probably serous high grade) III Negative Not tested Not tested PAP 536 67 NA Serous adenocarcinoma serous (probably serous high grade) IV Positive Not tested Not tested PAP 537 45 NA Ovarian Serous adenocarcinoma (probably serous high grade) IV Negative Not tested Not tested PAP 540 69 NA Ovarian Serous adenocarcinoma (probably serous high grade) III Negative Not tested Not tested PAP 541 65 NA Ovarian Serous adenocarcinoma (probably serous high grade) IV Positive Not tested Not tested PAP 54 2 64 NA Ovarian Serous adenocarcinoma (probably serous high grade) III Negative Not tested Not tested PAP 544 45 NA Endometrium Endometrial adenocarcinoma I Negative Not tested Not tested PAP 549 72 NA Endometrium Endometrial adenocarcinoma I Positive Not tested Not tested PAP 550 60 NA Endometrium Adenocarcinoma endometrium I Negative Not tested Not tested PAP 551 49 NA Ovarian Serous adenocarcinoma III Negative Not tested Not tested PAP 552 56 NA Ovarian Serous adenocarcinoma (probably serous high grade) III Positive Not tested Not tested PAP 554 59 NA Endometrium Mixed adenocarcinoma with cell character transp & serosa IV Positive Not tested Not tested PAP 555 71 NA Endometrium Endometrial adenocarcinoma IV Positive Not tested Not tested PAP 556 62 NA Endometrium Endometrial adenocarcinoma III Positive Not tested Not tested PAP 557 79 NA Endometrium NA IV Positive Not tested PAP 558 59 NA Ovarian Serous adenocarcinoma (probably serous high grade) III Negative Not tested Not tested PAP 560 59 NA Endometrium Endometrial adenocarcinoma I Positive Not tested Not tested PAP 561 66 NA Endometrium Endometrial adenocarcinoma I Negative Not tested Not tested PAP 562 73 NA Endometrium Endometrial adenocarcinoma I Positive Not tested Not tested PAP 563 68 NA Ovarian Adenocarcinoma serous III Negative Not tested Not tested

PAP 564 65 NA Endometrium Serous adenocarcinoma IV Positive Not tested Not tested PAP 565 56 NA Endometrium Adenocarcinoma endometrium well differentiated and undifferentiated I Positive Not tested Not tested PAP 566 60 NA Ovarian Serous adenocarcinoma (probably serous high grade) IV Negative Not tested Not tested PAP 567 58 NA Ovarian Serous Adenocarcinoma IV Negative Not Tested Not Tested PAP 568 53 NA Ovarian Serous Adenocarcinoma (Probably Serous High Grade) III Negative Not Tested PAP 569 45 NA Ovarian Serous Adenocarcinoma (Probably Serous High Grade) IV Positive Not Tested Not Tested tested PAP 570 60 NA Endometrium Carcinosarcoma III Positive Not tested Not tested PAP 572 68 NA Endometrium Serous adenocarcinoma III Positive Not tested Not tested PAP 573 69 NA Ovarian Serous adenocarcinoma (probably serous high grade) IV Negative Not tested Not tested PAP 574 52 NA Endometrium Endometrial adenocarcinoma III Positive Not tested Not tested PAP 575 42 NA Ovarian Serous Adenocarcinoma (probably serous high grade) III Negative Not tested Not tested PAP 576 69 NA Ovarian Serous Adenocarcinoma (probably serous high grade) III Positive Not tested Not tested PAP 579 43 Asian Ovarian Papillary serous cystadenocarcinoma (probably serous high grade) I Negative Not tested Negative PAP 581 53 Asian Endometrium Endometrial adenocarcinoma, viloglandular type I Positive Not tested Not tested PAP 582 64 Asian Endometrium Endometrial adenocarcinoma, viloglandular type I Positive Not tested Not tested PAP 583 58 Asian Endometrial Endometrial adenocarcinoma, Positive cribriform type I Positive tested Not tested PAP 584 66 Asian Endometrium Endometrial adenocarcinoma, type Viloglandular I Positive Not tested Not tested PAP 585 65 Asian Endometrium Endometrial adenocarcinoma, type Viloglandular I Positive Not tested Not tested PAP 587 41 Asian Endometrium Endometrial Adenocarcinoma, Type non-viloglandular I Positive Not tested PAP 588 62 Asian Endometrial Endometrial adenocarcinoma, viloglandular type I Positive Not tested Not tested PAP 589 74 Asian Endometrial Endometrial adenocarcinoma, poorly differentiated IV Positive Not tested Not tested PAP 590 56 Asian Endometrial Endometrial adenocarcinoma, Type viloglandular I Positive Not tested tested PAP 591 63 Asian Endometrium Endometrial adenocarcinoma, viloglandular type I Negative Not tested Not tested PAP 592 56 Asian Ovarian Adenosquamous cell carcinoma II Positive Not tested Not tested PAP 593 68 Asian Endometrial Endometrial adenocarcinoma, Viloglandular type I Positive Not tested PAP4 594 51 Asian Endometrial Endometrial adenocarcinoma, type I villoglandular I Positive Not tested Not tested PAP 595 60 Asian Endometrium Adenocarcinoma, endometrial type I Positive Not tested Not tested PAP 596 50 Asian Endometrial endometrial adenocarcinoma, type I secretion Positive not tested o Not tested PAP 599 79 Caucasian Endometrium Endometrial adenocarcinoma II Positive Not tested Not tested PAP 600 69 Caucasian Endometrium Adenocarcinoma cell carcinoma transp III Negative Not tested Not tested PAP 601 59 Caucasian Endometrium Serous papillary adenocarcinoma II Positive Not tested PAP 604 69 Caucasian Endometrium Endometrial adenocarcinoma with component, transp cell I Positive Not tested Not tested PAP 606 56 Caucasian Ovarian Serous adenocarcinoma (probably serous high grade) III Negative Not tested Not tested PAP 607 60 Caucasian Ovarian Serous papillary adenocarcinoma (probably serous high grade) III Negative No tested Not tested PAP 608 73 Caucasian Ovarian Serous adenocarcinoma (probably serous high grade) III Negative Not tested Not tested PAP 609 90 Caucasian Endometrium Endometrial adenocarcinoma I Positive Not tested Not tested PAP 610 49 Caucasian Endometrium Endometrial adenocarcinoma with component, transp cell III Pos positive Not tested Not tested PAP 612 59 Caucasian Ovarian Serous papillary adenocarcinoma (probably serous high grade) III Negative Not tested Not tested PAP 613 57 Caucasian Endometrium Endometrial adenocarcinoma I Negative Not tested Not tested PAP 616 68 Caucasian Endometrium Endometrial adenocarcinoma I Positive Not tested No tested PAP 617 74 Caucasian Endometrium Serous Papillary Adenocarcinoma III Positive Not tested Not tested PAP 618 76 Caucasian Endometrium Endometrial Adenocarcinoma I Positive Not tested Not tested PAP 619 59 Caucasian Endometrium Endometrial Adenocarcinoma III Positive Not tested Not tested PAP 621 56 Caucasian Endometrial Adenocarcinoma Serous Positive Not tested Not tested PAP 622 56 Caucasian Ovarian Papillary serous adenocarcinoma III Negative Not tested Not tested PAP 625 59 Caucasian Endometrial endometrial adenocarcinoma I Positive Not tested Not tested PAP 627 77 Caucasian Endometrial endometrial adenocarcinoma I Negative N Not Tested Not Tested PAP 628 53 Caucasian Ovarian Serous Papillary Carcinoma (Probably Serous High Degree) I Negative Not Tested PAP 629 56 Caucasian Endometrium Endometrial Adenocarcinoma I Positive Not Tested Not Tested PAP 632 66 Caucasian Endometrium Carcinosarcoma I Positive Not Tested PAP 633 71 Caucasian Endometrium Endometrial adenocarcinoma I Positive Not tested Not tested PAP 636 63 Caucasian Ovarian Serous adenocarcinoma high grade III Negative negative Not tested PAP 637 77 NA Endometrium Serous carcinoma high grade I Negative Positive Not tested PAP 638 67 NA Endometrium Serous carcinoma high grade IV Positive Positive Not tested PAP 641 53 Asian Ovarian Trans cell carcinoma II Negative Not tested negative PAP 642 53 Asian Ovarian Serous adenocarcinoma I Negative not tested Positive PAP 643 46 Asian Ovarian Endometrial adenocarcinoma I Negative Not tested Positive PAP 644 63 Asian Ovarian Carcinosarcoma II Negative No forehead do Positive PAP 645 63 Asian Ovarian Papillary Serous Cystadenocarcinoma I Negative Not tested Positive PAP 646 73 Asian Endometrium Endometrial adenocarcinoma, solid type II Positive Not tested Not tested PAP 647 49 Asian Endometrial Endometrial adenocarcinoma, villoglandular type I Positive Not tested Not tested PAP 648 29 Asian Endometrium Endometrial adenocarcinoma, villoglandular type I Positive Not tested Not tested PAP 649 60 Asian Endometrium Serous papillary adenocarcinoma I Positive Not tested Not tested PAP 650 59 Asian Endometrium Endometrial adenocarcinoma, Positive type Not tested Not tested PAP 651 66 Asian Endometrium Adenocarcinoma , villoglandular type I Negative Not tested Not tested PAP 652 43 Asian Endometrial Endometrial adenocarcinoma, villoglandular type I Positive Not tested Not tested

PAP 653 63 Asian Endometrium Endometrial adenocarcinoma, type II villaglandular Negative Not tested Not tested PAP 654 54 Asian Endometrium Adenocarcinoma endometrium, type villilland I Positive not tested Not tested PAP 655 74 Asian Endometrial papillary adenocarcinoma II Negative Not tested Not tested PAP 663 53 Caucasian Endometrial Endometrial adenocarcinoma I Positive Not tested Not tested PAP 665 81 Caucasian Ovarian Mucinous adenocarcinoma I Negative Not tested Not tested PAP 667 68 Caucasian Endometrium Endometrial adenocarcinoma I Positive Not tested Not tested PAP 668 68 Caucasian Endometrial Adenocarcinoma endometrium II Positive 6 Not tested PAP69 Caucasian Endometrium Endometrial adenocarcinoma I Positive Not tested Not tested PAP 670 57 Caucasian Ovarian Serous adenocarcinoma (probably serous high grade) III Negative Not tested Not tested PAP 671 56 Caucasian Endometrial Endometrial adenocarcinoma I Positive Not tested Not tested P AP 672 64 Caucasian Ovarian Serous Adenocarcinoma III Negative Not Tested Not Tested PAP 674 59 Caucasian Ovarian Serous Adenocarcinoma (Probably Serous High Grade) IV Negative Not Tested PAP 675 60 Caucasian Serosopapillary Endometrium Serous Papillary Adenocarcinoma I Positive Not Tested PAP 676 83 Caucasian Endometrial Serous Adenocarcinoma III Negative Not tested Not tested PAP 678 48 Caucasian Ovarian Transp cell carcinoma III Negative Not tested Not tested PAP 679 72 Caucasian Endometrium Endometrial adenocarcinoma I Positive Not tested Not tested PAP 681 64 Caucasian Endometrium Endometrial adenocarcinoma I Positive Not tested Not tested PAP 684 68 Caucasian Endometrium Endometrial adenocarcinoma I Positive Not tested Not tested PAP 685 61 Caucasian Endometrium Endometrial cancer I Positive Positive not tested PAP 686 57 Caucasian Endometrium Extensive mucinous endometrial adenocarcinoma d I Negative Positive Not tested PAP 687 86 Caucas iano Endometrium High grade endometrial carcinoma I Positive Positive Not tested PAP 688 59 NA Endometrium Endometrial adenocarcinoma I Positive Positive Not tested PAP 689 76 NA Endometrium Serious high degree endometrial carcinoma with metastasis III Positive Positive Not tested PAP 690 56 Caucasian Endometrial endometrial adenocarcinoma low grade I Positive Positive Not tested PAP 691 60 Caucasian Endometrium High-grade endometrial carcinoma a I Positive Positive Not tested PAP 692 58 African Ovarian Papillary thyroid carcinoma, (right); mature cystic teratoma (lI Negative Negative Not tested PAP 694 62 Asian Endometrium Endometrial adenocarcinone, viloglandular type I Negative Not tested Not tested PAP 695 66 Asian Endometrium Serous Adenocarcinoma II Positive Not tested Not tested PAP 696 71 Asian Endometrioid Adenocarcinone, Positive viloglandular type III Not tested Not tested PAP 697 56 Asian Endometrioid Endometrioid Adenocarcinone, type Viloglandular I Positive Not tested Not tested PAP 698 55 Asian Endometrial Endometrioid Adenocarcinone, type Viloglandular I Negative Not tested Not tested PAP 699 59 Asian Endometrioid Endometrioid Adenocarcinone I, Type Viloglandular Not tested PAP 701 51 Asian Ovarian Cystadenocarcinoma seros papillary (probably high-grade serous) I Negative Not tested Negative PAP 702 59 Asian Endometrium Endometrial adenocarcinoma I Negative Not tested Not tested PAP 704 59 Asian Ovarian Carcinom transp I Negative Not te Status Negative PAP 705 53 Asian Ovarian Carcinom cell transp I Positive Not tested Negative PAP 707 54 Asian Ovarian Serous papillary adenocarcinoma (probably serous high grade) III Positive Not tested Positive PAP 708 49 Asian Ovarian Carcinom cell transp II Negative Not tested Negative PAP 709 52 Asian Ovarian Mucinous Adenocarcinoma II Negative Not Tested Negative PAP 710 51 Asian Ovarian Carcinom Cell Transp III Positive Not Tested Negative PAP 711 57 Asian Ovarian Serous Papillary Adenocarcinoma (Probably Serous High Grade) III Positive Not Tested Positive PAP 715 64 Asian Ovarian Adenocarcinoma Poorly Differentiated ( probable serous high gradeI Negative Not tested negative PAP 717 51 Asian Endometrium Endometrial adenocarcinoma III Positive Not tested Not tested PAP 719 76 African Endometrial Endometrial carcinoma with mucinous differentiation I Positive Not tested Not tested PAP 721 75 Caucasian Ovarian Serous Adenocarcinoma high grade III Positive Not tested Not tested PAP 722 70 African Ovarian Serous adenocarcinoma high grade III Negative Not tested Not tested PAP 726 67 Caucasian Endometrial carcinoma with character, mucinous I Positive Not tested Not tested PAP 727 34 Caucasian Endometrial Endometrium with squamous morphological metaplasia and effective treatment I I Negative Not tested Not tested PAP 728 61 Caucasian Endometrium Endometrial adenocarcinoma I Positive Not tested Not tested PAP 730 7 ?? Other Endometrium Metastatic endometrial carcinoma to multiple lymph nodes III Positive Not tested Not tested PAP 735 65 Caucasian Endometrium Endometrial adenocarcinoma I Positive Not tested Not tested PAP 736 62 African Endometrial Endometrial carcinoma with focal squamous differentiation I Positive Not tested PAP 742 72 Caucasian Ovarian Carcinoma serous high grade IV Positive Not tested Not tested PAP 744 59 African Endometrial Endometrioid carcinoma metastatic to multiple lymph nodes III Positive Not tested Not tested PAP 746 56 Asian Ovarian Serous adenocarcinoma high grade III Negative Not tested Not tested PAP 748 57 Caucasian Endometrial High serous carcinoma grade I Positive Not tested Not tested PAP 749 75 Caucasian Ovarian Serous low carcinoma grade I Positive Not tested Not tested PAP 751 63 Caucasian Ovarian serous adenocarcinoma high grade III Negative Not tested Not tested PAP 753 72 Caucasian Endometrial serous carcinoma high grade IV Posi Not tested Not tested PAP 754 71 Caucasian Endometrium Endometrial adenocarcinoma I Positive Not tested Not tested PAP 757 56 Caucasian Endometrium Endometrial carcinoma with focal squamous differentiation III Positive Not tested Not tested PAP 758 45 Caucasian Endometrium Endometrial carcinoma I Negative Not tested Not tested PAP 761 79 African Endometrium Serous carcinoma high grade I Positive Not tested Not tested PAP 763 50 Caucasian Ovarian high serous adenocarcinoma III Positive Positive Not tested Not tested PAP 767 57 Caucasian Ovarian Serous adenocarcinoma high grade III Negative Not tested Not tested PAP 768 68 Caucasian Endometrial Serous carcinoma high grade with focal endometrial differentiation I Positive Not tested Not tested

PAP 770 76 African Endometrium Endometrial Adenocarcinoma I Positive Not tested Not tested PAP 775 79 African Endometrium Endometrial Adenocarcinoma I Positive Not tested Not tested PAP 777 65 Caucasian Ovarian Serous Adenocarcinoma High Grade III Negative Not tested Not tested PAP 778 35 Caucasian Endometrial Adenocarcinoma Negative I Not tested Not tested PAP 787 78 Other Ovarian NA IV Negative Not tested Not tested PAP 789 61 Caucasian Endometrial endometrial adenocarcinoma with squamous differentiation III Positive Not tested Not tested PAP 790 60 African Endometrium Carcinosarcoma III Positive Not tested Not tested PAP 791 64 Caucasian Endometrium Adenocarcinoma endometrium III Positive Not tested Not tested PAP 799 39 Asian Endometrium Endometrial adenocarcinoma I Negative Not tested Not tested PAP 800 56 Caucasian Endometrium Endometrioid carcinoma with squamous differentiation IV Positive Not tested Not tested PAP 802 68 African Endometrium Adenocarcino Endometrium I Positive Not tested Not tested PAP 804 59 Caucasian Endometrium Endometrioid carcinoma with squamous differentiation I Positive Not tested Not tested PAP 805 50 African Endometrium Endometrioid carcinoma with squamous differentiation I Positive Not tested Not tested PAP 807 65 Other Endometrium Endometrial adenocarcinoma Positive No tested Not tested PAP 809 38 Caucasian Ovarian High-grade adenocarcinoma with character. cell transp III Negative Not tested Not tested PAP 812 77 Caucasian Endometrium Endometrial adenocarcinoma I Positive Not tested Not tested PAP 813 64 Caucasian Endometrium Endometrial adenocarcinoma I Positive Not tested Not tested PAP 815 67 Caucasian Ovarian high serous carcinoma grade III Negative Not tested Negative PAP 816 61 Caucasian Endometrial Endometrial Adenocarcinoma I Positive Not tested Not tested PAP 824 64 NA Endometrial Endometrial Adenocarcinoma I Positive Positive Not tested PAP 829 75 Asian Endometrial Endometrial Adenocarcinoma I Positive Negative Not tested PAP 830 69 Asian Ovarian Endometrial Adenocarcinoma I Negative 68 Not tested PAP 824 Arabic Endometrium Serous carcinoma of high endometrium III Positive Positive Not tested PAP 832 81 Caucasian Endometrium High degree uterine leiomyosarcoma Negative Positive Not tested PAP 835 73 NA Endometrium Serous carcinoma high grade with metastasis to lymph nodes III Positive Positive No test PAP 836 76 NA Endometrium Endometrial adenocarcinoma I Negative Positive Not tested PAP 837 74 NA Endometrium Endometrial adenocarcinoma I Negative Positive Not tested PAP 839 68 Arabic Endometrium Serous endometrial carcinoma High grade III Positive Positive Not tested PAP 841 66 NA Endometrium Endometrial adenocarcinoma III Positive Positive Not tested PAP 842 70 NA Endometrium High grade endometrial carcinoma III Positive Positive Not tested PAP 845 67 Caucasian Ovarian Serous adenocarcinoma high grade IV Negative Positive Not tested PAP 850 63 Asian Endometrium High grade IV endometrial carcinoma Positive Positive Not tested PAP 851 72 Caucasian Endometrium Adenocarcinoma well differentiated endometrium with essamoso III Positive Positive Not tested PAP 853 53 NA Endometrium Adenocarcinoma endometrium I Negative Positive not tested PAP 858 66 Asian Endometrium Serous carcinoma high grade III Negative Positive Not tested PAP 860 65 NA Endometrium Endometrial adenocarcinoma I Positive Posi Non-tested PAP 862 66 Caucasian Endometrium Serous high-grade serous carcinoma III Not tested Positive Not tested PAP 865 73 Asian Endometrium Endenium Adenocarcinoma, villoglandular type I Positive Not tested Not tested PAP 866 61 Asian Endometrium Endometrium Adenocarcinoma, villoglandular type I Positive Not tested Not tested PAP 867 64 Asian Endometrium Endometrium Adenocarcinoma, viloglandular type I Positive Not tested Not tested PAP 868 53 Asian Endometrium Endometrium Adenocarcinoma, viloglandular type I Positive Not tested Not tested PAP 869 62 Asian Endometrium Endometrium adenocarcinoma, solid and mixed viloglandular type Positive Not tested tested PAP 870 56 Asian Endometrium Endometrium Adenocarcinoma, viloglandular type I Positive Not tested Not tested PAP 871 67 Asian Endometrium Endometrium adenocarcinoma, solid type II Positive Not tested Not tested PAP 873 58 Asian Endometrium Endometrium Adenocarcinoma, viloglandular type I Negative No t state Not tested PAP 874 64 Asian Endometrial Squamous cell carcinoma, transp cell type I Positive Not tested Not tested PAP 875 64 Asian Endometrium Endometrium Adenocarcinoma, viloglandular type I Positive Not tested Not tested PAP 876 76 Asian Endometrium Endometrium Adenocarcinoma, vile type land I Positive Non tested Not tested PAP 877 45 Asian Endometrium Endometrium Adenocarcinoma, type Viloglandular I Positive Not tested Not tested PAP 878 62 Asian Endometrium Endometrium Adenocarcinoma, type Viloglandular II Positive Not tested Not tested PAP 879 75 Asian Endometrium Endometrium Adenocarcinoma, Type Viloglandular I Positive Not tested tested PAP 881 60 African Endometrium Endometrial adenocarcinoma III Positive Not tested Not tested PAP 884 57 NA Endometrium Endometrial adenocarcinoma I Positive Positive Not tested PAP 889 53 NA Endometrium Endometrial adenocarcinoma, endometrioid type, well differentiated Negative Negative Not tested PAP 891 51 NA Ovarian Serous Adenocarcinoma High Grade III Negative Negative Not tested PAP 892 61 NA Endometrium Endometrium adenocarcinoma, endometrioid-type, well-differentiIII Positive Positive Not tested PAP 894 59 Caucasian Endometrium Endometrial adenocarcinoma I Positive Positive Not tested PAP 897 81 Caucasian Endometrium Adenocarcinoma Positive Positive Not tested PAP 900 45 Caucasian Ovarian High-grade mullerian undifferentiated carcinoma III Negative Not tested Not tested PAP 901 55 Caucasian Endometrial endometrial adenocarcinoma, squamous endometrioid and II Positive Positive Not tested PAP 902 61 NA Endometrial endometrial adenocarcinoma III Positive Not tested PAP 904 63 Asian Endometrium Serous adenocarcinoma high grade III Positive Positive Not tested PAP 907 69 Caucasian Endometrium Endometrial carcnoma, type squamous and mucous III Positive Positive Not tested PAP 910 81 NA Endometrium Endometrial adenocarcinoma III Positive Positive Not tested PAP 914 68 Arabic Ovarian Serous carcinoma high grade I Negative Positive Not tested PAP 919 57 NA Ovarian Endometrial adenocarcinoma, well differentiated II Negative Negative Not tested

PAP 922 54 NA Endometrium Endometrial adenocarcinoma I Positive Positive Not tested PAP 925 51 Asian Ovarian Serous high-grade carcinoma of ovarian tube III Negative Positive Not tested PAP 926 57 NA Endometrium Well differentiated endometrial adenocarcinoma I Positive Positive Not tested PAP 927 56 NA Endometrium Endometrium: FIGO grade I well differentiated aden endometrium I Positive Positive Not tested PAP 928 64 Latino Ovarian Serous high-grade carcinoma of ovarian tube II Positive Positive Not tested PAP 931 61 Caucasian Endometrium Endometrial endometrial adenocarcinoma with scaly d II Positive Not tested PAP 934 59 Caucasian Endometrial Endometrial Adenocarcinoma I Positive Positive Not Tested PAP 936 61 NA Endometrial Endometrial Adenocarcinoma I Positive Positive Not Tested PAP 937 55 NA Endometrial Endometrial Adenocarcinoma I Negative Negative Not Tested PAP 989 74 NA Endometrium Carcinosarcoma Uterine II Positive 9 Test Positive No Tested 9 Positive Positive Not tested 63 Arabi c Endometrium Endometrial adenocarcinoma I Positive Positive Not tested PAP 991 63 Asian Endometrium Endometrial adenocarcinoma, endometrioid with difference. escam III Positive Positive Not tested PAP 993 75 Arabic Ovarian High-grade serous carcinoma arising from the fallopian tube III Positive Positive Not tested PAP 994 65 Asian Endometrial Endometrium adenocarcinoma, endometrioid type with focus esc I Positive Negative Not tested PAP 995 58 Caucasian Endometrial Endometrial carcinoma , endometrioid 90% with 10% with transpII cell Positive Positive Not tested PAP 996 68 Caucasian Endometrium Endometrial adenocarcinoma I Positive Positive Not tested PAP 998 71 NA Endometrium Endometrium adenocarcinoma, mucinous type I Positive Positive Not tested PAP 999 66 NA Endometrium High endometrial carcinoma serious grade III Positive Positive Not tested PAPA 1001 53 Asian Endometrium Endometrial adenocarcinoma, endometrioid type with di mucinosaIII Positive Positive Not tested PAPA 1002 68 NA Endometrium Endometrial adenocarcinoma with mucinous and squamous I Positive Positive Not tested PAPA 1003 49 NA Endometrium Adenocarcinoma endometrium non-scaly III Positive Positive Not tested PAPA 1005 72 Caucasian Endometrium Serious high grade with endometrioid and scaly characteristics II Positive Positive not tested PAPA 1010 55 Caucasian Endometrium Endometrial adenocarcinoma III Positive Positive Not tested PAPA 1011 64 Asian Endometrium Endometrial adenocarcinoma Negative Positive NO 1012 73 Caucasian Endometrium Endometrial Carcinoma, undifferentiated III Positive Positive Not tested PAPA 1013 80 Caucasian Endometrium Serous carcinoma high grade III Positive Positive Not tested PAPA 1016 77 Other Endometrioid endometrioid endometrium with squamous metaplasia Positive Not tested PAPA 1019 63 NA Endometrium Adenium endometrium with villous and papillary focal III Positive Positive Not tested PAPA 1026 59 African Endometrium Serous carcinoma high grade I Negative Not tested Not tested PAPA 1033 76 Caucasian Ovarian Serous adenocarcinoma high grade III Negative Not tested Not tested P APA 1035 65 Caucasian Endometrium Serous carcinoma high grade II Positive Not tested Not tested PAPA 1037 60 Caucasian Ovarian Serous adenocarcinoma high grade III Positive Not tested Positive PAPA 1040 79 Caucasian Endometrium Endometrial adenocarcinoma I Positive Not tested Not tested PAPA 1043 57 Asian Endometrial Endometrial carcinoma with mucinous differentiation I Positive Not tested Not tested PAPA 1048 74 African Endometrial Serous carcinoma high grade I Positive Not tested Not tested PAPA 1057 73 Caucasian Endometrium Serous carcinoma high grade I Positive Not tested Not tested PAPA 1058 48 Caucasian Endometrium Endometrial adenocarcinoma I Negative Not tested No tested PAPA 1059 56 Caucasian Endometrium Endometrioid carcinoma with differ, mucinous and secretory I Positive Not tested Not tested PAPA 1062 66 Caucasian Ovarian Serous adenocarcinoma high grade IV Negative Not tested negative PAPA 1065 50 Other Endometrium Endometrial adenocarcinoma NA Negative No test do Not tested PAPA 1066 66 Caucasian Endometrium Endometrioid carcinoma with squamous differentiation I Positive Not tested Not tested PAPA 1069 56 Caucasian Endometrium Endometrioid carcinoma with prominent squamous difference I Positive Not tested Not tested PAPA 1072 39 Caucasian Ovarian Serous carcinoma low grade III Negative Not tested Negative PAPA 1074 48 Caucasian Ovarian Serous carcinoma poorly differentiated high grade III Negative Not tested negative PAPA 1076 58 African Endometrial Endometrioid carcinoma with secretory and metaplastic changesIII Positive Not tested Not tested PAPA 1077 53 Caucasian Endometrial Endometrioid carcinoma with mucinous differentiation I Positive Not tested Not tested 1078 40 Caucasian Endometrium Endometrial adenocarcinoma I Negative Not tested Not tested PAPA 1080 63 Caucasian Endometrium Endometrial adenocarcinoma I Positive Not tested Not tested PAPA 1081 73 Caucasian Endometrium Endometrial adenocarcinoma III Positive Not tested No the tested PAPA 1084 40 Caucasian Ovarian High-grade mucinous adenocarcinoma II Negative Not tested negative PAPA 1086 64 African Endometrium Multifocal serous endometrial intraepithelial carcinoma I Positive Not tested Not tested PAPA 1092 63 African Endometrial Endometrial adenocarcinoma I Positive Not tested PAPA 1094 52 African Ovar Serous adenocarcinoma high grade IV Positive Not tested negative PAPA 1095 70 Caucasian Endometrium Endometrioid carcinoma with prominent mucinous differentiationI Positive Not tested Not tested PAPA 1096 60 Caucasian Ovarian Serous adenocarcinoma high grade III Negative Not tested Positive PAPA 1097 55 Caucasian Endometrial carcinoma Endometrial carcinoma, Endometrial carcinoma IV Positive Not tested Not tested PAPA 1098 31 Caucasian Ovarian Endometrial adenocarcinoma I Positive Not tested negative PAPA 1099 58 Caucasian Endometrium Endometrial adenocarcinoma I Negative Not tested Not tested PAPA 1100 65 Caucasian Endometrium Ad endometrial enocarcinoma I Positive Not tested Not tested PAPA 1103 51 Caucasian Endometrium Endometrial adenocarcinoma III Positive Not tested Not tested PAPA 1106 51 Caucasian Endometrium Endometrial adenocarcinoma I Negative Not tested Not tested PAPA 1110 55 Asian Ovarian non-differentiated (probably serous high grade) II Positive Not tested Negative PAPA 1112 52 Asian Ovarian Mucinous cystadenocarcinoma I Positive Not tested Negative PAPA 1113 60 Asian Ovarian Papillary serous adenocarcinoma (probably serous high grade) III Positive Not tested Positive PAPA 1115 57 Asian Ovarian serous fibroma (likely serous high grade III) Positive Not tested Positive PAPA 1116 48 Asian Ovarian Carcinoma cell transp, mixed type I Negative Not tested Negative

PAPA 1117 67 Asian Ovarian Papillary Serous Adenocarcinoma (Probably Serous High Grade) III Negative Not Tested Positive PAPA 1118 37 Asian Ovarian Endometrial Adenocarcinoma, Villoglandular Type I Positive Not Tested Negative PAPA 1119 41 Asian Ovarian Mucinous Cystadenocarcinoma I Negative Not Tested 62 Negative PAPA 11 Ovarian Endometrial adenocarcinoma, mixed type I Positive Not tested Negative PAPA 1121 53 Asian Ovarian Carcinom cell transp, type tubulo-cystic I Negative Not tested negative PAPA 1122 56 Asian Ovarian Carcinom cell transp, papillary type I Negative Not tested Positive PAPA 1126 72 Caucasian Endometrium Adenocarcinoma, endometrioid type, well to moderate, difference III Positive Positive Not tested PAPA 1128 54 Caucasian Endometrium Endometrial adenocarcinoma I Negative Positive Not tested PAPA 1129 68 NA Endometrium Endometrial adenocarcinoma III Negative Positive Not tested PAPA 1130 51 NA Endometrium Endometrial adenocarcinoma with different endometrium Mucinous ions I Positive Positive Not tested PAPA 1131 39 NA Endometrium Endometrial adenocarcinoma I Positive Positive Not tested PAPA 1132 59 NA Endometrium Endometrial adenocarcinoma I Positive Positive Not tested PAPA 1133 52 NA Endometrium Endometrial adenocarcinoma III Positive Positive Not tested PAPA 1134 58 NA Endometrium Carometrium Endometrium papillary and squamous endometrioid I Positive Positive Not tested PAPA 1135 57 NA Endometrial Serious carcinoma high grade IV Positive Positive not tested PAPA 1136 60 Caucasian Ovarian Serous adenocarcinoma high grade IV Positive Positive Not tested PAPA 1137 73 Other Endometrial endometrial adenocarcinoma I Positive Positive Not tested PAPA 1138 56 Caucasian Ovarian Carcinom cell transp III Negative Negative Not tested PAPA 1139 70 NA Endometrium Endometrial adenocarcinoma I Negative negative Not tested PAPA 1140 75 Caucasian Endometrium Endometrial adenocarcinoma III Positive Positive Not tested PAPA 1141 71 African Endometrial Endometrium rio adenocarcinoma, cell transp, bad differentiated IV Positive Positive Not tested PAPA 1142 63 NA Endometrium Endometrial adenocarcinoma, with squamous differentiation I Positive Positive Not tested PAPA 1143 63 NA Endometrium Endometrial adenocarcinoma I Positive Positive Not tested PAPA 1144 65 Ovarian Caucasian NA Negative Negative Not tested PAPA 1145 53 NA Endometrium Endometrial adenocarcinoma I Positive Positive Not tested PAPA 1146 60 NA Endometrium Endometrial adenocarcinoma I Negative Positive Not tested PAPA 1147 56 NA Endometrium Endometrial adenocarcinoma Positive Positive Not tested PAPA 1148 65 NA Endometrium Endometrial carcinoma with different. endometrioid and serous III Positive Positive Not tested PAPA 1149 67 NA Endometrium Endometrial adenocarcinoma I Positive Positive Not tested PAPA 1150 63 Arabic Endometrium Serous adenocarcinoma high grade I Positive Positive Not tested PAPA 1152 53 Arabic Endometrium Endometrial adenocarcinoma Positive Positive Not tested PAPA 1153 83 Caucas Endometrium Endometrium carcinoma, cell type transp III Positive Positive Not tested PAPA 1154 70 NA Endometrium Endometrial adenocarcinoma I Positive Positive Not tested PAPA 1155 50 Asian Endometrium Endometrial adenocarcinoma I Positive Positive Not tested PAPA 1157 80 Asian Endometrium CÇancer serous high grade III Positive Positive tested PAPA 1158 56 NA Endometrium Endometrial adenocarcinoma I Positive Negative Not tested PAPA 1159 75 NA Ovarian Serous adenocarcinoma III Negative Positive Not tested PAPA 1160 52 NA Endometrium Endometrial adenocarcinoma I Positive Positive Not tested PAPA 1161 60 NA Endometrium Adenoca endometrial rcinoma I Positive Positive Not tested PAPA 1162 62 NA Endometrium Endometrial adenocarcinoma I Positive Positive not tested PAPA 1164 64 NA Endometrium Endometrial adenocarcinoma I Positive Positive not tested PAPA 1167 47 NA Ovarian adenocarcinoma serous high grade III Negative Negative PAPA 1169 59 Arabic Adenossarcom Mullerian of the endometrium with sarcomatous ove I Negative Negative Not tested PAPA 1172 57 NA Ovarian Serous adenocarcinoma high grade III Positive Not tested Not tested PAPA 1176 57 NA Ovarian Adenocarcinoma serous high grade III Negative Negative Not tested PAPA 1177 54 Caucasian Endometrial Endometrial carcinoma , endometrioid type I Positive Positive Not tested PAPA 1180 50 Other Ovarian Carcinoma high-grade serous (40%) and endometrioid (60%) I Negative Negative Not tested PAPA 1181 56 Asian Endometrium Carcinossarcoma with overcurrent, sarcomatous 15% endometrioid aIV Negative Not tested PAPA 1184 61 Ca ucasian Endometrium Endometrial adenocarcinoma I Negative Positive Not tested PAPA 1185 74 Other Endometrium Endometrial adenocarcinoma I Positive Positive Not tested PAPA 1188 77 NA Endometrium Cancer endometrial mixed type high grade (serous 90% and endom I Positive Positive not tested PAPA 1189 81 Serous Ovarian Caucasian Adenocarcinoma high grade III Negative Positive Not tested PAPA 1191 63 Asian Endometrium Endometrial adenocarcinoma I Positive Not tested Not tested PAPA 1192 73 Asian Endometrium Endometrial adenocarcinoma II Positive Not tested Not tested PAPA 1193 69 Asian Endometrial Endometrial adenocarcinoma I Positive Not tested Not tested PAPA 1194 68 Asian Endometrium NA I Positive Not tested Not tested PAPA 1198 61 African Endometrium High-grade carcinoma with mixed serous carcinoma characteristics I Positive Not tested Not tested PAPA 1200 46 Caucasian Endometrium Endometrial adenocarcinoma I Negative Not tested Not tested PAPA 1202 55 Caucasian Endometrium Carc high grade serous inoma I Positive Not tested Not tested PAPA 1204 72 Caucasian Endometrium Endometrial adenocarcinoma I Positive Not tested Not tested PAPA 1207 66 Caucasian Endometrium Endometrial carcinoma with metaplastic characteristics III Positive Not tested Not tested PAPA 1208 30 Ovarian Caucasian well-differentiated mucinous carcinoma I Positive Not tested Negative PAPA 1209 64 Caucasian Endometrium Endometrial adenocarcinoma I Positive Not tested Not tested PAPA 1210 67 Caucasian Ovarian Serous adenocarcinoma high grade III Positive Not tested Not tested PAPA 1211 58 Caucasian Endometrium Endometrial adenocarcinoma III Positive Not tested Not tested PAPA 1213 67 Ovarian Caucasian Ovar serous high grade III Positive Not tested Positive

PAPA 1214 33 African Endometrium Endometrial adenocarcinoma III Positive Not tested Not tested PAPA 1215 51 Caucasian Ovarian Serous adenocarcinoma high grade III Negative Not tested negative PAPA 1217 68 Caucasian Ovarian Serous adenocarcinoma High grade III Positive Not tested Not tested PAPA 1218 52 African Endometrial Adenocarcinoma endometrium Positive Not tested Not tested PAPA 1221 60 Caucasian Endometrium Endometrial adenocarcinoma I Positive Not tested Not tested PAPA 1222 57 Caucasian Endometrium Endometrioid carcinoma with mucinous differentiation I Negative Not tested Not tested PAPA 1223 65 Caucasian Endometrial Endometrioid carcinoma with mucinous differentiation I Positive Not tested PAPA 1224 61 Caucasian Endometrium Endometrial adenocarcinoma I Positive Not tested Not tested PAPA 1225 60 Caucasian Ovarian Carcinoma cell transp high grade I Negative Not tested Not tested PAPA 1226 75 African Endometrium Endometrial adenocarcinoma I Positive No tes not tested PAPA 1230 43 Caucasian Endometrium Endometrial adenocarcinoma I Negative Not tested Not tested PAPA 1232 60 Caucasian Endometrium Endometrioid carcinoma with focal mucinous differentiation I Positive Not tested Not tested PAPA 1233 41 Caucasian Endometrium Endometrial adenocarcinoma I Positive Not tested PAPA 1235 42 Ovarian Serous Carcinoma Low Grade III Positive Not Tested Negative PAPA 1237 43 Caucasian Ovarian Serous Adenocarcinoma Serous High Grade III Negative Not Tested Positive PAPA 1239 63 Caucasian Endometrium Endometrial Adenocarcinoma High Grade I Positive Not Tested Not Tested PAPA 1240 70 African Ovarian Serous Adenocarcinoma Serous High Grade III Negative Not tested Positive PAPA 1242 64 Caucasian Endometrium High grade adenocarcinoma with endometrioid characteristic I Positive Not tested Not tested PAPA 1243 41 Caucasian Endometrium Endometrial adenocarcinoma I Negative Not tested Not tested PAPA 1246 29 Asian Ovarian Carcinoma mu cinoso well differentiated I Negative Not tested Negative PAPA 1247 59 African Endometrial Serous carcinoma high grade I Negative Not tested Not tested PAPA 1249 58 Caucasian Endometrium Endometrial adenocarcinoma I Negative Positive Not tested PAPA 1251 64 NA Endometrium Endometrial adenocarcinoma I Positive Positive Not tested PAPA 1253 77 NA Ovarian Serous Adenocarcinoma High Grade III Negative Positive Not Tested PAPA 1254 70 NA Endometrium Adenocarcinoma Endometrial I Negative Positive Not Tested PAPA 1255 67 Caucasian Endometrium Adenocarcinoma Endometrium I Positive Positive Not Tested PAPA 1256 70 Caucasian Endometrium Endometrial Cancer Serous High Grade I Negative PAPA 1257 80 NA Ovarian High Serous Carcinoma of the Fallopian Tube III Positive Negative Not tested PAPA 1258 63 Other Ovarian Serous Adenocarcinoma High Grade IV Negative Positive Not tested PAPA 1259 77 Other Ovarian Serous Ovarian Cancer High Degree of Fibria TIC III Negative No tested PAPA 1260 64 NA Ovarian Endometrioid Type (60%) and sex cord (40%) I Negative Negative Not tested PAPA 1262 38 Caucasian Ovarian Mucinous heterogeneous mucinous ovarian tumor, intestinal type I Negative Negative Not tested PAPA 1263 45 Caucasian Ovarian Serous high adenocarcinoma grade IV Negative Positive Not tested PAPA 1264 56 NA Endometrium Endometrial adenocarcinoma I Positive Positive Not tested PAPA 1284 41 Other Endometrium Endometrial adenocarcinoma I Negative Not tested Not tested PAPA 1286 72 Caucasian Endometrium Serum endometrial carcinoma I Negative Not tested PAPA 1292 65 Ovarian Carcinom cell transp III Positive Not tested Positive PAPA 1293 62 African Endometrium Endometrial adenocarcinoma I Positive Not tested Not tested PAPA 1295 52 Caucasian Endometrium Endometrioid carcinoma with difference. prominent mucinosa I Negative Not tested Not tested PAPA 1296 50 Caucasian Ovarian Serous Adenocarcinoma High Grade III Positive Not tested Negative PAPA 1297 41 Other Ovarian Serous Adenocarcinoma High Grade III Positive Not Tested Positive PAPA 1298 73 Caucasian Ovarian Serous Adenocarcinoma Serous High Fallopian Tube III Positive Not tested Positive PAPA 1299 51 Caucasian Endometrium Endometrial adenocarcinoma I Positive Not tested Not tested PAPA 1300 63 Caucasian Endometrium High grade adenocarcinoma with endometrioid characteristic I Positive Not tested Not tested PAPA 1301 65 Caucasian Ovarian high grade endometrioid carcinoma Negative Not tested Negative PAPA2 72 Caucasian Ovarian Endometrial Adenocarcinoma I Positive Not tested Not tested PAPA 1303 72 African Endometrial Serous carcinoma high grade IV Positive Not tested Not tested PAPA 1307 79 African Endometrium Carcinom cell transp high grade IV Negative Not tested Not tested PAPA 1308 65 Caucasia Endometrial Endometrium Adenocarcinoma I Positive Not tested Not tested PAPA 1309 34 African Endometrium Mucinous carcinoma I Positive Not tested PAPA 1315 39 Asian Ovarian Carcinomcel transp III Negative Not tested negative PAPA 1320 52 Asian Ovarian Serous Adenocarcinoma high grade IV Negative Not tested Positive PAPA 1321 50 Asian Ovarian Transitional Cell Carcinoma III Negative Not tested Positive PAPA 1322 53 Asian Ovarian Carcinoma Transitional Cell II Positive Not tested Positive PAPA 1323 53 Asian Ovarian Serous Adenocarcinoma High Grade II Negative Not tested Negative PAPA 1325 57 Asian Ovarian Cisstadenocarcinoma serous low serous Negative Not tested negative PAPA 1326 47 Asian Ovarian Mucinous adenocarcinoma I Positive Not tested Positive PAPA 1327 46 Asian Ovarian Endometrial adenocarcinoma I Negative Not tested Positive PAPA 1328 56 Asian Ovarian Endometrial adenocarcinoma III Positive Not tested Negative PA PA 1329 73 Asian Endometrial Viloglandular Endometrial Adenocarcinoma I Negative Not Tested Not Tested PAPA 1359 78 NA Ovarian Serous Adenocarcinoma High Grade IV Negative Negative Not Tested PAPA 1361 61 Other Ovarian High Serous Adenocarcinoma of the Fallopian Tube II Negative Positive Not Tested PAPA 1362 45 Caucasian Ovarian Carcinom cell transp II Not tested Negative Not tested PAPA 1363 63 NA Ovarian high grade serous adenocarcinoma III Negative negative Not tested PAPA 1364 49 Caucasian Ovarian High grade endometrioid carcinoma with focus cell type II Positive Not tested Not tested PAPA 1365 50 NA Ovarian Adenocarcinoma serous high grade III Negative Negative Not tested

PAPA 1369 37 Caucasian Ovarian Mucinous Adenocarcinoma II Negative Negative Not Tested PAPA 1370 73 Caucasian Ovarian Serous Adenocarcinoma High Grade III Negative Not Tested PAPA 1371 67 NA Ovarian Serous Adenocarcinoma High Grade III Negative Negative Not Tested PAPA 1695 65 Caucasian Ovarian Adenocarcinoma Serous (Probably Serous Adenocarcinoma Serous high positive) IV Positive Not tested Not tested PAPA 1696 69 Caucasian Ovarian Cancer tubal III Positive Not tested Not tested PAPA 1697 54 Caucasian Ovarian Serous adenocarcinoma (probably serous high grade) III Positive Not tested Not tested PAPA 1698 58 Caucasian Ovarian Serous adenocarcinoma (probably serous high grade) II Negative Not tested Not tested PAPA 1699 64 Caucasian Ovarian Cancer tubal (probably serous high grade) IV Positive Not tested Not tested PAPA 1700 62 Caucasian Ovarian Serous adenocarcinoma (probably serous high grade) II Positive Not tested Not tested PAPA 1701 53 Caucasian O varian Serous adenocarcinoma (probably serous high grade) III Negative Not tested Not tested PAPA 1702 65 Caucasian Ovarian Serous adenocarcinoma (probably serous high grade) III Negative Not tested Not tested PAPA 1703 55 Caucasian Ovarian Serous adenocarcinoma (probably serous high grade) III Negative No tested Not tested PAPA 1705 76 Caucasian Ovarian Endometrial adenocarcinoma I Positive Not tested Not tested PAPA 1706 60 Caucasian Ovarian serous and mixed carcinosarcoma II Positive Not tested Not tested PAPA 1707 78 Caucasian Ovarian Serous adenocarcinoma (probably serous high grade) III Negative Not tested No tested PAPA 1710 71 Caucasian Ovarian Serous adenocarcinoma III Positive Not tested Not tested PAPA 1712 65 Caucasian Ovarian Cancer tubal (probably serous high grade) I Negative Not tested Not tested PAPA 1713 59 Caucasian Ovarian Serous adenocarcinoma III Negative Not tested PAPA 1714 48 Caucas Ovarian iano Mucinous adenocarcinoma III Negative Not tested Not tested PAPA 1715 62 Caucasian Ovarian Adenocarcinoma serous I Negative Not tested Not tested PAPA 1716 74 Caucasian Ovarian Serous adenocarcinoma II Positive Not tested Not tested PAPA 1717 66 Caucasian Ovarian adenocarcinoma serous (probably serous high grade III) Negative Not tested Not tested PAPA 1720 69 Caucasian Ovarian Serous adenocarcinoma (probably serous high grade) III Negative Not tested Not tested PAPA 1721 78 Caucasian Ovarian Serous adenocarcinoma III Negative Not tested Not tested PAPA 1722 83 Caucasian Ovarian Serous adenocarcinoma IV Negative Not tested Not tested PAPA 1724 68 Caucasian Ovarian Serous adenocarcinoma (probably serous high grade) III Negative Not tested Not tested PAPA 1725 55 Caucasian Ovarian Endometrial adenocarcinoma I Positive Not tested Not tested PAPA 1727 56 Caucasian Ovarian Serous adenocarcinoma I Negative Not tested PA PA 1728 51 Caucasian Ovarian Carcinom cell transp I Negative Not tested Not tested PAPA 1730 71 Caucasian Ovarian Serous adenocarcinoma (probably serous high grade) III Negative Not tested Not tested PAPA 1732 65 Caucasian Ovarian Serous adenocarcinoma (probably serous high grade) II Negative Not tested Not tested PAPA 1734 72 Caucasian Ovarian Serous adenocarcinoma (probably serous high grade) IV Positive Not tested Not tested PAPA 1735 57 Caucasian Ovarian Serous adenocarcinoma III Negative Not tested Not tested PAPA 1736 70 Caucasian Ovarian Endometrial adenocarcinoma II Positive Not tested PAPA 1737 59 Caucasian Ovarian Endometrioid Mixed Transp Cell II Negative Not Tested Not Tested PAPA 1738 70 Caucasian Ovarian Serous Adenocarcinoma (Probably Serous High Grade) III Negative Not Tested PAPA 1739 57 Caucasian Ovarian Mucinous Adenocarcinoma I Negative Not Tested PAPA 1816 54 Caucasian Ovarian A endometrial denocarcinoma with squamous differentiation I Negative Negative Not tested PAPA 1817 67 NA Ovarian high-grade fallopian tube fallopian III Negative Negative Not tested PAPA 1819 53 Asian Ovarian Carcinoma high-grade fallopian tube III Positive Positive Not tested PAPA 1820 82 NA Endometrium Carcinossarcoma , mullerian type, carcinomatous comp 20-25% III Positive Positive Not tested PAPA 1821 73 NA Ovarian Cancer tubal, atypical hyperplasia in the II Negative Positive Not tested PAPA 1823 83 Caucasian Ovarian Serous carcinoma of the fallopian tube IV Negative Positive Not tested PAPA 1826 56 Asian Ovarian Endometrial adenocarcinoma; endometriosis, Endometrium hyperp I Positive Positive Not tested PAPA 1828 55 Caucasian Ovarian Serous adenocarcinoma high grade III Positive Negative Not tested PAPA 1829 67 Arabic Ovarian Serous adenocarcinoma high grade; endoetriosis, leiomyoma and III Negative Negative Not tested PAPA 1832 70 Caucasian Ovarian Serous adenocarcinoma high grade III Not tested Positive Not tested PAPA 1833 51 NA Ovarian Carcinom cel transp III Negative Negative Not tested PAPA 1834 65 Another Ovarian Papillary thyroid carcinoma, follicular variant, encapsulated , I Negative Positive Not tested PAPA 1836 60 NA Endometrium Endometrial adenocarcinoma I Not tested Positive Not tested PAPA 1837 57 Caucasian Endometrium Endometrial adenocarcinoma I Not tested Positive Not tested PAPA 1838 34 NA Ovarian low grade III serous carcinoma Not tested Negative Not tested PAPA 1839 70 NA Endometrium Endometrial adenocarcinoma, endometrioid type I Not tested Positive Not tested PAPA 1840 82 Caucasian Endometrium Endometrial adenocarcinoma I Not tested Positive Not tested PAPA 1842 70 Caucasian Endometrium Endometrial adenocarcinoma, with papillary vile I Not tested Positive Not tested PAPA 1843 64 NA Endometrium trio I Not tested Positive Not tested PAPA 1844 60 NA Ovarian high grade serous adenocarcinoma III Not tested Positive Not tested PAPA 1845 57 NA Ovarian high grade serous adenocarcinoma III Not tested Positive Not tested PAPA 1847 55 NA Endometrium Endometrioid carcinoma with papillary villus III No tested Positive Not tested PAPA 1848 51 Caucasian Endometrium Endometrial adenocarcinoma, endometrioid type, small focus of m III Not tested Positive Not tested PAPA 1849 55 NA Endometrium Endometrioid adenocarcinoma endometrium I Not tested Positive Not tested PAPA 1850 59 NA Ovarian Adenocarcinoma serous high grade III No tested Positive Not tested PAPA 1852 56 NA Endometrium High-grade carcinoma of the endometrium, mixed (80% cell transp III Not tested Positive Not tested PAPA 1855 55 NA Endometrium Endometrioid adenocarcinoma endometrium I Not tested Positive Not tested PAPA 1856 45 Other Serous high-grade Ovarian Adenocarcinoma fallopian tube III Not tested Negative No tested PAPA 1857 75 NA Endometrium Carcinosarcoma III Not tested Positive Not tested

PAPA 1858 66 NA Endometrium High grade endometrial cancer, endometrioid and no difference III Not tested Positive Not tested PAPA 1859 67 NA Endometrium Endometrioid type endometrial adenocarcinoma, well differ I Not tested Positive Not tested PAPA 1860 57 NA Endometrial endometrial adenocarcinoma I Not tested Positive No tested PAPA 1861 69 NA Endometrium Endometrial adenocarcinoma IV Not tested Positive Not tested PAPA 1862 74 NA Endometrium Endometrial adenocarcinoma I Not tested Positive Not tested PAPA 1864 62 NA Ovarian High grade serous adenocarcinoma III Not tested Negative Not tested PAPA 1865 65 NA Endometrial carcinoma & leiomyoma Stromal LO Hyperplasia, I Not tested Positive Not tested PAPA 1866 38 Asian Ovarian Low grade serous carcinoma III Not tested Negative Not tested PAPA 1867 34 NA Endometrium Endometrial adenocarcinoma I Not tested Positive Not tested NA: Not available

Table 14. Samples tested with PapSEEK for somatic mutations and aneuploidy.

SampleID PatientID Cancer Type Etag Type Sample Somatic mutations Aneuploidy mutations Combined Assay

PAP 001 S PAP 001 Endometrium I Sample esc Pap Positive Positive Positive PAP 002 S PAP 002 Ovarian III Sample esc Pap Positive Negative Positive PAP 003 S PAP 003 Endometrium I Sample esc Pap Positive Positive Positive PAP 006 S PAP 006 Ovarian III Sample esc Pap Negative Negative Negative PAP 007 S PAP 007 Ovarian III Sample esc Pap Negative Negative PAP 010 S PAP 010 Endometrium I Sample esc Pap Positive Positive Positive PAP 011 S PAP 011 Endometrium I Sample esc Pap Positive Negative Positive PAP 024 S PAP 024 Endometrium I Sample esc Pap Negative Negative PAP 025 S PAP 025 Endometrium I Sample esc Pap Positive Negative Positive PAP 026 S PAP 026 Endometrium I Sample esc Pap Negative Negative PAP 034 S PAP 034 Endometrium I Sample esc Pap Positive Positive Positive PAP 036 S PAP 036 Ovarian I Sample esc Pap Negative Negative PAP 037 S PAP 037 Endometrium I Sample esc Pap Pap Negative Positive PAP 038 S PAP 038 Ovarian I Sample esc Pap Negative Negative Negative PAP 039 S PAP 039 Ovarian I Sample esc Pap Positive Negative Positive PAP 040 S PAP 040 Endometrium I Sample esc Pap Negative Negative PAP 041 S PAP 041 Endometrium I Sample esc Negative Negative PAP PAP 058 S PAP 058 Ovarian NA Sample esc Pap Negative Negative PAP 060 S PAP 060 Ovarian III Sample esc Pap Negative Negative Negative PAP 061 S PAP 061 Ovarian III Sample esc Pap Negative Negative Negative PAP 062 S PAP 062 Ovarian III Sample esc Pap Positive Negative Positive PAP 063 S PAP 063 Ovarian III Sample esc Pap Pap Negative Negative PAP 065 S PAP 065 Endometrium I Sample esc Pap Positive Positive Positive PAP 066 S PAP 066 Endometrium III Sample esc Pap Positive Positive Positive PAP 067 S PAP 067 Endometrium I Sample esc Pap Positive Negative Positive PAP 068 S PAP 068 Endometrium I Sample esc Pap Negative Negative PAP 069 S PAP 069 Endometrium I Sample esc Pap Positive Positive Positive PAP 070 S PAP 070 Endometrium II Sample esc Pap Positive Positive Positive PAP 071 S PAP 071 Endometrium I Sample esc Pap Negative Negative Negative PAP 072 S PAP 072 Ovarian IV Sample esc Pap Negative Negative Negative PAP 073 S PAP 073 Ovarian III Sample esc Pap Negative Negative Negative PAP 074 S PAP 074 Ovarian IV Sample esc Pap Negative Negative PAP 075 S PAP 075 Ovarian III Sample esc Pap Negative Negative PAP 076 S PAP 076 Ovarian III Sample esc Negative Negative Negative

PAP 078 S PAP 078 Ovarian III Sample esc Pap Negative Negative PAP 079 S PAP 079 Ovarian IV Sample esc Pap Positive Negative Positive PAP 080 S PAP 080 Endometrium III Sample esc Pap Positive Positive Positive PAP 081 S PAP 081 Ovarian NA Sample esc Negative Negative Negative PAP 084 S PAP 084 Endometrium III Sample esc Pap Positive Positive Positive PAP 119 S PAP 119 Endometrium I Sample esc Pap Positive Positive PAP 120 S PAP 120 Endometrium I Sample esc Pap Positive Positive Positive PAP 121 S PAP 121 Endometrium I Sample esc Pap Positive Negative Positive PAP 122 S PAP 122 Endometrium I Sample esc Pap Positive Negative Positive PAP 123 S PAP 123 Endometrium I Sample esc Negative Negative Negative PAP 125 S PAP 125 Endometrium I Sample esc Negative Negative Negative PAP 126 S PAP 126 Endometrium I Sample esc Pap Negative Negative PAP 128 S PAP 128 Endometrium I Sample esc Pap Positive Positive Positive PAP 129 S PAP 129 Endometrium I Amost ra esc Pap Negative Negative Negative PAP 131 S PAP 131 Endometrium I Sample esc Pap Positive Negative Positive PAP 132 S PAP 132 Endometrium I Sample esc Pap Positive Positive Positive PAP 168 S PAP 168 Ovarian IV Sample esc Pap Positive Positive Positive PAP 169 S PAP 169 Ovarian IV Sample esc Pap Positive Positive Positive PAP 172 S PAP 172 Ovarian I Sample esc Pap Negative Negative Negative PAP 176 S PAP 176 Endometrium I Sample esc Pap Positive Positive Positive PAP 177 S PAP 177 Endometrium I Sample esc Pap Positive Negative Positive PAP 178 S PAP 178 Endometrium I Sample esc Pap Positive Positive Positive PAP 179 S PAP 179 Endometrium I Sample esc Pap Positive Positive Positive PAP 180 S PAP 180 Endometrium I Sample esc Pap Positive Negative Positive PAP 181 S PAP 181 Endometrium I Sample esc Pap Positive Positive Positive PAP 182 S PAP 182 Ovarian I Sample esc Pap Negative Negative PAP PAP 183 S PAP 183 Endometrium III Sample esc Pap Pap Negative Negative PAP 185 S PAP 185 Ovarian I Sample esc Pap Negative Negative PAP 193 S PAP 193 Ovarian III Sample esc Pap Negative Negative PAP 195 S PAP 195 Ovarian III Sample esc Pap Positive Positive Positive PAP 196 S PAP 196 Ovarian I Sample esc Pap Negative Negative Negative PAP 197 S PAP 197 Ovarian I Sample esc Pap Negative Negative PAP 198 S PAP 198 Ovarian III Sample esc Pap Negative Negative PAP 199 S PAP 199 Ovarian I Sample esc Pap Positive Positive Positive PAP 200 S PAP 200 Endometrium I Sample esc Pap Positive Negative Positive PAP 201 S PAP 201 Endometrium I Sample esc Pap Positive Negative Positive PAP 202 S PAP 202 Endometrium I Sample esc Pap Positive Positive PAP 203 S PAP 203 Endometrium I Sample esc Pap Positive Positive Positive PAP 204 S PAP 204 Endometrium I Sample esc Pap Positive Positive Positive PAP 205 S PAP 205 Endometrium I Sample esc Pap Positive Negative Positive PAP 207 S PAP 207 Endometrium I Sample esc Pap Pos positive Positive PAP 209 S PAP 209 Endometrium II Sample esc Pap Positive Positive PAP 210 S PAP 210 Ovarian I Sample esc Pap Negative Negative PAP 212 S PAP 212 Endometrium NA Sample esc Pap Negative Negative Negative

PAP 213 S PAP 213 Endometrium II Sample esc Pap Positive Positive Positive PAP 214 S PAP 214 Endometrium I Sample esc Pap Positive Negative Positive PAP 215 S PAP 215 Endometrium I Sample esc Pap Positive Positive Positive PAP 216 S PAP 216 Endometrium I Sample esc Pap Positive Negative Positive PAP 217 S PAP 217 Endometrium I Sample esc Pap Negative Negative PAP 219 S PAP 219 Endometrium III Sample esc Pap Positive Negative Positive PAP 221 S PAP 221 Endometrium III Sample esc Pap Positive Negative Positive PAP 222 S PAP 222 Endometrium I Sample esc Pap Positive Positive Positive PAP 224 S PAP 224 Endometrium III Sample esc Pap Positive Positive PAP 225 S PAP 225 Endometrium I Sample esc Pap Positive Negative Positive PAP 226 S PAP 226 Endometrium I Sample esc Pap Negative Negative PAP 227 S PAP 227 Endometrium III Sample esc Pap Positive Positive Positive PAP 228 S PAP 228 Endometrium II Sample esc Pap Positive Positive Positive PAP 229 S PAP 229 Endometrium I Sample esc Pap Positive Positive Positive PAP 230 S PAP 230 Ovarian III Sample esc Pap Positive Negative Positive PAP 231 S PAP 231 Endometrium I Sample esc Pap Positive Negative Positive PAP 232 S PAP 232 Endometrium III Sample esc Pap Positive Positive Positive PAP 233 S PAP 233 Endometrium III Sample esc Pap Positive Negative Positive PAP 234 S PAP 234 Endometrium I Sample esc Pap Positive Negative Positive PAP 235 S PAP 235 Endometrium NA Sample esc Pap Positive Positive Positive PAP 236 S PAP 236 Endometrium NA Sample esc Pap Positive Negative Positive PAP 237 S PAP 237 Endometrium NA Sample esc Pap Positive Negative Positive PAP 238 S PAP 238 Endometrium NA Sample esc Pap Positive Negative Positive PAP 239 S PAP 239 Endometrium NA Sample esc Pap Positive Positive Positive PAP 240 S PAP 240 Endometrium NA Sample esc Pap Positive Positive Positive PAP 242 S PAP 242 Endometrium NA Sample esc Pap Positive Positive Positive PAP 243 S PAP 243 Endometrium NA Sample esc Pap Positiv o Positive Positive PAP 244 S PAP 244 Endometrium NA Sample esc Pap Positive Negative Positive PAP 245 S PAP 245 Endometrium NA Sample esc Pap Positive Positive PAP 246 S PAP 246 Endometrium NA Sample esc Pap Positive Negative Positive PAP 247 S PAP 247 Endometrium NA Sample esc Pap Positive Negative Positive PAP 248 S PAP 248 Ovarian III Sample esc Pap Negative Negative PAP 250 S PAP 250 Ovarian IV Sample esc Pap Positive Negative Positive PAP 251 S PAP 251 Ovarian I Sample esc Negative Negative PAP PAP 252 S PAP 252 Ovarian I Sample esc Pap Negative Negative PAP 253 S PAP 253 Ovarian III Sample esc Pap Negative Negative PAP 254 S PAP 254 Ovarian I Sample esc Pap Pap Positive Positive PAP 255 S PAP 255 Ovarian III Sample esc Pap Pap Positive Negative PAP 257 S PAP 257 Ovarian I Sample esc Pap Negative Negative PAP 258 S PAP 258 Ovarian I Sample esc Pap Negative Negative PAP 259 S PAP 259 Ovarian o I Sample esc Pap Negative Negative PAP 262 S PAP 262 Ovarian III Sample esc Pap Pap Negative Positive PAP 263 S PAP 263 Ovarian I Sample esc Pap Negative Negative PAP 264 S PAP 264 Ovarian I Sample esc Pap Pap Positive Positive

PAP 266 S PAP 266 Endometrium I Sample esc Pap Positive Negative Positive PAP 267 S PAP 267 Endometrium I Sample esc Pap Positive Negative Positive PAP 268 S PAP 268 Endometrium I Sample esc Pap Positive Positive Positive PAP 269 S PAP 269 Endometrium I Sample esc Pap Positive Positive Positive PAP 270 S PAP 270 Endometrium I Sample esc Pap Positive Positive Positive PAP 271 S PAP 271 Endometrium I Sample esc Pap Positive Negative Positive PAP 272 S PAP 272 Endometrium II Sample esc Pap Positive Positive Positive PAP 273 S PAP 273 Endometrium I Sample esc Pap Positive Negative Positive PAP 274 S PAP 274 Endometrium I Sample esc Pap Positive Positive PAP 276 S PAP 276 Endometrium I Sample esc Pap Positive Positive Positive PAP 277 S PAP 277 Endometrium I Sample esc Positive Pap Positive Negative PAP 524 S PAP 524 Ovarian III Sample esc Pap Positive Negative POS PAP 525 S PAP 525 Ovarian III Sample esc Pap Pap Negative Negative PAP 526 S PAP 526 Ovarian IV Sample esc Pap Negative Negative PAP 527 S PAP 527 Ovarian III Sample esc Pap Negative Negative PAP 528 S PAP 528 Ovarian III Sample esc Pap Pap Positive Negative Positive PAP 529 S PAP 529 Ovarian III Sample esc Negative Negative PAP PAP 530 S PAP 530 Ovarian III Sample esc Pap Positive Negative Positive PAP 531 S PAP 531 Ovarian II Sample esc Pap Positive Negative Positive PAP 535 S PAP 535 Ovarian III Sample esc Pap Negative Negative Negative PAP 536 S PAP 536 Ovarian IV Sample esc Pap Positive Negative Positive PAP 537 S PAP 537 Ovarian IV Sample esc Pap Negative Negative PAP 540 S PAP 540 Ovarian III Sample esc Pap Negative Negative PAP 541 S PAP 541 Ovarian IV Sample esc Pap Positive Negative Positive PAP 542 S PAP 542 Ovarian III Sample esc Pap Negative Negative PAP 549 S PAP 549 Endometrium I Sample esc Pap Positive Negative Positive PAP 550 S PAP 550 Endometrium I Sample esc Pap Negative Negative PAP 55 1 S PAP 551 Ovarian III Sample esc Pap Negative Negative PAP 552 S PAP 552 Ovarian III Sample esc Pap Positive Positive Positive PAP 554 S PAP 554 Endometrium IV Sample esc Pap Positive Negative Positive PAP 556 S PAP 556 Endometrium III Sample esc Pap Positive Negative PAP Positive 557 S PAP 557 Endometrium IV Sample esc Pap Positive Negative Positive PAP 558 S PAP 558 Ovarian III Sample esc Pap Negative Negative PAP 560 S PAP 560 Endometrium I Sample esc Pap Positive Negative Positive PAP 561 S PAP 561 Sample Esc Pap Pap Negative Negative Negative PAP 563 S PAP 563 Ovarian III Sample esc Pap Negative Negative PAP 564 S PAP 564 Endometrium IV Sample esc Pap Positive Positive Positive PAP 565 S PAP 565 Endometrium I Sample esc Pap Positive Negative Positive PAP 566 S PAP 566 Ovarian IV Sample esc Pap Negative Negative PAP 567 S PAP 567 Ovarian IV Sample esc Pap Negative Negative PAP 568 S PAP 568 Ovarian III Sample es c Pap Negative Negative Negative PAP 569 S PAP 569 Ovarian IV Sample esc Pap Positive Negative Positive PAP 572 S PAP 572 Endometrium III Sample esc Pap Positive Positive Positive PAP 573 S PAP 573 Ovarian IV Sample esc Negative Negative Pap

PAP 574 S PAP 574 Endometrium III Sample esc Pap Positive Negative Positive PAP 575 S PAP 575 Ovarian III Sample esc Pap Negative Negative Negative PAP 576 S PAP 576 Ovarian III Sample esc Pap Positive Negative Positive PAP 579 S PAP 579 Ovarian I Sample esc Pap Negative Negative Negative PAP 581 S PAP 581 Endometrium I Sample esc Pap Positive Positive Positive PAP 583 S PAP 583 Endometrium I Sample esc Pap Positive Positive Positive PAP 584 S PAP 584 Endometrium I Sample esc Pap Positive Positive Positive PAP 585 S PAP 585 Endometrium I Sample esc Pap Positive Positive Positive PAP 587 S PAP 587 Endometrium I Sample esc Pap Positive Negative Positive PAP 589 S PAP 589 Endometrium IV Sample esc Pap Positive Negative Positive PAP 592 S PAP 592 Ovarian II Sample esc Pap Negative Positive Positive PAP 593 S PAP 593 Endometrium I Sample esc Pap Positive Positive Positive PAP 594 S PAP 594 Endometrium I Sample esc Pap Positive Positive Positive PAP 595 S PAP 595 Endometrium I Sample esc Pap Positive Positive Positive PAP 596 S PAP 596 Endometrium I Sample esc Pap Positive Negative Positive PAP 599 S PAP 599 Endometrium II Sample esc Pap Positive Negative Positive PAP 600 S PAP 600 Endometrium III Sample esc Pap Negative Negative Negative PAP 604 S PAP 604 Endometrium I Sample esc Pap Positive Negative Positive PAP 606 S PAP 606 Ovarian III Sample esc Pap Negative Negative Negative PAP 607 S PAP 607 Ovarian III Sample esc Pap Negative Negative Negative PAP 609 S PAP 609 Endometrium I Sample esc Pap Positive Negative Positive PAP 610 S PAP 610 Endometrium III Sample esc Pap Negative Positive Positive PAP 612 S PAP 612 Ovarian III Sample esc Pap Negative Negative Negative PAP 613 S PAP 613 Endometrium I Sample esc Pap Negative Negative PAP PAP 617 S PAP 617 Endometrium III Sample esc Pap Positive Negative PAP PAP 619 S PAP 619 Endometrium III Sample esc Pap Positive Negative Positive PAP 621 S PAP 621 Endometrium I Sample esc Pap Positive Negative Po PAP 622 S PAP 622 Ovarian III Sample esc Pap Negative Negative Negative PAP 625 S PAP 625 Endometrium I Sample esc Pap Positive Negative Positive PAP 628 S PAP 628 Ovarian I Sample esc Pap Negative Negative PAP 629 S PAP 629 Endometrium I Sample esc Pap Pap Positive Negative Positive PAP 632 S PAP 632 Endometrium I Sample esc Pap Positive Negative PAP 633 S PAP 633 Endometrium I Sample esc Pap Positive Negative Positive PAP 636 S1 PAP 636 Ovarian III Sample esc Pap Negative Negative PAP 636 S2 PAP 636 Ovarian III Sample esc Tao Negative Negative Negative PAP 637 S1 PAP 637 Endometrium I Sample esc Pap Negative Negative Negative PAP 637 S2 PAP 637 Endometrium I Sample esc Tao Positive Negative Positive PAP 638 S1 PAP 638 Endometrium IV Sample esc Pap Positive Positive Positive PAP 638 S2 PAP 638 Endometrium IV Sample esc Tao Positive Positive Positive PAP 641 S1 PAP 641 Ovarian II Sample esc Pap Negative Negative Negative PAP 642 S1 PAP 642 Ovaria no I Sample esc Pap Negative Negative PAP 643 S1 PAP 643 Ovarian I Sample esc Pap Negative Negative PAP PAP 644 S1 PAP 644 Ovarian II Sample esc Pap Negative Negative PAP 646 S1 PAP 646 Endometrium II Sample esc Pap Pap Negative Positive

PAP 647 S1 PAP 647 Endometrium I Sample esc Pap Positive Negative Positive PAP 648 S1 PAP 648 Endometrium I Sample esc Pap Positive Positive Positive PAP 649 S1 PAP 649 Endometrium I Sample esc Pap Positive Positive Positive PAP 650 S1 PAP 650 Endometrium I Sample esc Pap Positive Positive Positive PAP 652 S1 PAP 652 Endometrium I Sample esc Pap Positive Positive Positive PAP 654 S1 PAP 654 Endometrium I Sample esc Positive Positive Negative PAP 663 S1 PAP 663 Endometrium I Sample esc Positive Pap Positive Positive PAP 665 S1 PAP 665 Ovarian I Sample esc Pap Negative Negative PAP 667 S1 PAP 667 Endometrium I Sample esc Pap Positive Positive Positive PAP 668 S1 PAP 668 Endometrium II Sample esc Pap Positive Negative Positive PAP 669 S1 PAP 669 Endometrium I Sample esc Pap Positive Negative Positive PAP 670 S1 PAP 670 Ovarian III Sample esc Pap Negative Negative PAP 671 S1 PAP 671 Endometrium I Sample esc Pap Pap Negative Positive PAP 672 S1 PAP 672 Ovarian o III Sample esc Pap Negative Negative PAP 674 S1 PAP 674 Ovarian IV Sample esc Pap Negative Negative PAP PAP 676 S1 PAP 676 Endometrium III Sample esc Pap Negative Negative PAP 678 S1 PAP 678 Ovarian III Sample esc Pap Negative Negative PAP 679 S1 PAP 679 Endometrium I Sample esc Pap Positive Negative Positive PAP 681 S1 PAP 681 Endometrium I Sample esc Pap Positive Negative Positive PAP 684 S1 PAP 684 Endometrium I Sample esc Pap Positive Negative Positive PAP 685 S1 PAP 685 Endometrium I Sample esc Positive Pap Positive Negative PAP PAP 685 S2 PAP 685 Endometrium I Sample esc Tao Positive Negative Positive PAP 686 S1 PAP 686 Endometrium I Sample esc Pap Negative Negative PAP 686 S2 PAP 686 Endometrium I Sample esc Tao Positive Negative PAP 687 S1 PAP 687 Endometrium I Sample esc Pap Positive Negative Positive PAP 687 S2 PAP 687 Endometrium I Sample esc Tao Positive Negative Positive PAP 688 S1 PAP 688 Endometrium I Sample esc Pap Po Positive Positive PAP 688 S2 PAP 688 Endometrium I Sample esc Tao Positive Positive PAP 689 S1 PAP 689 Endometrium III Sample esc Pap Positive Positive Positive PAP 689 S2 PAP 689 Endometrium III Sample esc Tao Positive Positive PAP 690 S1 PAP 690 Endometrium I Sample esc Pap Positive Positive Positive PAP 690 S2 PAP 690 Endometrium I Sample esc Tao Positive Positive Positive PAP 691 S1 PAP 691 Endometrium I Sample esc Pap Positive Negative Positive PAP 691 S2 PAP 691 Endometrium I Sample esc Tao Positive Negative Positive PAP 692 S1 PAP 692 Ovarian I Sample esc Pap Negative Negative PAP PAP 692 S2 PAP 692 Ovarian I Sample esc Tao Negative Negative PAP PAP 694 S1 PAP 694 Endometrium I Sample esc Pap Pap Negative Negative PAP 695 S1 PAP 695 Endometrium II Sample esc Pap Pap Positive Positive PAP PAP 696 S1 PAP 696 Endometrium III Sample esc Pap Positive Positive PAP 697 S1 PAP 697 Endometrium I Sample esc Pap Positive Positive Positi vo PAP 698 S1 PAP 698 Endometrium I Sample esc Pap Negative Negative PAP 699 S1 PAP 699 Endometrium I Sample esc Pap Positive Positive Positive PAP 701 S1 PAP 701 Ovarian I Sample esc Pap Negative Negative PAP 702 S1 PAP 702 Endometrium I Sample esc Pap Pap Negative Negative Negative

PAP 704 S1 PAP 704 Ovarian I Sample esc Pap Negative Negative PAP 705 S1 PAP 705 Ovarian I Sample esc Pap Positive Negative Positive PAP 707 S1 PAP 707 Ovarian III Sample esc Pap Positive Negative Positive PAP 709 S1 PAP 709 Ovarian II Sample esc Pap Negative Negative Negative PAP 710 S1 PAP 710 Ovarian III Sample esc Pap Negative Positive Positive PAP 711 S1 PAP 711 Ovarian III Sample esc Pap Positive Negative Positive PAP 715 S1 PAP 715 Ovarian I Sample esc Pap Negative Negative PAP 717 S1 PAP 717 Endometrium III Sample esc Pap Positive Positive Positive PAP 719 S1 PAP 719 Endometrium I Sample esc Pap Positive Negative Positive PAP 721 S1 PAP 721 Ovarian III Sample esc Pap Positive Negative Positive PAP 722 S1 PAP 722 Ovarian III Sample esc Negative Negative Negative PAP 726 S1 PAP 726 Endometrium I Sample esc Pap Positive Negative Positive PAP 727 S1 PAP 727 Endometrium I Sample esc Pap Negative Negative Negative PAP 728 S1 PAP 728 Endometrium I Sample esc Pap Positive Negative Positive PAP 730 S1 PAP 730 Endometrium III Sample esc Pap Positive Positive Positive PAP 735 S1 PAP 735 Endometrium I Sample esc Pap Positive Negative Positive PAP 736 S1 PAP 736 Endometrium I Sample esc Positive Pap Negative Positive PAP 742 S1 PAP 742 Ovarian IV Sample esc Pap Positive Negative Positive PAP 744 S1 PAP 744 Endometrium III Sample esc Pap Positive Positive Positive PAP 746 S1 PAP 746 Ovarian III Sample esc Pap Negative Negative PAP PAP 748 S1 PAP 748 Endometrium I Sample esc Positive Pap Positive Positive PAP 749 S1 PAP 749 Ovarian I Sample esc Pap Positive Negative Positive PAP 751 S1 PAP 751 Ovarian III Sample esc Pap Negative Negative PAP PAP 754 S1 PAP 754 Endometrium I Sample esc Pap Positive Positive Positive PAP 758 S1 PAP 758 Endometrium I Sample esc Pap Negative Negative Negative PAP 761 S1 PAP 761 Endometrium I Sample esc Pap Positive Positive Positive PAP 763 S1 PAP 763 Ovarian III Sample esc Pap Positi vo Negative Positive PAP 767 S1 PAP 767 Ovarian III Sample esc Pap Negative Negative PAP 768 S1 PAP 768 Endometrium I Sample esc Pap Positive Positive Positive PAP 770 S1 PAP 770 Endometrium I Sample esc Pap Positive Negative Positive PAP 775 S1 PAP 775 Endometrium I Sample esc Pap Positive Negative Positive PAP 777 S1 PAP 777 Ovarian III Sample esc Pap Negative Negative PAP 778 S1 PAP 778 Endometrium I Sample esc Pap Negative Negative Negative PAP 787 S1 PAP 787 Ovarian IV Sample esc Pap Negative Negative PAP 789 S1 PAP 789 Endometrium III Sample esc Pap Positive Positive Positive PAP 790 S1 PAP 790 Endometrium III Sample esc Pap Positive Positive Positive PAP 791 S1 PAP 791 Endometrium III Sample esc Pap Positive Negative Positive PAP 802 S1 PAP 802 Endometrium I Sample esc Pap Positive Negative Positive PAP 804 S1 PAP 804 Endometrium I Sample esc Pap Positive Negative Positive PAP 805 S1 PAP 805 Endometrium I Sample esc Pap Positive Positive Positive PAP 807 S1 PAP 807 Endometrium I Sample esc Pap Positive Negative Positive PAP 809 S1 PAP 809 Ovarian III Sample esc Pap Negative Negative Negative PAP 812 S1 PAP 812 Endometrium I Sample esc Pap Positive Positive Positive PAP 813 S1 PAP 813 Endometrium I Sample esc Pap Positive Positive Negative

PAP 815 S1 PAP 815 Ovarian III Sample esc Pap Negative Negative PAP 816 S1 PAP 816 Endometrium I Sample esc Pap Positive Positive Positive PAP 824 S1 PAP 824 Endometrium I Sample esc Pap Positive Negative Positive PAP 824 S2 PAP 824 Endometrium I Sample so Tao Positive Positive Positive PAP 829 S1 PAP 829 Endometrium I Sample esc Pap Positive Negative Positive PAP 829 S2 PAP 829 Endometrium I Sample esc Tao Negative Negative PAP 830 S1 PAP 830 Ovarian I Sample esc Pap Negative Negative PAP 830 S2 PAP 830 Ovarian I Sample esc Tao Negative Positive Positive PAP 831 S1 PAP 831 Endometrium III Sample esc Pap Positive Positive Positive PAP 831 S2 PAP 831 Endometrium III Sample esc Tao Positive Negative Positive PAP 832 S1 PAP 832 Endometrium I Sample esc Pap Negative Negative PAP 832 S2 PAP 832 Endometrium I Sample esc Tao Positive Negative Positive PAP 835 S1 PAP 835 Endometrium III Sample esc Pap Positive Negative Positive PAP 835 S2 PAP 835 Endo metric III Sample esc Tao Positive Negative Positive PAP 836 S1 PAP 836 Endometrium I Sample esc Pap Negative Negative Negative PAP 836 S2 PAP 836 Endometrium I Sample esc Tao Positive Negative Positive PAP 837 S1 PAP 837 Endometrium I Sample esc Pap Negative Negative PAP 837 S2 PAP 837 Endometrium I Sample esc Tao Positive Negative Positive PAP 839 S1 PAP 839 Endometrium III Sample esc Pap Positive Negative Positive PAP 839 S2 PAP 839 Endometrium III Sample esc Tao Positive Negative Positive PAP 841 S1 PAP 841 Endometrium III Sample esc Pap Positive Negative Positive PAP 841 S2 PAP 841 Endometrium III Sample esc Tao Positive Positive Positive PAP 842 S1 PAP 842 Endometrium III Sample esc Pap Positive Negative Positive PAP 842 S2 PAP 842 Endometrium III Sample esc Tao Positive Negative Positive PAP 845 S1 PAP 845 Ovarian IV Sample esc Pap Negative Negative PAP 845 negative S2 PAP 845 Ovarian IV Sample esc Tao Positive Negative positive PAP 850 S1 PAP 850 Endometrium IV Amo stra esc Pap Positive Positive Positive PAP 850 S2 PAP 850 Endometrium IV Sample esc Tao Positive Positive PAP 851 S1 PAP 851 Endometrium III Sample esc Pap Positive Negative Positive PAP 851 S2 PAP 851 Endometrium III Sample esc Tao Positive Positive Positive PAP 853 S1 PAP 853 Endometrium I Sample esc Pap Negative Negative PAP 853 S2 PAP 853 Endometrium I Sample esc Tao Positive Positive Positive PAP 858 S1 PAP 858 Endometrium III Sample esc Pap Negative Negative PAP 858 S2 PAP 858 Endometrium III Sample esc Tao Positive Positive Positive PAP 860 S1 PAP 860 Endometrium I Sample esc Pap Positive Negative Positive PAP 860 S2 PAP 860 Endometrium I Sample esc Tao Positive Positive Positive PAP 862 S2 PAP 862 Endometrium III Sample esc Tao Positive Positive Positive PAP 867 S1 PAP 867 Endometrium I Sample esc Positive Pap Positive Positive PAP 868 S1 PAP 868 Endometrium I Sample esc Pap Positive Negative Positive PAP 869 S1 PAP 869 Endometrium I Sample esc Pap P os Positive Positive PAP 870 S1 PAP 870 Endometrium I Sample esc Pap Positive Negative Positive PAP 871 S1 PAP 871 Endometrium II Sample esc Pap Positive Positive Positive PAP 873 S1 PAP 873 Endometrium I Sample esc Pap Negative Negative PAP 874 S1 PAP 874 Endometrium I Sample esc Pap Positive Positive Positive

PAP 875 S1 PAP 875 Endometrium I Sample esc Pap Positive Positive Positive PAP 876 S1 PAP 876 Endometrium I Sample esc Pap Positive Positive Positive PAP 877 S1 PAP 877 Endometrium I Sample esc Pap Positive Negative Positive PAP 878 S1 PAP 878 Endometrium II Sample esc Pap Positive Positive Positive PAP 879 S1 PAP 879 Endometrium I Sample esc Pap Positive Negative Positive PAP 881 S1 PAP 881 Endometrium III Sample esc Pap Positive Positive Positive PAP 884 S1 PAP 884 Endometrium I Sample esc Pap Positive Negative Positive PAP 884 S2 PAP 884 Endometrium I Sample esc Tao Positive Negative Positive PAP 889 S1 PAP 889 Endometrium I Sample esc Pap Negative Negative PAP 889 S2 PAP 889 Endometrium I Sample esc Tao Negative Negative Negative PAP 891 S1 PAP 891 Ovarian III Sample esc Negative Negative PAP PAP 891 S2 PAP 891 Ovarian III Sample esc Tao Negative Negative PAP 892 S1 PAP 892 Endometrium III Sample esc Pap Positive Negative Positive PAP 892 S2 PAP 892 E ndometric III Sample esc Tao Positive Negative Positive PAP 894 S1 PAP 894 Endometrium I Sample esc Pap Positive Negative Positive PAP 894 S2 PAP 894 Endometrium I Sample esc Tao Positive Positive Positive PAP 897 S1 PAP 897 Endometrium III Sample esc Pap Positive Negative Positive PAP 897 S2 PAP 897 Endometrium III Sample esc Tao Positive Positive PAP 900 S1 PAP 900 Ovarian III Sample esc Pap Negative Negative Negative PAP 901 S1 PAP 901 Endometrium II Sample esc Pap Positive Positive Positive PAP 901 S2 PAP 901 Endometrium II Sample esc Tao Positive Positive Positive PAP 902 S1 PAP 902 Endometrium III Sample esc Pap Positive Positive Positive PAP 902 S2 PAP 902 Endometrium III Sample esc Tao Positive Positive Positive PAP 904 S1 PAP 904 Endometrium III Sample esc Pap Positive Positive Positive PAP 904 S2 PAP 904 Endometrium III Sample esc Tao Positive Positive Positive PAP 907 S1 PAP 907 Endometrium III Sample esc Pap Positive Negative Positive PAP 907 S2 PAP 907 Endometric io III Sample esc Tao Positive Negative Positive PAP 910 S1 PAP 910 Endometrium III Sample esc Pap Positive Negative Positive PAP 910 S2 PAP 910 Endometrium III Sample esc Tao Positive Positive Positive PAP 914 S1 PAP 914 Ovarian I Sample esc Pap Negative Negative PAP 914 S2 PAP 914 Ovarian I Sample esc Tao Positive Negative Positive PAP 919 S1 PAP 919 Ovarian II Sample esc Pap Negative Negative PAP 919 S2 PAP 919 Ovarian II Sample esc Tao Negative Negative PAP 922 S1 PAP 922 Endometrium I Sample esc Positive Pap Positive Positive PAP 922 S2 PAP 922 Endometrium I Sample esc Tao Positive Positive Positive PAP 925 S1 PAP 925 Ovarian III Sample esc Pap Negative Negative PAP PAP 925 S2 PAP 925 Ovarian III Sample esc Tao Positive Positive PAP 926 S1 PAP 926 Endometrium I Sample esc Positive Pap Positive Positive PAP 926 S2 PAP 926 Endometrium I Sample esc Tao Positive Negative Positive PAP 927 S1 PAP 927 Endometrium I Sample esc Pap Posi Positive Negative PAP 927 S2 PAP 927 Endometrium I Sample esc Tao Positive Positive PAP 928 S1 PAP 928 Ovarian II Sample esc Pap Positive Negative Positive PAP 928 S2 PAP 928 Ovarian II Sample esc Tao Positive Negative PAP 931 S1 PAP 931 Endometrium II Sample esc Pap Positive Positive Positive

PAP 931 S2 PAP 931 Endometrium II Sample esc Tao Positive Positive Positive PAP 934 S1 PAP 934 Endometrium I Sample esc Pap Positive Negative Positive PAP 934 S2 PAP 934 Endometrium I Sample esc Tao Positive Positive Positive PAP 936 S2 PAP 936 Endometrium I Sample esc Tao Positive Positive Positive PAP 937 S2 PAP 937 Endometrium I Sample esc Tao Negative Negative PAP 989 S1 PAP 989 Endometrium II Sample esc Pap Positive Positive Positive PAP 989 S2 PAP 989 Endometrium II Sample esc Tao Positive Positive PAP 990 S1 PAP 990 Endometrium I Sample esc Pap Positive Negative Positive PAP 990 S2 PAP 990 Endometrium I Sample esc Tao Positive Positive Positive PAP 991 S1 PAP 991 Endometrium III Sample esc Pap Positive Positive Positive PAP 991 S2 PAP 991 Endometrium III Sample esc Tao Positive Negative Positive PAP 993 S1 PAP 993 Ovarian III Sample esc Pap Positive Positive Positive PAP 993 S2 PAP 993 Ovarian III Sample esc Tao Positive Positive Positive PAP 994 S1 PAP 994 Endometrium I Sample esc Pap Positive Negative Positive PAP 994 S2 PAP 994 Endometrium I Sample esc Tao Negative Negative Negative PAP 995 S1 PAP 995 Endometrium II Sample esc Pap Positive Positive Positive PAP 995 S2 PAP 995 Endometrium II Sample esc Tao Positive Positive Positive PAP 996 S1 PAP 996 Endometrium I Sample esc Pap Positive Positive Positive PAP 996 S2 PAP 996 Endometrium I Sample esc Tao Positive Positive Positive PAP 998 S1 PAP 998 Endometrium I Sample esc Pap Positive Negative Positive PAP 998 S2 PAP 998 Endometrium I Sample esc Tao Positive Negative Positive PAP 999 S1 PAP 999 Endometrium III Sample esc Pap Positive Positive Positive PAP 999 S2 PAP 999 Endometrium III Sample esc Tao Positive Positive Positive PAPA 1001 S1 PAPA 1001 Endometrium III Sample esc Pap Positive Negative Positive PAPA 1001 S2 PAPA 1001 Endometrium III Sample esc Tao Positive Positive Positive PAPA 1002 S1 PAPA 1002 Endometrium I Sample esc Pap Positive Negative Positive PAPA 1002 S2 PAPA 100 2 Endometrium I Sample esc Tao Positive Positive Positive PAPA 1003 S1 PAPA 1003 Endometrium III Sample esc Pap Positive Negative Positive PAPA 1003 S2 PAPA 1003 Endometrium III Sample esc Tao Positive Positive Positive PAPA 1005 S1 PAPA 1005 Endometrium II Sample esc Positive Positive PAP PAPA 1005 S2 PAPA 1005 Endometrium II Sample esc Tao Positive Negative Positive PAPA 1010 S1 PAPA 1010 Endometrium III Sample esc Pap Positive Negative Positive PAPA 1010 S2 PAPA 1010 Endometrium III Sample esc Tao Positive Positive PAPA 1011 S1 PAPA 1011 Endometrium I Sample esc Negative Negative Negative PAPA 1011 S2 PAPA 1011 Endometrium I Sample esc Tao Positive Negative Positive PAPA 1012 S2 PAPA 1012 Endometrium III Sample esc Tao Positive Positive Positive PAPA 1013 S1 PAPA 1013 Endometrium III Sample esc Pap Positive Positive Positive PAPA 1013 S2 PAPA 1013 Endometrium III Sample esc Tao Positive Positive Positive PAPA 1016 S1 PAPA 1016 Endometrium I Sample esc Pap Posit Positive Positive PAPA 1016 S2 PAPA 1016 Endometrium I Sample esc Tao Positive Positive Positive PAPA 1019 S1 PAPA 1019 Endometrium III Sample esc Pap Positive Negative Positive PAPA 1019 S2 PAPA 1019 Endometrium III Sample esc Tao Positive Negative Positive PAPA 1026 S1 PAPA 1026 Endometrium I Sample esc Pap Negative Negative PAPA 1033 S1 PAPA 1033 Ovarian III Sample esc Pap Pap Negative Negative

PAPA 1035 S1 PAPA 1035 Endometrium II Sample esc Pap Positive Positive Positive PAPA 1037 S1 PAPA 1037 Ovarian III Sample esc Pap Positive Negative Positive PAPA 1040 S1 PAPA 1040 Endometrium I Sample esc Pap Positive Negative Positive PAPA 1043 S1 PAPA 1043 Endometrium I Sample esc Pap Positive Negative Positive PAPA 1048 S1 PAPA 1048 Endometrium I Sample esc Pap Positive Positive PAPA 1057 S1 PAPA 1057 Endometrium I Sample esc Pap Positive Positive Positive PAPA 1058 S1 PAPA 1058 Endometrium I Sample esc Pap Negative Negative PAPA 1059 S1 PAPA 1059 Endometrium I Sample esc Pap Positive Negative Positive PAPA 1062 S1 PAPA 1062 Ovarian IV Sample esc Pap Negative Negative PAPA 1065 S1 PAPA 1065 Endometrium NA Sample esc Pap Negative Negative Negative PAPA 1066 S1 PAPA 1066 Endometrium I Sample esc Pap Positive Negative Positive PAPA 1069 S1 PAPA 1069 Endometrium I Sample esc Pap Positive Negative Positive PAPA 1072 S1 PAPA 1072 Ovarian III Sample esc Pap Negative Negative Negative PAPA 1074 S1 PAPA 1074 Ovarian III Sample esc Pap Negative Negative PAPA 1076 S1 PAPA 1076 Endometrium III Sample esc Pap Positive Positive Positive PAPA 1077 S1 PAPA 1077 Endometrium I Sample esc Pap Positive Negative Positive PAPA 1078 S1 PAPA 1078 Endometrium I Sample esc Pap Negative Negative Negative PAPA 1081 S1 PAPA 1081 Endometrium III Sample esc Pap Pap Negative Positive Positive PAPA 1084 S1 PAPA 1084 Ovarian II Sample esc Pap Negative Negative Negative PAPA 1092 S1 PAPA 1092 Endometrium I Sample esc Pap Positive Negative Positive PAPA 1094 S1 PAPA 1094 Ovarian IV Sample esc Pap Positive Negative Positive PAPA 1095 S1 PAPA 1095 Endometrium I Sample esc Pap Positive Negative Positive PAPA 1096 S1 PAPA 1096 Ovarian III Sample esc Pap Negative Negative Negative PAPA 1097 S1 PAPA 1097 Endometrium IV Sample esc Positive Pap Positive Positive PAPA 1098 S1 PAPA 1098 Ovarian I Sample esc Pap Positive Negative Positive PAPA 1099 S1 PAPA 1099 Endometrium I Sample esc Pap Negative Negative PAPA 1100 S1 PAPA 1100 Endometrium I Sample esc Pap Positive Negative Positive PAPA 1103 S1 PAPA 1103 Endometrium III Sample esc Pap Positive Negative Positive PAPA 1106 S1 PAPA 1106 Endometrium I Sample esc Pap Negative Negative PAPA 1110 S1 PAPA 1110 Ovarian II Sample esc Pap Positive Negative Positive PAPA 1112 S1 PAPA 1112 Ovarian I Sample esc Pap Positive Negative Positive PAPA 1113 S1 PAPA 1113 Ovarian III Sample esc Pap Positive Positive Positive PAPA 1115 S1 PAPA 1115 Ovarian III Sample esc Pap Positive Negative Positive PAPA 1116 S1 PAPA 1116 Ovarian I Sample esc Pap Negative Negative PAPA 1117 S1 PAPA 1117 Ovarian III Sample esc Pap Negative Negative PAPA 1118 S1 PAPA 1118 Ovarian I Sample esc Pap Negative Positive Positive PAPA 1119 S1 PAPA 1119 Ovarian I Sample esc Pap Negative Negative PAPA 1120 negative S1 PAPA 1120 Ovarian I Sample esc Pap Negative Positive Positive PAPA 1121 S1 PAPA 1121 Ovarian I Sample esc Pap Negative Negative PAPA 1122 S1 PAPA 1122 Ovarian I Sample esc Pap Negative Negative PAPA 1126 S1 PAPA 1126 Endometrium III Sample esc Pap Positive Negative Positive PAPA 1126 S2 PAPA 1126 Endometrium III Sample so Tao Positive Positive Positive PAPA 1128 S1 PAPA 1128 Endometrium I Sample esc Pap Negative Negative PAPA 1128 S2 PAPA 1128 Endometrium I Sample esc Tao Positive Positive Positive

PAPA 1129 S1 PAPA 1129 Endometrium III Sample esc Pap Negative Negative Negative PAPA 1129 S2 PAPA 1129 Endometrium III Sample esc Tao Positive Positive Positive PAPA 1130 S1 PAPA 1130 Endometrium I Sample esc Pap Positive Negative Positive PAPA 1130 S2 PAPA 1130 Endometrium I Sample esc Tao Positive Positive Positive PAPA 1131 S1 PAPA 1131 Endometrium I Sample esc Pap Positive Positive PAPA 1131 S2 PAPA 1131 Endometrium I Sample esc Tao Positive Positive Positive PAPA 1132 S1 PAPA 1132 Endometrium I Sample esc Positive Pap Positive Negative PAPA 1132 S2 PAPA 1132 Endometrium I Sample esc Tao Positive Positive Positive PAPA 1133 S1 PAPA 1133 Endometrium III Sample esc Pap Positive Negative Positive PAPA 1133 S2 PAPA 1133 Endometrium III Sample esc Tao Positive Positive Positive PAPA 1134 S1 PAPA 1134 Endometrium I Sample esc Positive Pap Positive Positive PAPA 1134 S2 PAPA 1134 Endometrium I Sample esc Tao Positive Positive Positive PAPA 1135 S1 PAPA 1135 Endometrium IV Sample esc Pap Positive Negative Positive PAPA 1135 S2 PAPA 1135 Endometrium IV Sample esc Tao Positive Positive Positive PAPA 1136 S1 PAPA 1136 Ovarian IV Sample esc Pap Positive Negative Positive PAPA 1136 S2 PAPA 1136 Ovarian IV Sample esc Tao Negative Positive Positive PAPA 1137 S1 PAPA 1137 Endometrium I Sample esc Pap Positive Negative Positive PAPA 1137 S2 PAPA 1137 Endometrium I Sample esc Tao Positive Positive Positive PAPA 1138 S1 PAPA 1138 Ovarian III Sample esc Pap Negative Negative Negative PAPA 1138 S2 PAPA 1138 Ovarian III Sample esc Tao Negative Negative PAPA 1139 S1 PAPA 1139 Endometrium I Sample esc Pap Negative Negative PAPA 1139 S2 PAPA 1139 Endometrium I Sample esc Tao Negative Negative PAPA 1140 S1 PAPA 1140 Endometrium III Sample esc Pap Positive Positive Positive PAPA 1140 S2 PAPA 1140 Endometrium III Sample esc Tao Positive Positive PAPA 1141 S1 PAPA 1141 Endometrium IV Sample esc Pap Positive Positive Positive PAPA 1141 S2 PAPA 1141 Endometrium IV Sample esc Tao Positive Positive Positive PAPA 1142 S1 PAPA 1142 Endometrium I Sample esc Pap Positive Positive Positive PAPA 1142 S2 PAPA 1142 Endometrium I Sample esc Tao Positive Positive Positive PAPA 1143 S1 PAPA 1143 Endometrium I Sample esc Positive Pap Positive Negative PAPA 1143 S2 PAPA 1143 Endometrium I Sample esc Tao Positive Positive Positive PAPA 1144 S1 PAPA 1144 Ovarian IV Sample esc Pap Negative Negative PAPA 1144 S2 PAPA 1144 Ovarian IV Sample esc Tao Negative Negative PAPA 1145 S1 PAPA 1145 Endometrium I Sample esc Pap Positive Negative Positive PAPA 1145 S2 PAPA 1145 Endometrium I Sample esc Tao Positive Negative Positive PAPA 1146 S1 PAPA 1146 Endometrium I Sample esc Pap Negative Negative negative PAPA 1146 S2 PAPA 1146 Endometrium I Sample esc Tao Positive Positive Positive PAPA 1147 S1 PAPA 1147 Endometrium I Sample esc Pap Positive Negative Positive PAPA 1147 S2 PAPA 1147 Endometrium I Sample esc Tao Positivo Posi Positive POS PAPA 1148 S1 PAPA 1148 Endometrium III Sample esc Pap Positive Positive PAPA 1148 S2 PAPA 1148 Endometrium III Sample esc Tao Positive Positive Positive PAPA 1149 S1 PAPA 1149 Endometrium I Sample esc Positive Pap Positive Positive PAPA 1149 S2 PAPA 1149 Endometrium I Sample esc Tao Positive Negative Positive PAPA 1150 S1 PAPA 1150 Endometrium I Sample esc Pap Positive Negative Positive PAPA 1150 S2 PAPA 1150 Endometrium I Sample esc Tao Positive Positive Positive

PAPA 1152 S1 PAPA 1152 Endometrium I Sample esc Pap Positive Negative Positive PAPA 1152 S2 PAPA 1152 Endometrium I Sample esc Tao Positive Negative Positive PAPA 1153 S1 PAPA 1153 Endometrium III Sample esc Pap Positive Negative Positive PAPA 1153 S2 PAPA 1153 Endometrium III Sample so Tao Positive Positive Positive PAPA 1154 S1 PAPA 1154 Endometrium I Sample esc Pap Positive Negative Positive PAPA 1154 S2 PAPA 1154 Endometrium I Sample esc Tao Positive Positive Positive PAPA 1155 S1 PAPA 1155 Endometrium I Sample esc Positive Pap Positive Positive PAPA 1155 S2 PAPA 1155 Endometrium I Sample esc Tao Positive Positive Positive PAPA 1157 S1 PAPA 1157 Endometrium III Sample esc Pap Positive Positive Positive PAPA 1157 S2 PAPA 1157 Endometrium III Sample esc Tao Positive Positive Positive PAPA 1158 S1 PAPA 1158 Endometrium I Sample esc Positive Pap Positive Negative PAPA 1158 S2 PAPA 1158 Endometrium I Sample esc Tao Negative Negative PAPA 1159 S1 PAPA 1159 Ovarian III Sample esc Pap Negative Negative PAPA 1159 S2 PAPA 1159 Ovarian III Sample esc Tao Positive Negative Positive PAPA 1160 S1 PAPA 1160 Endometrium I Sample esc Pap Positive Negative Positive PAPA 1160 S2 PAPA 1160 Endometrium I Sample esc Tao Positive Negative Positive PAPA 1161 S1 PAPA 1161 Endometrium I Sample esc Pap Positive Negative Positive PAPA 1161 S2 PAPA 1161 Endometrium I Sample esc Tao Positive Negative Positive PAPA 1162 S1 PAPA 1162 Endometrium I Sample esc Pap Positive Negative Positive PAPA 1162 S2 PAPA 1162 Endometrium I Sample esc Tao Positive Positive Positive PAPA 1164 S1 PAPA 1164 Endometrium I Sample esc Pap Negative Positive Positive PAPA 1164 S2 PAPA 1164 Endometrium I Sample esc Tao Positive Positive Positive PAPA 1167 S1 PAPA 1167 Ovarian III Sample esc Pap Negative Negative PAPA 1167 S2 PAPA 1167 Ovarian III Sample esco Negative Negative PAPA 1169 S1 PAPA 1169 Endometrium I Sample esc Pap Negative Negative PAPA 1169 S2 PAPA 1169 Endometrium I Sample esc Tao Negative Negative PAPA 1172 S1 PAPA 1172 Ovarian III Sample esc Pap Positive Negative Positive PAPA 1176 S1 PAPA 1176 Ovarian III Sample esc Pap Negative Negative PAPA 1176 S2 PAPA 1176 Ovarian III Sample esco Negative Negative PAPA 1177 S1 PAPA 1177 Endometrium I Sample esc Pap Positive Negative Positive PAPA 1177 S2 PAPA 1177 Endometrium I Sample esc Tao Positive Positive Positive PAPA 1180 S1 PAPA 1180 Ovarian I Sample esc Negative Negative Negative PAPA 1180 S2 PAPA 1180 Ovarian I Sample esc Tao Negative Negative Negative PAPA 1181 S2 PAPA 1181 Endometrium IV Sample esc Tao Negative Negative Negative PAPA 1184 S1 PAPA 1184 Endometrium I Sample esc Pap Negative Negative PAPA 1184 S2 PAPA 1184 Endometrium I Sample esc Tao Positive Positive Positive PAPA 1185 S1 PAPA 1185 Endometrium I Sample esc Pap Positive Positive Positive PAPA 1185 S2 PAPA 1185 Endometrium I Sample esc Tao Positive Positive Po sitivo PAPA 1188 S1 PAPA 1188 Endometrium I Sample esc Pap Positive Positive Positive PAPA 1188 S2 PAPA 1188 Endometrium I Sample esc Tao Positive Positive Positive PAPA 1189 S2 PAPA 1189 Ovarian III Sample esc Tao Positive Negative Positive PAPA 1191 S1 PAPA 1191 Endometrium I Sample esc Pap Positive Negative Positive PAPA 1192 S1 PAPA 1192 Endometrium II Sample esc Pap Positive Positive Positive PAPA 1193 S1 PAPA 1193 Endometrium I Sample esc Pap Positive Positive Positive

PAPA 1194 S1 PAPA 1194 Endometrium I Sample esc Pap Positive Negative Positive PAPA 1198 S1 PAPA 1198 Endometrium I Sample esc Pap Positive Positive Positive PAPA 1200 S1 PAPA 1200 Endometrium I Sample esc Pap Negative Negative PAPA 1202 S1 PAPA 1202 Endometrium I Sample esc Pap Positive Negative Positive PAPA 1204 S1 PAPA 1204 Endometrium I Sample esc Pap Positive Negative Positive PAPA 1207 S1 PAPA 1207 Endometrium III Sample esc Pap Positive Negative Positive PAPA 1208 S1 PAPA 1208 Ovarian I Sample esc Pap Positive Negative Positive PAPA 1209 S1 PAPA 1209 Endometrium I Sample sci Pap Positive Positive Positive PAPA 1210 S1 PAPA 1210 Ovarian III Sample esc Pap Positive Negative Positive PAPA 1211 S1 PAPA 1211 Endometrium III Sample esc Pap Positive Negative Positive PAPA 1213 S1 PAPA 1213 Ovarian III Sample esc Pap Positive Negative Positive PAPA 1214 S1 PAPA 1214 Endometrium III Sample esc Pap Positive Negative Positive PAPA 1215 S1 PAPA 1215 Ovarian III Sample esc Pap Negative Negative PAPA 1217 S1 PAPA 1217 Ovarian III Sample esc Pap Positive Negative PAPA 1218 S1 PAPA 1218 Endometrium I Sample esc Pap Positive Negative Positive PAPA 1221 S1 PAPA 1221 Endometrium I Sample esc Positive Pap Positive Positive PAPA 1222 S1 PAPA 1222 Endometrium I Sample esc Pap Negative Negative PAPA 1223 S1 PAPA 1223 Endometrium I Sample esc Pap Pap Negative Positive PAPA 1224 S1 PAPA 1224 Endometrium I Sample esc Pap Pap Negative Positive PAPA 1225 S1 PAPA 1225 Ovarian I Sample esc Pap Negative Negative PAPA 1226 S1 PAPA 1226 Endometrium I Sample esc Pap Positive Negative Positive PAPA 1230 S1 PAPA 1230 Endometrium I Sample esc Pap Negative Negative Negative PAPA 1232 S1 PAPA 1232 Endometrium I Sample esc Pap Positive Negative Positive PAPA 1233 S1 PAPA 1233 Endometrium I Sample esc Positive Pap Positive Negative PAPA 1235 S1 PAPA 1235 Ovarian III Sample esc Pap Positive Negative Positive PAPA 1237 S1 PAPA 1237 Ovarian III Sample esc Pap Negative Negative PAPA 1239 S1 PAPA 1239 Endometrium I Sample esc Pap Pap Positive Positive Positive PAPA 1240 S1 PAPA 1240 Ovarian III Sample esc Pap Negative Negative Negative PAPA 1242 S1 PAPA 1242 Sample End Pap I Positive esc Pap Positive Positive PAPA 1243 S1 PAPA 1243 Endometrium I Sample esc Pap Negative Negative Negative PAPA 1246 S1 PAPA 1246 Ovarian I Sample esc Pap Negative Negative Negative PAPA 1247 S1 PAPA 1247 Endometrium I Sample esc Pap Negative Negative Negative PAPA 1249 S1 PAPA 1249 Endometrium I Sample esc Pap Negative Negative PAPA 1249 S2 PAPA 1249 Endometrium I Sample esc Tao Negative Positive Positive PAPA 1253 S1 PAPA 1253 Ovarian III Sample esc Negative Negative PAPA 1253 S2 PAPA 1253 Ovarian III Sample esc Tao Positive Negative Positive PAPA 1254 S1 PAPA 1254 Endometrium I Sample esc Pap Negative Negative Negative PAPA 1254 S2 PAPA 1254 Endometrium I Sample esc Tao Positive Positive Positive PAPA 1255 S1 PAPA 1255 Endometrium I Sample esc Pap Positive Negative Positive PAPA 1256 S1 PAPA 1256 Endometrium I Sample esc Pap Positive Negative Positive PAPA 1256 S2 PAPA 1256 Endometrium I Sample esc Tao Negative Negative PAPA 1257 S1 PAPA 1257 Ovarian III Sample esc Pap Pap Negative Positive Positive PAPA 1257 S2 PAPA 1257 Ovarian III Sample esc Tao Negative Negative PAPA 1258 S1 PAPA 1258 Ovarian IV Sample esc Pap Negative Negative Negative

PAPA 1258 S2 PAPA 1258 Ovarian IV Sample esc Tao Positive Negative Positive PAPA 1259 S1 PAPA 1259 Ovarian III Sample esc Pap Negative Negative PAPA 1259 S2 PAPA 1259 Ovarian III Sample esc Tao Positive Negative Positive PAPA 1260 S1 PAPA 1260 Ovarian I Sample esc Pap Negative Negative Negative PAPA 1260 S2 PAPA 1260 Ovarian I Sample esc Tao Negative Negative PAPA 1262 S1 PAPA 1262 Ovarian I Sample esc Pap Negative Negative PAPA 1263 S1 PAPA 1263 Ovarian IV Sample esc Pap Negative Negative PAPA 1263 S2 PAPA 1263 Ovarian IV Sample esc Tao Positive Negative Positive PAPA 1264 S1 PAPA 1264 Endometrium I Sample esc Pap Positive Negative Positive PAPA 1264 S2 PAPA 1264 Endometrium I Sample esc Tao Positive Positive Positive PAPA 1284 S1 PAPA 1284 Endometrium I Sample esc Negative Negative Negative PAPA 1286 S1 PAPA 1286 Endometrium I Sample esc Pap Negative Negative PAPA 1292 S1 PAPA 1292 Ovarian III Sample esc Pap Positive Negative Positive PAPA 1293 S1 PAPA 1293 Endometrium I Sample esc Pap Positive Negative Positive PAPA 1295 S1 PAPA 1295 Endometrium I Sample esc Pap Negative Negative PAPA 1296 S1 PAPA 1296 Ovarian III Sample esc Pap Positive Positive Positive PAPA 1297 S1 PAPA 1297 Ovarian III Sample escar Pap Positive Negative Positive PAPA 1298 S1 PAPA 1298 Ovarian III Sample esc Pap Positive Positive Positive PAPA 1300 S1 PAPA 1300 Endometrium I Sample esc Pap Positive Positive Positive PAPA 1301 S1 PAPA 1301 Ovarian I Sample esc Negative Negative Negative PAPA 1302 S1 PAPA 1302 Ovarian I Sample esc Pap Negative Positive Positive PAPA 1303 S1 PAPA 1303 Endometrium IV Sample esc Pap Positive Positive Positive PAPA 1307 S1 PAPA 1307 Endometrium IV Sample esc Pap Negative Negative Negative PAPA 1308 S1 PAPA 1308 Endometrium I Sample esc Positive Pap Positive Negative PAPA 1309 S1 PAPA 1309 Endometrium I Sample esc Pap Positive Negative Positive PAPA 1315 S1 PAPA 1315 Ovarian III Sample esc Pap Negative Negative PAPA 1320 S1 PAPA 1320 Ovarian IV Sample esc Pap Negative Negative Negative PAPA 1321 S1 PAPA 1321 Ovarian III Sample esc Pap Negative Negative Negative PAPA 1322 S1 PAPA 1322 Ovarian II Sample esc Pap Positive Negative Positive PAPA 1323 S1 PAPA 1323 Ovarian II Sample esc Pap Negative Negative PAPA 1325 S1 PAPA 1325 Ovarian III Sample esc Pap Negative Negative PAPA 1326 S1 PAPA 1326 Ovarian I Sample esc Pap Pap Positive Positive PAPA 1327 S1 PAPA 1327 Ovarian I Sample esc Pap Negative Negative PAPA 1328 S1 PAPA 1328 Ovarian III Sample esc Pap Positive Negative Positive PAPA 1329 S1 PAPA 1329 Endometrium I Sample esc Pap Negative Negative Negative PAPA 1359 S1 PAPA 1359 Ovarian IV Sample esc Pap Negative Negative Negative PAPA 1359 S2 PAPA 1359 Ovarian IV Sample esc Tao Negative Negative PAPA 1361 S1 PAPA 1361 Ovarian II Sample esc Pap Negative Negative Negative PAPA 1361 S2 PAPA 1 361 Ovarian II Sample esc Tao Positive Negative Positive PAPA 1362 S2 PAPA 1362 Ovarian II Sample esc Tao Negative Negative PAPA 1363 S1 PAPA 1363 Ovarian III Sample esc Pap Negative Negative PAPA 1363 S2 PAPA 1363 Ovarian III Sample esco Negative Negative PAPA 1364 S1 PAPA 1364 Ovarian II Sample esc Pap Positive Negative Positive PAPA 1365 S1 PAPA 1365 Ovarian III Sample esc Pap Negative Negative Negative

PAPA 1365 S2 PAPA 1365 Ovarian III Sample esc Tao Negative Negative Negative PAPA 1369 S1 PAPA 1369 Ovarian II Sample esc Pap Negative Negative PAPA 1369 S2 PAPA 1369 Ovarian II Sample esc Tao Negative Negative PAPA 1370 S1 PAPA 1370 Ovarian III Sample esc Pap Negative Negative Negative PAPA 1370 S2 PAPA 1370 Ovarian III Sample esc Tao Negative Negative PAPA 1371 S1 PAPA 1371 Ovarian III Sample esc Pap Negative Negative PAPA 1371 S2 PAPA 1371 Ovarian III Sample esc Tao Negative Negative PAPA 1695 S1 PAPA 1695 Ovarian IV Sample escar Pap Positive Negative Positive PAPA 1696 S1 PAPA 1696 Ovarian III Sample esc Pap Positive Positive Positive PAPA 1697 S1 PAPA 1697 Ovarian III Sample esc Pap Positive Negative Positive PAPA 1698 S1 PAPA 1698 Ovarian II Sample esc Negative Negative Negative PAPA 1699 S1 PAPA 1699 Ovarian IV Sample esc Pap Negative Positive Positive PAPA 1700 S1 PAPA 1700 Ovarian II Sample esc Pap Papiti vo Negative Positive PAPA 1701 S1 PAPA 1701 Ovarian III Sample esc Pap Negative Negative PAPA 1702 S1 PAPA 1702 Ovarian III Sample esc Pap Negative Negative Negative PAPA 1703 S1 PAPA 1703 Ovarian III Sample esc Pap Negative Negative PAPA 1705 S1 PAPA 1705 Ovarian I Sample esc Pap Positive Negative Positive PAPA 1706 S1 PAPA 1706 Ovarian II Sample esc Pap Positive Positive Positive PAPA 1707 S1 PAPA 1707 Ovarian III Sample esc negative Pap Negative Negative PAPA 1710 S1 PAPA 1710 Ovarian III Sample esc Pap Negative Positive Positive PAPA 1712 S1 PAPA 1712 Ovarian I Sample esc Pap Negative Negative PAPA 1713 S1 PAPA 1713 Ovarian III Sample esc Pap Negative Negative PAPA 1714 S1 PAPA 1714 Ovarian III Sample esc Pap Negative Negative PAPA 1715 S1 PAPA 1715 Ovarian I Sample esc Pap Negative Negative PAPA 1716 S1 PAPA 1716 Ovarian II Sample esc Pap Positive Negative Positive PAPA 1717 S1 PAPA 1717 Ovarian III Amo stra esc Pap Negative Negative PAPA 1720 S1 PAPA 1720 Ovarian III Sample esc Pap Negative Negative PAPA 1721 S1 PAPA 1721 Ovarian III Sample esc Pap Negative Negative Negative PAPA 1722 S1 PAPA 1722 Ovarian IV Sample esc Pap Negative Negative PAPA 1724 S1 PAPA 1724 Ovarian III Sample esc Pap Negative Negative PAPA 1725 S1 PAPA 1725 Ovarian I Sample esc Pap Positive Negative Positive PAPA 1727 S1 PAPA 1727 Ovarian I Sample esc Pap Negative Negative Negative PAPA 1728 S1 PAPA 1728 Ovarian I Sample esc Pap Negative Negative PAPA 1730 S1 PAPA 1730 Ovarian III Sample esc Pap Negative Negative Negative PAPA 1732 S1 PAPA 1732 Ovarian II Sample esc Pap Negative Negative Negative PAPA 1734 S1 PAPA 1734 Ovarian IV Sample esc Pap Positive Negative Positive PAPA 1735 S1 PAPA 1735 Ovarian III Sample esc Negative Negative PAPA Negative 1736 S1 PAPA 1736 Ovarian II Sample esc Pap Positive Negative Positive PAPA 1737 S1 PAPA 1737 Ovarian II Sample esc Pap Negative Negative Negative PAPA 1738 S1 PAPA 1738 Ovarian III Sample esc Pap Negative Negative Negative PAPA 1739 S1 PAPA 1739 Ovarian I Sample esc Pap Negative Negative Negative PAPA 1816 S1 PAPA 1816 Ovarian I Sample esc Tao Negative Negative PAPA 1816 S2 PAPA 1816 Ovarian I Sample esc Pap Negative Negative Negative PAPA 1817 S1 PAPA 1817 Ovarian III Sample esc Tao Negative Negative Negative

PAPA 1817 S2 PAPA 1817 Ovarian III Sample esc Pap Negative Negative Negative PAPA 1819 S1 PAPA 1819 Ovarian III Sample esc Tao Positive Negative Positive PAPA 1819 S2 PAPA 1819 Ovarian III Sample esc Pap Positive Negative Positive PAPA 1820 S1 PAPA 1820 Endometrium III Sample esc Tao Positivo Positive Positive PAPA 1820 S2 PAPA 1820 Endometrium III Sample esc Pap Positive Negative Positive PAPA 1821 S1 PAPA 1821 Ovarian II Sample esc Tao Positive Positive Positive PAPA 1821 S2 PAPA 1821 Ovarian II Sample esc Pap Negative Negative PAPA 1823 S1 PAPA 1823 Ovarian IV Sample esc Tao Positive Negative Positive PAPA 1823 S2 PAPA 1823 Ovarian IV Sample esc Pap Negative Negative PAPA 1826 S1 PAPA 1826 Ovarian I Sample esc Tao Positive Negative Positive PAPA 1826 S2 PAPA 1826 Ovarian I Sample esc Pap Negative Positive Positive PAPA 1828 S1 PAPA 1828 Ovarian III Sample esc Tao Negative Negative PAPA 1828 S2 PAPA 1828 Ovarian III Sample esc Pap Posi Positive Negative PAPA 1829 S1 PAPA 1829 Ovarian III Sample esc Tao Negative Negative PAPA 1829 S2 PAPA 1829 Ovarian III Sample esc Pap Negative Negative PAPA 1832 S1 PAPA 1832 Ovarian III Sample esc Tao Positive Negative PAPA 1833 S1 PAPA 1833 Ovarian III Sample esc Tao Negative Negative PAPA 1833 S2 PAPA 1833 Ovarian III Sample esc Pap Negative Negative Negative PAPA 1834 S1 PAPA 1834 Ovarian I Sample esc Tao Positive Negative Positive PAPA 1834 S2 PAPA 1834 Ovarian I Sample esc Negative Negative Negative PAPA 1836 S1 PAPA 1836 Endometrium I Sample esc Tao Positive Negative Positive PAPA 1837 S1 PAPA 1837 Endometrium I Sample esc Tao Positive Positive Positive PAPA 1838 S1 PAPA 1838 Ovarian III Sample esc Tao Negative Negative Negative PAPA 1839 S1 PAPA 1839 Endometrium I Sample esc Tao Positive Positive PAPA 1840 S1 PAPA 1840 Endometrium I Sample esc Tao Positive Positive Positive PAPA 1842 S1 PAPA 1842 Endometrium I Sample esc Tao Positive Positive Positive PAPA 1843 S1 PAPA 1843 Endometrium I Sample esc Tao Positive Positive Positive PAPA 1844 S1 PAPA 1844 Ovarian III Sample esc Tao Positive Negative Positive PAPA 1845 S1 PAPA 1845 Ovarian III Sample esc Tao Positive Positive PAPA 1847 S1 PAPA 1847 Endometrium III Sample esc Tao Positive Positive PAPA 1848 S1 PAPA 1848 Endometrium III Sample esc Tao Positive Positive PAPA 1849 S1 PAPA 1849 Endometrium I Sample esc Tao Positive Positive PAPA 1850 S1 PAPA 1850 Ovarian III Sample esc Tao Positive Positive PAPA 1852 S1 PAPA 1852 Endometrium III Sample esc Tao Positive Positive Positive PAPA 1855 S1 PAPA 1855 Endometrium I Sample esc Tao Positive Positive PAPA 1856 S1 PAPA 1856 Ovarian III Sample esc Tao Negative Negative PAPA 1857 S1 PAPA 1857 Endometrium III Sample esc Tao Positive Positive PAPA 1858 S1 PAPA 1858 Endometrium III Sample esc Tao Positivo Positivo Posit ivo PAPA 1859 S1 PAPA 1859 Endometrium I Sample esc Tao Positive Positive PAPA 1860 S1 PAPA 1860 Endometrium I Sample esc Tao Positive Negative Positive PAPA 1861 S1 PAPA 1861 Endometrium IV Sample esc Tao Positive Positive Positive PAPA 1862 S1 PAPA 1862 Endometrium I Sample esco Positive Negative Positive PAPA 1864 S1 PAPA 1864 Ovarian III Sample esc Tao Negative Negative PAPA 1865 S1 PAPA 1865 Endometrium I Sample esc Tao Positive Positive Positive

PAPA 1866 S1 PAPA 1866 Ovarian III Sample esc Tao Negative Negative Negative PAPA 1867 S1 PAPA 1867 Endometrium I Sample esc Tao Positive Positive Positive PAP 033 S PAP 033 Endometrium I Sample esc Pap Positive Negative Positive PAP 042 S PAP 042 Ovarian III Sample esc Pap Positive Positive Positive PAP 261 S PAP 261 Ovarian I Sample esc Pap Negative Negative PAP 532 S PAP 532 Ovarian III Sample esc Pap Negative Negative PAP 533 S PAP 533 Ovarian III Sample esc Pap Negative Negative PAP 544 S PAP 544 Endometrium I Sample esc Pap Negative Negative PAP 562 S PAP 562 Endometrium I Sample esc Pap Positive Positive Positive PAP 570 S PAP 570 Endometrium III Sample esc Pap Positive Positive Positive PAP 588 S PAP 588 Endometrium I Sample esc Positive Pap Positive Negative PAP 590 S PAP 590 Endometrium I Sample esc Pap Positive Negative Positive PAP 601 S PAP 601 Endometrium II Sample esc Pap Positive Positive Positive PAP 645 S1 PAP 645 Ovarian I Sample esc Pap Negative Negative Negative PAP 651 S1 PAP 651 Endometrium I Sample esc Pap Negative Negative Negative PAP 653 S1 PAP 653 Endometrium II Sample esc Pap Negative Negative Negative PAP 655 S1 PAP 655 Endometrium II Sample esc Pap Negative Negative PAP 027 S PAP 027 Ovarian NA Sample esc Pap Negative Negative Negative PAP 582 S PAP 582 Endometrium I Sample esc Pap Positive Negative Positive PAP 083 S PAP 083 Endometrium I Sample esc Pap Negative Negative Negative PAP 265 S PAP 265 Endometrium II Sample esc Pap Positive Negative Positive PAP 555 S PAP 555 Endometrium IV Sample esc Pap Positive Positive Positive PAP 618 S PAP 618 Endometrium I Sample esc Pap Positive Negative Positive PAP 059 S PAP 059 Ovarian NA Sample esc Pap Positive Negative Positive PAP 174 S PAP 174 Ovarian I Sample esc Pap Negative Negative Negative PAP 175 S PAP 175 Endometrium IV Sample esc Pap Positive Positive Positive PAP 184 S PAP 184 Ovarian I Sample esc Pap Positive Positive PAP Positive 032 S PAP 032 Endometrium I Sample esc Pap Negative Negative PAP 035 S PAP 035 Endometrium IV Sample esc Pap Positive Positive PAP 206 S PAP 206 Endometrium III Sample esc Pap Positive Positive Positive PAP 218 S PAP 218 Sample end Pap Pap Positive Positive Positive PAP 186 S PAP 186 Ovarian II Sample esc Pap Positive Negative Positive PAP 220 S PAP 220 Endometrium III Sample esc Pap Negative Positive Positive PAP 173 S PAP 173 Ovarian I Sample esc Pap Positive Positive Positive PAP 194 S PAP 194 Ovarian I Sample esc Pap Positive Positive Positive PAP 241 S PAP 241 Ovarian NA Sample esc Pap Negative Negative PAP 608 S PAP 608 Ovarian III Sample esc Pap Negative Negative Negative PAP 616 S PAP 616 Endometrium I Sample esc Pap Positive Negative Positive PAP 627 S PAP 627 Endometrium I Sample esc Pap Negative Negative PAP 275 S PAP 275 Endometrium I Sample esc Pap Positive Negative Positive PAP 708 S1 PAP 708 Ovarian II Sample esc Pap Negative Negative PAP 675 S1 PAP 675 Endometrium I Sample esc Pap Positive Positive Positive PAPA 1012 S1 PAPA 1012 Endometrium III Sample esc Pap Positive Positive Positive PAP 208 S PAP 208 Endometrium I Sample esc Positive Pap Positive Negative Positive

PAP 936 S1 PAP 936 Endometrium I Sample esc Pap Positive Negative Positive PAP 937 S1 PAP 937 Endometrium I Sample esc Pap Negative Negative PAP 753 S1 PAP 753 Endometrium IV Sample esc Pap Positive Positive Positive PAP 757 S1 PAP 757 Endometrium III Sample esc Pap Positive Positive Positive PAP 799 S1 PAP 799 Endometrium I Sample esc Pap Negative Negative PAP 800 S1 PAP 800 Endometrium IV Sample esc Pap Positive Positive PAP 866 S1 PAP 866 Endometrium I Sample esc Pap Positive Negative Positive PAP 865 S1 PAP 865 Endometrium I Sample esc Pap Positive Positive Positive PAPA 1080 S1 PAPA 1080 Endometrium I Sample esc Pap Positive Negative Positive PAPA 1086 S1 PAPA 1086 Endometrium I Sample esc Pap Positive Negative Positive PAPA 1181 S1 PAPA 1181 Endometrium IV Sample esc Negative Negative Negative PAPA 1189 S1 PAPA 1189 Ovarian III Sample esc Pap Negative Negative PAPA 1251 S2 PAPA 1251 Endometrium I Sample esc Tao Positive Positive Positive PAPA 1255 S2 PAPA 1255 Endometrium I Sample esc Tao Positive Positive Positive PAPA 1262 S2 PAPA 1262 Ovarian I Sample esc Tao Negative Negative PAPA 1251 S1 PAPA 1251 Endometrium I Sample esc Pap Positive Negative Positive PAPA 1299 S1 PAPA 1299 Endometrium I Sample esc Pap Positive Positive Positive PAP 030 S PAP 030 Endometrium I Sample esc Pap Negative Negative PAP 591 S PAP 591 Endometrium I Sample esc Pap Negative Negative PAP 182 PLS PAP 182 Ovarian I Plasma sample Negative Not tested on PAP 184 PLS PAP 184 Ovarian I Plasma sample Positive Not tested NA PAP 185 PLS PAP 185 Ovarian I Plasma sample Negative Not tested NA PAP 186 PLS PAP 186 Ovarian II Plasma sample Positive Not tested NA PAP 193 PLS PAP 193 Ovarian III Plasma sample Positive Not tested NA PAP 195 PLS PAP 195 Ovarian III Plasma sample Negative Not tested NA PAP 196 PLS PAP 196 Ovarian I Plasma sample Positive Not tested NA PAP 197 PLS PAP 197 Ovarian I Plasma sample Posi Not tested NA PAP 198 PLS PAP 198 Ovarian III Plasma sample Positive Not tested NA PAP 199 PLS PAP 199 Ovarian I Plasma sample Positive Not tested NA PAP 210 PLS PAP 210 Ovarian I Plasma sample Positive Not tested NA PAP 230 PLS PAP 230 Ovarian III Plasma sample Negative Not tested NA PAP 241 PLS PAP 241 Ovarian NA Plasma sample Negative Not tested NA PAP 248 PLS PAP 248 Ovarian III Plasma sample Negative Not tested NA PAP 250 PLS PAP 250 Ovarian IV Plasma sample Positive Not tested NA PAP 251 PLS PAP 251 Ovarian I Plasma sample Positive Not tested NA PAP 252 PLS PAP 252 Ovarian I Plasma sample Negative Not tested NA PAP 253 PLS PAP 253 Ovarian III Plasma sample Negative Not tested NA PAP 254 PLS PAP 254 Ovarian I Plasma sample Negative Not tested NA PAP 255 PLS PAP 255 Ovarian III Plasma sample Positive Not tested NA PAP 257 PLS PAP 257 Ovarian I Plasma sample Negative Not tested NA PAP 258 PLS PAP 258 Ovarian I Plasma sample Negative Not tested NA PAP 259 PLS PAP 259 Ovarian I Plasma sample Negative Not tested NA PAP 261 PLS PAP 261 Ovarian I Plasma sample Positive Not tested NA PAP 262 PLS PAP 262 Ovarian III Plasma sample Positive Not tested NA

PAP 263 PLS PAP 263 Ovarian I Plasma sample Negative Not tested NA PAP 264 PLS PAP 264 Ovarian I Plasma sample Negative Not tested NA PAP 579 PLS PAP 579 Ovarian I Plasma sample Negative Not tested NA PAP 641 PLS PAP 641 Ovarian II Plasma sample Negative Not tested NA PAP 642 PLS PAP 642 Ovarian I Plasma sample Positive Not tested NA PAP 643 PLS PAP 643 Ovarian I Plasma sample Positive Not tested NA PAP 644 PLS PAP 644 Ovarian II Plasma sample Positive Not tested NA PAP 645 PLS PAP 645 Ovarian I Plasma sample Positive Not tested NA PAP 701 PLS PAP 701 Ovarian I Plasma sample Negative Not tested NA PAP 704 PLS PAP 704 Ovarian I Plasma sample Negative Not tested NA PAP 705 PLS PAP 705 Ovarian I Plasma sample Negative Not tested NA PAP 707 PLS PAP 707 Ovarian III Plasma sample positive Not tested NA PAP 708 PLS PAP 708 Ovarian II Plasma sample negative Not tested NA PAP 709 PLS PAP 709 Ovarian II Plasma sample negative Not tested NA PAP 710 PLS PAP 710 Ovarian III Plasma sample Negative Not tested NA PAP 711 PLS PAP 711 Ovarian III Plasma sample Positive Not tested NA PAP 715 PLS PAP 715 Ovarian I Plasma sample Negative Not tested NA PAP 815 PLS PAP 815 Ovarian III Plasma sample Negative Not tested IN PAPA 1037 PLSPAPA 1037 Ovarian III Plasma Sample Positive Not tested ON PAPA 1062 PLSPAPA 1062 Ovarian IV Plasma Sample Negative Not tested ON PAPA 1072 PLSPAPA 1072 Ovarian III Plasma Sample Negative Not tested ON PAPA 1074 PLSPAPA 1074 Ovarian III Plasma Sample Negative Not tested ON PAPA 1084 PLSPAPA 1084 Ovarian II Plasma sample Negative Not tested ON PAPA 1094 PLSPAPA 1094 Ovarian IV Plasma sample Negative Not tested ON PAPA 1096 PLSPAPA 1096 Ovarian III Plasma sample Positive Not tested ON PAPA 1098 PLSPAPA 1098 Ovarian I Plasma sample Negative Not tested ON PAPA 1110 PLSPAPA 1110 Ovarian II Plasma sample Negative Not tested ON PAPA 1112 PLSPAPA 1112 Ovarian I Plasma sample Negative Not tested ON PAPA 1113 PLSP APA 1113 Ovarian III Plasma Sample Positive Not tested ON PAPA 1115 PLSPAPA 1115 Ovarian III Plasma Sample Positive Not tested ON PAPA 1116 PLSPAPA 1116 Ovarian I Plasma Sample Negative Not tested ON PAPA 1117 PLSPAPA 1117 Ovarian III Plasma Sample Positive Not tested ON PAPA 1118 PLSPAPA 1118 Ovarian I Plasma Sample Negative Not tested ON PAPA 1119 PLSPAPA 1119 Ovarian I Plasma Sample Negative Not tested ON PAPA 1120 PLSPAPA 1120 Ovarian I Plasma Sample Negative Not tested ON PAPA 1121 PLSPAPA 1121 Ovarian I Plasma Sample Negative Not tested ON PAPA 1122 PLSPAPA 1122 Ovarian I Plasma sample Positive Not tested ON PAPA 1208 PLSPAPA 1208 Ovarian I Plasma sample Negative Not tested ON PAPA 1213 PLSPAPA 1213 Ovarian III Plasma sample Positive Not tested ON PAPA 1215 PLSPAPA 1215 Ovarian III Plasma sample Negative Not tested ON PAPA 1235 PLSPAPA 1235 Ovarian III Plasma sample Negative Not tested NA PAPA 1237 PLSPAPA 1237 Ovarian III Plasma sample Positive Not te sted NA PAPA 1240 PLSPAPA 1240 Ovarian III Plasma sample Positive Not tested NA PAPA 1246 PLSPAPA 1246 Ovarian I Plasma sample Negative Not tested NA

PAPA 1292 PLSPAPA 1292 Ovarian III Plasma Sample Positive Not tested ON PAPA 1296 PLSPAPA 1296 Ovarian III Plasma Sample Positive Not tested ON PAPA 1297 PLSPAPA 1297 Ovarian III Plasma Sample Positive Not tested ON PAPA 1298 PLSPAPA 1298 Ovarian III Plasma Sample Positive Not tested ON PAPA 1301 PLSPAPA 1301 Ovarian I Plasma Sample Negative Not tested ON PAPA 1315 PLSPAPA 1315 Ovarian I Plasma Sample Negative Not tested ON PAPA 1320 PLSPAPA 1320 Ovarian IV Plasma Sample Positive Not tested ON PAPA 1321 PLSPAPA 1321 Ovarian III Plasma Sample Positive Not tested ON PAPA 1322 PLSPAPA Ovarian II Plasma Sample Positive Not tested ON PAPA 1323 PLSPAPA 1323 Ovarian II Plasma Sample Negative Not tested ON PAPA 1325 PLSPAPA 1325 Ovarian III Plasma Sample Negative Not tested ON PAPA 1326 PLSPAPA 1326 Ovarian I Plasma Sample Positive Not tested ON PAPA 1327 PLSPAPA 1327 Ovarian I Plasma sample Positive Not tested IN PAPA 1328 PLSPAPA 1328 Ovarian III Plasma samples tra Negative Not tested NA

Table 15. Mutations identified in Pap brush and Tao brush samples from patients with ovarian or endometrial cancer.

Cancer Sample WT MT WT Mutant Site Exon MAF Total # PatientID Type Chrom Type Position * Base MutType Base Gene Nucleotid.

Codon Codon Codon Junction End% Supermutants PAP 001 Endometrium Sample esc cr cr 6 67591119 Indel TAT PIK3R1 1712 571 I NULL NN 0.76 63 PAP 010 Endometrium Sample esc pap cr 5 67589602 Indel GTTTCAAGA PIK3R1 1365 455 Q NULL PAP NN 11.37 011 Endometrium Sample esc Pap cr 5 67589619 Indel GAG PIK3R1 1382 461 R NULL NN 3.56 311 PAP 034 Endometrium Sample esc Pap cr 5 67591116 Indel TTATCC PIK3R1 1709 570 L NULL NN 21.07 2481 PAP 066 Endometrium Sample esc Pap cr 17 7578235 SBS TC TP53 614 205 YCNN 8.35 357 PAP 066 Endometrium Esc sample Pap cr 9 21974705 Indel GGTGCGTTGGGCAGCGCCCC (SEQ ID NO: 749) CDKN2A 122 41 P NULL NN 0.61 73 PAP 067 Endometrium Sample sc Pap cr 5 CT 67589618 SBS 1381 461 R * NN 2.41 1187 PAP 069 Endometrium Sample esc Pap cr 17 7579359 SBS GA TP53 328 110 RCNN 15.22 2175 PAP 079 Ovarian Sample esc Pap cr 17 7579349 Indel AAG TP53 338 113 F NULL NN 0.11 55 PAP 084 Endometrium Sample esc Pap cr 5 112174371 Indel A APC 3080 1027 Y NULO NN 0.52 217 PAP 084 Endometrium Sample esc cr cr 5 112175973 Indel AGGA APC 4682 1561 K NULL NN 0.14 71 PAP 119 Endometrium Sample esc cr cr 5 67589590 Indel ATA PIK3R1 1353 451 AND NULL NN 0.42 20 PAP 120 Endometrium Sample esc Pap cr 10 89692818 SBS TC PTEN 302 101 ITNN 3.84 437 PAP 120 Endometrium esc Pap cr 10 89720693 Indel TT PTEN 844 282 G NULL NN 1.87 483 PAP 131 Endometrium Sample esc Pap cr 5 112175856 SBS TG APC 4565 1522 L * NN 0.11 21 PAP 131 Endometrium Sample esc Pap cr 5 67589618 SBS CT PIK3R1 1381 461 R * NN 5.21 399 PAP 131 Endometrium Sample esc Pap cr 5 67591116 Indel TTATCC PIK3R1 1709 570 L NULL NN 0.31 16 PAP 132 Endometrium Sample esc Pap cr 5 112175852 SBS GT APC 4561 1521 E * NN 43.3.29 2057 PAP 168 Ovarian Sample esc Pap cr 17 7577536 SBS TC TP53 745 249 RGNN 13.3.61 1900 PAP 176 Endometrium Sample esc Pap cr 5 112175957 Indel A APC 4666 1556 T NULL NN 0.57 20 PAP 180 Endometrium Sample esc Pap cr 5 67589609 SBS GT PIK3R1 1372 458 E * NN 2.70 137 PAP 202 Endometrium Sample esc Pap cr 17 7577534 SBS CA TP53 747 249 RSNN 2.34 182 PAP 202 Endometrium Sample esc cr cr 17 7577565 SBS TC TP53 716 239 NSNN 0.50 39 PAP 202 Endometrium Sample esc Pap Pap cr 17 7579419 Indel T TP53 268 90 S NULL NN 0.24 28 PAP 204 Endometrium Sample esc Pap cr 17 7579419 Indel T TP53 268 90 S NULL NN 16.26 1400 PAP 215 Endometrium Sample esc Pap cr 17 7577538 SBS CA TP53 743 248 RLNN 73.92 5722 PAP 216 Endometrium Sample esc Pap cr 5 67589623 Indel ATATGA PIK3R1 1386 462 E NULL NN 0.25 24 PAP 219 Endometrium Sample esc Pap cr 3 178952018 SBS AG PIK3CA 3073 1025 TANN 20.03 578 PAP 224 Endometrium Sample esc Pap cr 17 7577094 SBS GA TP53 844 282 RWNN 4.88 470 PAP 227 Endometrium Sample esc Pap cr 17 7577568 SBS CT TP53 713 238 CYNN 0.69 53 PAP 228 Endometrium Sample esc Pap cr 10 89720757 Indel G PTEN 908 303 I NULL NN NN 15.89 1514 PAP 230 Ovarian Sample esc Pap cr 17 7579374 SBS CG TP53 313 105 GRNN 0.09 19 PAP 234 Endometrium Sample esc Pap cr 17 7577548 SBS CA TP53 733 245 GCNN 1.52 296 PAP 237 Endometrium Sample esc Pap cr 5 67589589 Indel TGA PIK3R1 1352 451 AND NULL NN 1.29 66 PAP 246 Endometrium Sample esc cr cr 10 89717650 SBS TG PTEN 675 225 Y * NN 2.86 112 PAP 246 Endometrium Sample esc Pap cr 5 67589613 Indel AAA PIK3R1 1376 459 K NULL NN 1.48 79 PAP 247 Endometrium Sample esc Pap cr 5 112176020 SBS GT APC 4729 1577 E * NN 1, 43 68 PAP 254 Ovarian Sample esc Pap cr 9 21971117 Indel GTCGGGTGAGAGTGGCGGGG (SEQ ID NO: 750) CDKN2A 241 81 P NULL NN 8.25 67 PAP 255 Ovarian Sample esc cr cr 17 7577102 SBS CT TP53 836 279 GENN 1.18 243 PAP 255 Ovarian Sample esc Pap cr 5 67589590 Indel ATA PIK3R1 1353 451 E NULL NN 0.18 8 PAP 255 Ovarian Sample esc cr cr 5 67591116 Indel TTATCC PIK3R1 1709 570 L NULL NN 0.05 6 PAP 262 Ovarian Sample esc Pap cr 17 7574004 Indel G TP53 1023 341 F NULL NN 0.23 41 PAP 264 Ovarian Sample esc Pap cr 17 7578460 SBS AT TP53 470 157 VD NN 14.56 1565 PAP 266 Endometrium Sample esc Pap cr 5 67591121 Indel C PIK3R1 1714 572 Q NULL NN 0.62 26 PAP 267 Endometrium Sample esc Pap cr 10 89653808 SBS GT PTEN 106 36 G * NN 29.21 678 PAP 267 Endometrium Sample esc Pap cr 10 89692930 SBS TG PTEN 414 138 Y * NN 2.64 50 PAP 267 Endometrium Sample esc Pap cr 12 133250289 SBS CA POLE 1231 411 VLNN 27.16 1216

PAP 267 Endometrium Sample esc Pap cr 17 7578212 SBS GA TP53 637 213 R * NN 7.65 191 PAP 267 Endometrium Sample esc cr cr 75758550 SBS GA TP53 380 127 SFNN 6.45 147 PAP 267 Endometrium Sample esc Pap cr 17 7579312 SBS CT TP53 375 125 TTN Yes 2.11 124 PAP 267 Endometrium Sample esc cr 3 178952007 SBS AG PIK3CA 3062 1021 YCNN 27.59 226 PAP 267 Endometrium Sample esc Pap cr 3 41266097 SBS GA CTNNB1 94 32 DNNN 6.08 223 PAP 267 Endometrium Sample esc Pap cr 3 41266098 SBS AG CTNNB1 95 32 DGNN 0.41 15 PAP 267 Endometrium Sample esc Pap cr 3 41266124 SBS AG CTNNB1 121 41 TANN 3.25 119 PAP 268 Endometrium Sample esc Pap cr 17 7577580 SBS TC TP53 701 234 YCNN 3.25 499 PAP 269 Endometrium Sample esc Pap cr 17 7578275 SBS GA TP53 574 192 Q * NN 6.00 824 PAP 270 Endometrium Sample esc Pap cr 3 178916876 SBS GA PIK3CA 263 88 RQNN 6.87 1218 PAP 270 Endometrium Sample esc Pap cr 4 153247289 SBS GA FBXW7 1513 505 RCNN 8.31 1254 PAP 271 Endometrium Sample e sc Pap cr 5 67590457 SBS GT PIK3R1 1519 507 E * NN 12,06 48 PAP 273 Endometrium Sample esc cr 10 89624245 SBS GT PTEN 19 7 E * NN 7,04 126 PAP 273 Endometrium Sample esc esc cr 10 89653803 SBS CA PTEN 101 34 DNA 9.88 335 PAP 273 Endometrium Sample esc cr 10 89720744 SBS GT PTEN 895 299 E * NN 3.33 597 PAP 273 Endometrium Sample esc Pap cr 12 133253184 SBS GC POLE 857 286 PRNN 11.95 340 PAP 273 Endometrium Sample esc Pap cr 17 7578212 SBS GA TP53 637 213 R * NN 3.74 105 PAP 273 Endometrium Sample esc Pap cr 17 7578263 SBS GA TP53 586 196 R * NN 3.99 234 PAP 273 Endometrium Sample esc Pap cr 3 178952085 SBS AG PIK3CA 3140 1047 HRNN 11.65 433 PAP 273 Endometrium Sample esc Pap cr 5 112175105 Indel T APC 3814 1272 S NULL NN 3.75 61 PAP 273 Endometrium Sample esc Pap cr 5 112175639 SBS CT APC 4348 1450 R * NN 3.64 139 PAP 274 Endometrium Sample esc Pap cr 5 112175190 Indel ATACCCTGCAAATA (SEQ ID N: 751) APC 3899 1300 NULL NN 12.93 1456 PAP 276 Endometrium Sample esc Pap cr 10 89717613 Indel C PTEN 638 213 P NULL NN 39.06 407 PAP 276 Endometrium Sample esc Pap cr 3 178952085 SBS AG PIK3CA 3140 1047 HRNN 16.57 395 PAP 429 Healthy contr Sample esc Pap cr 5 67589590 Indel ATA PIK3R1 1353 451 E NULL NN 0.22 14 PAP 528 Ovarian Sample esc Pap cr 17 7579349 SBS AG TP53 338 113 FSNN 0.08 20 PAP 531 Ovarian Sample esc cr cr 17 7579443 Indel G TP53 244 82 P NULL NN 0.23 52 PAP 556 Endometrium Sample esc Pap cr 5 67591117 Indel T PIK3R1 1710 570 L NULL NN 0.48 19 PAP 557 Endometrium Sample esc cr cr 17 7577094 SBS GA TP53 844 282 RWNN 1.90 140 PAP 560 Endometrium Sample esc cr cr 17 7577567 Indel A TP53 714 238 C NULL NN 0.17 8 PAP 564 Endometrium Sample esc Pap cr 17 7577548 SBS CT TP53 733 245 GSNN 17.13 620 PAP 569 Ovarian Sample esc Pap cr 17 7577545 SBS TC TP53 736 246 MVNN 1.77 67 PAP 574 Endometrium Sample esc Pap cr 5 67589618 SBS CT PIK3R1 1381 461 R * NN 1.74 280 PAP 576 Ovarian Sample esc Pap cr 1 7 7579439 Indel GC TP53 248 83 A NULL NN 0.06 12 PAP 584 Endometrium Sample esc Pap cr 5 67589609 Indel GAA PIK3R1 1372 458 AND NULL NN 16.92 2302 PAP 587 Endometrium Sample esc Pap cr 17 7577536 SBS TA TP53 745 249 RWNN 2.68 198 PAP 587 Endometrium Sample esc Pap cr 5 112175957 Indel A APC 4666 1556 T NULL NN 1.36 359 PAP 589 Endometrium Sample esc Pap cr 17 7576851 SBS AG TP53 0 0 NULL NULL 1 N 3.30 84 PAP 589 Endometrium Sample esc Pap cr 17 7579369 SBS GC TP53 318 106 SRNN 2.33 485 PAP 595 Endometrium Sample esc Pap cr 17 7577533 Indel ATGGG TP53 748 250 P NULL NN 4.31 52 PAP 595 Endometrium Sample esc Pap cr 17 7577538 SBS CT TP53 743 248 RQNN 4.64 56 PAP 604 Endometrium Sample esc cr 177577580 SBS TC TP53 701 234 YCNN 0.40 39 PAP 609 Endometrium Sample esc Pap cr 3 178952018 SBS AG PIK3CA 3073 1025 TANN 5.38 395 PAP 617 Endometrium Sample esc Pap cr 17 7577539 SBS GA TP53 742 248 RWNN 1.32 152 PAP 617 Endometrium Sample esc Pap cr 17 7578271 SBS T C TP53 578 193 HRNN 0.24 23 PAP 619 Endometrium Sample Pap cr 3 178952018 SBS AG PIK3CA 3073 1025 TANN 6.41 152 PAP 621 Endometrium Sample sample Pap cr 5 67591120 Indel ACCAGACCTTATC (SEQ ID N: 777) PIK3R1 1713 571 I NULL NN 1.66 43 PAP 625 Endometrium Sample esc Pap cr 10 89711983 SBS GT PTEN 601 201 E * NN 1.97 365 PAP 632 Endometrium Sample esc Pap cr 17 7577094 SBS GA TP53 844 282 RWNN 2.14 400 PAP 632 Sample Endometrium esc Pap cr 3 178952018 SBS AT PIK3CA 3073 1025 TSNN 2.26 214 PAP 633 Endometrium Sample esc Pap cr 5 67589617 Indel TCG PIK3R1 1380 460 S NULL NN 1.17 116 PAP 637 Endometrium Sample esc Tao cr 17 7577559 SBS GT TP53 722 241 SYNN 3.47 76 PAP 638 Endometrium Sample esc Pap cr 17 7577556 SBS CA TP53 725 242 CFNN 3.67 43 PAP 638 Endometrium Sample esc Tao cr 17 7577556 SBS CA TP53 725 242 CFNN 30.79 315 PAP 646 Endometrium Sample esc Pap cr 10 89692900 SBS GT PTEN 384 128 KNNN 1.02 37 PAP 648 Endometrium Sample esc Pap cr 17 7577538 SBS CT TP53 743 248 RQNN 24.64 924 PAP 650 Endometrium Sample esc cr 5 112175957 Indel A APC 4666 1556 T NULL NN 9.68 1188 PAP 654 Endometrium Sample esc Pap cr 17 7577548 SBS CT TP53 733 245 GSNN 2.41 469 PAP 654 Endometrium Sample esc Pap cr 4 153247373 SBS CT FBXW7 1429 477 GSNN 2.33 93 PAP 654 Endometrium Sample esc Pap cr 5 112174424 SBS CT APC 3133 1045 Q * NN 1.79 210 PAP 654 Endometrium Sample esc Pap cr 5 112175945 SBS APC GT 4654 1552 E * NN 2.05 123 PAP 663 Endometrium Sample esc cr cr 17878201201 SBS AG PIK3CA 3073 1025 TANN 1.13 26 PAP 663 Endometrium Sample esc Pap cr 5 112176020 SBS GT APC 4729 1577 E * NN 6.78 94 PAP 663 Endometrium Sample esc Pap cr 5 67588951 SBS CT PIK3R1 1042 348 R * NN 12.80 219 PAP 667 Endometrium Sample esc Pap cr 10 89690819 Indel T PTEN 226 76 Y NULO NN 49.33 962 PAP 669 Endometrium Sample esc Pap cr 17 7577556 SBS CG TP53 725 242 CSNN 0.37 54 PAP 671 Endometrium Sample esc Pap cr 10 89717650 SBS TG PTEN 675 225 Y * NN 0.24 9 PAP 679 Endometrium Sample esc Pap cr 17 7577089 Indel GCGCCGGT TP53 849 283 R NULL NN 1.52 36 PAP 681 Endometrium Sample esc Pap cr 5 67589590 Indel ATA PIK3R1 1353 451 E NULL NN 2.40 150 PAP 684 Endometrium Sample esc Pap cr 5 67589607 SBS AC PIK3R1 1370 457 QPNN 0.18 7 PAP 688 Endometrium Sample esc Pap cr 10 89720734 Indel AT PTEN 885 295 L NULL NN 1.95 174 PAP 688 Endometrium Sample esc Tao cr 10 89720734 Indel AT PTEN 885 295 L NULL NN 10.47 714 PAP 689 Endometrium Sample esc cr 17 7577538 SBS CT TP53 743 248 RQNN 10.90 326 PAP 689 Endometrium Sample esc Tao cr 17 7577538 SBS CT TP53 743 248 RQNN 61.68 3016 PAP 691 Endometrium Sample esc Pap cr 3 178952019 SBS CA PIK3CA 3074 1025 TNNN 5.62 337 PAP 691 Endometrium Sample esc Cr cr 17878201201 SBS CA PIK3CA 3074 1025 TNNN 21.16 1086 PAP 697 Endometrium Sample esc cr cr 5 112175840 SBS CT APC 4549 1517 Q * NN 8.72 49 PAP 699 Endometrium Sample esc Pap cr 10 89690819 Indel TAT PTEN 226 76 Y NULO N N 41.34 592 PAP 699 Endometrium Sample esc Pap cr 5 112175957 Indel A APC 4666 1556 T NULL N N 28.44 1521

PAP 705 Ovarian Sample esc Pap cr 17 7577538 SBS CT TP53 743 248 RQNN 0.83 180 PAP 711 Ovarian Sample esc Pap cr 5 67589604 Indel AACTCAGTT PIK3R1 1367 456 F NULL NN 0.46 19 PAP 719 Endometrium Sample esc Pap cr 10 89720716 Indel A PTEN 867 289 K NULL NN 0.58 35 PAP 726 Endometrium Sample Pap cr 5 67589628 Indel ATA PIK3R1 1391 464 D NULL NN 2.32 358 PAP 726 Endometrium Sample esc Pap cr 5 67589629 Indel TAGATTATATGA (SEQ ID N: 752) PIK3R1 1392 464 D NULL NN 0.11 17 PAP 730 Endometrium Sample esc cr 10 89690819 Indel TAT PTEN 226 76 Y NULL NN 1.41 29 PAP 730 Endometrium Sample esc Pap cr 10 89720749 Indel C PTEN 900 300 I NULL NN 11, 21 1081 PAP 730 Endometrium Sample esc Pap cr 5 112174742 Indel GAA APC 3451 1151 E NULO NN 10.97 2039 PAP 730 Endometrium Sample esc Pap cr 5 67589633 Indel ATGATAGAT PIK3R1 1396 466 L NULL NN 20.63 3314 PAP 744 Endometrium Sample esc Pap cr 17 7572968 Indel T TP53 1141 381 K NULL NN 1.77 347 PAP 754 Endometrium Sample esc Pap cr 1 7 7577579 SBS GT TP53 702 234 Y * NN 0.83 72 PAP 768 Endometrium Sample esc cr cr 17 7577094 SBS GA TP53 844 282 RWNN 1.07 92 PAP 775 Endometrium Sample esc cr cr 5 67589590 Indel ATATAA PIK3R1 1353 451 E NULO NN 3.00 339 PAP 790 Endometrium Sample esc cr cr 17 7577534 SBS CA TP53 747 249 RSNN 9.16 272 PAP 804 Endometrium Sample esc cr cr 17 7579366 SBS GT TP53 321 107 Y * NN 0.68 38 PAP 804 Endometrium Sample esc Pap cr 5 112173893 SBS GT APC 2602 868 E * NN 11.11 1014 PAP 813 Endometrium Sample esc Pap cr 10 89717650 SBS TG PTEN 675 225 Y * NN 0.82 49 PAP 813 Endometrium Sample esc Pap cr 5 67591116 SBS TC PIK3R1 1709 570 LPNN 1.31 99 PAP 829 Endometrium Sample esc Pap cr 5 67589630 Indel AGAT PIK3R1 1393 465 R NULL NN 0.17 12 PAP 831 Endometrium Sample esc Pap cr 17 7577538 SBS CT TP53 743 248 RQNN 36.99 2189 PAP 831 Endometrium Sample esc Tao cr 17 7577538 SBS CT TP53 743 248 RQNN 21.27 1191 PAP 835 Endometrium Sample esc Pap cr 17 7577539 SBS GA TP53 742 248 RWNN 73.09 3313 PAP 835 Endometrium Sample esc Tao cr 17 7577539 SBS GA TP53 742 248 RWNN 72.63 2563 PAP 836 Endometrium Sample esc Tao cr 17 7577547 SBS CT TP53 734 245 GDNN 4.20 328 PAP 841 Endometrium Sample esc Pap cr 17 7579358 SBS CA TP53 329 110 RLNN 10.00 159 PAP 841 Endometrium Sample esc Tao cr 17 7579358 SBS CA TP53 329 110 RLNN 35.42 8229 PAP 850 Endometrium Sample esc Pap cr 17 7579469 SBS AG TP53 218 73 VANN 46 , 51 1080 PAP 850 Endometrium Sample esc Tao cr 17 7579469 SBS AG TP53 218 73 VANN 31.13 2131 PAP 851 Endometrium Sample esc Tao cr 17 7577538 SBS CT TP53 743 248 RQNN 35.62 1900 PAP 860 Endometrium Sample esc Pap cr 17 7577538 SBS CT TP53 743 248 RQNN 1.91 310 PAP 879 Endometrium Sample esc Pap cr 5 67589607 SBS AC PIK3R1 1370 457 QPNN 0.12 13 PAP 884 Endometrium Sample esc Pap cr 17 7577094 SBS GA TP53 844 282 RWNN 5.13 155 PAP 884 Endometrium Sample esc Tao cr 17 7577094 SBS GA TP53 844 282 RWNN 22,28 172 7 PAP 884 Endometrium Sample esc Tao cr 17 7577548 SBS CT TP53 733 245 GSNN 5.91 357 PAP 901 Endometrium Sample esc Tao cr 17 7579550 Indel G TP53 137 46 S NULL NN 0.56 38 PAP 904 Endometrium Sample esc Pap cr 3 178952018 SBS AG PIK3CA 3073 1025 TANN 17.38 380 PAP 904 Endometrium Sample esc Tao cr 3 178952018 SBS AG PIK3CA 3073 1025 TANN 36.93 942 PAP 910 Endometrium Sample esc Pap cr 10 89692847 SBS TC PTEN 331 111 WRNN 1.74 98 PAP 910 Endometrium Sample esc Tao cr 10 89692847 SBS TC PTEN 331 111 WRNN 29.71 773 PAP 922 Endometrium Sample esc Pap cr 5 67589613 Indel AAA PIK3R1 1376 459 K NULL NN 2.16 64 PAP 922 Endometrium Sample esc Tao cr 5 67589613 Indel AAA PIK3R1 1376 459 K NULO NN 23.50 553 PAP 925 Ovarian Sample esc Tao cr 17 7577548 SBS CA TP53 733 245 GCNN 2.30 218 PAP 931 Endometrium Sample esc Pap cr 10 89711973 Indel G PTEN 591 197 K NULL NN 3.58 86 PAP 931 Endometrium Sample esc Tao cr 10 89711973 Indel G PTEN 591 197 K NULL NN 23,35 1939 PAP 993 Ovarian Sample esc Pap cr 17 7577556 SBS CT TP53 725 242 CYNN 1.33 108 PAP 993 Ovarian Sample esc cr cr 5 67588968 Indel G PIK3R1 1059 353 G NULL NN 0.55 51 PAP 993 Ovarian Sample esc cr Tao cr 17 7577556 SBS CT TP53 725 242 CYNN 2.82 423 PAP 995 Endometrium Sample esc Pap cr 17 7577099 SBS CG TP53 839 280 RTNN 14.92 1777 PAP 995 Endometrium Sample esc Tao cr 17 7577099 SBS CG TP53 839 280 RTNN 14.92 1340 PAP 998 Endometrium Sample esc Tao cr 17 7577552 SBS CG TP53 729 243 MINN 0.32 45 PAPA 1001 Endometrium Sample esc Tao cr 17 7577565 Indel GGT TP53 716 239 NULL NN NN 6.40 348 PAPA 1011 Endometrium Sample esc Tao cr 5 112173917 SBS CT APC 2626 876 R * NN 6.58 327 PAPA 1013 Endometrium Sample esc Pap cr 17 7579358 SBS CG TP53 329 110 RPNN 0.29 51 PAPA 1013 Endometrium Sample esc Tao cr 17 7579358 SBS CG TP53 329 110 RPNN 0.30 27 PAPA 1035 Endometrium Sample esc Pap cr 5 67589607 Indel GTTTCA PIK3R1 1370 457 Q NULL NN 7.14 285 PAPA 1043 Endometri o Sample esc Pap cr 17 7577090 SBS CT TP53 848 283 RHNN 44.60 2220 PAPA 1048 Endometrium Sample esc Pap cr 17 7577094 SBS GA TP53 844 282 RWNN 7.97 654 PAPA 1059 Endometrium Sample esc Pap cr 10 89692839 SBS TG PTEN 323 108 LRNN 10.59 656 PAPA 1069 Endometrium Sample esc Pap cr 3 178952018 SBS AG PIK3CA 3073 1025 TANN 2.16 169 PAPA 1081 Endometrium Sample esc Pap cr 17 7579418 SBS GA TP53 269 90 SFNN 0.83 131 PAPA 1081 Endometrium Sample esc Pap cr 5 67589618 SBS CT PIK3R1 1381 461 R * NN 21.10 1375 PAPA 1094 Ovarian esc sample Pap cr 5 67589590 Indel ATA PIK3R1 1353 451 AND NULL NN 0.17 9 PAPA 1113 Ovarian Sample esc pap cr 17 7577568 SBS CG TP53 713 238 CSNN 1.66 235 PAPA 1126 Endometrium Sample esc Pap cr 10 89692904 SBS CG PTEN 388 130 RGNN 0.21 15 PAPA 1126 Endometrium Sample esc Tao cr 10 89692904 SBS CG PTEN 388 130 RGNN 26.91 3214 PAPA 1126 Endometrium Sample esc Tao cr 3 41266113 SBS CT CTNNB1 110 37 SFNN 22.67 836 POPE 1128 Endometrium Sample esc Tao cr 10 89720803 Indel T PTEN 954 318 L NULL NN 39.07 586 PAPA 1128 Endometrium Sample esc Tao cr 1 115256536 SBS CT NRAS 175 59 ATNN 18.11 2107 PAPA 1128 Endometrium Sample esc Tao cr 3 178936082 SBS GC PIK3CA 1624 542 EQNN 17.18 376 PAPA 1128 Endometrium Sample esc Tao cr 3 41266104 SBS GA CTNNB1 101 34 GENN 6.12 193 PAPA 1129 Endometrium Sample esc Tao cr 17 7577556 SBS CT TP53 725 242 CYNN 14.89 2919 PAPA 1130 Endometrium Sample esc Pap cr 10 89624245 SBS GT PTEN 19 7 E * NN 0,05 12 PAPA 1130 Endometrium Sample esc Pap cr 10 89685281 SBS CA PTEN 176 59 S * NN 8.20 1881 PAPA 1130 Endometrium Sample esc Pap cr 10 89720870 SBS TG PTEN 1021 341 FVNN 10.66 114 PAPA 1130 Endometrium Sample esc cr cr 133253184 SBS GC POLE 857 286 PRNN 7.56 171 PAPA 1130 Endometrium Sample esc Pap cr 5 112175216 SBS GT APC 3925 1309 E * NN 8.54 677 PAPA 1130 Endometrium Sample esc Pap cr 5 67588951 SBS CT PIK3R1 1042 348 R * NN 7.54 318 PAPA 1130 Endometrics io Sample esc Pap cr 5 67589595 Indel ACA PIK3R1 1358 453 N NULL N N 0.12 9 PAPA 1130 Endometrium Sample esc Pap cr 5 67589629 Indel TAGATTATATGA (SEQ ID NO: 753) PIK3R1 1392 464 D NULL N N 0.97 76

PAPA 1130 Endometrium Sample esc Tao cr 10 89685281 SBS CA PTEN 176 59 S * NN 37.81 11182 PAPA 1130 Endometrium Sample esc Tao cr 10 89720870 SBS TG PTEN 1021 341 FVNN 46.86 812 PAPA 1130 Endometrium Sample sam Tao cr 12 133253184 SBS GC POLE 857 286 PRNN 40.16 1026 PAPA 1130 Endometrium Sample esc Tao cr 5 112175216 SBS GT APC 3925 1309 E * NN 38.89 5084 PAPA 1130 Endometrium Sample esc Tao cr 5 67588951 SBS CT PIK3R1 1042 348 R * NN 38.75 2023 PAPA 1131 Endometrium Sample esc Pap cr 10 123279677 SBS GC FGFR2 755 252 SWNN 0.82 100 PAPA 1131 Endometrium Sample esc Pap cr 10 89725042 SBS AG PTEN 0 0 NULL NULL 1 N 4.92 156 PAPA 1131 Endometrium Sample esc Pap cr 3 41266101 SBS CA CTNNB1 98 33 SYNN 1.89 78 PAP 1131 Endometrium Sample esc cr 5 67591106 SBS AG PIK3R1 1699 567 KENN 2.13 132 PAPA 1131 Sample Endometrium Esc Tao cr 10 123279677 SBS GC FGFR2 755 252 SWNN 11.53 1186A 1131 Endometrium Sample esc Tao cr 10 89725042 SBS AG PTEN 0 0 NULL NULL 1 N 64.10 2537 PAPA 1131 Endometrium Sample esc Tao cr 3 41266101 SBS CA CTNNB1 98 33 SYNN 30.45 1382 PAPA 1131 Endometrium Sample esc Tao cr 5 67591106 SBS AG PIK3R1 1699 567 KENN 30.40 2938 PAPA 1132 Endometrium Sample esc Pap cr 10 89624296 SBS GC PTEN 70 24 DHNN 0.54 86 PAPA 1132 Sample endoscope Pap cr 3 41266137 SBS CT CTNNB1 134 45 SFNN 0.23 5 PAPA 1132 Sample endometrium esc Pap cr 5 67591092 Indel GTATGAACAGCATTAAAC (SEQ ID N: 754) PIK3R1 1685 562 R NULL NN 0.19 11 PAPA 1132 Sample endometrium Tao cr 10 123279677 SBS GC FGFR2 755 252 SWNN 7.01 570 PAPA 1132 Sample endometrium Esc Tao cr 10 89624296 SBS GC PTEN 70 24 DHNN 42.81 10046 PAPA 1132 Sample endometrium Tao cr 17 7578406 SBS CT TP53 524 175 RHNN 3.21 201 PAPA 1132 Sample endometrium Tao cr 5 67591092 Indel GTATGAACAGCATTAAAC (SEQ ID NO: 755) PIK3R1 1685 562 R NULL NN 26.45 1141 PAPA 1133 Sample endometrium Pap cr 10 89712017 SBS GC PTEN 0 0 NULL NULL 1 N 0.41 31 POPE 1133 Endometrium Sample esc Pap cr 10 89717775 Indel A PTEN 800 267 K NULL NN 1.32 106 PAPA 1133 Endometrium Sample esc Pap cr 17 56435167 Indel C RNF43 1970 657 R NULL NN 1.32 68 PAPA 1133 Endometrium Sample esc cr cr 5 67591106 SBS AG PIK3R1 1699 567 KENN 0.60 39 PAPA 1133 Endometrium Sample esc Tao cr 10 89712017 SBS GC PTEN 0 0 NULL NULL 1 N 6.60 351 PAPA 1133 Endometrium Sample esc Tao cr 10 89717775 Indel A PTEN 800 267 K NUL NN 19 , 11 1244 PAPA 1133 Endometrium Sample esc Tao cr 17 56435167 Indel C RNF43 1970 657 R NULL NN 9.01 363 PAPA 1133 Endometrium Sample esc Tao cr 5 67591106 SBS AG PIK3R1 1699 567 KENN 11.58 696 PAPA 1134 Endometrium Sample esc Pap cr 12 25398284 SBS CT KRAS 35 12 GDNN 5.34 465 PAPA 1134 Endometrium Sample esc cr 17 56435167 Indel C RNF43 1970 657 R NULL NN 7.56 296 PAPA 1134 Endometrium Sample esc Pap cr 3 178936091 SBS GA PIK3CA 1633 545 EKNN 5, 24 101 PAPA 1134 Endometrium Sample esc Pap cr 3 41266125 SBS CT CTNNB1 122 41 T INN 0.93 29 PAPA 1134 Endometrium Sample esc Tao cr 12 25398284 SBS CT KRAS 35 12 GDNN 30.32 2699 PAPA 1134 Endometrium Sample esc Tao cr 17 56435167 Indel C RNF43 1970 657 R NULL NN 29.06 1162 PAPA 1134 Endometrium Sample esc Tao cr 3 178936091 SBS GA PIK3CA 1633 545 EKNN 32.26 590 PAPA 1134 Endometrium Sample esc Tao cr 9 21970966 SBS CT CDKN2A 392 131 RHNN 9.12 461 PAPA 1135 Endometrium Sample esc cr 17 7577120 SBS CT TP53 818 273 RHNN 4, 18 467 PAPA 1135 Sample Endometrium esc Pap cr 17 7577556 SBS CT TP53 725 242 CYNN 2.23 376 PAPA 1135 Sample Endometrium Tao cr 17 7577120 SBS CT TP53 818 273 RHNN 26.76 2342 PAPA 1135 Sample Endometrium Tao cr 17 7577556 SBS CT TP53 725 242 CYNN 16.04 1968 PAPA 1135 Endometrium Sample esc Tao cr 17 7577565 SBS TC TP53 716 239 NSNN 2.10 346 PAPA 1136 Ovarian Sample esc pap Pap 10 123279677 SBS GC FGFR2 755 252 SWNN 0.22 29 PAPA 1136 Ovarian Sample esc Pap cr 3 178952077 SBS TG PIK3CA 3132 1044 N KNN 0.18 15 PAPA 1136 Ovarian Sample esc Pap cr 5 67589613 Indel AAA PIK3R1 1376 459 K NULL NN 0.17 21 PAPA 1136 Ovarian Sample esc Pap cr 5 67591134 Indel C PIK3R1 1727 576 T NULL NN 0.45 31 PAPE 1137 Endometrium Sample esc Pap cr 10 89624245 SBS GT PTEN 19 7 E * NN 9.63 1679 PAPA 1137 Endometrium Sample esc Pap cr 10 89692905 SBS GA PTEN 389 130 RQNN 3.99 262 PAPA 1137 Sample end Esc esc Pap cr 12 133250289 SBS CA POLE 1231 411 VLNN 12.28 700 PAPA 1137 Endometrium Sample esc cr 4 153247367 SBS GA FBXW7 1435 479 R * NN 9.07 194 PAPA 1137 Endometrium Sample esc Pap cr 5 112176051 SBS CA APC 4760 1587 S * NN 11.06 346 PAPA 1137 Sample endometrium Tao cr 10 89624245 SBS GT PTEN 19 7 E * NN 33.26 8803 PAPA 1137 Sample endometrium Tao cr 10 89692905 SBS GA PTEN 389 130 RQNN 15.98 1226 PAPA 1137 Sample endometrium Tao cr 12 133250289 SBS CA POLE 1231 411 VLNN 26.72 1178 PAPA 1137 Endometrium Sample esc Tao cr 4 153247367 SBS GA FBXW7 143 5 479 R * NN 28.42 661 PAPA 1137 Endometrium Sample esc Tao cr 5 112176051 SBS CA APC 4760 1587 S * NN 26.86 896 PAPA 1140 Endometrium Sample esc Pap cr 10 123247516 SBS TC FGFR2 1975 659 KENN 10.89 823 PAPA 1140 Endometrium Sample esc Pap cr 10 123258034 SBS AT FGFR2 1647 549 NKNN 15.06 481 PAPA 1140 Endometrium Sample esc Pap cr 10 89624285 SBS GA PTEN 59 20 GENN 0.07 18 PAPA 1140 Endometrium Sample esc Pap cr 10 89717695 SBS CA PTEN 720 240 Y * NN 37.17 11022 PAPA 1140 Sample endoscope Pap cr 17 7577121 SBS GA TP53 817 273 RCNN 4.20 342 PAPA 1140 Sample endometrium esc Pap cr 17 7578500 SBS GA TP53 430 144 Q * NN 1.63 112 PAPA 1140 Endometrium Sample esc Tao cr 10 89717695 SBS CA PTEN 720 240 Y * NN 34.63 7142 PAPA 1140 Endometrium Sample esc Tao cr 17 7577121 SBS GA TP53 817 273 RCNN 2.27 220 PAPA 1140 Endometrium Sample tao cr 17 7577551 SBS CA TP53 730 244 GCNN 24.42 3007 PAPA 1141 Endometrium Sample esc Pap cr 17 7574007 Indel TTCC TP53 1020 340 M NULL NN 76.40 4650 PAPA 1141 Endometrium Sample esc Tao cr 17 7574007 Indel TTCC TP53 1020 340 M NULL NN 59.39 1952 PAPA 1142 Endometrium Sample esc Pap cr 3 178952085 SBS AG PIK3CA 3140 1047 HRNN 4.21 489 PAPA 1142 Endometrium Sample esc cr cr 41266098 SBS AG CTNNB1 95 32 DGNN 3.33 242 PAPA 1142 Endometrium Sample esc Tao cr 3 178952085 SBS AG PIK3CA 3140 1047 HRNN 10.39 927 PAPA 1142 Endometrium Sample esc Tao cr 3 41266098 SBS AG CTNB1 32 DGNN 7.84 308 PAPA 1143 Endometrium Sample esc cr cr 8989692904 SBS CG PTEN 388 130 RGNN 0.69 95 PAPA 1143 Endometrium Sample esc pap cr 10 89692905 SBS GT PTEN 389 130 RLNN 0.39 54 PAPA 1143 Endometrium Sample esc Pap Pap cr 5 67591067 Indel GCTGAGTATCGAGAAATTGAC (SEQ ID NO: 756) PIK3R1 1660 554 A NULL NN 0.24 25 PAPA 1143 Endometrium Sample esc Tao cr 10 89692904 SBS CG PTEN 388 130 RGNN 14,02 1038 PAPA 1143 Endometrium Sample 89 ta2 cr2 SBS GT PTEN 389 130 RLNN 7.14 529 PAPA 1143 End ometrium Sample esc Tao cr 12 25398284 SBS CT KRAS 35 12 GDNN 22.09 2200 PAPA 1143 Endometrium Sample esc Tao cr 3 178952074 SBS GT PIK3CA 3129 1043 MINN 19.17 1790 PAPA 1143 Endometrium Sample tao cr 5 67591067 Indel GCTGAGTATA AGG N: 757) PIK3R1 1660 554 A NULL NN 12.89 1004 PAPA 1145 Endometrium Sample esc Pap cr 17 7577565 SBS TC TP53 716 239 NSNN 2.39 299

PAPA 1145 Endometrium Sample esc Tao cr 12 133253184 SBS GC POLE 857 286 PRNN 25.69 552 PAPA 1145 Endometrium Sample esc Tao cr 17 7577064 SBS TG TP53 874 292 KQNN 0.55 46 PAPA 1145 Endometrium Sample tao cr 17 7577565 SBS TC TP53 716 239 NSNN 24.65 3384 PAPA 1145 Endometrium Sample esc Tao cr 17 7578212 SBS GA TP53 637 213 R * NN 22.14 636 PAPA 1145 Endometrium Sample esc Tao cr 19 52715982 SBS CT PPP2R1A 547 183 RWNN 27.09 1255 PAPA 1145 Endometrium Sample esc Tao cr 3 178952074 SBS GT PIK3CA 3129 1043 MINN 12.17 1045 PAPA 1146 Endometrium Sample esc Tao cr 10 89717615 SBS CT PTEN 640 214 Q * NN 11.22 226 PAPA 1146 Endometrium Sample tao cr 12 25398284 SBS CT KRAS 35 12 GDNN 14.60 1827 PAPA 1146 Endometrium Sample esc Tao cr 3 178952072 SBS AG PIK3CA 3127 1043 MVNN 8.67 878 PAPA 1147 Endometrium Sample esc Pap cr 10 123247516 SBS TC FGFR2 1975 659 KENN 0.42 27 PAPA 1147 Endometrium Sample esc Pap cr 10 89692905 SBS GA PTEN 389 130 RQNN 1.28 121 PAPA 1147 Endometrium Sample esc Pap cr 10 89692935 SBS TG PTEN 419 140 L * NN 1.06 101 PAPA 1147 Endometrium Sample esc Pap cr 12 133250289 SBS CA POLE 1231 411 VLNN 2.79 267 PAPA 1147 Endometrium Sample esc Pap cr 3 178952007 SBS AG PIK3CA 3062 1021 YCNN 3.74 50 PAPA 1147 Endometrium Sample esc Pap cr 3 41266097 SBS GA CTNNB1 94 32 DNNN 0.31 15 PAPA 1147 Endometrium Sample esc Pap cr 3 41266098 SBS AC CTNNB1 95 32 DANN 0.16 8 PAPA 1147 Endometrium Sample esc Pap cr 3 41266113 SBS CT CTNNB1 110 37 SFNN 0.49 24 PAPA 1147 Endometrium Sample esc Pap cr 4 153249385 SBS GA FBXW7 1393 465 RCNN 2.22 149 PAPA 1147 Endometrium Sample esc Tao cr 10 123247516 SBS TC FGFR2 1975 659 KENN 26.03 1780 PAPA 1147 Endometrium Sample esc Tao cr 10 89692905 SBS GA PTEN 389 130 RQNN 15.40 1332 PAPA 1147 Endometrium Sample esc Tao cr 10 89693007 Indel A PTEN 491 164 K NULL NN 16.48 609 PAPA 1147 Endometrium Sample esc Tao cr 12 133250289 SBS CA POLE 1231 411 VL NN 24.74 2194 PAPA 1147 Endometrium Sample esc Tao cr 3 178952007 SBS AG PIK3CA 3062 1021 YCNN 24.27 281 PAPA 1148 Endometrium Sample esc Pap cr 10 89692839 SBS TG PTEN 323 108 LRNN 62.86 4865 PAPA 1148 Endometrium Sample esc Pap cr 10 89692904 SBS CG PTEN 388 130 RGNN 29.35 3059 PAPA 1148 Endometrium Sample esc Pap cr 17 7577538 SBS CT TP53 743 248 RQNN 9.00 1538 PAPA 1148 Endometrium Sample esc Pap cr 3 41266124 SBS AG CTNNB1 121 41 TANN 40.95 1882 PAPA 1148 Endometrium Sample esc Tao cr 10 89692839 SBS TG PTEN 323 108 LRNN 75.36 5762 PAPA 1148 Endometrium Sample esc Tao cr 10 89692904 SBS CG PTEN 388 130 RGNN 33.00 3198 PAPA 1148 Endometrium Sample esc Tao cr 17 7577538 SBS CT TP53 743 248 RQNN 1.24 256 PAPA 1148 Endometrium Sample esc Tao cr 3 41266124 SBS AG CTNNB1 121 41 TANN 43.24 1806 PAPA 1149 Endometrium Sample esc Pap cr 10 123258034 SBS AT FGFR2 1647 549 NKNN 24.43 713 PAPA 1149 Endometrium Sample esc Pap cr 14 105246551 SBS CT AKT1 49 17 EKNN 44.41 2856 PAPA 1149 Endometrium Sample esc Tao cr 14 105246551 SBS CT AKT1 49 17 EKNN 5.30 324 PAPA 1150 Endometrium Sample esc Pap cr 17 7577120 SBS CT TP53 818 273 RHNN 3.89 294 PAPA 1150 Endometrium Sample esc Tao cr 17 7577120 SBS CT TP53 818 273 RHNN 61.75 5936 PAPA 1150 Endometrium Sample esc Tao cr 17 7578541 SBS AC TP53 389 130 LRNN 0.11 8 PAPA 1150 Endometrium Sample esc Tao cr 19 52716323 SBS CT PPP2R1A 767 256 SFNN 52, 82 7492 PAPA 1150 Endometrium Sample esc Tao cr 5 67590490 SBS GT PIK3R1 1552 518 E * NN 8.96 1348 PAPA 1152 Endometrium Sample esc Pap cr 5 112175957 Indel A APC 4666 1556 T NULL NN 0.36 59 PAPA 1152 Endometrium Sample tao cr 5 112175957 Indel A APC 4666 1556 T NULL NN 37.41 709 PAPA 1154 Endometrium Sample esc Tao cr 5 67589588 SBS GT PIK3R1 1351 451 E * NN 7.12 242 PAPA 1155 Endometrium Sample sc cr 17 7577094 SBS GC TP53 844 282 RGNN 8.04 431 PAPA 1155 Endometrium Sample esc Tao cr 17 7577094 SBS GC TP 53 844 282 RGNN 65,08 2708 PAPA 1157 Endometrium Sample esc Pap cr 17 7577538 SBS CT TP53 743 248 RQNN 38,55 3299 PAPA 1157 Endometrium Sample esc Tao cr 17 7577538 SBS CT TP53 743 248 RQNN 47,96 4353 PAPA 1159 Ovarian Sample esc Tao cr 17 7577539 SBS GA TP53 742 248 RWNN 0.80 92 PAPA 1160 Endometrium Sample esc Pap cr 10 89692905 SBS GA PTEN 389 130 RQNN 1.36 167 PAPA 1160 Endometrium Sample esc Pap cr 10 89711899 SBS CT PTEN 517 173 RCNN 2 , 02 96 PAPA 1160 Endometrium Sample esc Pap cr 12 133250289 SBS CA POLE 1231 411 VLNN 1.78 225 PAPA 1160 Endometrium Sample esc Pap cr 4 153247366 SBS CT FBXW7 1436 479 RQNN 1.65 60 PAPA 1160 Endometrium Sample esc cr cr 6 67591106 SBS AG PIK3R1 1699 567 KENN 2.02 195 PAPA 1161 Endometrium Sample Pap cr 10 89685293 Indel A PTEN 188 63 N NULL NN 0.65 241 PAPA 1161 Endometrium Sample esc Pap cr 5 67591115 Indel CTT PIK3R1 1708 570 L NULL NN 0, 39 33 PAPA 1161 Endometrium Sample esc Tao cr 10 89685293 Indel A PTEN 188 63 N NULL NN 3.08 889 PAPA 1162 Endometrium Sample esc cr 10 89685315 Indel GT PTEN 0 0 NULL NULL 1 N 1.71 633 PAPA 1162 Endometrium Sample esc Pap cr 10 89720804 Indel ACTT PTEN 955 319 T NULL NN 1 , 62 51 PAPA 1162 Endometrium Sample esc Tao cr 10 89685315 Indel GT PTEN 0 0 NULL NULL 1 N 32,87 9502 PAPA 1162 Endometrium Sample esc Tao cr 10 89720804 Indel ACTT PTEN 955 319 T NULL NN 33,25 656 PAPA 1162 Endometrium Sample esc Tao cr 12 25398284 SBS CT KRAS 35 12 GDNN 31.02 3984 POPE 1164 Endometrium Sample esc Tao cr 10 89711926 Indel TTAAAGAATCATCTGG (SEQ ID NO: 758) PTEN 544 182 L NULL NN 0.68 191 PAPA 1172 Ovarian Sample esc Pap cr 17 7577539 SBS GC TP53 742 248 RGNN 0.12 9 PAPA 1189 Ovarian Sample esc Tao cr 17 7577097 SBS CG TP53 841 281 DHNN 0.91 75 PAPA 1193 Endometrium Sample esc Pap cr 10 89692844 SBS CT PTEN 328 110 Q * NN 7, 67 561 PAPA 1194 Endometrium Sample esc Pap cr 10 89717650 SBS TG PTEN 675 225 Y * NN 0.15 54 PAPA 1202 Endometrium Amo stra esc Pap cr 17 7579414 SBS CT TP53 273 91 W * NN 0.58 89 PAPA 1209 Endometrium Sample esc cr cr 10 89717732 Indel AT PTEN 757 253 I NULL NN 6.42 2265 PAPA 1213 Ovarian Sample esc Pap cr 17 7577090 SBS CG TP53 848 283 RPNN 0.39 27 PAPA 1213 Ovarian Sample esc Pap cr 17 7579368 Indel A TP53 319 107 Y NULL NN 0.39 58 PAPA 1217 Ovarian Sample esc cr cr 17 7577583 Indel TGTAGT TP53 698 233 H NULL NN 0.13 17 PAPA 1223 Endometrium Sample esc Pap cr 5 67589593 Indel TAA PIK3R1 1356 452 Y NULL NN 0.34 9 PAPA 1239 Endometrium Sample esc Pap cr 17 7577548 SBS CT TP53 733 245 GSNN 4.63 461 PAPA 1242 Endometrium Sample esc cr 17 7577550 SBS CT TP53 731 244 GDNN 16.98 1743 PAPA 1256 Endometrium Sample esc Pap cr 5 67589613 Indel AAA PIK3R1 1376 459 K NULL NN 0.96 21 PAPA 1263 Ovarian Esc sample Tao cr 19 52715971 SBS CG PPP2R1A 536 179 PRNN 0.61 230 PAPA 1292 Ovarian Sample esc Pap cr 5 67589559 Indel A PIK3R1 1322 441 NULL NN NN 0.53 15 POPE 1297 Ovarian Sample esc Pap cr 17 7577556 SBS CG TP53 725 242 CSNN 0.35 34 PAPA 1308 Endometrium Sample esc Pap cr 17 7577539 SBS GA TP53 742 248 RWNN 29.43 3588 PAPA 1322 Ovarian Sample esc Pap cr 17 7578271 SBS TC TP53 578 193 HRNN 0.46 28

PAPA 1705 Ovarian Sample esc Pap cr 5 67589596 Indel C PIK3R1 1359 453 N NULL NN 0.46 34 PAPA 1736 Ovarian Sample esc Pap cr 17 7579368 SBS AT TP53 319 107 YNNN 0.10 35 PAPA 1821 Ovarian Sample esc Tao cr 17 7577534 SBS CA TP53 747 249 RSNN 8.62 2791 PAPA 1834 Ovarian Sample esc Tao cr 17 7577097 SBS CT TP53 841 281 DNNN 0.36 61 PAPA 1850 Ovarian Sample esc Tao cr 17 7579424 Indel G TP53 263 88 A NULL NN 14.74 3276 PAPA 1859 Endometrium Sample esc Tao cr 17 7577538 SBS CT TP53 743 248 RQNN 26.26 8779 PAPA 1865 Endometrium Sample esc Tao cr 17 7577538 SBS CT TP53 743 248 RQNN 4.23 1224 PAPA 1865 Endometrium Sample esc Tao cr 5 67589590 Indel ATA PIK33R 451 E NULL NN 21.36 642 PAP 010 Endometrium Sample esc cr cr 153247367 SBS GA FBXW7 1435 479 R * NN 10.43 39 PAP 010 Endometrium Sample esc Pap cr 10 123279677 SBS GC FGFR2 755 252 SWNN 10.08 67 PAP 010 Endometrium Sample esc Pap cr 10 89653791 Indel C PTEN 89 30 P NULL NN 13.23 254 PAP 010 Endometrium Sample esc Pap cr 10 89692904 SBS CG PTEN 388 130 RGNN 12.37 123 PAP 010 Endometrium Sample esc Pap cr 17 7579713 Indel T TP53 83 28 E NULL NN 1.56 28 PAP 033 Endometrium Sample esc esc cr 10 89717656 Indel C PTEN 681 227 S NULL NN 1.22 14 PAP 037 Endometrium Sample esc cr cr 5 112174631 SBS CT APC 3340 1114 R * NN 13.88 232 PAP 037 Endometrium Sample esc Pap cr 5 112175897 SBS GT APC 4606 1536 E * NN 10.02 57 PAP 037 Endometrium Sample esc Pap cr 12 133253151 SBS GA POLE 890 297 SFNN 13.65 74 PAP 037 Endometrium Sample esc Pap cr 10 89711913 SBS TG PTEN 531 177 Y * NN 16,94 173 PAP 042 Ovarian Sample esc Pap cr 17 7579350 Indel A TP53 337 113 F NULL NN 3.44 26 PAP 562 Endometrium Sample esc Pap cr 10 89653800 SBS TG PTEN 98 33 ISNN 4.71 146 PAP 570 Endometrium Sample esc Pap cr 17 7574021 SBS CA TP53 1006 336 E * NN 30, 83 967 PAP 583 Endometrium Sample esc Pap cr 3 41266137 SBS CG CTNNB1 134 45 SCNN 1.40 16 PAP 583 Endometrium Sample esc Pap c r 3 178916876 SBS GA PIK3CA 263 88 RQNN 8.66 35 PAP 583 Endometrium Sample esc Pap cr 5 67591135 Indel GAGAGACCA PIK3R1 1728 576 T NULL NN 9.02 144 PAP 583 Endometrium Sample esc cr cr 6 67591144 Indel CAA PIK3R1 1737 579 NN 0.63 10 PAP 583 Endometrium Sample esc Pap cr 10 89692919 SBS AG PTEN 403 135 IVNN 3.82 95 PAP 583 Endometrium Sample esc Pap cr 10 89720817 Indel A PTEN 968 323 NULL NN 12.10 107 PAP 583 Endometrium Sample esc Pap cr 17 7577530 SBS TA TP53 751 251 IFNN 4.07 33 PAP 584 Endometrium Sample esc Cr 3 41266104 SBS GA CTNNB1 101 34 GENN 23.03 401 PAP 584 Endometrium Sample esc Pap cr 10 89717703 Indel T PTEN 728 243 F NULO NN 11.06 182 PAP 584 Endometrium Sample esc cr cr 17 7578397 Indel TGGGGGCAGC (SEQ ID NO: 759) TP53 533 178 H NULL NN 1.97 23 PAP 588 Endometrium Sample esc cr cr 5 67591085 SBS GC PIK3R1 1678 560 DHNN 1.76 53 PAP 588 Endometrium Sample esc Pap cr 10 89717725 Indel TG PTEN 750 250 C NULL NN 4.68 39 PAP 590 Endom étrium Sample esc Pap cr 9 21974819 Indel GGCTCCATGCTGCTCCCCGCCGCC (SEQ ID NO: 760) CDKN2A 8 3 P NULL NN 54.66 938 PAP 594 Endometrium Sample esc Pap cr 14 105246551 SBS CT AKT1 49 17 EKNN 18.22 356 PAP 594 Endometrium Pap cr 3 41266104 SBS GT CTNNB1 101 34 GVNN 7.50 71 PAP 595 Endometrium Sample esc Pap cr 10 89692905 SBS GT PTEN 389 130 RLNN 3.90 24 PAP 601 Endometrium Sample esc Pap cr 3 178952090 SBS GC PIK3CA 3145 1049 GRNN 60, 22 2346 PAP 619 Endometrium Sample esc Pap cr 5 112175639 SBS CT APC 4348 1450 R * NN 3.73 82 PAP 619 Endometrium Sample esc Pap cr 3 178916876 SBS GA PIK3CA 263 88 RQNN 3.86 8 PAP 619 Endometrium Sample esc Pap cr 12 133253151 SBS GA POLE 890 297 SFNN 7.22 62 PAP 637 Endometrium Sample esc Tao cr 17 7579327 SBS CG TP53 360 120 KNNN 0.34 4 PAP 650 Endometrium Sample esc Pap cr 5 67591147 Indel CTTGATGTA PIK3R1 1740 580 Y NULO NN 15.62 157 PAP 650 Endometrium Sample esc Pap cr 10 89720804 Indel ACTT PTEN 955 319 T NUL ONN 22.43 155 PAP 001 Endometrium Sample esc Pap cr 12 25398284 SBS CG KRAS 35 12 GANN 13.64 1255 PAP 001 Endometrium Sample esc Pap cr 3 178952085 SBS AG PIK3CA 3140 1047 HRNN 5.43 514 PAP 001 Endometrium Sample esc Pap cr 10 89692904 SBS CG PTEN 388 130 RGNN 1.75 79 PAP 084 Endometrium Sample esc cr 5 112175682 Indel AGAG APC 4391 1464 AND NULL NN 0.07 25 PAP 084 Endometrium Sample esc Pap cr 5 112175752 Indel TTTATTAC APC 4461 1487 T NULL NN 0.12 10 PAP 084 Endometrium Sample esc cr cr 10 89720702 SBS GT PTEN 853 285 E * NN 8.52 1570 PAP 084 Endometrium Sample esc Pap cr 17 7576915 Indel CTGGTGTG TP53 931 311 NULL NN 34.37 5812 PAP 524 Ovarian Sample esc Pap cr 3 178952085 SBS AT PIK3CA 3140 1047 HLNN 0.32 12 PAP 530 Ovarian Sample esc Pap cr 12 25398282 SBS CT KRAS 37 13 GSNN 0.03 2 PAP 541 Ovarian Sample esc Pap cr 17 7578486 Indel A TP53 444 148 D NULL NN 0.22 15 PAP 549 Endometrium Sample esc Pap cr 10 89624268 Indel GG PTEN 42 14 R NULL N N 0.40 52 PAP 549 Endometrium Sample esc Pap cr 17 7579589 Indel G TP53 98 33 S NULL NN 0.25 41 PAP 554 Endometrium Sample esc Pap cr 3 178952090 SBS GC PIK3CA 3145 1049 GRNN 1.60 114 PAP 565 Endometrium Sample esc Pap cr 10 89692904 SBS CG PTEN 388 130 RGNN 0.47 17 PAP 565 Endometrium Sample esc Pap cr 10 89720805 Indel CTTT PTEN 956 319 T NULL NN 1.92 27 PAP 572 Endometrium Sample esc Pap cr 12 25398284 SBS CA KRAS 35 12 GVNN 13.41 1857 PAP 572 Endometrium Sample esc Pap cr 3 178952084 SBS CT PIK3CA 3139 1047 HYNN 12.24 1463 PAP 572 Endometrium Sample esc Pap cr 17 7578542 SBS GA TP53 388 130 LFNN 11.51 2352 PAP 574 Endometrium Sample esc Pap cr 3 41266113 SBS CA CTNNB1 110 37 SYNN 1.95 74 PAP 574 Endometrium Sample esc cr cr 10 89653786 SBS TG PTEN 84 28 IMNN 1.69 177 PAP 574 Endometrium Sample esc cr cr 17 7578212 SBS GA TP53 637 213 R * NN 2.40 130 PAP 582 Endometrium Sample esc Pap cr 5 67588951 SBS CT PIK3R1 1042 348 R * NN 3.29 14 PAP 582 End ometrium Sample esc Pap cr 12 133253184 SBS GC POLE 857 286 PRNN 3.01 107 PAP 582 Endometrium Sample esc cr cr 10 89720744 SBS GT PTEN 895 299 E * NN 1.40 90 PAP 582 Endometrium Sample esc Pap cr 17 7578212 SBS GA TP53 637 213 R * NN 3.51 162 PAP 585 Endometrium Sample esc Pap cr 3 41266097 SBS GA CTNNB1 94 32 DNNN 4.86 328 PAP 585 Endometrium Sample esc Pap cr 3 41266124 SBS AG CTNNB1 121 41 TANN 2.07 140 PAP 585 Endometrium Sample esc Pap cr 10 123279674 SBS GC FGFR2 758 253 PRNN 31.98 1799 PAP 585 Endometrium Sample esc Pap cr 5 67591135 Indel G PIK3R1 1728 576 T NULL NN 4.15 159 PAP 585 Sample end Pap esc cr 10 89653789 SBS TG PTEN 87 29 Y * NN 24.16 1809 PAP 585 Sample end esc Pap cr 10 89685293 Indel A PTEN 188 63 NULL NN NN 12.71 99 PAP 587 Endometrium Sample esc Pap cr 3 41266100 SBS TC CTNNB1 97 33 SPNN 1.50 78 PAP 587 Endometrium Sample esc Pap cr 3 41266125 SBS CT CTNNB1 122 41 TINN 3.32 173 PAP 587 Endometrium Sample esc Pap c r 4 153247373 SBS C T FBXW7 1429 477 G S N N 3.70 178 PAP 587 Endometrium Sample esc Pap cr 5 67591106 SBS A G PIK3R1 1699 567 K E N N 9.96 634

PAP 587 Endometrium Sample esc Pap cr 10 89720828 SBS AT PTEN 979 327 K * NN 13.71 205 PAP 646 Endometrium Sample esc Pap cr 9 21970976 SBS GA CDKN2A 382 128 RWNN 1.25 93 PAP 646 Endometrium Sample esc Pap cr 7 55241726 SBS CT EGFR 2174 725 TMNN 22.36 1878 PAP 646 Endometrium Sample esc Pap cr 4 153249384 SBS CT FBXW7 1394 465 RHNN 19.89 1191 PAP 646 Endometrium Sample esc Pap cr 3 178952074 SBS GA PIK3CA 3129 1043 MINN 19.51 1334 PAP 646 Endometrium Sample esc Pap cr 19 52715982 SBS CT PPP2R1A 547 183 RWNN 41.12 183 PAP 646 Endometrium Sample esc Pap cr 10 89624266 SBS AG PTEN 40 14 RGNN 0.34 51 PAP 646 Endometrium Sample esc Pap cr 10 89717609 SBS GT PTEN 0 0 NULL NULL 1 N 4.14 172 PAP 646 Endometrium Sample esc Pap cr 10 89717712 SBS CT PTEN 737 246 PLNN 6.12 663 PAP 646 Endometrium Sample esc Pap cr 17 7577121 SBS GA TP53 817 273 RCNN 5.45 518 PAP 646 Endometrium Sample esc Pap cr 17 7577022 SBS GA TP53 916 306 R * NN 20,20,03 811 PAP 646 Endome trio Sample esc Pap cr 17 7574018 SBS GA TP53 1009 337 RCNN 3.40 301 PAP 647 Endometrium Sample esc cr cr 10 123258034 SBS AC FGFR2 1647 549 NKNN 8.75 470 PAP 647 Endometrium Sample esc cr Pap 10 89692904 SBS CT PTEN 388 130 R * NN 1.90 80 PAP 648 Endometrium Sample esc Pap cr 3 41266113 SBS CA CTNNB1 110 37 SYNN 33.87 961 PAP 648 Endometrium Sample esc Pap cr 10 89692904 SBS CT PTEN 388 130 R * NN 23.66 767 PAP 649 Endometrium Sample esc Pap cr 19 52715971 SBS CG PPP2R1A 536 179 PRNN 9.31 57 PAP 649 Endometrium Sample esc Pap cr 17 7578383 Indel AGCAGCGCTCATGGTGGG (SEQ ID NO: 761) TP53 547 183 S NULL NN 16.54 552 PAP 176 Endometrium Sample Pap cr 12 25398284 SBS CT KRAS 35 12 GDNN 4.07 76 PAP 176 Endometrium Sample esc cr 3 178936094 SBS CA PIK3CA 1636 546 QKNN 37.83 457 PAP 176 Endometrium Sample esc Pap cr 10 89720716 Indel A PTEN 867 289 K NULL NN 0 , 46 64 PAP 177 Endometrium Sample esc Pap cr 3 41266113 SBS CT CTNNB1 110 37 SFNN 1.14 31 PA P 177 Endometrium Sample esc Pap cr 10 89720804 Indel ACTT PTEN 955 319 T NULL NN 1.20 47 PAP 200 Endometrium Sample esc Pap cr 3 178916854 SBS GA PIK3CA 241 81 EKNN 4.71 496 PAP 200 Endometrium Sample esc Pap cr 12 133250289 SBS CA POLE 1231 411 VLNN 4,50 202 PAP 200 Endometrium Sample esc Pap cr 10 89720876 SBS GA PTEN 0 0 NULL NULL 1 N 7.87 27 PAP 243 Endometrium Sample esc Pap cr 3 41266101 SBS CA CTNNB1 98 33 SYNN 22,50 602 PAP 243 Endometrium Sample esc Pap cr 3 41266124 SBS AG CTNNB1 121 41 TANN 13.50 361 PAP 243 Endometrium Sample esc cr cr 178916876 SBS GA PIK3CA 263 88 RQNN 10.25 1305 PAP 243 Endometrium Sample esc cr cr 10 89692905 SBS GA PTEN 389 130 RQNN 2.35 52 PAP 250 Ovarian Sample esc Pap cr 17 7577022 SBS GA TP53 916 306 R * NN 2.04 193 PAP 265 Endometrium Sample esc Pap cr 3 41266097 SBS GT CTNNB1 94 32 DYNN 1.52 64 PAP 267 Endometrium Sample esc Pap cr 3 41266112 SBS TC CTNNB1 109 37 SPNN 0.25 9 PAP 555 Endometrium Sample es c Pap cr 5 112175639 SBS CT APC 4348 1450 R * NN 25.99 563 PAP 555 Endometrium Sample esc Pap cr 5 67591146 Indel ACT PIK3R1 1739 580 Y NULL NN 31.09 415 PAP 555 Endometrium Sample esc Pap cr 17 7578263 SBS GA TP53 586 196 R * NN 27.89 1063 PAP 556 Endometrium Sample esc cr cr 10 89720749 Indel C PTEN 900 300 I NULL NN 0.13 36 PAP 618 Endometrium Sample esc Pap cr 12 25398284 SBS CT KRAS 35 12 GDNN 13.23 592 PAP 618 Endometrium Sample esc Pap cr 3 178952085 SBS AG PIK3CA 3140 1047 HRNN 8.25 170 PAP 003 Endometrium Sample esc Pap cr 5 112175639 SBS CT APC 4348 1450 R * NN 11.56 289 PAP 003 Endometrium Sample esc Pap cr 3 178916876 SBS GA PIK3CA 263 88 RQNN 7.56 1546 PAP 003 Endometrium Sample esc Pap cr 5 67588951 SBS CT PIK3R1 1042 348 R * NN 5.57 17 PAP 003 Endometrium Sample esc Pap cr 12 133250289 SBS CA POLE 1231 411 VLNN 9.79 525 PAP 003 Endometrium Sample esc Pap cr 10 89624245 SBS GT PTEN 19 7 E * NN 7.91 211 PAP 003 Endometrium Sample esc Pap cr 10 89711899 SBS CT PTEN 517 173 RCNN 2.72 69 PAP 169 Ovarian Sample esc cr cr 105246551 SBS CT AKT1 49 17 EKNN 31.02 2016 PAP 169 Ovarian Sample esc cr cr 10 123258034 SBS AC FGFR2 1647 549 NKNN 17.29 120 PAP 169 Ovarian Sample esc Pap cr 12 133253184 SBS GC POLE 857 286 PRNN 18.51 238 PAP 169 Ovarian Sample esc Pap cr 17 7578212 SBS GA TP53 637 213 R * NN 22.24 226 PAP 175 Endometrium Sample esc Pap cr 10 123258034 SBS AC FGFR2 1647 549 NKNN 0.42 12 PAP 175 Endometrium Sample esc Pap cr 12 25398284 SBS CT KRAS 35 12 GDNN 3.45 142 PAP 175 Endometrium Sample esc Pap cr 3 178952074 SBS GT PIK3CA 3129 1043 MINN 7.60 147 PAP 175 Endometrium Sample esc Pap cr 5 67591144 Indel A PIK3R1 1737 579 Q NULL NN 11.95 455 PAP 175 Endometrium Sample Pap cr 12 133253184 SBS GC POLE 857 286 PRNN 0.60 30 PAP 175 Endometrium Sample sample Pap cr 10 89692904 SBS CG PTEN 388 130 RGNN 3 , 11 104 PAP 175 Endometrium Sample esc Pap cr 10 89720741 SBS CT PTEN 89 2 298 Q * NN 4.04 522 PAP 184 Ovarian Sample esc Pap cr 3 41266113 SBS CT CTNNB1 110 37 SFNN 11.83 227 PAP 184 Ovarian Sample esc Pap cr 3 178952085 SBS AG PIK3CA 3140 1047 HRNN 12.27 208 PAP 184 Ovarian Sample esc Pap cr 10 89720716 Indel A PTEN 867 289 K NULL NN 14.97 1549 PAP 201 Endometrium Sample esc Pap cr 10 123279677 SBS GC FGFR2 755 252 SWNN 0.31 13 PAP 201 Endometrium Sample esc Pap cr 10 89711928 Indel A PTEN 546 182 L NULL NN 1.88 30 PAP 201 Endometrium Sample esc Pap cr 10 89711930 Indel A PTEN 548 183 K NULL NN 0.87 64 PAP 205 Endometrium Sample esc Pap cr 3 178916876 SBS GA PIK3CA 263 88 RQNN 1.29 142 PAP 235 Endometrium Sample esc Pap cr 1 115258747 SBS CT NRAS 35 12 GDNN 1.34 44 PAP 235 Endometrium Sample esc cr cr 1 115256528 SBS TG NRAS 183 61 QHNN 0.36 12 PAP 235 Endometrium Sample esc cr cr 1 115256530 SBS GT NRAS 181 61 QKNN 3.58 120 PAP 235 Endometrium Sample esc Pap cr 3 178936091 SBS GA PIK3CA 1633 545 EKNN 41.95 1001 PAP 235 Endometrium Sample esc Pap cr 17 7579312 SBS CT TP53 375 125 TTN Yes 25.57 1504 PAP 238 Endometrium Sample esc Pap cr 12 25398281 SBS CT KRAS 38 13 GDNN 6.31 155 PAP 238 Endometrium Sample esc cr cr 5 67591106 SBS AG PIK3R1 1699 567 KENN 0.53 16 PAP 245 Endometrium Sample esc Pap cr 12 25398284 SBS CT KRAS 35 12 GDNN 9.26 263 PAP 245 Endometrium Sample esc Pap cr 3 178952085 SBS AG PIK3CA 3140 1047 HRNN 8.06 173 PAP 245 Endometrium Sample esc Pap cr 10 89653829 SBS GT PTEN 127 43 E * NN 7.37 159 PAP 011 Endometrium Sample esc cr cr 12 25398284 SBS CG KRAS 35 12 GANN 3.53 93 PAP 011 Endometrium Sample esc Pap cr 5 67588976 Indel T PIK3R1 1067 356 L NULL NN 4.15 10 PAP 011 Endometrium Sample esc Pap cr 10 89692893 Indel C PTEN 377 126 A NULL NN 4.66 23 PAP 066 Endometrium Sample esc Pap cr 12 25398285 SBS CA KRAS 34 12 GCNN 5.21 149 PAP 225 Endometrium Sample esc Pap cr 3 178936091 SBS GA PIK3CA 1633 545 EKNN 1.17 22 PAP 225 Endometrium Sample a esc Pap cr 10 89720817 Indel A PTEN 968 323 NULL NULL N N 1.02 81

PAP 034 Endometrium Sample esc Pap cr 3 41266103 SBS GA CTNNB1 100 34 GRNN 20,20,13 2734 PAP 034 Endometrium Sample esc Pap cr 5 67591040 Indel TTGGAAGAAGA (SEQ ID N: 762) PIK3R1 1633 545 L NULL NN 0.50 35 PAP 034 Endometrium Sample esc Pap cr 5 67591110 Indel CAGACC PIK3R1 1703 568 P NULL NN 0.16 19 PAP 034 Endometrium Sample esc Pap cr 10 89692904 SBS CG PTEN 388 130 RGNN 4.83 441 PAP 039 Ovarian Sample esc Pap cr 17 7577519 Indel G TP53 762 254 I NULL NN 5.82 428 PAP 065 Endometrium Sample esc cr cr 105 105246551 SBS CT AKT1 49 17 EKNN 70.67 10077 PAP 067 Endometrium Sample esc Pap cr 12 133253184 SBS GC POLE 857 286 PRNN 2.49 253 PAP 067 Endometrium Sample esc Pap cr 19 52716329 SBS GA PPP2R1A 773 258 RHNN 5.28 865 PAP 067 Endometrium Sample esc Pap cr 10 89690847 SBS GA PTEN 0 0 NULL NULL 1 N 2.16 116 PAP 067 Endometrium Sample esc Pap cr 10 89624245 SBS GT PTEN 19 7 E * NN 0.14 12 PAP 067 Endometrium Sample esc Pap cr 17 7578212 SBS GA TP53 637 213 R * NN 2.42 212 PAP 069 Endometrium Sample esc Pap cr 12 133253184 SBS GC POLE 857 286 PRNN 16.50 754 PAP 069 Endometrium Sample esc Pap cr 10 89692890 SBS AC PTEN 374 125 KTNN 13.07 164 PAP 069 Endometrium Sample esc Pap Pap cr 10 89711899 SBS CT PTEN 517 173 RCNN 4.39 120 PAP 069 Endometrium Sample esc cr cr 898911910 SBS TG PTEN 528 176 Y * NN 0.66 18 PAP 069 Endometrium Sample esc Pap cr 10 89720716 Indel A PTEN 867 289 K NULL NN 0.33 31 PAP 069 Endometrium Sample esc Pap cr 10 89720870 SBS TG PTEN 1021 341 FVNN 23.84 123 PAP 069 Endometrium Sample esc Pap cr 17 7577022 SBS GA TP53 916 306 R * NN 14.95 1064 PAP 080 Endometrium Sample esc Pap cr 17 7578283 Indel G TP53 566 189 A NULL NN 78.59 11296 PAP 232 Endometrium Sample esc Pap cr 5 67592056 Indel GAATG PIK3R1 1872 624 W NULL NN 0.18 15 PAP 232 Endometrium Sample esc Pap cr 10 89720741 SBS CT PTEN 892 298 Q * NN 13.14 3286 PAP 234 Endometrium Sample esc Pap cr 12 25398281 SBS CT KRAS 38 13 GDNN 1.64 4 5 PAP 234 Endometrium Sample esc Pap cr 5 67589581 Indel ATTACA PIK3R1 1344 448 K NULL NN 1.06 35 PAP 234 Endometrium Sample esc Pap cr 5 67589582 Indel TTACAT PIK3R1 1345 449 L NULL NN 0.23 28 PAP 234 Endometrium Sample esc Pap cr 10 89717678 Indel G PTEN 703 235 E NULL NN 0.18 8 PAP 236 Endometrium Sample esc Pap cr 10 89653799 Indel ATT PTEN 97 33 I NULL NN 0.72 49 PAP 236 Endometrium Sample esc Pap cr 10 89692975 Indel T PTEN 459 153 D NULL NN 0.56 31 PAP 242 Endometrium Sample esc Pap cr 4 153249384 SBS CT FBXW7 1394 465 RHNN 5.29 269 PAP 242 Endometrium Sample esc Pap cr 10 89692904 SBS CG PTEN 388 130 RGNN 4.11 150 PAP 652 Endometrium Sample esc Pap Pap cr 5 112174093 Indel TTAC APC 2802 934 T NULL NN 0.70 32 PAP 652 Endometrium Sample esc Pap cr 5 112175639 SBS CT APC 4348 1450 R * NN 8.22 458 PAP 652 Endometrium Sample esc Pap cr 3 178952085 SBS AG PIK3CA 3140 1047 HRNN 8.28 502 PAP 652 Endometrium Sample esc Pap cr 10 89692904 SBS CG PTEN 388 130 RGNN 7.48 337 PAP 652 Endometrium Sample esc Pap cr 17 7578406 SBS CT TP53 524 175 RHNN 7.69 419 PAP 002 Ovarian Sample esc Pap cr 17 7578490 SBS AT TP53 440 147 VDNN 0.26 14 PAP 025 Endometrium Sample esc cr Pap 17 7579395 Indel GG TP53 292 98 P NULL NN 10.13 245 PAP 035 Endometrium Sample esc Pap cr 12 25398284 SBS CT KRAS 35 12 GDNN 4.18 21 PAP 035 Endometrium Sample esc Pap cr 3 178916854 SBS GA PIK3CA 241 81 EKNN 3.92 155 PAP 119 Endometrium Sample esc Pap cr 5 67589651 Indel GCACATCCCAGGCCC (SEQ ID NO: 778) PIK3R1 1414 472 R NULL NN 0.27 13 PAP 119 Endometrium Sample esc Pap cr 10 89692932 SBS TA PTEN 416 139 L * NN 0.37 9 PAP 121 Endometrium Sample esc Pap cr 12 25398284 SBS CA KRAS 35 12 GVNN 2.92 61 PAP 121 Endometrium Sample esc cr cr 10 89692904 SBS CG PTEN 388 130 RGNN 1.48 27 PAP 178 Endometrium Sample esc cr Pap 25 25398284 SBS CT KRAS 35 12 GDNN 4.60 97 PAP 178 Endometrium Sample esc Pap cr 3 178952052 SBS TC PIK3CA 3107 1036 LSNN 4.39 108 PA P 178 Endometrium Sample esc cr cr 10 89692905 SBS GC PTEN 389 130 RPNN 1.52 55 PAP 179 Endometrium Sample esc cr cr 5 67589568 Indel CTGTAGGGAAAAAATTACATGAAT (SEQ ID NO: 763) PIK3R1 1331 444 A NULL NN 3,57 51 PAP 179 Endometrium Sample esc Pap cr 17 7578265 SBS AG TP53 584 195 ITNN 6.16 227 PAP 180 Endometrium Sample esc cr cr 12 133253184 SBS GC POLE 857 286 PRNN 2.78 90 PAP 180 Endometrium Sample esc esc Pap cr 10 89711899 SBS CT PTEN 517 173 RCNN 1.84 31 PAP 199 Ovarian Sample esc Pap cr 17 7578265 SBS AG TP53 584 195 ITNN 5.68 473 PAP 202 Endometrium Sample esc Pap cr 3 178936083 SBS AC PIK3CA 1625 542 EANN 1.16 29 PAP 202 Endometrium Sample esc Pap cr 3 178952090 SBS GA PIK3CA 3145 1049 GSNN 1.16 56 PAP 202 Sample endoscope Pap cr 10 89720716 Indel A PTEN 867 289 K NULL NN 4.53 846 PAP 202 Sample endometrium Esc Pap cr 10 89720817 Indel A PTEN 968 323 N NULL NN 15 , 49 1405 PAP 202 Endometrium Sample esc Pap cr 17 7578553 SBS TC TP53 377 126 Y CNN 1.26 31 PAP 202 Endometrium Sample esc Pap cr 17 7577524 SBS TG TP53 757 253 TPNN 13.92 1083 PAP 206 Endometrium Sample esc Pap cr 10 123258034 SBS AT FGFR2 1647 549 NKNN 2.23 38 PAP 206 Endometrium Sample esc cr cr 5 67591130 Indel AAGACGAGA PIK3R1 1723 575 K NULL NN 0.26 7 PAP 209 Endometrium Sample Pap cr 9 21971099 SBS GA CDKN2A 259 87 RWNN 6.04 62 PAP 209 Endometrium Sample esc Pap cr 3 41266100 SBS TG CTNNB1 97 33 SANN 22, 72 556 PAP 209 Endometrium Sample esc Pap cr 3 41266104 SBS GA CTNNB1 101 34 GENN 0.82 20 PAP 209 Endometrium Sample esc Pap cr 5 67592091 Indel A PIK3R1 1907 636 NULL NN 0.62 14 PAP 209 Endometrium Sample esc Pap cr 10 89690805 SBS GA PTEN 212 71 CYNN 8.58 23 PAP 209 Endometrium Sample esc cr cr 89892905 SBS GA PTEN 389 130 RQNN 7.82 88 PAP 209 Endometrium Sample esc Pap cr 10 89692911 SBS GA PTEN 395 132 GDNN 6.48 73 PAP 218 Endometrium Sample esc Pap cr 3 178952085 SBS AG PIK3CA 3140 1047 HRNN 51.44 946 P AP 218 Endometrium Sample esc Pap cr 19 52715971 SBS CG PPP2R1A 536 179 PRNN 27.54 76 PAP 218 Endometrium Sample esc Pap cr 17 7578427 SBS TC TP53 503 168 HRNN 72.66 760 PAP 229 Endometrium Sample esc cr cr 3 41266125 SBS CT CTNNB1 122 41 TINN 2.54 148 PAP 229 Endometrium Sample esc Pap cr 4 153247289 SBS GA FBXW7 1513 505 RCNN 2.69 311 PAP 229 Endometrium Sample esc Pap cr 3 178916876 SBS GA PIK3CA 263 88 RQNN 3.10 622 PAP 240 Endometrium Sample esc Pap cr 19 52715971 SBS CG PPP2R1A 536 179 PRNN 17.07 164 PAP 240 Endometrium Sample esc Pap cr 17 7578290 SBS CT TP53 0 0 NULL NULL 1 N 22.31 1283 PAP 240 Endometrium Sample esc Pap cr 17 7577141 SBS CT TP53 797 266 GENN 23 , 71 468 PAP 247 Endometrium Sample esc Pap cr 3 41266104 SBS GA CTNNB1 101 34 GENN 0.47 17 PAP 247 Endometrium Sample esc Pap cr 5 67591147 SBS CA PIK3R1 1740 580 Y * NN 1.50 43 PAP 247 Endometrium Sample esc Pap cr 12 133253184 SBS GC POLE 857 286 PRNN 2.39 79 PAP 247 Endometrium A show esc Pap cr 10 89720870 SBS T G PTEN 1021 341 F V N N 5.08 30 PAP 070 Endometrium Sample esc cr cr 10 123258034 SBS A T FGFR2 1647 549 N K N N 0.31 6

PAP 070 Endometrium Sample esc Pap cr 3 178916876 SBS GA PIK3CA 263 88 RQNN 23.62 3349 PAP 070 Endometrium Sample esc Pap cr 17 7578556 SBS TC TP53 0 0 NULL NULL 1 N 7.08 148 PAP 131 Endometrium Sample esc Pap cr 4 153249385 SBS GA FBXW7 1393 465 RCNN 2.55 144 PAP 131 Endometrium Sample esc cr cr 25258562 SBS CT KRAS 436 146 ATNN 34.03 1433 PAP 131 Endometrium Sample esc Pap cr 3 178916876 SBS GA PIK3CA 263 88 RQNN 33.88 6544 PAP 131 Endometrium Sample esc Pap cr 10 89685308 SBS AG PTEN 203 68 YCNN 43.57 122 PAP 186 Ovarian Sample esc Pap cr 17 7579589 Indel G TP53 98 33 S NULL NN 0.49 34 PAP 195 Ovarian Sample esc Pap cr 3 178916854 SBS GA PIK3CA 241 81 EKNN 0.27 30 PAP 195 Ovarian Sample esc Pap cr 12 133250289 SBS CA POLE 1231 411 VLNN 0.33 25 PAP 195 Ovarian Sample esc pap cr 17 7578486 Indel A TP53 444 148 D NULL NN 3.39 221 PAP 219 Endometrium Sample esc Pap cr 3 178916876 SBS GA PIK3CA 263 88 RQNN 17.52 2099 PAP 219 Endometrium Amost ra esc Pap cr 10 89624245 SBS GT PTEN 19 7 E * NN 22.28 123 PAP 219 Endometrium Sample esc cr 1089624270 SBS GA PTEN 44 15 RKNN 19.75 109 PAP 222 Endometrium Sample esc esc Pap cr 3 41266097 SBS GA CTNNB1 94 32 DNNN 0.56 16 PAP 222 Endometrium Sample esc Pap cr 3 41266112 SBS TC CTNNB1 109 37 SPNN 2.06 59 PAP 222 Endometrium Sample esc Pap cr 4 153249385 SBS GA FBXW7 1393 465 RCNN 13.32 325 PAP 222 Endometrium Sample esc Pap Pap cr 10 123247516 SBS TC FGFR2 1975 659 KENN 0.78 17 PAP 222 Endometrium Sample esc cr 12 25378562 SBS CT KRAS 436 146 ATNN 6.94 128 PAP 222 Endometrium Sample esc Pap cr 3 178952085 SBS AG PIK3CA 3140 1047 HRNN 22.33 527 PAP 222 Endometrium Sample esc Pap cr 5 67591150 Indel GATGTAAGTATT (SEQ ID NO: 764) PIK3R1 1743 581 L NULL NN 0.29 6 PAP 222 Endometrium Sample esc Pap cr 10 89692904 SBS CG PTEN 388 130 RGNN 3.25 46 PAP 233 Endometrium Sample esc Pap cr 3 178952085 SBS AG PIK3CA 3140 1047 HRNN 20,20,39 1011 PAP 233 Endome trio Sample esc Pap cr 10 89692904 SBS CG PTEN 388 130 RGNN 8.62 369 PAP 233 Endometrium Sample esc Pap cr 10 89720817 Indel A PTEN 968 323 N NULL NN 18.54 1973 PAP 237 Endometrium Sample esc Pap cr 3 41266097 SBS GA CTNNB1 94 32 DNNN 0.59 22 PAP 237 Endometrium Sample esc Pap cr 10 89624249 Indel T PTEN 23 8 I NULL NN 4.00 50 PAP 237 Endometrium Sample esc Pap cr 10 89720781 Indel A PTEN 932 311 N NULL NN 1.29 98 PAP 239 Endometrium Sample esc Pap cr 17 7577108 SBS CA TP53 830 277 CFNN 80.44 1197 PAP 244 Endometrium Sample esc Pap cr 4 153249393 SBS GA FBXW7 1385 462 SFNN 4.07 205 PAP 244 Endometrium Sample esc cr cr 17 7578413 SBS CT TP53 517 173 VMNN 5.90 315 PAP 244 Endometrium Sample esc Pap cr 17 7576911 Indel GTGTTGTTGGGCAG (SEQ ID NO: 765) TP53 935 312 T NULL NN 4.10 389 PAP 062 Ovarian Sample esc pap cr 17 7577120 SBS CT TP53 818 273 RHNN 4 , 16 79 PAP 204 Endometrium Sample esc Pap cr 5 112174631 SBS CT APC 3340 1114 R * NN 4.39 199 PAP 204 Endo metric Sample esc Pap cr 3 41266113 SBS CA CTNNB1 110 37 SYNN 16.06 624 PAP 204 Endometrium Sample esc Pap cr 12 25380272 SBS CA KRAS 186 62 EDNN 0.52 31 PAP 204 Endometrium Sample esc esc cr 3 178916876 SBS GA PIK3CA 263 88 RQNN 11.96 1811 PAP 204 Endometrium Sample esc Pap cr 10 89692905 SBS GA PTEN 389 130 RQNN 3.29 93 PAP 204 Endometrium Sample esc Pap cr 10 89720744 SBS GT PTEN 895 299 E * NN 0.11 12 PAP 204 Endometrium Sample esc Pap cr 17 7579702 SBS GT TP53 94 32 LMNN 6.87 296 PAP 204 Endometrium Sample esc Pap cr 17 7579348 SBS GT TP53 339 113 FLNN 1.52 139 PAP 204 Endometrium Sample esc Pap cr 17 7578463 SBS CT TP53 467 156 RHNN 15, 39 862 PAP 213 Endometrium Sample esc Pap cr 12 25398284 SBS CA KRAS 35 12 GVNN 10.46 266 PAP 213 Endometrium Sample esc Pap cr 3 178936091 SBS GA PIK3CA 1633 545 EKNN 20.20,94 422 PAP 213 Endometrium Sample esc cr cr 10 89720817 Indel A PTEN 968 323 NULL NULL NN 18.36 1483 PAP 216 Endometrium Sample esc Pap cr 10 123258034 SBS AC FGFR2 1647 549 NKNN 1.72 47 PAP 216 Endometrium Sample esc Pap cr 10 89653809 Indel G PTEN 107 36 G NULL NN 1.24 48 PAP 227 Endometrium Sample esc Pap cr 12 25398284 SBS CG KRAS 35 12 GANN 15, 76 410 PAP 227 Endometrium Sample esc Pap cr 3 178952088 SBS AG PIK3CA 3143 1048 HRNN 3.31 129 PAP 228 Endometrium Sample esc cr 3 41266097 SBS GA CTNNB1 94 32 DNNN 23.44 546 PAP 228 Endometrium Sample esc Pap cr 5 67591097 SBS AG PIK3R1 1690 564 NDNN 25.65 1646 PAP 231 Endometrium Sample esc cr 17 7578268 SBS AC TP53 581 194 LRNN 0.82 67 PAP 122 Endometrium Sample esc Pap cr 10 89692904 SBS CG PTEN 388 130 RGNN 0.15 7 PAP 132 Endometrium Sample esc Pap cr 5 112174631 SBS CT APC 3340 1114 R * NN 41.74 3872 PAP 132 Endometrium Sample esc Pap cr 3 178952074 SBS GT PIK3CA 3129 1043 MINN 25.70 1627 PAP 132 Endometrium Sample esc Pap cr 5 67588951 SBS CT PIK3R1 1042 348 R * NN 18.08 113 PAP 132 Endometrium Sample esc Pap cr 5 67589562 SB STG PIK3R1 1325 442 ISNN 0.23 12 PAP 132 Endometrium Sample esc cr cr 10 89624245 SBS GT PTEN 19 7 E * NN 50.74 4886 PAP 132 Endometrium Sample esc Pap cr 10 89624270 SBS GT PTEN 44 15 RINN 23.30 2244 PAP 132 Endometrium Sample esc Pap cr 10 89711900 SBS GA PTEN 518 173 RHNN 3.17 393 PAP 132 Endometrium Sample esc cr cr 10 89720744 SBS GT PTEN 895 299 E * NN 3.67 1282 PAP 132 Endometrium Sample esc cr cr 17 7578211 SBS CT TP53 638 213 RQNN 0.99 124 PAP 132 Endometrium Sample esc Pap cr 17 7577138 SBS CT TP53 800 267 RQNN 12.60 1032 PAP 173 Ovarian Sample esc pap cr 3 41266101 SBS CG CTNNB1 98 33 SCNN 0.72 24 PAP 173 Ovarian Sample esc Pap cr 3 178936091 SBS GA PIK3CA 1633 545 EKNN 2.47 47 PAP 181 Endometrium Sample esc Pap cr 19 52716323 SBS CT PPP2R1A 767 256 SFNN 68.96 6377 PAP 181 Endometrium Sample esc pap cr 17 7578190 SBS TC TP53 659 220 YCNN 62 , 45 1791 PAP 194 Ovarian Sample esc Pap cr 17 7578484 Indel G TP53 446 149 S NULL NN 5 , 75 338 PAP 203 Endometrium Sample esc Pap cr 3 178952077 SBS TA PIK3CA 3132 1044 NKNN 9.45 387 PAP 203 Endometrium Sample esc Pap cr 5 67589579 Indel AAA PIK3R1 1342 448 K NULL NN 2.47 78 PAP 203 Endometrium Sample esc Pap cr 10 89712018 SBS TC PTEN 0 0 NULL NULL 1 N 15.58 1111 PAP 203 Endometrium Sample esc Pap cr 10 89720716 Indel A PTEN 867 289 K NULL NN 0.57 57 PAP 203 Endometrium Sample esc Pap cr 10 89720761 SBS CA PTEN 912 304 C * NN 8.20 373 PAP 207 Endometrium Sample esc Pap cr 12 25378561 SBS GA KRAS 437 146 AVNN 26.74 878 PAP 207 Endometrium Sample esc Pap cr 5 67592099 SBS CT PIK3R1 1915 639 R * NN 32.58 1518 PAP 214 Endometrium Sample esc Pap cr 12 25398284 SBS CT KRAS 35 12 GDNN 4.94 198 PAP 214 Endometrium Sample esc Pap cr 10 89692904 SBS CG PTEN 388 130 RGNN 0.44 22 PAP 214 Endometrium Sample esc Pap cr 17 7578212 SBS GA TP53 637 213 R * NN 2.96 166 PAP 221 Endometrium Sample esc Pap cr 3 178936092 SBS AC PIK3CA 1634 545 EANN 2.28 42 PAP 6 09 Endometrium Sample esc Pap cr 12 133250289 SBS C G POLE 1231 411 V L N N 6.28 951

PAP 609 Endometrium Sample esc Pap cr 17 7577102 Indel C TP53 836 279 G NULL NN 0.16 30 PAP 616 Endometrium Sample esc Pap cr 12 25398284 SBS CA KRAS 35 12 GVNN 6.49 149 PAP 616 Endometrium Sample esc Pap cr 3 178916876 SBS GA PIK3CA 263 88 RQNN 1.09 153 PAP 616 Endometrium Sample esc Pap cr 5 67591135 Indel G PIK3R1 1728 576 T NULL NN 1.96 29 PAP 616 Endometrium Sample esc Pap cr 10 89692905 SBS GA PTEN 389 130 RQNN 2.31 31 PAP 621 Endometrium Sample esc Pap cr 1 115258747 SBS CG NRAS 35 12 GANN 2.29 70 PAP 629 Endometrium Sample esc cr cr 5 67591098 Indel ACGTATGAA PIK3R1 1691 564 NULL NN 0.30 25 PAP 596 Endometrium Sample esc cr cr 10 89720767 Indel A PTEN 918 306 I NULL NN 0.19 15 PAP 633 Endometrium Sample esc Pap cr 3 41266098 SBS AT CTNNB1 95 32 DVNN 0.63 52 PAP 633 Endometrium Sample esc Pap cr 12 25398282 SBS CA KRAS 37 13 GCNN 1.89 100 PAP 633 Endometrium Sample esc Pap cr 5 67591106 SBS AG PIK3R1 1699 567 KENN 2.12 197 PAP 633 Endometrium A show esc Pap cr 10 89692904 SBS CG PTEN 388 130 RGNN 0.60 61 PAP 272 Endometrium Sample esc cr 4 153247288 SBS CT FBXW7 1514 505 RHNN 61.62 12245 PAP 272 Endometrium Sample esc esc cr 10 123279677 SBS GC FGFR2 755 252 SWNN 12.52 879 PAP 272 Endometrium Sample esc Pap cr 3 178916854 SBS GA PIK3CA 241 81 EKNN 57.20 10210 PAP 272 Endometrium Sample esc Pap cr 12 133250289 SBS CA POLE 1231 411 VLNN 30.12 4672 PAP 272 Endometrium Sample esc Pap cr 10 89624245 SBS GT PTEN 19 7 E * NN 46.63 788 PAP 272 Endometrium Sample esc cr cr 10 89692905 SBS GA PTEN 389 130 RQNN 7.08 151 PAP 272 Endometrium Sample esc cr cr 17 7577117 SBS AG TP53 821 274 VANN 83.83 1835 PAP 274 Endometrium Sample esc Pap cr 14 105246551 SBS CT AKT1 49 17 EKNN 20,20,36 2957 PAP 274 Endometrium Sample esc Pap cr 5 112175681 Indel GA APC 4390 1464 AND NULL NN 10,22 992 PAP 274 Endometrium Sample esc Pap cr 5 112175684 Indel AG APC 4393 1465 S NULL NN 11.13 1652 PAP 274 Am Endometrium oyster esc Pap cr 12 25398284 SBS CT KRAS 35 12 GDNN 10.16 463 PAP 277 Endometrium Sample esc cr 10123279677 SBS GC FGFR2 755 252 SWNN 1.21 254 PAP 552 Ovarian Sample esc esc Cr 17 7578556 SBS TC TP53 0 0 NULL NULL 1 N 3.12 87 PAP 581 Endometrium Sample esc Pap cr 5 67591123 Indel GCTGAG PIK3R1 1716 572 Q NULL NN 8.14 262 PAP 581 Endometrium Sample esc Pap cr 10 89692920 SBS TA PTEN 404 135 IKNN 1.74 82 PAP 593 Endometrium Sample esc Pap cr 3 41266113 SBS CG CTNNB1 110 37 SCNN 0.60 26 PAP 593 Endometrium Sample esc Pap cr 3 41266113 SBS CT CTNNB1 110 37 SFNN 0.21 9 PAP 593 Endometrium Sample esc Pap cr 10 89692893 SBS CT PTEN 377 126 AVNN 16.18 296 PAP 599 Endometrium Sample esc Pap cr 10 89653799 Indel ATT PTEN 97 33 I NULL NN 0.98 68 PAP 625 Endometrium Sample esc Pap cr 3 178916876 SBS GA PIK3CA 263 88 RQNN 2.62 830 PAP 625 Endometrium Sample esc Pap Pap cr 3 178952007 SBS AG PIK3CA 3062 1021 YCNN 5.57 78 PAP 625 Endometrium Sample esc Pap cr 12 133253184 SBS GC POLE 857 286 PRNN 4.91 297 PAP 625 Endometrium Sample esc cr cr 17 7578212 SBS GA TP53 637 213 R * NN 5.91 260 PAP 632 Endometrium Sample esc cr cr 4 153247366 SBS CT FBXW7 1436 479 RQNN 1.81 82 PAP 632 Endometrium Sample esc Pap cr 19 52716327 SBS GT PPP2R1A 771 257 WCNN 3.76 301 PAP 271 Endometrium Sample esc Pap cr 3 178916876 SBS GA PIK3CA 263 88 RQNN 7.85 68 PAP 271 Endometrium Sample esc cr cr 12 133253151 SBS GA POLE 890 297 SFNN 11.88 41 PAP 271 Endometrium Sample esc Pap cr 10 89720744 SBS GT PTEN 895 299 E * NN 13.89 75 PAP 275 Endometrium Sample esc Pap cr 3 41266104 SBS GT CTNNB1 101 34 GVNN 9.50 32 PAP 686 Endometrium Sample esc Tao cr 10 89692905 SBS GC PTEN 389 130 RPNN 1.43 330 PAP 686 Endometrium Sample esc Tao cr 10 89717610 Indel A PTEN 635 212 NULL NULL 5.15 152 PAP 687 Endometrium Sample esc Pap cr 17 7578397 SBS TG TP53 533 178 HPNN 0.41 20 PAP 691 Endometrium Sample esc Pap cr 5 112174631 SBS CT APC 3340 1 114 R * NN 8.23 352 PAP 691 Endometrium Sample esc cr cr 123 123258036 SBS TG FGFR2 1645 549 NHNN 8.51 233 PAP 691 Endometrium Sample esc Pap cr 3 178916876 SBS GA PIK3CA 263 88 RQNN 4.17 35 PAP 691 Endometrium Sample esc Pap cr 12 133253184 SBS GC POLE 857 286 PRNN 9.13 240 PAP 691 Endometrium Sample esc Pap cr 10 89624245 SBS GT PTEN 19 7 E * NN 7.13 1671 PAP 691 Endometrium Sample esc Pap cr 10 89692905 SBS GA PTEN 389 130 RQNN 2.87 343 PAP 691 Endometrium Sample esc Pap cr 17 7577027 SBS GT TP53 911 304 TNNN 0.33 32 PAP 691 Endometrium Sample esc Tao cr 5 112174631 SBS CT APC 3340 1114 R * NN 25.90 967 PAP 691 Endometrium Sample esc Tao cr 10 123258036 SBS TG FGFR2 1645 549 NHNN 28.73 507 PAP 691 Endometrium Sample esc Tao cr 3 178916876 SBS GA PIK3CA 263 88 RQNN 12.80 54 PAP 691 Endometrium Sample esc Tao cr 12 133253184 SBS GC POLE 857 286 PRNN 26, 11 377 PAP 691 Endometrium Sample esc Tao cr 10 89624245 SBS GT PTEN 19 7 E * NN 25.51 4937 PAP 691 Endometrium Sample esc Tao cr 10 89692905 SBS GA PTEN 389 130 RQNN 10.03 838 PAP 707 Ovarian Sample esc Pap cr 17 7578271 SBS TC TP53 578 193 HRNN 1.05 79 PAP 824 Endometrium Sample esc Pap cr 10 89692904 SBS CG PTEN 388 130 RGNN 0.12 26 PAP 829 Endometrium Sample esc Pap cr 3 178916854 SBS GA PIK3CA 241 81 EKNN 2.83 31 PAP 829 Endometrium Sample esc Pap cr 3 178916876 SBS GA PIK3CA 263 88 RQNN 2.47 27 PAP 829 Endometrium Sample esc Pap Pap cr 5 67590993 Indel A PIK3R1 1586 529 D NULL NN 0.52 11 PAP 829 Endometrium Sample esc cr 12 133253184 SBS GC POLE 857 286 PRNN 6.29 167 PAP 829 Endometrium Sample esc Pap cr 10 89692995 SBS CT PTEN 479 160 TINN 5 , 65 157 PAP 829 Endometrium Sample esc Pap cr 10 89711891 SBS GA PTEN 509 170 SNNN 5.05 103 PAP 832 Endometrium Sample esc Tao cr 17 7578539 Indel T TP53 391 131 NULL NN 1.73 108 PAP 839 Endometrium Sample tao cr 5 67591097 SBS AG PIK3R1 1690 564 NDNN 37.48 1366 PAP 841 Endometrium Sample and sc Pap cr 17 7577022 SBS GA TP53 916 306 R * NN 12.47 244 PAP 690 Endometrium Sample esc Pap cr 3 41266113 SBS CT CTNNB1 110 37 SFNN 0.30 14 PAP 690 Endometrium Sample esc Pap cr 12 25398284 SBS CA KRAS 35 12 GVNN 14.68 1083 PAP 690 Endometrium Sample esc cr cr 178952085 SBS AG PIK3CA 3140 1047 HRNN 7.55 317 PAP 831 Endometrium Sample esc pap cr 19 52716323 SBS CT PPP2R1A 767 256 SFNN 39.91 1462 PAP 831 Endometrium Sample tao cr 19 52716323 SBS CT PPP2R1A 767 256 SFNN 32.93 753 PAP 836 Endometrium Sample esc Tao cr 10 89692905 SBS GC PTEN 389 130 RPNN 4.54 433 PAP 839 Endometrium Sample esc Pap cr 5 67591097 SBS AG PIK3R1 1690 564 NDNN 1.85 59 PAP 842 Endometrium Sample esc Tao cr 10 89720817 Indel A PTEN 968 323 N NULL NN 9.41 149 PAP 688 Endometrium Sample esc Pap cr 12 25398285 SBS CA KRAS 34 12 GCNN 2.40 201 PAP 688 Endometrium Sample esc Tao cr 12 25398285 SBS CA KRAS 34 12 GCNN 15.45 1018 PAP 688 Endometrium Sample esc Tao cr 5 675911 28 SBS G T PIK3R1 1721 574 R I N N 10.50 90

PAP 690 Endometrium Sample esc Tao cr 12 25398284 SBS CA KRAS 35 12 GVNN 28.98 3154 PAP 690 Endometrium Sample esc Tao cr 3 178952085 SBS AG PIK3CA 3140 1047 HRNN 16.15 1457 PAP 696 Endometrium Sample esc cr cr 41266101 SBS CG CTNNB1 98 33 SCNN 0.80 52 PAP 696 Endometrium Sample esc Pap cr 12 25398284 SBS CT KRAS 35 12 GDNN 48.69 5000 PAP 696 Endometrium Sample esc Pap cr 10 89653829 SBS GT PTEN 127 43 E * NN 74.40 5874 PAP 824 Endometrium Sample esc Tao cr 3 178952085 SBS AG PIK3CA 3140 1047 HRNN 38.71 2444 PAP 824 Endometrium Sample esc Tao cr 10 89692790 SBS GT PTEN 274 92 DYNN 22.52 93 PAP 824 Endometrium Sample esc Tao cr 10 89692904 SBS CG PTEN 388 130 RGNN 12.46 1807 PAP 835 Endometrium Sample esc Pap cr 3 178936082 SBS GA PIK3CA 1624 542 EKNN 42.89 1216 PAP 835 Endometrium Sample esc Tao cr 3 178936082 SBS GA PIK3CA 1624 542 EKNN 46.57 1268 PAP 841 Endometrium Sample esc Tao cr 17 7577022 SBS GA TP53 916 306 R * NN 35.37 5336 PAP 685 E ndométrio Sample esc Pap cr 10 89692845 Indel AATGGC PTEN 329 110 Q NULL NN 0.15 11 PAP 685 Endometrium Esc sample Tao cr 10 123247516 SBS TC FGFR2 1975 659 KENN 17.49 832 PAP 685 Endometrium Sample esc Tao cr 10 89692845 Indel AATGGC PTEN 329 Q NULL NN 16.92 1274 PAP 685 Endometrium Sample esc Tao cr 10 89692904 SBS CT PTEN 388 130 R * NN 7.16 1137 PAP 687 Endometrium Sample esc Tao cr 3 178952085 SBS AG PIK3CA 3140 1047 HRNN 29.65 1645 PAP 687 Endometrium Sample esc Tao cr 17 7578403 SBS CT TP53 527 176 CYNN 68.66 1512 PAP 695 Endometrium Sample esc Pap cr 3 178952085 SBS AG PIK3CA 3140 1047 HRNN 15.67 954 PAP 695 Endometrium Sample esc Pap cr 17 7577120 SBS CT TP53 818 273 RHNN 17.66 1090 PAP 697 Endometrium Sample esc Pap cr 5 112174257 Indel ATGATGAAAG (SEQ ID NO: 766) APC 2966 989 D NULL NN 6.79 154 PAP 697 Endometrium Sample esc Pap cr 10 89692918 Indel GAT PTEN 402 134 M NULL NN 6.34 413 PAP 699 Endometrium Sample esc Pap cr 5 112173791 Indel T APC 2500 834 S NULL NN 25.20 1214 PAP 699 Endometrium Sample esc cr cr 4 153247303 SBS TC FBXW7 1499 500 HRNN 11.56 743 PAP 699 Endometrium Sample esc Pap cr 10 89711899 SBS CT PTEN 517 173 RCNN 28.54 774 PAP 699 Endometrium Sample esc Pap cr 10 89717775 Indel A PTEN 800 267 K NULL NN 29,15 1123 PAP 711 Ovarian Sample esc Pap cr 5 67589596 Indel CACTCAGTTTCAAGAAAAAG (SEQ ID NO: 767) PIK3R1 1359 453 NULL NN 0.24 10 PAP 711 Ovarian Sample esc Pap cr 19 52716327 SBS GT PPP2R1A 771 257 WCNN 3.34 52 PAP 711 Ovarian Sample esc Pap cr 17 7578429 Indel C TP53 501 167 Q NULL NN 0.46 18 PAP 837 Endometrium Sample esc Tao cr 10 89692904 SBS CG PTEN 388 130 RGNN 1.28 291 PAP 837 Endometrium Sample esc Tao cr 17 7577120 SBS CG TP53 818 273 RPNN 0.27 31 PAP 842 Endometrium Sample esc Pap cr 12 25380275 SBS TG KRAS 183 61 QHNN 1.32 91 PAP 842 Endometrium Sample esc Pap cr 5 67590478 SBS CT PIK3R1 1540 514 RCNN 1.25 77 PAP 842 Endometrium Sample esc Pap cr 10 8972 0817 Indel A PTEN 968 323 N NULL NN 2.82 65 PAP 845 Ovarian Sample esc Tao cr 17 7577120 SBS CT TP53 818 273 RHNN 24.65 1480 PAP 675 Endometrium Sample esc Pap cr 5 112175684 Indel AG APC 4393 1465 S NULL NN 0 , 56 49 PAP 675 Endometrium Sample esc Pap cr 3 178952084 SBS CT PIK3CA 3139 1047 HYNN 10.14 262 PAP 675 Endometrium Sample esc Pap cr 10 89720867 Indel AATT PTEN 1018 340 NULL NN 3.03 8 PAP 675 Endometrium Sample esc Pap cr 17 56435083 Indel G RNF43 2054 685 T NULL NN 1.39 34 PAP 675 Endometrium Sample esc Pap cr 17 7577114 SBS CA TP53 824 275 CFNN 24.95 1142 PAP 850 Endometrium Sample esc Pap cr 3 41266113 SBS CT CTNNB1 110 37 SFNN 43, 61 1310 PAP 850 Endometrium Sample esc Pap cr 10 123279677 SBS GC FGFR2 755 252 SWNN 13.37 226 PAP 850 Endometrium Sample esc Pap cr 3 178936091 SBS GA PIK3CA 1633 545 EKNN 45.49 2906 PAP 850 Endometrium Sample esc Pap cr 10 89720852 SBS CT PTEN 1003 335 R * NN 91.18 2109 PAP 850 Endometrium Sample esc Tao cr 10 123 279677 SBS GC FGFR2 755 252 SWNN 17.12 839 PAP 850 Endometrium Sample esc Tao cr 3 178936091 SBS GA PIK3CA 1633 545 EKNN 46.00 3229 PAP 850 Endometrium Sample esc Tao cr 10 89720852 SBS CT PTEN 1003 335 R * NN 92.59 3871 PAP 850 Endometrium Sample esc Tao cr 17 56435167 Indel C RNF43 1970 657 R NULL NN 10.13 211 PAP 850 Endometrium Sample esc Tao cr 17 7578421 SBS GA TP53 509 170 TMNN 0.49 17 PAPA 1010 Endometrium Sample esc Tao cr 12 25398284 SBS CA KRAS 35 12 GVNN 32.34 1389 PAPA 1010 Endometrium Sample esc Tao cr 10 89692893 SBS CA PTEN 377 126 DNA 29.37 1775 PAPA 1010 Endometrium Sample esc Tao cr 17 7577124 SBS CT TP53 814 272 VMNN 21.10 882 PAPA 1011 Sample Endometrium esc Tao cr 10 123258036 SBS TG FGFR2 1645 549 NHNN 7.60 65 PAPA 1012 Endometrium Sample esc Tao cr 12 25398281 SBS CT KRAS 38 13 GDNN 28.11 610 PAPA 1012 Endometrium Sample esc Tao cr 3 178916876 SBS GA PIK3CA 263 88 RQNN 20 , 20,13 62 PAPA 1012 Endometrium Sample esc Tao cr 10 89717775 Indel A PTEN 800 267 K NULL NN 23.69 258 POPE 1012 Endometrium Sample esc Tao cr 10 89717775 Indel A PTEN 800 267 K NULL NN 27.09 295 PAPA 1013 Endometrium Sample esc Pap cr 17 7577141 SBS CT TP53 797 266 GENN 22 , 35 1500 PAPA 1016 Endometrium Sample esc Pap cr 10 123279677 SBS GC FGFR2 755 252 SWNN 9.62 282 PAPA 1016 Endometrium Sample esc Pap cr 3 178916836 SBS CG PIK3CA 223 75 QENN 20.20,03 152 PAPA 1016 Endometrium Sample esc Pap cr 5 67591152 Indel TGT PIK3R1 1745 582 M NULL N Yes 1.32 17 PAPA 1016 Endometrium Esc sample Pap cr 5 67592052 Indel CA PIK3R1 1868 623 T NULL NN 9.18 187 PAPA 1016 Endometrium Sample esc cr cr 89892904 SBS CG PTEN 388 130 RGNN 8.80 923 PAPA 1016 Endometrium Sample esc Tao cr 10 123279677 SBS GC FGFR2 755 252 SWNN 37.50 309 PAPA 1016 Endometrium Sample esc Tao cr 3 178916836 SBS CG PIK3CA 223 75 QENN 55.86 224 PAPA 1016 Endometrium Sample tao cr 5 67591152 Indel TGT PIK3R1 1745 582 M NULL N Yes 6.39 68 POPE 1016 Endometrium Sample esc Tao cr 5 67592052 Indel CA PIK3R1 1868 623 T NULL NN 33,15 648 PAPA 1016 Endometrium Sample esc Tao cr 10 89692904 SBS CG PTEN 388 130 RGNN 27,11 2378 PAP 120 Endometrium Sample esc Pap cr 3 41266101 SBS CG CTNNB1 98 33 SCNN 3.86 378 PAP 120 Endometrium Sample esc Cr 3 178952085 SBS AG PIK3CA 3140 1047 HRNN 6.76 700 PAP 853 Endometrium Sample esc Tao cr 3 41266101 SBS CG CTNNB1 98 33 SCNN 39.42 2871 PAP 853 Endometrium Sample esc Tao cr 3 178952085 SBS AG PIK3CA 3140 1047 HRNN 37.18 5264 PAP 853 Endometrium Sample esc Tao cr 10 89711899 SBS CT PTEN 517 173 RCNN 39.25 3020 PAP 860 Endometrium Sample esc Pap cr 3 41266101 SBS CT CTNNB1 98 33 SFNN 1, 04 159 PAP 860 Endometrium Sample esc Pap cr 3 178936091 SBS GA PIK3CA 1633 545 EKNN 0.92 294 PAPA 1010 Endometrium Sample esc Pap cr 17 7577124 SBS CT TP53 814 272 VMNN 1.26 84 PAPA 1013 Endometrium Sample esc Tao cr 17 7577141 SBS CT TP53 797 266 GENN 20,20,53 1279 POPE 101 9 Endometrium Sample esc Pap cr 10 89692961 SBS CT PTEN 445 149 Q * NN 0.13 25 PAPA 1019 Endometrium Sample esc Tao cr 4 153247366 SBS CT FBXW7 1436 479 RQNN 21.45 118 PAPA 1019 Endometrium Sample esc Tao cr 5 67591139 Indel GA PIK3R1 1732 578 D NULL NN 28.79 152

PAPA 1019 Endometrium Sample esc Tao cr 10 89692961 SBS CT PTEN 445 149 Q * NN 13.07 556 PAPA 1019 Endometrium Sample esc Tao cr 10 89717672 SBS CT PTEN 697 233 R * NN 9.33 358 PAPA 1019 Endometrium Sample esc Tao cr 17 7578388 SBS CT TP53 542 181 RHNN 30.55 172 PAP 671 Sample end esc Pap cr 10 123279677 SBS GC FGFR2 755 252 SWNN 0.75 17 PAP 671 Sample endoscope esc Pap cr 10 123258034 SBS AT FGFR2 1647 549 NKNN 3.97 86 PAP 671 Endometrium Sample esc Pap cr 10 89692922 SBS TC PTEN 406 136 CRNN 1.63 149 PAP 681 Endometrium Sample esc cr cr 10 89692902 SBS GT PTEN 386 129 GVNN 2.02 405 PAP 684 Endometrium Sample esc esc cr 10 89692904 SBS CG PTEN 388 130 RGNN 2.89 201 PAP 684 Endometrium Sample esc Pap cr 10 89720808 SBS TA PTEN 959 320 L * NN 6.48 49 PAP 851 Endometrium Sample esc Tao cr 3 41266124 SBS AG CTNNB1 121 41 TANN 36.57 918 PAP 851 Endometrium Sample esc Tao cr 4 153247367 SBS GC FBXW7 1435 479 RGNN 34.66 670 PAP 851 Endometrium Sample esc Tao cr 10 89653834 Indel C PTEN 132 44 G NULL NN 9.75 46 PAP 851 Endometrium Sample esc Tao cr 17 7578525 SBS GT TP53 405 135 C * NN 35.95 1141 PAP 860 Endometrium Sample esc Tao cr 3 178936091 SBS GA PIK3CA 1633 545 EKNN 11.82 612 PAP 860 Endometrium Sample esc Tao cr 17 56435167 Indel C RNF43 1970 657 R NULL NN 6.32 151 PAPA 1012 Endometrium Sample esc Pap cr 12 25398281 SBS CT KRAS 38 13 GDNN 17.35 369 PAPA 1012 Endometrium Sample esc Pap cr 3 178916876 SBS GA PIK3CA 263 88 RQNN 9.79 32 PAPA 1012 Endometrium Sample esc Pap cr 10 89717775 Indel A PTEN 800 267 K NULL NN 18.01 515 PAPA 1012 Endometrium Sample esc Pap cr 10 89717775 Indel A PTEN 800 267 K NULL NN 16.47 471 PAP 667 Endometrium Sample esc Pap cr 12 25398284 SBS CT KRAS 35 12 GDNN 49.63 2526 PAP 668 Endometrium Sample esc Pap cr 19 52715971 SBS CG PPP2R1A 536 179 PRNN 0.23 27 PAP 669 Endometrium Sample esc Pap cr 5 67591112 Indel G PIK3R1 1705 569 D NULL NN 0.22 19 PAP 669 Endometrium Sample esc Pap cr 10 89692904 SBS CG PTEN 388 130 RGNN 0.17 22 PAP 851 Endometrium Sample esc Pap cr 3 41266124 SBS AG CTNNB1 121 41 TANN 0.83 51 PAP 851 Endometrium Sample esc cr cr 4 153247367 SBS GC FBXW7 1435 479 RGNN 0.97 49 PAP 851 Endometrium Sample esc Pap cr 10 89653834 Indel C PTEN 132 44 G NULL NN 0.60 14 PAP 858 Endometrium Sample esc Tao cr 3 178952072 SBS AG PIK3CA 3127 1043 MVNN 38.59 3862 PAP 858 Endometrium Sample tao cr 17 7578175 SBS AC TP53 0 0 NULL NULL 1 N 74.82 3917 PAP 862 Endometrium Sample esc Tao cr 17 7578212 SBS GA TP53 637 213 R * NN 85.71 1662 PAP 128 Endometrium Sample esc Pap cr 10 123258034 SBS AT FGFR2 1647 549 NKNN 41.03 1931 PAP 128 Endometrium Sample esc cr 3 178952090 SBS GA PIK3CA 3145 1049 GSNN 28.74 1070 PAP 128 Endometrium Sample esc cr 5 57591128 SBS GC PIK3R1 1721 574 RTNN 21.02 641 PAP 663 Endometrium Sample esc Pap cr 4 153247367 SBS GA FBXW7 1435 479 R * NN 11.58 130 PAP 663 Endometrium Amost ra esc Pap cr 3 178916854 SBS GA PIK3CA 241 81 EKNN 15.92 124 PAP 663 Endometrium Sample esc cr 12 133253184 SBS GC POLE 857 286 PRNN 16.28 142 PAP 663 Endometrium Sample esc esc cr 10 89690835 SBS TG PTEN 242 81 FCNN 2.01 34 PAP 663 Endometrium Sample esc cr cr 10 89624245 SBS GT PTEN 19 7 E * NN 16.69 426 PAP 663 Endometrium Sample esc cr 10 89692905 SBS GA PTEN 389 130 RQNN 1.71 93 PAP 663 Endometrium Sample esc Pap cr 17 7578212 SBS GA TP53 637 213 R * NN 17.73 269 PAP 989 Endometrium Sample esc Pap cr 3 178936091 SBS GA PIK3CA 1633 545 EKNN 29,40 2667 PAP 989 Endometrium Sample esc Pap cr 17 7577121 SBS GA TP53 817 273 RCNN 13,85 1103 PAP 990 Endometrium Sample esc Tao cr 1 115258744 SBS CA NRAS 38 13 GVNN 40.79 2113 PAP 990 Endometrium Sample esc Tao cr 10 89692904 SBS CT PTEN 388 130 R * NN 54.79 9533 PAP 990 Endometrium Sample esc Tao cr 17 56448310 SBS GA RNF43 337 113 R * NN 18.34 350 PAP 990 Endometrium Sample esc Tao cr 17 564351 67 Indel C RNF43 1970 657 R NULL NN 35.49 368 PAP 994 Sample end esc Pap cr 10 89692921 Indel A PTEN 405 135 I NULL NN 0.27 62 PAP 994 Endometrium Sample esc Pap cr 10 89720804 Indel ACTT PTEN 955 319 T NUL NN 1.23 34 PAP 996 Endometrium Sample esc Pap cr 3 41266113 SBS CT CTNNB1 110 37 SFNN 7.97 421 PAP 996 Endometrium Sample esc Pap cr 10 123279677 SBS GC FGFR2 755 252 SWNN 2.82 136 PAP 996 Endometrium Sample esc cr cr 3 178936095 SBS AG PIK3CA 1637 546 QRNN 8.12 411 PAP 996 Endometrium Sample esc Pap cr 10 89693009 Indel G PTEN 0 0 NULL NULL 1 N 6.68 146 PAP 996 Endometrium Sample esc Pap cr 10 89725042 SBS AG PTEN 0 0 NULL NULL 1 N 8.64 408 PAP 998 Endometrium Sample esc Pap cr 9 21971186 Indel GGGCGCTGCCCATC (SEQ ID NO: 768) CDKN2A 172 58 R NULL NN 1.78 40 PAP 998 Sample Endoscope esc Pap cr 12 25380275 SBS TA KRAS 183 61 QHNN 2 .20 134 PAP 998 Endometrium Sample esc Pap cr 10 89720804 Indel A PTEN 955 319 T NULL NN 2.44 90 PAP 999 Endometrium Sample a esc Pap cr 12 25398281 SBS CT KRAS 38 13 GDNN 42,44 7827 PAP 999 Endometrium Sample esc cr 19 52715971 SBS CG PPP2R1A 536 179 PRNN 18,27 1666 PAP 999 Endometrium Sample esc Pap cr 17 7578406 SBS CT TP53 524 175 RHNN 32.61 2038 PAP 999 Endometrium Sample esc Tao cr 12 25398281 SBS CT KRAS 38 13 GDNN 63.85 15888 PAP 999 Endometrium Sample esc Tao cr 19 52715971 SBS CG PPP2R1A 536 179 PRNN 34.43 3006 PAP 999 Endometrium Sample tao cr 17 7578406 SBS CT TP53 524 175 RHNN 72.46 5025 PAPA 1001 Endometrium Sample esc cr 10 89624305 SBS TG PTEN 79 27 YDN Yes 0.14 36 PAPA 1001 Endometrium Sample esc Tao cr 12 25398284 SBS CA KRAS 35 12 GVNN 27.73 2467 PAPA 1001 Endometrium Sample esc Tao cr 5 67591094 Indel AACGTA PIK3R1 1687 563 M NULL NN 36.12 1215 PAPA 1001 Endometrium Sample esc Tao cr 10 89624305 SBS TG PTEN 79 27 YDN Yes 61.28 15665 PAPA 1002 Endometrium Sample esc cr cr 178916876 SBS GA PIK3CA 263 88 RQNN 3.30 56 PAPA 1002 Endometrium Sample esc Pap cr 3 178952077 SBS TG PIK3CA 3132 1044 NKNN 5.56 419 PAPA 1002 Endometrium Sample esc Pap cr 10 89624297 SBS AG PTEN 71 24 DGNN 0.25 61 PAPA 1002 Endometrium Sample esc Pap cr 10 89692905 SBS GA PTEN 389 130 RQNN 1.80 444 PAPA 1003 Endometrium Sample esc Tao cr 14 105246551 SBS CT AKT1 49 17 EKNN 68.94 1884 PAPA 1003 Endometrium Sample esc Tao cr 3 41266098 SBS AG CTNNB1 95 32 DGNN 34.85 2859 PAP 926 Endometrium Sample esc Pap cr 3 41266104 SBS GA CTNNB1 101 34 GENN 0.71 62 PAP 926 Endometrium Sample esc cr cr 133250289 SBS CG POLE 1231 411 VLNN 2.23 309 PAP 926 Endometrium Sample esc Pap cr 10 89692905 SBS GA PTEN 389 130 RQNN 1.19 280 PAP 926 Endometrium Sample esc Pap cr 10 89717672 SBS CT PTEN 697 233 R * NN 0.92 145 PAP 934 Endometrium Sample esc Tao cr 3 41266101 SBS CA CTNNB1 98 33 SYNN 31.73 2632 PAP 934 Endometrium Sample tao cr 3 178952085 SBS AG PIK3CA 3140 1047 HRNN 31,20 1862 PAP 990 Endometrium Sample esc Pap cr 1 115258744 SBS C A NRAS 38 13 G V N N 4.08 326 PAP 990 Endometrium Sample esc Pap cr 10 89692904 SBS C T PTEN 388 130 R * N N 4.94 782

PAP 990 Endometrium Sample esc Pap cr 17 56448303 Indel G RNF43 344 115 A NULL NN 7.03 37 PAP 991 Endometrium Sample esc Tao cr 5 67591134 Indel CGA PIK3R1 1727 576 T NULL NN 32.66 763 PAP 991 Endometrium Sample esc Tao cr 10 89692896 SBS GT PTEN 380 127 GVNN 33,50 3167 PAP 991 Endometrium Sample esc Tao cr 10 89692985 Indel G PTEN 469 157 E NULL NN 31,94 842 PAP 991 Endometrium Sample esc Tao cr 17 7578279 Indel AGG TP53 570 190 P NULO NN 0 , 54 88 PAP 996 Endometrium Sample esc Tao cr 3 41266113 SBS CT CTNNB1 110 37 SFNN 8.63 921 PAP 996 Endometrium Sample esc Tao cr 10 123279677 SBS GC FGFR2 755 252 SWNN 11.37 581 PAP 996 Endometrium Sample tao cr 3 178936095 SBS AG PIK3CA 1637 546 QRNN 25.71 2157 PAP 996 Endometrium Sample esc Tao cr 10 89693009 Indel G PTEN 0 0 NULL NULL 1 N 27.21 590 PAP 996 Endometrium Sample esc Tao cr 10 89725042 SBS AG PTEN 0 0 NULL NULL 1 N 31.25 3517 PAP 998 Endometrium Sample esc Tao cr 10 89720804 Indel A PTEN 955 319 T NULL NN 5.3 9 180 PAPA 1002 Endometrium Sample esc Tao cr 3 178916876 SBS GA PIK3CA 263 88 RQNN 15.45 306 PAPA 1002 Endometrium Sample esc Tao cr 3 178952077 SBS TG PIK3CA 3132 1044 NKNN 26.93 3461 PAPA 1002 Endometrium Sample esc Tao cr 10 89692905 GA PTEN 389 130 RQNN 11.09 3351 PAPA 1003 Endometrium Sample esc cr cr 105 105246551 SBS CT AKT1 49 17 EKNN 3.93 192 PAPA 1003 Endometrium Sample esc Pap cr 3 41266098 SBS AG CTNNB1 95 32 DGNN 1.47 160 PAPA 1005 Endometrium Sample esc Pap cr 4 153249384 SBS CT FBXW7 1394 465 RHNN 4.00 268 PAPA 1005 Endometrium Sample esc Pap cr 12 25398284 SBS CT KRAS 35 12 GDNN 4.87 513 PAPA 1005 Sample Endoscope esc Pap cr 5 67591130 Indel AAGACGAGA PIK3R1 1723 575 NULL NN 2.82 55 PAPA 1005 Endometrium Sample esc Pap cr 17 7578413 SBS CA TP53 517 173 VLNN 4.74 433 PAP 922 Endometrium Sample esc Pap cr 3 41266097 SBS GT CTNNB1 94 32 DYNN 1.66 121 PAP 922 Endometrium Sample esc Tao cr 3 41266097 SBS GT CTNNB1 94 32 DYNN 17.44 1686 PAP 926 Endometrium Sample esc Tao cr 12 133250289 SBS CG POLE 1231 411 VLNN 7.76 895 PAP 927 Endometrium Sample esc Tao cr 10 89692883 SBS CG PTEN 367 123 HDNN 51.94 4503 PAP 928 Ovarian Sample esc Tao cr 17 7577127 SBS CT TP53 811 271 EKNN 0.70 100 PAP 931 Endometrium Sample esc Tao cr 3 41266113 SBS CT CTNNB1 110 37 SFNN 44.11 3924 PAP 934 Endometrium Sample esc Pap cr 3 41266101 SBS CA CTNNB1 98 33 SYNN 0.26 22 PAP 936 Endometrium Sample esc Tao cr 4 153249384 SBS CT FBXW7 1394 465 RHNN 30.77 2312 PAP 936 Endometrium Sample esc Tao cr 3 178936082 SBS GA PIK3CA 1624 542 EKNN 28.74 2211 PAP 936 Endometrium Sample esc Tao cr 10 89692769 SBS GC PTEN 0 0 NUL NULL 1 N 34.13 443 PAP 936 Endometrium Sample esc Tao cr 10 89717775 Indel A PTEN 800 267 K NULL NN 27.45 342 PAP 936 Endometrium Sample esc Tao cr 17 56435167 Indel C RNF43 1970 657 R NULL NN 51.72 301 PAP 989 Endometrium Sample esc Tao cr 3 178936091 SBS GA PIK3CA 1633 545 EKNN 30.9 9 2697 PAP 989 Endometrium Sample esc Tao cr 17 7577121 SBS GA TP53 817 273 RCNN 12.99 1720 PAP 991 Endometrium Sample esc Pap cr 3 178936096 SBS GT PIK3CA 1638 546 QHNN 3.89 428 PAP 991 Endometrium Sample esc cr 3 178952085 SBS AG PIK3CA 3140 1047 HRNN 3.30 345 PAP 991 Endometrium Sample esc Pap cr 5 67591134 Indel CGA PIK3R1 1727 576 T NULL NN 6.06 285 PAP 991 Endometrium Sample esc Pap cr 10 89692896 SBS GT PTEN 380 127 GVNN 12.78 1618 PAP 991 Endometrium Sample esc Pap cr 10 89692985 Indel G PTEN 469 157 E NULL NN 12.36 616 PAP 208 Endometrium Sample esc Pap cr 3 41266101 SBS CA CTNNB1 98 33 SYNN 0.40 19 PAP 208 Endometrium Sample esc Pap cr 1 115256530 SBS GT NRAS 181 61 QKNN 6.38 306 PAP 208 Endometrium Sample esc cr 3 178952085 SBS AG PIK3CA 3140 1047 HRNN 8.21 308 PAP 208 Endometrium Sample esc Pap cr 10 89717775 Indel A PTEN 800 267 K NULL 2.48 6 PAP 208 Endometrium Sample esc Pap cr 10 89717775 Indel A PTEN 800 267 K NULL NN 5.37 13 PAP 208 Endometrium Sample esc Pap cr 17 7578479 Indel G TP53 451 151 P NULL NN 13.97 276 PAP 208 Endometrium Sample esc Pap cr 17 7578407 SBS GA TP53 523 175 RCNN 1.91 61 PAP 927 Endometrium Sample esc Pap cr 10 89692883 SBS CG PTEN 367 123 HDNN 0.72 30 PAP 928 Ovarian Sample esc Pap cr 17 7577498 SBS CT TP53 0 0 NULL NULL 1 N 0.66 52 PAP 931 Endometrium Sample esc Pap cr 3 41266113 SBS CT CTNNB1 110 37 SFNN 6.22 137 PAP 936 Endometrium Sample esc Pap cr 4 153249384 SBS CT FBXW7 1394 465 RHNN 2.98 62 PAP 936 Endometrium Sample esc Pap cr 10 89692769 SBS GC PTEN 0 0 NULL NULL 1 N 2.55 12 PAP 936 Endometrium Sample esc Pap cr 10 89717775 Indel A PTEN 800 267 K NULL NN 2.84 16 PAP 936 Endometrium Sample esc cr 17 56435167 Indel C RNF43 1970 657 R NULL NN 6.85 51 PAPA 1005 Endometrium Sample esc Tao cr 17 7578413 SBS CA TP53 517 173 VLNN 3, 44 83 PAP 728 Endometrium Sample esc Pap cr 10 89624272 Indel TATCAAGAGG (SEQ ID N: 769) PTEN 46 16 Y NULO NN 4.30 1067 PAP 728 Endometrium Sample esc Pap cr 10 89711893 SBS CT PTEN 511 171 Q * NN 5.61 307 PAP 728 Endometrium Sample esc Pap cr 10 89711899 SBS CT PTEN 517 173 RCNN 50.41 2760 PAP 728 Endometrium Sample esc Pap cr 17 7578402 Indel G TP53 528 176 C NULL NN 5.57 332 PAP 728 Endometrium Sample esc Pap cr 17 7577105 SBS GC TP53 833 278 PRNN 5.09 524 PAP 730 Endometrium Sample esc Pap cr 3 41266118 SBS GA CTNNB1 115 39 ATNN 0.48 89 PAP 730 Endometrium Sample esc Pap cr 3 41266124 SBS AG CTNNB1 121 41 TANN 1.05 196 PAP 730 Endometrium Sample esc Pap cr 3 41266136 SBS TC CTNNB1 133 45 SPNN 0.17 31 PAP 730 Endometrium Sample esc paper cr 12 25398281 SBS CT KRAS 38 13 GDNN 5.71 1936 PAP 730 Endometrium Sample esc Pap cr 12 25398284 SBS CT KRAS 35 12 GDNN 3.14 1064 PAP 730 Endometrium Sample esc Pap cr 12 25398285 SBS CA KRAS 34 12 GCNN 5.65 1913 PAP 730 Endometrium Sample esc Pap Pap cr 10 89624236 Indel A PTEN 10 4 I NULL NN 66.14 26888 PAP 730 Endometrium Sample esc Pap cr 10 89725042 SBS AG PTEN 0 0 NULL NULL 1 N 1.34 180 PAP 730 Endometrium Sample esc Pap cr 17 56435167 Indel C RNF43 1970 657 R NULL NN 23.41 1488 PAP 730 Endometrium Sample esc Pap cr 17 7579716 SBS GA TP53 80 27 PLNN 0.62 128 PAP 735 Endometrium Sample esc cr cr 6 67591125 SBS TC PIK3R1 1718 573 LPNN 3.95 53 PAP 735 Endometrium Sample esc Pap cr 5 67591145 SBS TG PIK3R1 1738 580 YDNN 1.79 24 PAP 735 Endometrium Sample esc Pap cr 10 89711916 SBS TG PTEN 534 178 Y * NN 7.31 253 PAP 736 Endometrium Sample esc Pap cr 3 41266137 SBS CG CTNNB1 134 45 SCNN 0.19 11 PAP 736 Endometrium Sample esc Pap cr 10 89692904 SBS CG PTEN 388 130 RGNN 0.35 49 PAP 789 Endometrium Sample esc Pap cr 3 41266113 SBS CG CTNNB1 110 37 SCNN 15.39 570 PAP 789 Endometrium Sample esc Pap cr 3 178952085 SBS AG PIK3CA 3140 1047 HRNN 14.09 514 PAP 789 Endometrium Sample esc Pap cr 10 89692904 SBS CT PTEN 388 130 R * NN 2.61 298 PAP 790 Endometrium Sample esc Pap cr 4 153249384 SBS C T FBXW7 1394 465 R H N N 15,13 77 PAP 790 Endometrium Sample esc Pap cr 17 7578206 SBS T C TP53 643 215 S G N N 20,20,33 669

PAP 791 Endometrium Sample esc Pap cr 12 25398282 SBS CA KRAS 37 13 GCNN 16.36 1113 PAP 791 Endometrium Sample esc Pap cr 17 56435167 Indel C RNF43 1970 657 R NULL NN 16.61 771 PAP 791 Endometrium Sample esc Pap cr 17 7579554 SBS GC TP53 133 45 LVNN 0.37 58 PAP 802 Endometrium Sample esc cr cr 412126098 SBS AT CTNNB1 95 32 DVNN 13.89 548 PAP 802 Endometrium Sample esc pap cr 3 178916854 SBS GA PIK3CA 241 81 EKNN 12.26 103 PAP 802 Endometrium Sample esc Pap cr 10 89711899 SBS CT PTEN 517 173 RCNN 15.52 382 PAP 802 Endometrium Sample esc Pap cr 10 89717778 SBS TG PTEN 0 0 NULL NULL 1 N 14.77 682 PAP 802 Endometrium Sample esc Pap cr 17 7573948 SBS CG TP53 1079 360 GANN 52.85 2114 PAP 717 Endometrium Sample Pap cr 3 178952085 SBS AG PIK3CA 3140 1047 HRNN 4.79 114 PAP 717 Endometrium Sample sample Pap cr 10 89692944 Indel G PTEN 428 143 G NULL NN 0.43 85 PAP 717 Endometrium Sample esc Pap cr 17 7578271 SBS TC TP53 578 193 HRNN 14.78 457 PAP 719 Endomét river Sample esc Pap cr 3 41266137 SBS CT CTNNB1 134 45 SFNN 0.17 18 PAP 749 Ovarian Sample esc Pap cr 1 115258747 SBS CT NRAS 35 12 GDNN 0.81 47 PAP 753 Endometrium Sample esc Pap cr 19 52715971 SBS CG PPP2R1A 536 179 PRNN 19.16 465 PAP 753 Endometrium Sample esc Pap cr 17 7577121 SBS GA TP53 817 273 RCNN 50.52 3183 PAP 763 Ovarian Sample esc Pap cr 17 7578263 SBS GA TP53 586 196 R * NN 2.19 120 PAP 775 Endometrium Sample esc Pap cr 12 25398284 SBS CG KRAS 35 12 GANN 4.48 321 PAP 775 Endometrium Sample esc cr 10 89711917 Indel AG PTEN 535 179 S NULL NN 3.98 132 PAP 744 Endometrium Sample esc Pap cr 12 25378562 SBS CT KRAS 436 146 ATNN 11.42 1223 PAP 744 Endometrium Sample esc Pap cr 3 178952085 SBS AG PIK3CA 3140 1047 HRNN 13.32 838 PAP 744 Endometrium Sample esc Pap cr 10 89720817 Indel A PTEN 968 323 NULL NN 11.62 620 PAP 748 Endometrium Sample esc Pap Pap cr 17 7579350 SBS AC TP53 337 113 FVNN 39.12 947 PAP 757 Endometrium Sample esc Pap cr 3 41266101 SBS CG CTNNB1 98 33 SCNN 10.28 985 PAP 757 Endometrium Sample esc Pap cr 12 25398285 SBS CG KRAS 34 12 GRNN 8.59 1059 PAP 757 Endometrium Sample esc Pap cr 5 67591134 Indel CGA PIK3R1 1727 576 T NULL NN 8, 78 165 PAP 757 Endometrium Sample esc Pap cr 10 89692904 SBS CG PTEN 388 130 RGNN 13.51 3149 PAP 761 Endometrium Sample esc Pap cr 3 178936091 SBS GA PIK3CA 1633 545 EKNN 17.38 1089 PAP 761 Endometrium Sample esc Pap cr 17 7578413 SBS CG TP53 517 173 VLNN 36.22 1122 PAP 770 Endometrium Sample esc cr cr 10 123279677 SBS GC FGFR2 755 252 SWNN 0.24 13 PAP 770 Endometrium Sample esc Pap cr 10 89692904 SBS CG PTEN 388 130 RGNN 0.65 63 PAP 805 Endometrium Sample esc Pap cr 3 178936074 SBS CG PIK3CA 1616 539 PRNN 4.89 300 PAP 805 Endometrium Sample esc Pap cr 17 7578403 SBS CT TP53 527 176 CYNN 2.16 190 PAP 721 Ovarian Sample esc Pap cr 10 89717749 Indel CC PTEN 774 258 F NULO NN 0.71 60 PAP 742 Ovarian Sample esc Pap cr 3 178952074 SBS G C PIK3CA 3129 1043 MINN 1.54 74 PAP 742 Ovarian Sample esc Pap cr 17 7577509 Indel TC TP53 772 258 E NULL NN 0.12 11 PAP 754 Endometrium Sample esc Pap cr 10 89653819 Indel A PTEN 117 39 A NULL NN 8.00 353 PAP 754 Endometrium Sample esc Pap cr 10 89720817 Indel A PTEN 968 323 N NULL NN 16.80 697 PAP 768 Endometrium Sample esc Pap cr 17 7577082 SBS CT TP53 856 286 EKNN 21.17 1825 PAP 800 Endometrium Sample esc cr cr 41266097 SBS GT CTNNB1 94 32 DYNN 2.61 145 PAP 800 Endometrium Sample esc cr cr 89892904 SBS CG PTEN 388 130 RGNN 1.29 118 PAP 800 Endometrium Sample esc cr cr 8989692905 Indel G PTEN 389 130 R NULL NN 2.79 255 PAP 804 Endometrium Sample esc Pap cr 5 112174631 SBS CT APC 3340 1114 R * NN 11.26 208 PAP 804 Endometrium Sample esc Pap cr 10 123279677 SBS GC FGFR2 755 252 SWNN 2.71 67 PAP 804 Endometrium Sample esc Pap cr 3 178952074 SBS GT PIK3CA 3129 1043 MINN 12.59 272 PAP 804 Endometrium Sample esc Pap cr 12 133253184 SBS GC POLE 857 286 PRNN 11.86 686 PAP 804 Endometrium Sample esc cr cr 898969905 SBS GA PTEN 389 130 RQNN 5.08 1132 PAP 804 Endometrium Sample esc cr 1089692964 SBS GT PTEN 448 150 E * NN 7.81 1740 PAP 804 Endometrium Sample esc Pap cr 10 89692973 SBS GT PTEN 457 153 DYNN 0.46 103 PAP 807 Endometrium Sample esc cr cr 10 89692904 SBS CG PTEN 388 130 RGNN 0.15 19 PAP 812 Endometrium Sample esc Pap cr 12 25398281 SBS CT KRAS 38 13 GDNN 2 , 85 159 PAP 812 Endometrium Sample esc Pap cr 5 67590991 SBS TA PIK3R1 1584 528 Y * NN 1.68 32 PAP 812 Endometrium Sample esc Pap cr 5 67591080 Indel AAA PIK3R1 1673 558 E NULL NN 2.72 84 PAP 812 Endometrium Sample esc Pap cr 10 89692905 Indel G PTEN 389 130 R NULL NN 3.71 439 PAP 866 Endometrium Sample esc Pap cr 17 7578280 SBS GA TP53 569 190 PLNN 5.64 503 PAP 877 Endometrium Sample esc Pap cr 10 89692920 SBS TA PTEN 404 135 IKNN 0.16 16 PAP 884 Endometrium Sample esc Pap cr 3 41266104 SBS GT CTNNB1 101 34 GVNN 2.61 84 PAP 884 Endometrium Sample esc Pap cr 4 153249366 Indel T FBXW7 1412 471 E NULL NN 2.60 125 PAP 884 Endometrium Sample esc Pap cr 10 123279677 SBS GC FGFR2 755 252 SWNN 3.77 179 PAP 884 Endometrium Sample esc Pap cr 5 67589138 SBS GA PIK3R1 1126 376 GRNN 2.63 20 PAP 884 Endometrium Sample esc Pap cr 12 133250289 SBS CA POLE 1231 411 VLNN 3.16 130 PAP 884 Endometrium Sample esc Pap cr 10 89692905 SBS GA PTEN 389 130 RQNN 2.80 260 PAP 884 Endometrium Sample esc Pap cr 10 89692976 SBS TG PTEN 460 154 FVNN 2.76 208 PAP 884 Endometrium Sample esc cr cr 17 7578475 SBS GA TP53 455 152 PLNN 4.74 181 PAP 892 Endometrium Sample esc Pap cr 5 67589639 Indel GAAGAATAT PIK3R1 1402 468 E NULO NN 4.30 130 PAP 892 Endometrium Sample esc Pap cr 10 89720771 Indel C PTEN 922 308 R NULL NN 1.39 21 PAP 892 Endometrium Sample esc Tao cr 5 67589639 Indel GAAGAATAT PIK3R1 1402 468 E NULL NN 7.93 220 PAP 892 Endometrium Sample esc Tao cr 10 89720771 Indel C PTEN 922 308 R NULL NN 5.41 85 PAP 897 Endometrium Sample esc Pap cr 12 25398284 SBS CT KRAS 35 12 GDNN 4.04 225 PAP 897 Endometrium Sample esc cr cr 3 178952007 SBS AG PIK3CA 3062 1021 YCNN 5.12 28 PAP 897 Endometrium Sample esc cr cr 10 89717775 Indel A PTEN 800 267 K NULO NN 4.80 156 PAP 897 Endometrium Sample esc cr 17 56435167 Indel C RNF43 1970 657 R NULL NN 8.44 216 PAP 897 Endometrium Sample esc Tao cr 12 25398284 SBS CT KRAS 35 12 GDNN 20.20, 69 952 PAP 897 Endometrium Sample esc Tao cr 3 178952007 SBS AG PIK3CA 3062 1021 YCNN 15.62 77 PAP 897 Endometrium Sample esc Tao cr 10 89717775 Indel A PTEN 800 267 K NULL NN 25.53 1287 PAP 897 Endometrium Sample sam Tao cr 17 56435167 Indel C RNF43 1970 657 R NULL NN 44.94 2136 PAP 901 Endometrium Sample esc Pap cr 12 25398284 SBS CT KRAS 35 12 GDNN 31.90 1359 PAP 901 Endometrium Sample esc Pap cr 5 67591130 Indel AA PIK3R1 1723 575 K NULO NN 32 , 98 187 PAP 901 Endometrium Sample esc Pap cr 17 56435167 Indel C RNF43 1970 657 R NULO N N 59.29 1445 PAP 901 Endometrium Sample esc Tao cr 12 25398284 SBS C T KRAS 35 12 G D N N 44.57 3934

PAP 901 Endometrium Sample esc Tao cr 5 67589581 Indel A PIK3R1 1344 448 K NULL NN 12,36 860 PAP 901 Endometrium Sample esc Tao cr 5 67591130 Indel AA PIK3R1 1723 575 K NULL NN 39,56 731 PAP 901 Endometrium Sample esc Tao cr 17 56435167 Indel C RNF43 1970 657 R NULL NN 83.28 1564 PAP 904 Endometrium Sample esc Pap cr 10 89624305 SBS TA PTEN 79 27 YNN Yes 16.66 2343 PAP 904 Endometrium Sample esc Pap cr 17 7578519 Indel C TP53 411 137 L NULL NN NN 40.70 2119 PAP 904 Endometrium Sample esc Pap cr 17 7578518 Indel C TP53 412 138 A NULL NN 45.35 795 PAP 904 Endometrium Sample esc Tao cr 10 89624305 SBS TA PTEN 79 27 YNN Yes 21.03 5169 PAP 904 Endometrium Sample esc Tao cr 17 7578519 Indel C TP53 411 137 L NULL NN 63.57 4506 PAP 904 Endometrium Sample esc Tao cr 17 7578518 Indel C TP53 412 138 A NULL NN 64.24 1202 PAP 910 Endometrium Sample esc Tao cr 10 123279677 SBS GC FGFR2 755 252 SWNN 15.79 422 PAP 910 Endometrium Sample esc Tao cr 3 178952085 SBS AG PIK3CA 3140 1047 HR NN 24.27 647 PAP 813 Endometrium Sample esc Pap cr 10 123279677 SBS GC FGFR2 755 252 SWNN 1.27 119 PAP 813 Endometrium Sample esc Pap cr 12 25398285 SBS CA KRAS 34 12 GCNN 1.21 73 PAP 813 Endometrium Sample esc Pap cr 17 56435167 Indel C RNF43 1970 657 R NULL NN 7.49 382 PAP 867 Endometrium Sample esc Pap cr 12 25380275 SBS TG KRAS 183 61 QHNN 7.55 447 PAP 867 Endometrium Sample esc Pap cr 10 89692904 SBS CT PTEN 388 130 R * NN 2.93 415 PAP 867 Endometrium Sample esc Pap cr 17 56435167 Indel C RNF43 1970 657 R NULL NN 0.87 93 PAP 868 Endometrium Sample esc Pap cr 10 89717684 SBS AT PTEN 709 237 K * NN 0.78 58 PAP 868 Endometrium Sample esc Pap cr 10 89720804 Indel ACTT PTEN 955 319 T NULL NN 5.06 69 PAP 869 Endometrium Sample esc pap cr 3 178952085 SBS AT PIK3CA 3140 1047 HLNN 44.98 4729 PAP 869 Endometrium Sample esc esc cr 17 7578448 Indel GGATGG TP53 482 161 A NULL NN 17.27 1196 PAP 869 Endometrium Sample esc Pap cr 17 7578204 SBS AC TP53 645 215 SRNN 1 9.74 696 PAP 878 Endometrium Sample esc Pap cr 10 123279677 SBS GC FGFR2 755 252 SWNN 0.45 26 PAP 878 Endometrium Sample esc Pap cr 5 67589642 Indel AATATGAAG PIK3R1 1405 469 E NULL NN 0.57 27 PAP 878 Endometrium Sample esc Pap cr 5 67589647 Indel GAAGAATAT PIK3R1 1410 470 Y NULL NN 0.34 22 PAP 879 Endometrium Sample Pap cr 10 89690847 SBS GC PTEN 0 0 NULL NULL 1 N 5.45 105 PAP 881 Endometrium Sample esc Pap cr 3 178936082 SBS GA PIK3CA 1624 542 EKNN 18.88 602 PAP 881 Endometrium Sample esc cr 19 52716323 SBS CA PPP2R1A 767 256 SYNN 21.32 397 PAP 881 Endometrium Sample esc Pap cr 17 7578268 SBS AC TP53 581 194 LRNN 15.58 1616 PAP 910 Endometrium Sample esc Pap Pap cr 10 123279677 SBS GC FGFR2 755 252 SWNN 1.34 88 PAP 865 Endometrium Sample esc Pap cr 17 7577121 SBS GA TP53 817 273 RCNN 8.02 363 PAP 870 Endometrium Sample esc Pap cr 10 123258034 SBS AT FGFR2 1647 549 NKNN 1.06 49 PAP 870 Endometrium Sample esc Pap cr 10 123247516 SBS TC FGFR2 1975 659 KENN 1.77 62 PAP 870 Endometrium Sample esc Pap cr 5 67588989 Indel CG PIK3R1 1080 360 A NULL NN 6.62 70 PAP 870 Endometrium Sample esc Pap cr 10 89692904 SBS CT PTEN 388 130 R * NN 2.27 418 PAP 871 Endometrium Sample esc Pap cr 3 41266100 SBS TG CTNNB1 97 33 SANN 0.51 28 PAP 871 Endometrium Sample esc Pap cr 3 41266112 SBS TC CTNNB1 109 37 SPNN 0.27 15 PAP 871 Endometrium Sample esc Pap cr 3 178916876 SBS GA PIK3CA 263 88 RQNN 27.06 266 PAP 871 Endometrium Sample esc Pap cr 10 89717672 SBS CT PTEN 697 233 R * NN 8.50 480 PAP 871 Endometrium Sample esc Pap cr 10 89725065 Indel A PTEN 1048 350 T NULL NN 3.85 86 PAP 871 Endometrium Sample esc Pap cr 17 56435168 Indel G RNF43 1969 657 R NULO NN 17.29 1289 PAP 871 Endometrium Sample esc pap cr 17 56435168 Indel GC RNF43 1969 657 R NULL NN 1.42 106 PAP 874 Endometrium Sample esc Pap cr 10 89690847 SBS GT PTEN 0 0 NULL NULL 1 N 31.21 437 PAP 874 Endometrium Sample esc Pap cr 10 89692905 Indel G PTEN 389 130 R NULL N N 15.68 1520 PAP 884 Endometrium Sample esc Tao cr 3 41266104 SBS GT CTNNB1 101 34 GVNN 21.72 1089 PAP 884 Endometrium Sample esc Tao cr 4 153249366 Indel T FBXW7 1412 471 E NULL NN 20.20.00 1195 PAP 884 Endometrium Sample esc Tao cr 10 123279677 SBS GC FGFR2 755 252 SWNN 14.98 834 PAP 884 Endometrium Sample esc Tao cr 12 133250289 SBS CA POLE 1231 411 VLNN 27.17 1521 PAP 884 Endometrium Sample tao cr 10 89692905 SBS GA PTEN 389 130 RQNN 15.99 1820 PAP 884 Endometrium Sample esc Tao cr 10 89692976 SBS TG PTEN 460 154 FVNN 26.62 1801 PAP 884 Endometrium Sample esc Tao cr 17 7578475 SBS GA TP53 455 152 PLNN 37.28 2010 PAP 894 Endometrium Sample esc Tao cr 3 178936082 SBS GA PIK3CA 1624 542 EKNN 18.89 512 PAP 894 Endometrium Sample esc Tao cr 10 89720817 Indel A PTEN 968 323 NULL NN N 39.93 825 PAP 914 Ovarian Sample esc Tao cr 17 7577139 SBS GA TP53 799 267 RWNN 4.65 361 PAP 816 Endometrium Sample esc Pap cr 14 105246551 SBS CT AKT1 49 17 EKNN 6.04 346 PAP 816 Endometrium Sample esc Pap cr 3 41266101 SBS CG CTNNB1 98 33 SCNN 3.13 239 PAP 875 Endometrium Sample esc Pap cr 3 41266124 SBS AG CTNNB1 121 41 TANN 0.55 49 PAP 875 Endometrium Sample esc Pap cr 10 89692855 Indel TGAAG PTEN 339 113 S NULL NN 86.54 4610 PAP 875 Endometrium Sample esc Pap cr 17 56435167 Indel C RNF43 1970 657 R NULL NN 41.31 3390 PAP 876 Endometrium Sample esc Pap cr 12 25398285 SBS CA KRAS 34 12 GCNN 6 , 98 669 PAP 876 Endometrium Sample esc Pap cr 5 67591030 Indel TAG PIK3R1 1623 541 S NULL NN 1.25 36 PAP 876 Endometrium Sample esc cr cr 10 89692980 SBS AG PTEN 464 155 YCNN 14.81 899 PAP 876 Endometrium Sample esc Pap cr 17 56435167 Indel C RNF43 1970 657 R NULL NN 5.76 483 PAP 894 Endometrium Sample esc Pap cr 10 89720817 Indel A PTEN 968 323 NULL NN 1.07 188 PAP 902 Endometrium Sample esc Pap cr 19 52715982 SBS CT PPP2R1A 547 183 RWNN 39.23 3035 PAP 902 Endometrium Sample esc Pap cr 10 89692904 SBS CG PTEN 388 130 RGNN 14.81 1441 PAP 902 Endometrium Sample esc Pap cr 10 89720804 Indel ACTT PTEN 955 319 T NULL NN 53.03 815 PAP 902 Endometrium Sample esc cr cr 17 56435167 Indel C RNF43 1970 657 R NULL NN 3.96 159 PAP 902 Endometrium Sample esc Tao cr 19 52715982 SBS CT PPP2R1A 547 183 RWNN 36.92 2698 PAP 902 Endometrium Sample esc Tao cr 10 89692904 SBS CG PTEN 388 130 RGNN 15.26 2711 PAP 902 Endometrium Sample esc Tao cr 10 89720804 Indel ACTT PTEN 955 319 TULO NULO NN 52.33 1753 PAP 907 Endometrium Sample esc Pap cr 10 89653832 Indel GAAG PTEN 130 44 G NULL NN 4.89 514 PAP 907 Endometrium Sample esc Pap cr 10 89653833 Indel AAGG PTEN 131 44 G NULL NN 3.93 37 PAP 907 Endometrium Sample esc Pap cr 17 56435167 Indel C RNF43 1970 657 R NULL NN 2.26 133 PAP 907 Endometrium Sample esc Tao cr 12 25398282 SBS CT KRAS 37 13 GSNN 0.03 2 PAP 907 Sample Endoscope Esc tao cr 10 89653832 Indel GAAG PTEN 130 44 G NULL NN 6.67 341 PAP 907 Endometrium Sample esc Tao cr 10 89653833 Indel AAGG PTEN 1 31 44 G NULL N N 7.87 98 PAPA 1135 Endometrium Sample esc cr cr 25258282 SBS C T KRAS 37 13 G S N N 0.02 2 PAPA 1145 Endometrium Sample esc Pap cr 12 133253184 SBS G C POLE 857 286 P R N N 1.97 68

PAPA 1145 Endometrium Sample esc Pap cr 17 7578212 SBS GA TP53 637 213 R * NN 2.50 97 PAPA 1160 Endometrium Sample esc Tao cr 4 153247366 SBS CT FBXW7 1436 479 RQNN 24.54 766 PAPA 1160 Endometrium Sample esc Tao cr 5 67591106 SBS AG PIK3R1 1699 567 KENN 30.60 4501 PAPA 1160 Sample endometrium Tao cr 12 133250289 SBS CA POLE 1231 411 VLNN 25.39 2932 PAPA 1160 Sample endometrium Esc Tao cr 10 89692905 SBS GA PTEN 389 130 RQNN 27.56 2991 PAPA 1160 Endometrium Sample esc Tao cr 10 89711899 SBS CT PTEN 517 173 RCNN 28.80 1407 PAPA 1160 Endometrium Sample esc Tao cr 17 7578212 SBS GA TP53 637 213 R * NN 9.82 491 PAPA 1136 Ovarian Sample esc Pap cr 5 67590390 Indel T PIK3R1 1452 484 I NULL NN 0.22 10 PAPA 1129 Endometrium Sample esc Tao cr 17 7577120 SBS CT TP53 818 273 RHNN 26.46 1658 PAPA 1135 Endometrium Sample esc Tao cr 17 7578212 SBS GA TP53 637 213 R * NN 2.20 41 PAPA 1035 Endometrium Sample esc Pap cr 19 52715971 SBS CG PPP2R1A 536 179 PRNN 4.9 0 149 PAPA 1035 Endometrium Sample esc Pap cr 17 7577046 SBS CA TP53 892 298 E * NN 7.34 423 PAPA 1037 Ovarian Sample esc Pap cr 17 7577117 SBS AC TP53 821 274 VGNN 69.69 1750 PAPA 1048 Endometrium Sample esc Pap cr 10 89692905 Indel G PTEN 389 130 R NULL NN 0.90 46 PAPA 1048 Endometrium Sample esc Pap cr 10 89692905 SBS GA PTEN 389 130 RQNN 1.35 69 PAPA 1080 Endometrium Sample esc Pap cr 10 89720724 Indel A PTEN 875 292 N NULL NN 2 , 53 13 PAPA 1086 Endometrium Sample esc Pap cr 10 89692791 SBS AC PTEN 275 92 DANN 2.80 108 PAPA 1092 Endometrium Sample esc Pap cr 3 41266104 SBS GT CTNNB1 101 34 GVNN 31.49 1031 PAPA 1092 Endometrium Sample esc cr cr 52715983 SBS GA PPP2R1A 548 183 RQNN 30.51 1114 PAPA 1095 Sample Endoscope esc Pap cr 10 89692904 SBS CG PTEN 388 130 RGNN 0.19 12 PAPA 1059 Sample Endometrium esc Pap cr 3 178916876 SBS GA PIK3CA 263 88 RQNN 7.91 792 PAPA 1081 Endometrium Sample esc Pap cr 3 41266124 SBS AG CTNNB1 121 41 TANN 19.73 540 PAPA 1081 Endometrium Sample esc Pap cr 3 178916876 SBS GA PIK3CA 263 88 RQNN 11.41 1170 PAPA 1081 Endometrium Sample esc Pap cr 3 178952074 SBS GT PIK3CA 3129 1043 MINN 17.28 1627 PAPA 1081 Endometrium Sample esc cr cr 10 89624245 SBS GT 7 E * NN 21.44 4758 PAPA 1081 Endometrium Sample esc cr cr 10 89692973 SBS GT PTEN 457 153 DYNN 0.24 19 PAPA 1081 Endometrium Sample esc cr 10 89711900 SBS GA PTEN 518 173 RHNN 18.38 506 PAPA 1098 Ovarian Sample esc Pap cr 10 123258034 SBS AT FGFR2 1647 549 NKNN 1.12 30 PAPA 1098 Ovarian Sample esc Pap cr 10 89624270 Indel GA PTEN 44 15 R NULL NN 1.72 364 PAPA 1098 Ovarian Sample esc Pap cr 10 89717775 Indel A PTEN 800 267 K NULO NN 1.20 60 PAPA 1103 Endometrium Sample esc Pap cr 10 89720716 Indel A PTEN 867 289 K NULO NN 3.80 25 PAPA 1103 Endometrium Sample esc Pap cr 17 56435167 Indel C RNF43 1970 657 R NULL NN 2.88 188 PAPA 1040 Endometrium Sample esc Pap cr 3 178916854 SBS GA PIK3CA 241 81 EKNN 2.20 349 PAPA 1040 Endometrium Sample esc Pap cr 3 178952007 SBS AG PIK3CA 3062 1021 YCNN 7.35 247 PAPA 1040 Endometrium Sample esc Pap cr 10 89624270 SBS GT PTEN 44 15 RINN 1.81 594 PAPA 1040 Endometrium Sample esc Pap cr 10 89692904 SBS CT PTEN 388 130 R * NN 1.02 203 PAPA 1040 Sample endoscope Pap cr 10 89720744 SBS GT PTEN 895 299 E * NN 9.35 79 PAPA 1066 Sample endometrium Pap cr 4 153247351 Indel C FBXW7 1451 484 R NULO NN 0.67 37 PAPA 1066 Endometrium Sample esc Pap cr 5 67590484 Indel GGCAATGAGAA (SEQ ID NO: 770) PIK3R1 1546 516 G NULL NN 0.72 95 PAPA 1066 Endometrium Sample esc Pap cr 5 67591141 Indel CCAATACTTGA (SEQ ID N: 771 ) PIK3R1 1734 578 D NULL NN 0.55 52 PAPA 1066 Endometrium Sample esc Cr 5 67591154 Indel TC PIK3R1 0 0 NULL NULL 1 N 0.41 39 PAPA 1066 Endometrium Sample esc Pap cr 10 89690810 SBS GT PTEN 217 73 E * NN 2.97 133 PAPA 1066 Endometrium Sample esc Pap cr 10 89692905 SBS GT PTEN 389 130 RLNN 0.76 127 PAPA 1077 Endometrium Am Oyster esc Pap cr 5 67589560 Indel TATTGA PIK3R1 1323 441 NULL NN NN 12.44 248 PAPA 1077 Endometrium Sample esc cr 10 89711903 Indel AT PTEN 521 174 Y NULO NN 1.17 44 PAPA 1077 Endometrium Sample esc Pap cr 10 89717775 Indel A PTEN 800 267 K NULL NN 7.60 318 PAPA 1077 Endometrium Sample esc Pap cr 10 89720804 Indel ACTT PTEN 955 319 T NULL NN 0.95 24 PAPA 1097 Endometrium Sample esc Pap cr 3 178952085 SBS AG PIK3CA 3140 1047 HRNN 15.86 1507 PAPA 1097 Endometrium Sample esc Pap cr 10 89690810 SBS GT PTEN 217 73 E * NN 37,79 947 PAPA 1097 Endometrium Sample esc cr 10 89692953 Indel T PTEN 437 146 L NULL NN 9,86 978 PAPA 1097 Endometrium Sample esc Pap cr 17 7579314 SBS TC TP53 373 125 TANN 11.72 461 PAPA 1100 Sample endoscope Pap cr 14 105246551 SBS CT AKT1 49 17 EKNN 0.72 40 PAPA 1100 Sample endometrium esc Pap cr 3 41266125 SBS CTNNB1 122 41 TINN 0.46 21 PAPA 1110 Ovarian Sample esc Pap cr 17 7578490 SBS AC TP53 440 147 VGNN 0.71 59 PAPA 1115 Ovarian Sample esc Pap cr 5 67589583 SBS TG PIK3R1 1346 449 L * NN 0.28 12 PAPA 1115 Ovarian Sample esc cr cr 17 7578263 SBS GA TP53 586 196 R * NN 1.56 78 PAPA 1057 Endometrium Sample esc cr cr 1 115258747 SBS CT NRAS 35 12 GDNN 38.24 4736 PAPA 1057 Endometrium Sample esc Pap cr 3 178936082 SBS GA PIK3CA 1624 542 EKNN 26.78 1638 PAPA 1057 Endometrium Sample esc Pap cr 17 7578406 SBS CT TP53 524 175 RHNN 4.59 369 PAPA 1057 Endometrium Sample esc Pap cr 17 7577141 SBS CT TP53 797 266 GENN 37.91 1877 PAPA 1069 Endometrium Sample esc Pap cr 3 178916876 SBS GA PIK3CA 263 88 RQNN 0.90 131 PAPA 1069 Sample Endoscope Sample esc cr cr 10 89690835 SBS TG PTEN 242 81 FCNN 0.63 18 PAPA 1069 Endometrium Sample esc Pap cr 10 89624245 SBS GT PTEN 19 7 E * NN 1.18 248 PAPA 1069 Endometrium Sample esc cr cr 10 89624270 SBS GT PTEN 44 15 RINN 1.70 356 PAPA 1076 Endometrium Sample esc Pap Pap cr 3 178952084 SBS CT PIK3CA 3139 1047 HYNN 3.36 245 PAPA 1112 Ovarian Am oyster esc Pap cr 3 41266137 SBS CG CTNNB1 134 45 SCNN 0.31 12 PAPA 1152 Endometrium Sample tao cr 4 153249384 SBS CT FBXW7 1394 465 RHNN 33.80 576 PAPA 1152 Endometrium Sample tao cr 5 67591106 SBS AG PIK3RN 1699 567 K7 567 K7 43.61 3021 PAPA 1153 Endometrium Sample esc Pap cr 3 41266100 SBS TG CTNNB1 97 33 SANN 2.33 100 PAPA 1153 Endometrium Sample esc Pap cr 3 178952085 SBS AG PIK3CA 3140 1047 HRNN 3.89 208 PAPA 1153 Endometrium Sample esc Pap cr 10 89692811 SBS GT PTEN 295 99 E * NN 7.16 29 PAPA 1153 Endometrium Sample esc Pap cr 17 56435167 Indel C RNF43 1970 657 R NULL NN 2.38 69 PAPA 1153 Endometrium Sample esc Pap cr 17 7578229 SBS TC TP53 620 207 DGNN 1 , 42 7 PAPA 1154 Endometrium Sample esc Pap cr 5 67591103 Indel AACGTATGAACAGCA (SEQ ID NO: 779) PIK3R1 1696 566 I NULL NN 0.44 19 PAPA 1154 Endometrium Sample esc Tao cr 10 123279677 SBS GC FGFR2 755 252 SWNN 7.65 507 PAPA 1154 Endometrium Sample esc Tao cr 5 67591103 Indel AACGTATGAACAGCA (SEQ I DN: 780) PIK3R1 1696 566 I NULL NN 35.33 2309 PAPA 1155 Endometrium Sample esc Pap cr 10 123279677 SBS GC FGFR2 755 252 SWNN 1.74 139 PAPA 1155 Endometrium Sample esc Pap cr 3 178952085 SBS AG PIK3CA 3140 1047 HRNN 8, 61 557 PAPA 1155 Endometrium Sample esc Pap cr 10 89692886 SBS TA PTEN 370 124 CSNN 7.39 313

PAPA 1155 Endometrium Sample esc Tao cr 10 123279677 SBS GC FGFR2 755 252 SWNN 12.90 769 PAPA 1155 Endometrium Sample esc Tao cr 3 178952085 SBS AG PIK3CA 3140 1047 HRNN 35.08 2025 PAPA 1155 Endometrium Sample tao cr 10 89692886 SBS TAEN PTEN 370 124 CSNN 65.68 2019 PAPA 1185 Endometrium Sample esc cr 10 89717698 Indel T PTEN 723 241 F NULL NN 0.61 351 PAPA 1153 Endometrium Sample esc Tao cr 3 178952085 SBS AG PIK3CA 3140 1047 HRNN 28.28 1338 PAPA 1153 Endometrium Sample esc Tao cr 10 89692811 SBS GT PTEN 295 99 E * NN 56.97 139 PAPA 1157 Endometrium Sample esc Pap cr 3 178952085 SBS AT PIK3CA 3140 1047 HLNN 34.61 1891 PAPA 1177 Sample endoscope Sample tao cr 10 123247516 SBS TC FGFR2 1975 659 KENN 49.21 17781 PAPA 1177 Endometrium Sample esc Tao cr 3 178952085 SBS AT PIK3CA 3140 1047 HLNN 26.68 1388 PAPA 1177 Endometrium Sample esc Tao cr 10 89692904 SBS CG PTEN 388 130 RGNN 20.20.77 97 PAPE 1188 Endometrium Sample esc Pap cr 17 7578406 SBS CT TP53 524 175 RHNN 24.02 541 PAPA 1254 Endometrium Sample esc Tao cr 12 25398284 SBS CT KRAS 35 12 GDNN 12.44 100 PAPA 1254 Endometrium Sample esc Tao cr 3 178952077 SBS TA PIK3CA 3132 1044 NKNN 13.99 735 PAPA 1254 Endometrium Sample esc Tao cr 10 89692904 SBS CG PTEN 388 130 RGNN 9.68 24 PAPA 1157 Endometrium Sample esc Tao cr 3 178952085 SBS AT PIK3CA 3140 1047 HLNN 32.88 1959 PAPA 1184 Endometrium Sample esc Tao cr 12 25398284 SBS CG KRAS 35 12 GANN 15 , 54 174 PAPA 1184 Endometrium Sample esc Tao cr 10 89692904 SBS CG PTEN 388 130 RGNN 11,97 31 PAPA 1184 Endometrium Sample esc Tao cr 10 89692905 Indel G PTEN 389 130 R NULL NN 8,88 23 PAPA 1185 Endometrium Sample Tao cr 10 89717698 Indel T PTEN 723 241 F NULL NN 0.61 380 PAPA 1188 Endometrium Sample esc Tao cr 17 7578406 SBS CT TP53 524 175 RHNN 64.47 1724 PAPA 1258 Ovarian Sample esc Tao cr 17 7578442 SBS TC TP53 488 163 YCNN 4, 32 188 PAPA 1258 Ovarian Sample esc Tao cr 17 7574010 SBS CGT P53 1017 339 EDNN 0.89 29 PAPA 1264 Endometrium Sample esc cr cr 41212113 SBS CT CTNNB1 110 37 SFNN 0.29 12 PAPA 1264 Endometrium Sample esc Pap cr 10 123279677 SBS GC FGFR2 755 252 SWNN 0.56 47 PAPA 1264 Endometrium Sample esc Pap cr 5 67591135 Indel G PIK3R1 1728 576 T NULL NN 3.06 17 PAPA 1264 Sample Endoscope esc Pap cr 10 89692904 SBS CG PTEN 388 130 RGNN 0.40 10 PAPA 1264 Sample Endometrium esc Tao cr 3 41266113 SBS CT CTNNB1 110 37 SFNN 7.75 360 PAPA 1264 Endometrium Sample esc Tao cr 10 123279677 SBS GC FGFR2 755 252 SWNN 10.13 668 PAPA 1264 Endometrium Sample esc Tao cr 3 178952085 SBS AG PIK3CA 3140 1047 HRNN 10.89 576 PAPA 1264 Endometrium Sample so large 5 67588989 Indel CG PIK3R1 1080 360 A NULL NN 10.82 102 PAPA 1264 Endometrium Dark sample 5 67591135 Indel G PIK3R1 1728 576 T NULL NN 36.43 161 PAPA 1264 Endometrium Sample dark as 10 89692904 SBS CG PTEN 388 130 RGNN 7.02 134 PAPA 1251 Endometrium Sample esc Tao cr 10 8962 4242 SBS AT PTEN 16 6 K * NN 70.28 3192 PAPA 1251 Endometrium Sample esc Tao cr 17 7577115 SBS AG TP53 823 275 CRNN 34.08 122 PAPA 1253 Ovarian Sample esc Tao cr 17 7578190 SBS TC TP53 659 220 YCNN 0.88 5 PAPA 1255 Endometrium Sample esc Pap cr 10 89624285 Indel GA PTEN 59 20 G NULL NN 0.45 26 PAPA 1255 Endometrium Sample esc Tao cr 10 89624285 Indel GA PTEN 59 20 G NULL NN 48.11 1664 PAPA 1256 Endometrium Sample esc Pap cr 17 7578394 SBS TC TP53 536 179 HRNN 7.77 122 PAPA 1152 Endometrium Sample esc cr 5 67591106 SBS AG PIK3R1 1699 567 KENN 0.43 46 PAPA 1158 Endometrium Sample esc Pap cr 10 89624245 SBS GT PTEN 19 7 E * NN 0, 14 37 PAPA 1177 Endometrium Sample esc Pap cr 10 123247516 SBS TC FGFR2 1975 659 KENN 3.21 699 PAPA 1177 Endometrium Sample esc cr 3 178952085 SBS AT PIK3CA 3140 1047 HLNN 1.53 99 PAPA 1177 Endometrium Sample esc cr 10 89692904 SBS CG PTEN 388 130 RGNN 1.16 201 PAPA 1251 Endometrium Sample esc Pap cr 10 89624242 SBS AT PTEN 16 6 K * NN 5.77 548 PAPA 1251 Endometrium Sample esc Pap cr 17 7577115 SBS AG TP53 823 275 CRNN 2.28 35 PAPA 1259 Ovarian Sample esc Tao cr 19 52716327 SBS GT PPP2R1A 771 257 WCNN 6.14 184 PAPA 1259 Ovarian Sample esc Tao cr 17 7578201 Indel CAC TP53 648 216 V NULL NN 0.32 3 PAPA 1198 Endometrium Sample esc cr 17 7577114 SBS CT TP53 824 275 CYNN 59.37 2559 PAPA 1209 Sample Endoscope esc Pap cr 10 89690810 SBS GT PTEN 217 73 E * NN 12.49 289 PAPA 1209 Endometrium Sample esc Pap cr 17 56435167 Indel C RNF43 1970 657 R NULL NN 0.85 86 PAPA 1209 Endometrium Sample esc Pap cr 17 7577022 SBS GA TP53 916 306 R * NN 14, 76 851 PAPA 1211 Endometrium Sample esc Pap cr 10 89692908 Indel CTGGTGTAA PTEN 392 131 T NULL NN 0.28 10 PAPA 1214 Endometrium Sample esc cr 1 115258744 SBS CT NRAS 38 13 GDNN 2.59 436 PAPA 1214 Endometrium Sample esc Pap cr 3 178952085 SBS AG PIK3CA 3140 1047 HRNN 4.04 170 PAPA 1214 Endometrium Sample esc Pap cr 10 89717775 I level A PTEN 800 267 K NULL NN 1.34 27 PAPA 1226 Endometrium Sample esc Pap cr 10 89693007 Indel A PTEN 491 164 K NULL NN 2.95 24 PAPA 1235 Ovarian Sample esc Pap cr 9 21971080 SBS GA CDKN2A 278 93 TMNN 1, 22 3 PAPA 1239 Endometrium Sample esc Pap cr 3 41266101 SBS CA CTNNB1 98 33 SYNN 0.47 66 PAPA 1239 Endometrium Sample esc Pap cr 3 41266113 SBS CT CTNNB1 110 37 SFNN 4.15 580 PAPA 1239 Endometrium Sample esc cr cr 41266137 SBS CT CTNNB1 134 45 SFNN 0.09 12 PAPA 1239 Endometrium Sample esc cr cr 1 115258747 SBS CT NRAS 35 12 GDNN 0.44 72 PAPA 1239 Endometrium Sample esc pap cr 3 178936092 SBS AG PIK3CA 1634 545 EGNN 21.19 275 PAPA 1239 Endometrium Sample esc Pap cr 10 89717615 SBS CT PTEN 640 214 Q * NN 18.23 1134 PAPA 1239 Endometrium Sample esc Pap cr 10 89720804 Indel A PTEN 955 319 T NULL NN 9.58 82 PAPA 1239 Endometrium Sample esc Pap cr 17 7579312 SBS CT TP53 375 125 TTN Yes 14.35 1498 PAPA 1293 Endometrium Sample esc Pap cr 3 41266125 SBS CT CTNNB1 122 41 TINN 0.35 34 PAPA 1299 Endometrium Sample esc Pap cr 3 41266104 SBS GA CTNNB1 101 34 GENN 19.53 2476 PAPA 1299 Endometrium Sample esc Pap cr 10 89624243 Indel AA PTEN 17 6 K NULL NN 22.56 3980 PAPA 1299 Endometrium Sample esc Pap cr 10 89711914 Indel T PTEN 532 178 Y NULO NN 21.83 429 PAPA 1300 Endometrium Sample esc Pap cr 5 67589634 Indel TATATGAAGAAT (SEQ ID N: 772) PIK3R1 1397 466 L NULL NN 9.56 256 PAPA 1300 Endometrium Sample esc Pap cr 10 89692802 SBS CG PTEN 286 96 PANN 16.06 128 PAPA 1300 Endometrium Sample esc Pap cr 10 89717750 Indel C PTEN 775 259 H NULL NN 13.47 277 PAPA 1300 Sample Endoscope Sample esc cr cr 10 89720741 SBS CT PTEN 892 298 Q * NN 12.66 71 PAPA 1300 Sample endoscope Pap cr 17 7578206 SBS TC TP53 643 215 SGNN 19.48 493 PAPA 1303 Endometrium Sample esc Pap cr 17 7577022 SBS GA TP53 916 306 R * NN 4.89 390 PAPA 1308 Endometrium Sample esc Pap cr 3 41266097 SBS GA CTNNB1 94 32 DNNN 0.56 72 PAPA 1308 Endometrium Sample a esc Pap cr 10 123258035 SBS TC FGFR2 1646 549 NSNN 1.23 21 PAPA 1308 Endometrium Sample esc cr cr 10 89624297 Indel AC PTEN 71 24 D NULL NN 0.14 29 PAPA 1192 Endometrium Sample esc esc cr 5 67591134 Indel CGAGAG PIK3R1 1727 576 T NULL NN 34.94 647

PAPA 1192 Endometrium Sample esc Pap cr 10 89692904 SBS CT PTEN 388 130 R * NN 8.96 306 PAPA 1192 Endometrium Sample esc Pap cr 10 89720669 Indel T PTEN 820 274 W NULL NN 34.16 221 PAPA 1192 Endometrium Sample esc Pap cr 17 7577114 SBS CT TP53 824 275 CYNN 19.06 710 PAPA 1192 Endometrium Sample esc Pap cr 17 7574017 SBS CA TP53 1010 337 RLNN 19.45 399 PAPA 1207 Endometrium Sample esc Pap cr 12 25398284 SBS CT KRAS 35 12 GDNN 11.90 1210 PAPA 1207 Endometrium Sample esc Pap cr 10 89717672 SBS CT PTEN 697 233 R * NN 5.49 2001 PAPA 1207 Endometrium Sample esc Pap cr 10 89717775 Indel A PTEN 800 267 K NULL NN 11.87 225 PAPA 1218 Endometrium Sample esc Pap cr 10 89720804 Indel ACTT PTEN 955 319 T NULL NN 3.63 30 PAPA 1218 Endometrium Sample Pap cr 10 89725042 SBS AC PTEN 0 0 NULL NULL 1 N 9.98 169 PAPA 1218 Endometrium Sample esc Pap cr 17 56435167 Indel C RNF43 1970 657 R NULL NN 11.25 1105 PAPA 1223 Endometrium Sample esc Pap cr 10 89624284 SBS GT PTEN 58 20 G * N N 1.83 328 PAPA 1242 Endometrium Sample esc Pap cr 12 25398284 SBS CT KRAS 35 12 GDNN 12.30 1580 PAPA 1298 Ovarian Sample esc Pap cr 17 7578413 SBS CA TP53 517 173 VLNN 7.52 576 PAPA 1326 Ovarian Sample esc Pap cr 5 112175639 SBS CT APC 4348 1450 R * NN 4.37 572 PAPA 1326 Ovarian Sample esc Pap cr 12 25398281 SBS CT KRAS 38 13 GDNN 12.18 1118 PAPA 1326 Ovarian Sample esc Pap cr 3 178916854 SBS GA PIK3CA 241 81 EKNN 1, 16 42 PAPA 1326 Ovarian Sample esc Pap cr 3 178952085 SBS AT PIK3CA 3140 1047 HLNN 0.25 14 PAPA 1326 Ovarian Sample esc Pap cr 17 7576900 Indel GGGG TP53 946 316 P NULL NN 5.72 216 PAPA 1204 Endometrium Sample esc Pap cr 12 25398284 SBS CA KRAS 35 12 GVNN 12.53 1730 PAPA 1204 Endometrium Sample esc cr cr 178916876 SBS GA PIK3CA 263 88 RQNN 7.15 272 PAPA 1204 Endometrium Sample esc Pap cr 10 89717762 SBS AT PTEN 787 263 K * NN 13.98 284 PAPA 1204 Endometrium Sample esc Pap cr 10 89720804 Indel ACTT PTEN 955 319 T NULO NN 13, 26 159 PAPA 1204 Endometrium Sample esc Pap cr 17 56435167 Indel C RNF43 1970 657 R NULL NN 28.52 2933 PAPA 1224 Endometrium Sample esc Pap cr 12 133250289 SBS CG POLE 1231 411 VLNN 0.21 22 PAPA 1233 Endometrium Sample esc Pap cr 3 41266097 SBS GT CTNNB1 94 32 DYNN 0.45 63 PAPA 1233 Endometrium Sample esc Pap cr 10 89653784 Indel A PTEN 82 28 I NULL NN 0.74 26 PAPA 1309 Endometrium Sample esc Pap cr 17 7579909 SBS CT TP53 4 2 EKNN 48.42 4775 PAPA 1328 Ovarian Sample esc Pap cr 12 133253184 SBS GC POLE 857 286 PRNN 0.77 74 PAPA 1191 Endometrium Sample esc cr 10 89685269 SBS GA PTEN 0 0 NULL NULL 1 N 0.18 44 PAPA 1193 Endometrium Sample esc Pap cr 3 41266097 SBS GT CTNNB1 94 32 DYNN 8.42 984 PAPA 1193 Endometrium Sample esc Pap cr 10 89692852 Indel AAGTGAAGATGACAAT (SEQ ID NO: 773) PTEN 336 112 L NULL NN 4.91 359 PAPA 1221 Sample Endoscope Pap cr 10 123279677 SBS GC FGFR2 755 252 SWNN 2.04 213 PAPA 1221 Endometrium Sample esc Pap cr 10 89711875 SBS GA PTEN 493 165 GRNN 5.73 291 PAPA 1221 Endometrium Sample esc Pap cr 10 89717736 Indel AAGTA PTEN 761 254 K NULL NN 1.87 85 PAPA 1232 Endometrium Sample esc Pap cr 17 7578263 SBS GA TP53 586 196 R * NN 1.32 75 PAPA 1292 Ovarian Sample esc Pap cr 17 7578397 SBS TG TP53 533 178 HPNN 0.29 22 PAPA 1296 Ovarian Sample esc Pap cr 17 7579312 Indel CGTGCAAGTCACAGAC (SEQ ID N: 774) TP53 375 125 T NULL N Yes 0.13 18 PAPA 1361 Ovarian Sample esc Tao cr 3 41266137 SBS CT CTNNB1 134 45 SFNN 0.85 107 PAPA 1364 Ovarian Sample esc Pap cr 10 123279677 SBS GC FGFR2 755 252 SWNN 0.14 32 PAP 536 Ovarian Sample esc cr cr 10 123279677 SBS GC FGFR2 755 252 SWNN 0.10 28 PAP 536 Ovarian sample esc Pap cr 3 178936091 SBS GA PIK3CA 1633 545 EKNN 4.57 159 PAP 536 Ovarian sample esc Pap cr 5 67590427 Indel C PIK3R1 1489 497 Q NULL NN 0.16 10 PAP 536 Ovarian Sample esc Pap cr 10 89692904 SBS CG PTEN 388 130 RGNN 2.16 229 PAP 536 Ovarian Sample esc Pap cr 10 897176 72 SBS CT PTEN 697 233 R * NN 4.52 1724 PAP 536 Ovarian Sample esc Pap cr 17 7578406 SBS CT TP53 524 175 RHNN 5.17 779 PAPA 1696 Ovarian Sample esc Pap cr 17 7577532 SBS GA TP53 749 250 PLNN 22.77 3899 PAPA 1697 Ovarian Sample esc Pap cr 17 7578370 SBS CT TP53 0 0 NULL NULL 1 N 0.92 171 PAPA 1700 Ovarian Sample esc Pap cr 5 67589567 Indel AAG PIK3R1 1330 444 A NULL NN 0.71 158 PAPA 1700 Ovarian Sample esc Pap cr 17 7578394 SBS TA TP53 536 179 HLNN 0.11 12 PAPA 1705 Ovarian Sample esc Pap cr 10 123258034 SBS AT FGFR2 1647 549 NKNN 0.35 22 PAPA 1706 Ovarian Sample esc Pap cr 17 7578555 SBS CT TP53 0 0 NULL NULL 1 N 5.00 952 PAPA 1716 Ovarian Sample esc Pap cr 17 7578203 SBS CT TP53 646 216 VMNN 0.48 46 PAPA 1716 Ovarian Sample esc cr 17 7577130 SBS AT TP53 808 270 FINN 0.41 38 PAPA 1725 Ovarian Sample esc Pap cr 3 41266113 SBS CT CTNNB1 110 37 SFNN 3.82 430 PAPA 1734 Ovarian Sample esc Pap cr 17 7577560 SBS AC TP53 721 241 SAN N 0.08 17 PAPA 1819 Ovarian Sample esc Tao cr 17 7577527 Indel GGA TP53 754 252 L NULL NN 0.16 62 PAPA 1819 Ovarian Sample esc Pap cr 10 123258035 SBS TC FGFR2 1646 549 NSNN 0.16 22 PAPA 1819 Ovarian Sample esc Pap cr 17 7577527 Indel GGA TP53 754 252 L NULL NN 0.13 43 PAPA 1819 Ovarian esc sample Pap cr 17 7577124 SBS CA TP53 814 272 VLNN 1.23 262 PAPA 1820 Endometrium Sample esc Tao cr 12 25398284 SBS CA KRAS 35 12 GVNN 58.32 13079 PAPA 1820 Endometrium Sample esc Tao cr 17 7578271 SBS TA TP53 578 193 HLNN 35.34 4849 PAPA 1820 Endometrium Sample esc Pap cr 12 25398284 SBS CA KRAS 35 12 GVNN 1.76 258 PAPA 1820 Endometrium Sample esc Pap cr 17 7578271 SBS TA TP53 578 193 HLNN 0.35 57 PAPA 1823 Ovarian Sample esc Tao cr 17 7578265 SBS AG TP53 584 195 ITNN 0.35 58 PAPA 1826 Ovarian Sample esc Tao cr 5 67591134 Indel CGA PIK3R1 1727 576 T NULL NN 6.59 82 PAPA 1828 Ovarian Sample esc Pap cr 17 7577106 SBS GA TP53 832 278 PSNN 0.23 32 PAPA 1 832 Ovarian Sample esc Tao cr 17 7577502 Indel G TP53 779 260 S NULL NN 0.19 49 PAPA 1836 Endometrium Sample esc Tao cr 3 41266101 SBS CT CTNNB1 98 33 SFNN 38.22 2700 PAPA 1836 Endometrium Sample esc Tao cr 3 178952085 SBS AT PIK3CA 3140 1047 HLNN 38.11 3542 PAPA 1836 Endometrium Sample tao cr 10 89692794 SBS AG PTEN 278 93 HRNN 40.00 110 PAPA 1837 Endometrium Sample esc Tao cr 3 178952085 SBS AG PIK3CA 3140 1047 HRNN 21.00 1502 PAPA 1837 Endometrium Sample esc Tao cr 17 56435167 Indel C RNF43 1970 657 R NULL NN 37.86 4648 PAPA 1839 Endometrium Sample esc Tao cr 10 123279677 SBS GC FGFR2 755 252 SWNN 26.83 2427 PAPA 1839 Endometrium Sample esc Tao cr 5 67592055 Indel TG PIK3R1 1871 6 W NULL NN 29.78 411 PAPA 1839 Endometrium Sample esc Tao cr 10 89692961 SBS CT PTEN 445 149 Q * NN 16.40 2986 PAPA 1839 Endometrium Sample esc Tao cr 17 56435167 Indel C RNF43 1970 657 R NULL NN 24.46 5736 PAPA 1840 Endometrium Sample esc Tao cr 10 89717708 SBS CT PTEN 73 3 245 Q * NN 9.98 609 PAPA 1840 Endometrium Sample esc Tao cr 17 56435167 Indel C RNF43 1970 657 R NULL NN 8.36 1514 PAPA 1840 Endometrium Sample esc Tao cr 17 7576903 Indel AG TP53 943 315 S NULL NN 0.40 6

PAPA 1842 Endometrium Sample esc Tao cr 3 41266113 SBS CT CTNNB1 110 37 SFNN 17.64 882 PAPA 1842 Endometrium Sample esc Tao cr 5 67591130 Indel AAGACGAGA PIK3R1 1723 575 K NULL NN 27,06 250 PAPA 1842 Endometrium Sample tao cr2 5 27 547 183 RWNN 16.85 1449 PAPA 1842 Endometrium Sample esc Tao cr 10 89720875 SBS GT PTEN 1026 342 KNN Yes 34.45 287 PAPA 1843 Endometrium Sample esc Tao cr 10 89692904 SBS CG PTEN 388 130 RGNN 12.83 5040 PAPA 1843 Endometrium Sample esc Tao cr 10 89692905 Indel GAACTGGTGTAATGATATGTG (SEQ ID NO: 775) PTEN 389 130 R NULL NN 4.23 1664 PAPA 1844 Ovarian Sample esc Tao cr 17 7578479 SBS GC TP53 451 151 PANN 0.13 45 PAPA 1845 Ovarian Sample esc Tao cr 17 7578223 Indel CT TP53 626 209 R NULL NN 3.34 202 PAPA 1847 Endometrium Sample esc Tao cr 12 25398284 SBS CG KRAS 35 12 GANN 57.65 7566 PAPA 1847 Endometrium Sample esc Tao cr 10 89692905 SBS GC PTEN 389 130 RPNN 21, 20 3245 PAPA 1848 Endometrium Sample esc Ta o cr 10 89717716 Indel A PTEN 741 247 L NULL NN 21,10 1044 PAPA 1848 Endometrium Dark sample 17 56435167 Indel C RNF43 1970 657 R NULL NN 60,84 7329 PAPA 1849 Endometrium Sample dark Tao cr 14 105246551 SBS CT AKT1 49 17 EKNN 79.80 23434 PAPA 1849 Endometrium Sample esc Tao cr 19 52715983 SBS GA PPP2R1A 548 183 RQNN 34.17 2362 PAPA 1849 Endometrium Sample esc Tao cr 17 7577082 SBS CT TP53 856 286 EKNN 48.53 3668 PAPA 1852 Endometrial Sample cr 3 178952074 SBS GA PIK3CA 3129 1043 MINN 36.48 2449 PAPA 1852 Endometrium Sample esc Tao cr 10 89692904 SBS CG PTEN 388 130 RGNN 29.60 8206 PAPA 1855 Endometrium Sample esc Tao cr 12 25398284 SBS CA KRAS 35 12 GVNN 31.60 4143 PAPA 1855 Endometrium Sample esc Tao cr 10 89692904 SBS CG PTEN 388 130 RGNN 21.51 5163 PAPA 1857 Endometrium Sample esc Tao cr 3 178952074 SBS GT PIK3CA 3129 1043 MINN 39.78 3596 PAPA 1857 Endometrium Sample tao cr 19 52715971 SBS PPP2R1A 536 179 PRNN 21.34 1762 POPE 1857 Endometrium Sample esc Tao cr 17 7578551 SBS AG TP53 379 127 SPNN 58.32 11338 PAPA 1858 Endometrium Sample esc Tao cr 3 178952085 SBS AT PIK3CA 3140 1047 HLNN 46.34 4129 PAPA 1858 Endometrium Sample tao cr 10 89692803 SBS CT PTEN 96 PLNN 46.88 165 PAPA 1858 Endometrium Sample esc Tao cr 10 89692929 SBS AG PTEN 413 138 YCNN 20.20.75 5651 PAPA 1859 Endometrium Sample esc Tao cr 10 123258034 SBS AT FGFR2 1647 549 NKNN 65.86 5734 PAPA 1859 Endometrium Sample esc Tao cr 3 178916876 SBS GA PIK3CA 263 88 RQNN 33.91 352 PAPA 1859 Sample endometrium esc Tao cr 10 89720737 SBS TA PTEN 888 296 C * NN 55.96 2150 PAPA 1860 Sample endometrium Tao cr 3 41266113 SBS CTNNB1 110 37 SFNN 8.40 516 PAPA 1860 Endometrium Sample esc Tao cr 5 67591086 SBS AG PIK3R1 1679 560 DGNN 7.77 521 PAPA 1861 Endometrium Sample esc Tao cr 3 178916876 SBS GA PIK3CA 263 88 RQNN 6.22 58 PAPA 1862 Endometrium Sample tao cr 10 89720817 Indel A PTEN 968 323 NULL NULL NN 5.76 193 PAPA 1862 Endometrium Sample esc Tao cr 10 89720845 Indel A PTEN 996 332 K NULL NN 6.46 97 PAPA 1865 Endometrium Sample esc Tao cr 10 89692935 SBS TG PTEN 419 140 L * NN 6.18 2564 PAPA 1865 Endometrium Sample esc Tao cr 17 7578419 SBS CA TP53 511 171 E * NN 2.00 339 PAPA 1865 Endometrium Sample esc Tao cr 17 7577120 SBS CT TP53 818 273 RHNN 45.33 7911 PAPA 1867 Endometrium Sample esc Tao cr 12 25398284 SBS CA KRAS 35 12 GVNN 53.24 12357 PAP 266 Endometrium Sample esc Pap cr 10 89720733 Indel T PTEN 884 295 L NULL NN 0.27 15 PAP 059 Ovarian Sample esc Pap cr 12 25398282 SBS CT KRAS 37 13 GSNN 0.10 6 PAP 581 Endometrium Sample esc Pap cr 5 67590364 SBS GA PIK3R1 1426 476 EKNN 1.15 14 PAP 208 Endometrium Sample esc pap cr 10 89720716 Indel A PTEN 867 289 K NULL NN 0.22 16 PAPA 1153 Endometrium Sample esc Tao cr 17 7578229 SBS TC TP53 620 207 DGNN 0.70 3 PAPA 1256 Endometrium Sample esc Pap cr 10 89685307 SBS TC PTEN 202 68 YHNN 0.28 14 PAPA 12 08 Ovarian Sample esc Pap cr 9 21971082 Indel G CDKN2A 276 92 D NULL NN 2.85 7 PAPA 1210 Ovarian Sample esc cr cr 9 21971080 SBS GA CDKN2A 278 93 TMNN 1.36 3 PAPA 1695 Ovarian Sample esc cr cr 17 7578535 SBS TC TP53 395 132 KRNN 0.05 10 PAPA 1697 Ovarian Sample esc Pap cr 17 7579541 SBS TC TP53 146 49 DGNN 0.09 23 PAPA 1140 Endometrium Sample esc pap cr 17 7577551 SBS CA TP53 730 244 GCNN 0.64 117 PAP 378 Healthy Contr Sample esc Pap cr 3 178936082 SBS GA PIK3CA 1624 542 EKNN 3,55 127 PAP 433 Healthy Contr Sample esc Pap cr 5 67591134 Indel CGA PIK3R1 1727 576 T NULL NN 0,60 10 PAP 829 Endometrium Sample esc Pap cr 5 67591134 Indel CGA PIK3R1 1727 576 T NULL NN 0.55 10 PAP 663 Endometrium Sample esc Pap cr 4 153247288 SBS CT FBXW7 1514 505 RHNN 1.51 41 PAP 922 Endometrium Sample esc Pap cr 10 89717672 SBS CT PTEN 697 233 R * NN 0.72 115 PAP 739 Contr Healthy Sample esc Pap cr 17 7577509 SBS CA TP53 772 258 E * NN 0.71 44 PAP 901 Endometrium Am Oyster esc Pap cr 5 67589581 Indel A PIK3R1 1344 448 K NULL NN 1.38 63 PAPA 1094 Ovarian Sample esc Pap cr 7 140453154 SBS TC BRAF 1781 594 DGNN 0.19 6 PAPA 1152 Endometrium Sample esc cr cr 4 153249384 SBS CT FBXW7 1394 465 RHNN 1.05 81 PAPA 1259 Ovarian Sample esc Tao cr 19 52715971 SBS CG PPP2R1A 536 179 PRNN 1.89 271 PAPA 1328 Ovarian Sample esc Pap cr 10 89653809 SBS GA PTEN 107 36 GENN 0.63 24 PAP 238 Endometrium Sample esc Pap cr 5 67589615 Indel A PIK3R1 1378 460 S NULL NN 0.18 19 PAPA 1145 Endometrium Sample tao cr 10 89685303 SBS GT PTEN 198 66 KNNN 1.71 415 PAP 671 Sample endometrium esc Pap cr 17 7578271 SBS TG TP53 578 193 HPNN 0 , 17 10 PAPA 1145 Endometrium Sample esc Pap cr 10 89685303 SBS GT PTEN 198 66 KNNN 0.34 148 PAPA 1077 Endometrium Sample esc Pap cr 10 89692793 SBS CT PTEN 277 93 HYNN 0.07 4 PAPA 1328 Ovarian Sample esc Pap cr 3 178916854 SBS GA PIK3CA 241 81 EKNN 0.39 12 PAP 142 Healthy Contr Sample esc Pap cr 17 7577518 Indel TGATGGTGA TP53 763 255 I NULL NN 0.48 22 PAP 255 Ovarian Sample esc Pap cr 19 52716325 SBS TG PPP2R1A 769 257 WGNN 0.11 9 PAP 583 Endometrium Sample esc Pap cr 3 178952019 SBS CA PIK3CA 3074 1025 TNNN 0.46 22 PAPA 1217 Ovarian Sample esc Pap cr 5 67591106 SBS AG PIK3R1 1699 567 KENN 0.26 13 PAP 386 Healthy Contr Sample esc cr 10 89692981 SBS TG PTEN 465 155 Y * NN 0.51 9 PAP 242 Endometrium Sample esc Pap cr 3 41266097 SBS GA CTNNB1 94 32 DNNN 0.21 14 PAP 277 Endometrium Sample esc Pap cr 5 112175684 Indel AG APC 4393 1465 S NULL NN 0.44 38 PAPA 1043 Endometrium Sample esc Pap cr 12 25398284 SBS CA KRAS 35 12 GVNN 2.44 82 PAPA 1218 Endometrium Sample esc Pap cr 17 7577105 SBS GT TP53 833 278 PHNN 0.67 21 PAP 277 Endometrium Sample esc Pap cr 5 67589588 Indel GAATAT PIK3R1 1351 451 AND NULL NN 0.31 18 PAPA 1145 Endometrium Sample esc Tao cr 10 89624245 SBS GT PTEN 19 7 E * NN 0.35 83 PAP 235 Endometrium Sample esc Pap cr 10 123247516 SBS T C FGFR2 1975 659 K E N N 0.65 13 PAP 034 Endometrium Sample esc cr 17 7578253 SBS C T TP53 596 199 G E N N 0.16 19 PAP 685 Endometrium Sample esc Pap cr 10 123247516 SBS T C FGFR2 1975 659 K E N N 0.21 8

PAP 208 Endometrium Sample esc Pap cr 12 25398284 SBS CG KRAS 35 12 GANN 1.91 71 PAP 869 Endometrium Sample esc cr cr 10 89720670 SBS GA PTEN 821 274 W * NN 0.76 20 PAPA 1160 Endometrium Sample esc Tao cr 17 7578412 SBS AG TP53 518 173 VANN 0.18 19 PAPA 1110 Ovarian Sample esc Pap cr 12 25398284 SBS CG KRAS 35 12 GANN 1.13 82 PAPA 1239 Endometrium Sample esc pap cr 17 7577115 SBS AC TP53 823 275 CGNN 0.14 6 PAPA 1328 Ovarian Sample esc Pap cr 10 89624245 SBS GT PTEN 19 7 E * NN 0.07 7 PAPA 1705 Ovarian Sample esc Pap cr 3 178936074 SBS CG PIK3CA 1616 539 PRNN 0.51 27 PAP 204 Endometrium Sample esc Pap cr 17 7577099 SBS CA TP53 839 280 RINN 0.15 12 PAP 280 Contr Healthy Sample esc Pap cr 5 67589598 Indel ACACAC PIK3R1 1361 454 T NULL NN 0.11 13 PAP 569 Ovarian Sample esc pap cr 17 7578235 SBS TC TP53 614 205 YCNN 0.21 8 PAPA 1832 Ovarian Sample esc Tao cr 17 7577099 SBS CA TP53 839 280 RINN 0.27 31 PAP 280 Healthy Contr Sample esc Pap cr 5 67589596 Indel CAC PIK3R1 1359 453 N NULL NN 0.10 12 PAP 177 Endometrium Sample esc Pap cr 10 89717620 Indel T PTEN 645 215 F NULL NN 1.72 22 PAP 557 Endometrium Sample esc Pap cr 17 7577018 SBS CA TP53 0 0 NULL NULL 1 N 0.34 25 PAP 236 Endometrium Sample esc Pap cr 10 89692977 Indel T PTEN 461 154 F NULL NN 0.22 7 PAP 998 Endometrium Sample esc Tao cr 9 21971186 Indel GGGCGCTGCCCATC (SEQ ID N: 776) CDKN2A 172 58 R NULL NN 2.77 162 PAPA 1218 Endometrium Sample esc Pap cr 17 7579312 SBS CA TP53 375 125 TTN Yes 1.24 118 PAPA 1823 Ovarian Sample esc Tao cr 17 7578492 SBS CT TP53 438 146 W * NN 0.17 67 PAPA 1860 Endometrium Sample esc Tao cr 3 178952085 SBS AT PIK3CA 3140 1047 HLNN 1.03 99 PAP 003 Endometrium Sample esc Pap cr 10 89692905 SBS GA PTEN 389 130 RQNN 1.32 56 PAP 069 Endometrium Sample esc Pap cr 5 67589609 SBS GT PIK3R1 1372 458 E * NN 0.28 19 PAPA 1140 Endometrium Sample esc Pap cr 17 7577557 SBS AC TP53 724 242 CGNN 0.07 13 PAP 270 Endometrium Amo stra esc Pap cr 3 178936095 SBS AG PIK3CA 1637 546 QRNN 1.10 19 PAP 654 Endometrium Sample esc cr 3 178952085 SBS AG PIK3CA 3140 1047 HRNN 2.10 212 PAP 998 Endometrium Sample esc Tao cr 17 7577100 SBS TC TP53 838 280 RGNN 0.64 66 PAPA 1162 Endometrium Sample esc Pap cr 12 25398284 SBS CT KRAS 35 12 GDNN 1.63 243 PAP 378 Healthy Contr Sample esc Pap cr 4 153247247 SBS AC FBXW7 1555 519 YDNN 3.13 110 PAP 574 Endometrium Sample esc Pap cr 3 178916876 SBS GA PIK3CA 263 88 RQNN 1.95 17 PAP 320 Healthy Contr sc Sample Pap cr 10 123279674 SBS GC FGFR2 758 253 PRNN 0.86 20 PAP 320 Healthy Contr Scopic Sample Pap cr 17 7578529 SBS AC TP53 401 134 FCNN 0.36 17 PAP 145 Healthy ContrSample sc Pap cr 17 7578182 SBS GA TP53 667 223 PSNN 1.46 38 PAP 145 Healthy Contrsample Pap cr 17 7577105 SBS GC TP53 833 278 PRNN 1.37 23 PAP 229 Endometrial Sample esc Pap cr 12 25398284 SBS CT KRAS 35 12 GDNN 3.27 108 PAP 936 Endometrium Sample esc Pap cr 3 178936082 SBS GA PIK3CA 1624 542 EKNN 2.89 88 PAP 726 Endometrium Sample esc Pap cr 12 25398284 SBS CT KRAS 35 12 GDNN 2.94 168 PAP 879 Endometrium Sample esc Pap cr 12 25398284 SBS CT KRAS 35 12 GDNN 3.36 156 PAPA 1043 Endometrium Sample esc Pap cr 3 178936082 SBS GA PIK3CA 1624 542 EKNN 2.44 75 PAPA 1066 Endometrium Sample esc Pap cr 12 25398284 SBS CT KRAS 35 12 GDNN 2.56 279 PAPA 1155 Endometrium Sample esc Tao cr 17 7577120 SBS CT TP53 818 273 RHNN 0.65 2 PAPA 1189 Ovarian Sample esc Tao cr 17 7577120 SBS CT TP53 818 273 RHNN 0.76 6 PAPA 1264 Endometrium Sample esc Pap cr 5 67588989 Indel CG PIK3R1 1080 360 A NULL NN 1.08 10 PAPA 1191 Endometrium Sample esc Pap cr 3 178936095 SBS AG PIK3CA 1637 546 QRNN 0.49 15 PAPA 1202 Endometrium Sample esc Pap cr 17 7577120 SBS CT TP53 818 273 RHNN 1.23 96

* Coordinates refer to the human reference hg19 genome release (Genome Reference Consortium GRCh37, Feb 2009), Total # Mutation in UIDs Primary Tumor 8.317 Tumor tissue unavailable 5.900 Tissue tissue unavailable 8.737 Tissue tissue unavailable 11.773 Tissue tissue unavailable 4,278 Tissue tissue unavailable 12,038 Tissue tissue unavailable 49,208 Tissue tissue unavailable 14,290 Tissue tissue unavailable 48,916 Tissue tissue unavailable 41,873 N t Present

51,075 N t Present 4,771 N t Present 11,369 Present 25,810 Present 19,946 N t Present 7,658 N t Present 5,146 N t Present 4,752 Present 13,958 Present 3,535 N t Present 5,072 Present 7,780 N t Present 7,780 N t Present 11,553 N t Present 8,610 Present 7,741 Present 9,561 N t Present 2,885 Present 9,629 Present 7,643 N t Present 9,527 Present 21,539 Insufficient neoplasia 19,529 Present 5,132 Present 3,917 Present 5,338 Present 4,753 Present 812 Insufficient neoplasm 20,529 Present 4,494 N t Present

11,168 N t Present 17,497 Present 10,746 with insufficient sequence 4,194 Present 2,321 Present 1,891 with insufficient sequence 4,477 Present 2,498 N t Present 2,278 N t Present 5,889 N t Present 819 Present 3,667 N t Present 3,667 N t Present 3,667 N t Present 15,370 Present 13,737 Present 17,720 with insufficient sequence 15,094 N t Present 398 with insufficient sequence 1,791 Present 3,389 Present 17,945 with insufficient sequence 2,844 Present 2,805 with insufficient sequence 5.862 Present 1,626 with insufficient sequence 3,821 with insufficient sequence 11,263 Present 1,042 Present

2,384 Present 6,479 Tumor tissue unavailable 23,572 with insufficient sequence 22,984 Present 3,959 Present 7,385 Present 4,692 N t Present 3,619 Present 3,792 Present 16,046 Present 19,257 with insufficient sequence 13,605 Present 7,379 Present 26,387 Present 2,544 Present 20,833 Present 1,207 Present 1,207 Present 9,865 N t Present 7,343 Present 11,511 Present 9,699 N t Present 2,373 Present 2,593 Present 18,514 Present 18,654 Present 9,489 Present 9,903 Present 2,192 Present 1,172 Present

1,023 Present 3,618 Present 3,750 N t Present 12,276 Present 19,446 Present 3,987 Present 11,708 Present 5,995 Present 2,302 N t Present 1,387 Present 1,711 Present 1,950 Present 14,566 N t Present 3,780 Present 2,365 N t Present 6,238 Present 3,916 N t Present 8,913 Present 6,820 Present 2,992 Present 4,890 Present 5,999 Tissue tissue unavailable 5,133 Tissue tissue unavailable 562 Present 1,432 Present 5,348 Present 21,758 N t Present 4,101 N t Present 6,071 Present 15,454 Present

15,454 Present 2,051 Tissue tissue unavailable 9,646 Tissue tissue unavailable 18,593 Tissue tissue unavailable 16,061 Tissue tissue unavailable 19,559 N t Present 8.662 N t Present 8,619 Present 11,312 Present 2,971 Tumor tissue unavailable 5,599 Present 9,130 Present 5,994 Tissue tissue unavailable 7,542 Tissue unavailable of tumor unavailable 6,957 N t Present 5,918 Present 5,600 Present 4,533 Present 3,529 Present 7,813 Present 1,590 Present 23,232 Present 2,322 Tumor tissue unavailable 6,846 Tumor tissue unavailable 5,334 Present 16,264 N t Present 11,271 N t Present 3,024 Tumor tissue unavailable 7,753 Tumor tissue unavailable 6.038 tumor tissue unavailable

6,771 N t Present 2,186 Present 2,551 Present 5,646 Present 2,602 Present 2,960 Present 2,353 Present 9,493 Present 2,405 Present 8,303 Present 8,141 Present 9,275 N t Present 14,975 Present 11,907 Present 8,981 Present 14,065 N t Present 5,435 N t Present 4,968 Present 17,348 N t Present 9,141 N t Present 3,992 Present 4,978 Tumor tissue unavailable 8,204 Present 6,192 N t Present 7,841 Tumor tissue unavailable 15,827 N t Present 6,516 Present 5,280 Insufficient neoplasm 14,141 Present 7,117 Present

11,943 Present 3,687 N t Present 1,500 Present 11,634 Present 2,189 Present 3,153 Present 19,601 Insufficient neoplasm 24,630 N t Present 22,950 Present 1,069 Present 2,262 Present 7,926 Present 4,217 Present 7,825 N t Present 7,825 N t Present 29,578 Present 1,733 Present 2,555 Present 13,074 Present 5,220 Present 12,181 Present 3,168 Present 4,125 Present 6,183 Present 10,284 Present 3,958 Present 4,538 Present 9,664 Present 15,861 Present 2,161 N t Present

5,677 Present 8,132 Present 23,467 Present 6,266 N t Present 4,314 Present 7,532 Present 8,020 Present 5,160 Present 6,540 Present 5,317 Present 6,509 Present 4,030 Present 6,010 Present 8,701 N t Present 3,913 N t Present 1,928 Present 3,117 Present 8,902 N t Present 3,999 N t Present 1,829 Present 5,053 N t Present 11,163 Present 16,883 Present 8,751 Present 12,268 Present 16,479 N t Present 13,209 Insufficient neoplasm 8,423 Insufficient neoplasia 12,688 Insufficient neoplasia 6,881 Insufficient neoplasia

17,440 Present 6,569 Present 5,699 Present 2,139 Present 3,127 Present 26,469 Present 7,674 Present 4,409 Present 2,326 Present 3,336 Present 7,559 N t Present 3,193 N t Present 24,646 N t Present 29,656 Present 8,152 Present 6,881 N t Present 20,626 Present 9,707 Present 12,315 N t Present 6,086 Present 3,287 Present 11,627 Present 7,278 Present 8,918 Present 3,929 Present 13,811 Present 13,811 Present 10,597 Present 7,406 Present 7,406 Present

9,961 Present 9,337 Present 7,789 Present 12,490 Present 2,149 Present 8,384 N t Present 13,727 Present 2,873 Present 4,633 Present 8,588 N t Present 2,015 Tissue tissue unavailable 12,513 Tumor tissue unavailable 10,127 Tumor tissue unavailable 6,419 Present 9,484 Present 9,484 N t Present 9,570 Present 1,336 Present 4,851 N t Present 4,851 N t Present 4,851 N t Present 6,709 Present 6,839 Present 8,649 Present 3,695 Present 8,869 Present 1,158 Present 7,739 Present 10,421 Present 17,090 Present

4,596 Present 7,646 Present 9,691 Present 20,690 Present 4,177 Present 2,919 Present 6,431 Present 6,117 Present 7,559 Tissue tissue unavailable 9,613 Tissue tissue unavailable 14,196 Tumor tissue unavailable 15,043 Tumor tissue unavailable 16,619 Present 1,895 Present 3,398 Present 5,364 Present 4,161 Present 8,557 Present 9,076 Present 11,571 N t Present 12,288 Present 4,754 Present 12,662 Present 3,643 Present 9,676 Present 37,116 Present 8,409 Present 28,861 Present 37,034 Present

3,148 Present 28,906 Present 1,973 Present 12,845 Present 28,286 Insufficient neoplasm 7,379 N t Present 8,213 Insufficient neoplasm 7,317 Present 35,552 Present 15,426 N t Present 35,305 Present 6,920 Present 15,051 N t Present 13.204 Tumor tissue unavailable 2,626 Present 9,950 Present 10,262 Present 2,187 N t Present 38,012 N t Present 2,830 N t Present 9,800 Present 12,192 Present 6,117 Present 7,383 with insufficient sequence 34,241 N t Present 32,383 Present 16,824 N t Present 22,218 Present 33,427 Present 28,914 N t Present

3,006 Present 374 Tissue tissue unavailable 665 Tissue tissue unavailable 1,920 Tissue tissue unavailable 994 Tissue tissue unavailable 1,750 Tissue tissue unavailable 1,171 Tissue tissue unavailable 569 Tissue tissue unavailable 542 Tissue tissue unavailable 1,021 Tissue tissue tumor unavailable 755 tumor tissue unavailable 3,098 present 3,137 present 1,144 insufficient neoplasia 404 present 1,597 present 1,597 insufficient neoplasm 2,490 present 884 present 810 insufficient neoplasm 1,741 present 1,646 present 1,165 with insufficient sequence 3,017 present 834 present 1,716 present 1,954 present 947 present 616 present

3,896 Present 2,198 Present 207 Present 859 Present 1,174 N t Present 1,005 Present 691 Present 9,202 Tissue tissue unavailable 9,468 Tissue tissue unavailable 4,511 Tissue tissue unavailable 35,417 N t Present 8,176 N t Present 18,418 Present 16,911 Present 3,754 N t Present 7,957 with sequence insufficient 6,867 Present 12,871 Present 16,594 Present 7,122 Present 3,649 Present 1,408 Present 13,843 Present 11,957 Present 20,434 Present 3,793 Present 10,476 Present 5,426 Present 426 Present 3,560 Present

6,439 Present 4,614 Present 6,752 Present 6,752 N t Present 5,626 Present 3,828 Present 7,488 Present 779 Present 5,214 Present 5,214 N t Present 6,807 N t Present 6,365 Present 1,495 Present 7,425 Present 8,400 Present 5,989 Present 6,839 Present 445 Present 14,934 N t Present 4,157 Present 10,833 Present 9,507 Present 4,048 Present 8,865 N t Present 5,370 Present 4,204 Present 2,837 Present 3,242 Present 612 Present 3,337 Present

1,868 N t Present 1,208 Present 14,050 N t Present 2,730 Present 3,906 with insufficient sequence 10,527 Present 4,484 Present 343 Present 2,675 N t Present 2,675 Present 12,736 Present 2,216 Present 9,479 Present 4,218 Present 3,667 N t Present 2,166 Present 1,335 Present 3,811 Present 27,573 Present 4,474 Fabric tumor tissue unavailable 2,060 tumor tissue unavailable 2,499 tumor tissue unavailable 20,438 tumor tissue unavailable 305 tumor tissue unavailable 5,362 tumor tissue unavailable 2,667 tumor tissue unavailable 2,534 tumor tissue unavailable 6,499 gift 694 gift 1,286 gift

1,016 Present 2,825 N t Present 4,117 Present 1,935 Present 3,807 Present 5,005 N t Present 3,345 Present 12,921 Present 1,919 Present 1,695 Present 10,347 Present 4,229 Present 1,598 Present 11,004 Present 3,284 Present 3,356 N t Present 3,356 N t Present 2,386 Present 5,883 Present 2,456 Present 3,000 N t Present 2,841 Present 2,147 Present 2,157 Present 2,635 Tissue tissue unavailable 241 Tissue tissue unavailable 494 Tissue tissue unavailable 2,859 Tissue tissue unavailable 1,881 Present

7,926 Present 13,583 Tissue tissue unavailable 7,020 Tissue tissue unavailable 9,732 Tissue tissue unavailable 7,359 Tissue tissue unavailable 14,259 Tissue tissue unavailable 10,181 Tissue tissue unavailable 16,382 Tissue tissue unavailable 5,363 Tissue tissue unavailable 8,615 Tissue tissue unavailable tumor unavailable 8,749 tumor tissue unavailable 4,569 tumor tissue unavailable 1,255 tumor tissue unavailable 2,735 tumor tissue unavailable 2,735 tumor tissue unavailable 9,408 tumor tissue unavailable 516 tumor tissue unavailable 7,119 tumor tissue unavailable 14,373 tumor tissue unavailable 8,113 N t Present 25,013 Present 2,749 Present 3,316 Present 12,296 Present 4,533 Present 6,761 Present 5,570 Present 5,085 Present 3,649 Present

4,571 N t Present 5,571 N t Present 6,064 Present 4,505 Present 5,449 Present 5,299 Tissue tissue unavailable 2,418 Tissue tissue unavailable 502 Tissue tissue unavailable 3,958 Tumor tissue unavailable 4,771 N t Present 2,461 Present 2,090 Present 1,819 Present 2,107 Present 2,461 Present 3,614 Present 1,429 Present 3,686 Present 3,238 Present 1,681 Present 8,326 Present 2,503 N t Present 4,846 N t Present 18,667 Present 9,069 Present 2,454 N t Present 7,780 Present 1,704 Present 2,714 Present 1,027 N t Present

2,447 N t Present 2,447 N t Present 2,270 Present 268 N t Present 1,126 Present 1,126 Present 1,839 Present 276 Present 1,046 Present 5,827 Present 11,574 Present 20,042 Present 961 Present 5,750 Present 1,974 Present 3,652 N t Present 2,866 Present 3,311 Present 591 Present Tumor tissue unavailable 14,177 Tissue tissue unavailable 2,090 Tissue tissue unavailable 5,656 N t Present 4,211 Present 19,318 Present 280 Present 6,872 Present 11,106 with insufficient sequence 7,494 with insufficient sequence 6,521 Present

11,980 Present 552 Present 552 Present 2,862 N t Present 2,862 Present 2,440 Present 2,191 N t Present 1,845 N t Present 2,360 Present 2,047 N t Present 1,416 Present 4,958 Present 4,279 Present 10,639 Present 3,701 Present 1,249 Present 7,609 Present 1,488 Present 5,035 Present 5,340 Present 9,488 Present 1,900 Tumor tissue unavailable 4,537 Present 3,886 Present 6,016 N t Present 15,140 Present 2,830 Present 11,051 N t Present 4,310 Present 9,116 N t Present

5,601 Present 2,544 with insufficient sequence 2,015 with insufficient sequence 8,076 with insufficient sequence 2,736 Present 3,886 Present 2,601 Present 3,898 N t Present 2,329 Present 6,417 Present 8,167 Present 4,762 Present 9,276 Present 6,331 Present 625 Present 5,113 with insufficient sequence 9,630 Present 9,630 Present 12,378 with insufficient sequence 34,978 N t Present 12,515 with insufficient sequence 8.188 N t Present 3,339 N t Present 1,901 Present 9,247 Present 2,868 Present 5,874 Present 4,094 Present 3,155 N t Present 7,133 Present

10.021 N t Present 4,550 Present 3,283 Present 4,660 Present 4,007 Present 4,983 Present 5,601 Present 1,844 Present 15,152 Present 19,302 N t Present 2,296 Present 13,991 Present 1,482 Present 1,341 Present 3,059 Present 8,451 Present 7,881 Present 8,199 N t Present 5,290 Present 9,303 Present 10,192 Present 19,873 Present 7,022 Present 17,849 Present 15,510 Present 1,690 Present 2,134 Present 2,189 Present 14,521 Present 9,704 Present

14,840 Present 4,556 Present 20,977 Present 2,787 Present 3,218 Present 4,706 Present 4,358 N t Present 4,358 N t Present 1,829 Present 6,944 Present 31,671 Present 1,401 Present 6,045 Present 4,403 Present 4,541 Present 7,998 Present 866 coefficient sequence 345 coefficient sequence 540 coefficient sequence 337 Present 23,046 Present 2,951 Present 4,921 N t Present 4,279 Tissue tissue unavailable 2,739 Tissue tissue unavailable 839 Tissue tissue unavailable 2,628 Tissue tissue unavailable 23,422 Tissue tissue unavailable 11,970 Tissue tissue unavailable 9,807 Tissue tissue unavailable

3,733 Tissue tissue unavailable 1,765 Tissue tissue unavailable 422 Tissue tissue unavailable 1,444 Tissue tissue unavailable 19,353 Tissue tissue unavailable 7,498 Present 22,367 Present 1,095 Present 1,095 Present 2,098 N t Present 2,657 Present 2,780 Present 2,041 Present 6,227 Present 3,645 Present 1,957 Present 4,680 Tissue tissue unavailable 7,378 Tissue tissue unavailable 4,199 Tissue tissue unavailable 3,663 Present 2,287 Present 9,536 Present 3,192 Present 1,583 Present 8,587 Present 6,587 Present 857 Present 10,885 Tissue tissue unavailable 9,021 Tumor tissue unavailable

6.512 N t Present 10,270 Present 7,895 Present 6,314 Present 413 Present 14,504 Present 2,835 Present 2,723 Present 15,086 Present 7,557 Present 4,756 Present 7,529 Present 15,588 Present 5,548 Present 2,202 Present 6,087 Present 6,173 Present 2,269 Present 6,516 Present 4,817 Present 6,425 N t Present 2,712 Present 3,852 Present 4,101 N t Present 1,556 N t Present 3,902 Present 22,750 Present 11,284 N t Present 6,887 Present 6,166 Present

2,302 Present 6,004 Tissue tissue unavailable 8,697 N t Present 2,583 N t Present 264 Present 2,447 N t Present 4,578 Present 3,004 Tissue tissue unavailable 1,690 Tissue tissue unavailable 6,388 Tissue tissue unavailable 2,313 Tissue tissue unavailable 4,901 Tissue tissue unavailable 7,019 Tissue tumor tissue unavailable 4,181 tumor tissue unavailable 2,082 tumor tissue unavailable 3,456 tumor tissue unavailable 4,295 tumor tissue unavailable 4,180 tumor tissue unavailable 855 present 2,170 present 308 present 1,089 present 1,089 present 6,711 present 2,931 n t present 759 N t Present 1,289 N t Present 2,036 N t Present 10,485 N t Present

824 N t Present 401 N t Present 1,064 N t Present 1,955 N t Present 8,772 N t Present 9,797 Present 10,352 Present 7,284 Present 14,160 Present 7,694 Present 15,332 Present 31,946 Present 6,666 Tumor tissue unavailable 6,229 Present 18,726 N t Present 550 N t Present 528 N t Present 4,255 N t Present 3,836 N t Present 563 N t Present 2,260 Present 2,168 Present 9,128 Present 20,083 Present 6,957 Present 756 Present 2,510 Present 1,933 Present 472 Present 3,174 Present

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$ #% & '()% * +, &, & -! ./ & 01 23 * '4 56 7 7 #! 8 * 9 8 * 9 * 9: '4 8 - &8; 23 <0 8 = .1>. 6. 5 0 8 = .1> * 66 6 6;? @ 0 8 = .1>. 0? 5 0 8 = .1>. *;? @ 0 8 = .1> 0 * * 3;? @ 0 8 = .1>. * 2 0 8 = .1>. 6;? @ 0 8 = .1> 2A * 0 0 6 B = 0 8 = .1> * 7 * 6 - * 0 8 = .1>. * 6 = 6 0 8 = .1>. 000 0;? @ 0 8 = .1> A = 0- * 0 6 C 0 8 = .1>. * 6 = 6 0 8 = .1> A = 0- * 6 6 0 0 8 = .1> * 7 * 6 - * 0 8 = .1> 2A = *;? @;? @ - 0 8 =. 1> 2A = 0;? @;? @ - 0 8 = .1> = 0- * D = 0 8 = .1> 0A *. A 0 8 = .1> * 7 6 0 6 = 0 8 = .1> 161 = 0 A 0 8 = .1> 2A = 6 * = 0 8 = .1>. 6 1 C 0 8 = .1> @ ?. 6 * = 0 8 = .1> * 7 6 - 0 0 8 = .1>. 0 6 E * 0 8 = .1> A = 0- * 0 0 8 = .1> 2A * 0 6 0 = D 0 8 = .1>. 6. 5 0 8 = .1>. 6 = 2 0 8 = .1> 2A * 0 6 & 2 0 8 = .1> 0 * * = 5 0 8 = .1> 0 * 6. 5 0 8 = .1> 2A = * = 5 0 8 = .1> * = 6 0 8 = .1> 161 = 0 * A 0 8 = .1> @ ?. 6 * = 0 8 = .1> 0A *. A 0 8 = .1> 6 0 = 5 0 8 = .1> 6 0 D 5 0 8 = .1> 2A * 0 6 0. A 0 8 = .1>. 0 *;? @ 0 8 = .1> A = 0- * 0 6 C 0 8 = .1> * 7 * - 1 0 8 = .1>. * 6 = 6 0 8 = .1>. * D 5 0 8 = .1> A = 0- * 6 3 0 8 = .1> 2A * 0 6 & 2 0 8 = .1> 2A = 0 D;? @ 0 8 = .1> A = 0 - * 0 0 8 = .1> 2A * 0 * 0 DA 0 8 = .1>. 1;? @ 0 8 = .1> * 7 * - 1 0 8 = .1>. 0 * 0 2;? @ 0 8 = .1>. 6 * = 0 8 = .1> A = 0- * 6 3 0 8 = .1> 2A * 0 *? - 0 8 = .1> 0 6 2

0 8 = .1> 2A = * 6 0666000000 0 * 0 600 F-.D 23 G H 0;? @ 0 8 = .1>. * = * 0 8 = .1> @ ?. 6 * = 0 8 = .1> 2A = 6. 5 0 8 = .1> 2A = * = 5 0 8 = .1> * E * 0 8 = .1> = 0 * - 1 0 8 = .1> * 0 C? 0 8 = .1> 0 6 2 0 8 = .1>. *? ;? @ 0 8 = .1>. 0 A;? @ 0 8 = .1> 2A * 0 0 6 B = 0 8 = .1> * 7 * - 1 0 8 = .1> * = D 0 8 = .1> 6 -;? @ 0 8 = .1> * 6 3 0 8 = .1> 6 -;? @ 0 8 = .1> 0 3;? @ 0 8 = .1> * 0;? @;? @ - 0 8 = .1 > * E * 0 8 = .1> * 6;? @ 0 8 = .1> 0 6 2 0 8 = .1>. 6 0;? @;? @ - 0 8 = .1> @ ?. * 0 C? 0 8 = .1> 2A * 0 6 0. A 0 8 = .1> 161 = 6 * -, 0 8 = .1>. 0? ;? @ 0 8 = .1>. 0 A;? @ 0 8 = .1> * 7 6 0 3 0 8 = .1> * 7 * - 0 8 = .1>. 0 A;? @ 0 8 = .1>. 0;? @ 0 8 = .1> 6 0 8 = .1>. *;? @;? @ - 0 8 = .1>. * 0 * 5 0 8 = .1> 2A * 0 0 A 0 8 = .1>. 6 0 = D 0 8 = .1> * = B 0 8 = .1> -;? @ 0 8 = .1> 6? & 0 8 = .1> 2A * 0 6 0 = D 0 8 = .1> * 7 * 0 - E 0 8 = .1> 0 * * = 5 0 8 = .1> 0 * 6. 5 0 8 = .1>. *;? @ 0 8 = .1> 2A * 0 6 0 = D 0 8 = .1> 161 = 0 A 0 8 = .1> 2A = 0060 * 6060 A;? @ 0 8 = .1> A = 0- 6 0 0 C 0 8 = .1> 2A = * = 5 0 8 = .1> * 3A 0 * 0;? @ 0 8 = .1> = 0- 6 DA 0 8 = .1> 6; ? @ 0 8 = .1> 2A * 0 0 6 B = 0 8 = .1>. 6 0 = D 0 8 = .1>. 6 0 6 3 0 8 = .1> 2A = 0;? @ 0 8 = .1> A = 0- * 0 6 C 0 8 = .1> 2A = 6. 5 0 8 = .1>. * 6 = 6 0 8 = .1>. 0;? @ 0 8 = .1> A = 0- * 6 3 0 8 = .1> 6 0 = 5 0 8 = .1> 2A * 0 6 0. A 0 8 = .1> * 0 =? 0 8 = .1> 161 = 0 * A

0 8 = .1>. 6 6;? @ 0 8 = .1>. * = 5 0 8 = .1>. *;? @ 0 8 = .1>. 6 * * - 0 8 = .1> 2A * 0 6 0 & 2 0 8 = .1> * 7 6 3 E 0 8 = .1> * B = 0 8 = .1> = 0 * 6 = 0 8 = .1> 2A * 0 0 6 B = 0 8 = .1>. 6. 5 0 8 = .1>. 6 0 = A 0 8 = .1> 2A * 0 6 0 = D 0 8 = .1> 2A * 0 0 6 0 0 8 = .1>. * = 5 0 8 = .1>. 6 6;? @ 0 8 = .1> A = 0- * 0 6 C 0 8 = .1> 2A * 0 0 *. 0 0 8 = .1>. 6 0, 5 0 8 = .1>. * 6 = 6 0 8 = .1> 2A * 0 0 6 B = 0 8 = .1> * 7 * - 0 8 = .1> 17I, 6 0 = * 0 8 = .1> 6 0 =, 0 8 = .1>. 0;? @ 0 8 = .1> 2A * 0 6 0. A 0 8 = .1>. * 6 = 6 0 8 = .1> 2A * 0 6 0. A 0 8 = .1> A = 0- * 6 6 0 0 8 = .1>. 6 2;? @ 0 8 = .1> * 7 6 0 3 0 8 = .1> 2A = 0 6 3 0 8 = .1> A = 0- * 6 3 0 8 = .1> 2A * 0 6 0 = D 0 8 = .1> * 7 * 2 0 8 = .1> 17I, 6 0 = * 0 8 = .1> 0 *? = 0 8 = .1> = 0 * - 1 0 8 = .1> 2A = * = 5 0 8 = .1>. * D 5 0 8 = .1>. * 6 = 6 0 8 = .1>. 0;? @ 0 8 = .1> 2A * 0 0 6 B = 0 8 = .1>. 6. ;? @ 0 8 = .1> A = 0- * 6 3 0 8 = .1> * 0 6 * 0 8 = .1> 2A = 0 0 * 0 A;? @ 0 8 = .1> 2A = 0 * 0? ;? @ 0 8 = .1> = 0- * 6 3 0 8 = .1> * - 0 8 = .1> 2A * 0 6 0. A 0 8 = .1>. 0 2;? @ 0 8 = .1>. 3;? @ 0 8 = .1>. 1;? @ 0 8 = .1> = 1 * =;? @ 0 8 = .1> = 0 * =, 0 8 = .1>. 2;? @ 0 8 = .1>. 6. 5 0 8 = .1>. 0;? @ 0 8 = .1> 2A * 0 0 6 B = 0 8 = .1> * 7 6 0 3 0 8 = .1> 2A = 60. ;? @ 0 8 = .1>. 1;? @ 0 8 = .1>. 0 A;? @ 0 8 = .1>. 0 A;? @ 0 8 = .1> A = 0- * 6 3 0 8 = .1> = 1 * =;? @

0 8 = .1> 2A = 0 -;? @ 0 8 = .1> * 0 * 1 0 8 = .1> * 6. 0 8 = .1> *;? @;? @ - 0 8 = .1> = 0 * 6 = 0 8 = .1> * & C 0 8 = .1> 161 = 6 0 -? 0 8 = .1>. * 6 = 6 0 8 = .1> 17I, * = B 0 8 = .1>. 6 0 = D 0 8 = .1>. 0? ;? @ 0 8 = .1> 2A * 0 6 0 = D 0 8 = .1> * 7 0 6 0 0 8 = .1> 6 6 6 666 * 06 F-.D 23 GH;? @ 0 8 = .1> * C & 0 8 = .1> 17I, 6 0 - 1 0 8 = .1>. 6. 5 0 8 = .1> A = 0- * 6 3 0 8 = .1> 2A * 0 0 6 B = 0 8 = .1>. 6 E 5 0 8 = .1> 2A = 000 A;? @ 0 8 = .1>. 6 0 = D 0 8 = .1>. 6 1 C 0 8 = .1> @ ?. 6 * = 0 8 = .1> 0 * * = 5 0 8 = .1> 0 * 6. 5 0 8 = .1> 2A = * = 5 0 8 = .1> 2A = * 0 E 5 0 8 = .1> 6 0 = 5 0 8 = .1> * * E 0 8 = .1> * ;? @;? @ - 0 8 = .1> * 6. 0 8 = .1> = 0- * D = 0 8 = .1> A = 0- * 6 3 0 8 = .1> * 0. 5 0 8 = .1> * 3A 0 06;? @ 0 8 = .1> * 3A 00 -;? @ 0 8 = .1> 2A * 0 0 D? 0 8 = .1> 0 6 1? 0 8 = .1> 6 1;? @ 0 8 = .1> * 7 6 3 E 0 8 = .1>. ? ;? @ 0 8 = .1>. 0 *;? @ 0 8 = .1> A = 0- * 0 6 C 0 8 = .1> 2A = * D;? @ 0 8 = .1>. 6 6 5 0 8 = .1>. 0 6 3 6 0 8 = .1> @ ?. * 0 C? 0 8 = .1> 2A * 0 0 6 E * 0 8 = .1> * E * 0 8 = .1> 6 0 D 5 0 8 = .1> 2A * 0 6 0. A 0 8 = .1> 161 = 6 * -, 0 8 = .1>. 6. 5 0 8 = .1>. 6 0 = D 0 8 = .1> @ ?. * 0 C? 0 8 = .1> 0 6 C 0 0 8 = .1> 2A * 0 6 0. A 0 8 = .1> 17I, * = B 0 8 = .1>. 6. 5 0 8 = .1>. * 0 0 3 0 8 = .1> @ ?. 6 * = 0 8 = .1> 2A * 0 0 6 B = 0 8 = .1> A = 0- * 6 3 0 8 = .1> 0A *. A 0 8 = .1> 0 * 0 0 *** 6 * 000 0 F-.D 23 G H;

0 8 = .1> 0 * 60. ;? @ 0 8 = .1> 0 * 06 -;? @ 0 8 = .1>. 0 2;? @ 0 8 = .1> = 06 0 = D 0 8 = .1> * 7 6 6 C 0 8 = .1>. *;? @ 0 8 = .1> 2A * 0 0 6 B = 0 8 = .1> 161 = 6 * -, 0 8 = .1> * = B 0 8 = .1> * = B 0 8 = .1> 6 60 60 66 606 F-.D 23 GH;? @ 0 8 = .1> * A = 0 8 = .1> 6 0 =, 0 8 = .1> 0 6? 0 8 = .1> 6;? @ 0 8 = .1> * B = 0 8 = .1> 0 *? = 0 8 = .1> 6 0 = * 0 8 = .1> 0 6 1 - 0 8 = .1> 0 0;? @ 0 8 = .1> * E * 0 8 = .1> 0 3; ? @ 0 8 = .1> * E * 0 8 = .1>. 6 0;? @;? @ - 0 8 = .1> * 7 * 6 - * 0 8 = .1>. 66 =;? @ 0 8 = .1> 6 0 =, 0 8 = .1> 6 -;? @ 0 8 = .1> A = 0- * 0 6 C 0 8 = .1> *;? @ ;? @ - 0 8 = .1> 2A * 0 6 * 6 = 0 8 = .1>. 6 0;? @;? @ - 0 8 = .1> 6 0 = 5 0 8 = .1> 0 * * = 5 0 8 = .1> 2A = 0 * E;? @ 0 8 = .1> . 6 0 = D 0 8 = .1>. * 2;? @ 0 8 = .1> A = 0- * 0 6 * 0 8 = .1> 2A =? ;? @ 0 8 = .1>. * 0 * 5 0 8 = .1> 6 0 =, 0 8 = .1> 6 -;? @ 0 8 = .1>. 6 & = 0 8 = .1> * 7 6 3 E 0 8 = .1>. 0;? @ 0 8 = .1> 2A * 0 0 6 D = 0 8 = .1>. 6 2 - 0 8 = .1> A = 0- * 6 3 0 8 = .1> 2A * 0 0 6 B = 0 8 = .1> * 6 - 0 8 = .1>. * 6 = 6 0 8 = .1>. *;? @ 0 8 = .1>;? @ 0 8 = .1> * E * 0 8 = .1> * & C 0 8 = .1> * 0. 5 0 8 = .1> A = 0- * 0 6 C 0 8 = .1> 6 0? 1 0 8 = .1> 2A * 0 * B E 0 8 = .1> * 6 3 0 8 = .1>. 6 2 & 0 8 = .1> * 0 3 E 0 8 = .1> 6 0 = 5 0 8 = .1> * 7 * 0 - E 0 8 = .1> 2A = * = 5 0 8 =. 1> 6 0 =,

0 8 = .1> 0060 6 * 606606666 ** 060 ** 0060 F-.D 23 G H 2;? @

0 8 = .1> 6 B 0 8 = .1> * = B 0 8 = .1>. 0 6 B = 0 8 = .1>. 0 2 A 0 8 = .1> 2A = 6 * 606 D;? @ 0 8 = .1>. 6. 5 0 8 = .1> @ ?. 6 * = 0 8 = .1> 6 0 = 5 0 8 = .1> 2A = * = 5 0 8 = .1>. 0 6 2 C 0 8 = .1>. 0;? @ 0 8 = .1> 2A * 0 6 0 = D 0 8 = .1> 2A = 606060 ** 0;? @ 0 8 = .1>. 1;? @ 0 8 = .1> * 7 6 0 6. 0 8 = .1> 2A = 600. ;? @ 0 8 = .1> 161 = 6 * = 0 8 = .1>. 6 E 5 0 8 = .1>. 0;? @ 0 8 = .1> * 7 6 0 3 0 8 = .1> 2A = 6;? @ 0 8 = .1>. 0 A 5 0 8 = .1> 0 =, 0 8 = .1> * 7 * - 0 8 = .1> 0 * 0 A;? @ 0 8 = .1> 0 * 0;? @ 0 8 = .1> 0 * 0;? @ 0 8 = .1> 2A = 0 6 A. 0 8 = .1>. 6 *;? @ 0 8 = .1> 2A = 6 * 3 B 0 8 = .1> 0 6;? @;? @ - 0 8 = .1> 6 * - = 0 8 = .1> * 3A 0 66 * ** 0 6 * 6 * **** 6 ** 6 ** F-.D 23 GH;

0 8 = .1> 161 = 6 * -, 0 8 = .1>. * 6 = 6 0 8 = .1> 2A * 0 6 0. A 0 8 = .1>. * 0 C 0 8 = .1> 0A *. A 0 8 = .1> * 7 6 6 C 0 8 = .1>. 6 =? 0 8 = .1> 0 666;? @ 0 8 = .1> * = D 0 8 = .1> 161 = 6 * = 0 8 = .1>. 0 2;? @ 0 8 = .1> 2A = 060 0 0 60 F-.D 23 G H 3;? @ 0 8 = .1>. 0 2;? @ 0 8 = .1> A = 0- * 6 3 0 8 = .1> * 0 6 C 0 8 = .1> * * 0 * 00 ** ** 6 * 0 6 6 * F -.D 23 GH =;? @ 0 8 = .1> 2A * 0 6 * 6 = 0 8 = .1>. ? ;? @ 0 8 = .1> * = B 0 8 = .1> 2A = 6 =;? @ 0 8 = .1> *. 6 0 8 = .1> * =;? @ 0 8 = .1> * B = 0 8 = .1> 2A = 000 A;? @ 0 8 = .1>. 6. 5 0 8 = .1>. 0 A;? @ 0 8 = .1> @ ?. * 6 C? 0 8 = .1> 2A * 0 0 6 0 0 8 = .1> *, 5 0 8 = .1>. 6 0 = D

0 8 = .1> A = 0- * 0 6 C 0 8 = .1> 2A * 0 6 0 = D 0 8 = .1> 2A = 6;? @ 0 8 = .1> 6 0 =, 0 8 = .1> = 0 * 0 - E 0 8 = .1> @ ?. 6 0 - 1 0 8 = .1> 2A * 0 6 0 = D 0 8 = .1> 2A * 0 0 6 0 0 8 = .1> 0 * * = 5 0 8 = .1> = 0- * 6 6 0 0 8 = .1> 2A = 0 ** 060 ** 0 * F-.D 23 GH 2;? @ 0 8 = .1> 7 = 01 0 C. 0 8 = .1>. 6 A 0 8 = .1>. 6. 5 0 8 = .1> @ ?. 6 * = 0 8 = .1> 6 0 = 5 0 8 = .1> 2A * 0 6 0 = D 0 8 = .1> 2A * 0 0 6 E * 0 8 = .1>. 6 0 = D 0 8 = .1>. 6. 5 0 8 = .1> A = 0- * 0 6 * 0 8 = .1> 2A * 0 0 *. 0 0 8 = .1> 2A = * D 5 0 8 = .1> * = B 0 8 = .1>. 6 * = 0 8 = .1>. 6;? @;? @ - 0 8 = .1> A = 0- * 0 6 C 0 8 = .1> 2A = 0 * 6 0 600 F-.D 23 GH;? @ 0 8 = .1> 6 0 =, 0 8 = .1> = 06, * 0 8 = .1> 2A * 0 0 - 0 8 = .1> 17I, * = D 0 8 = .1>. * 6 = 6 0 8 = .1> A = 0- * 0 6 * 0 8 = .1> 2A = * 6 -;? @ 0 8 = .1> 2A = 0 6 A. 0 8 = .1>. * 6 = 6 0 8 = .1>. * = 5 0 8 = .1> A = 0- * 6 3 0 8 = .1> * = B 0 8 = .1> * 6. 0 8 = .1> A;? @ 0 8 = .1> 6 - E 0 8 = .1> 17I, * = B 0 8 = .1> 2A = 60. ;? @ 0 8 = .1> * 0 * 1 0 8 = .1> 0A *. A 0 8 = .1> = 0 * =, 0 8 = .1> = 0 * =, 0 8 = .1> 2A * 0 6 0. A 0 8 = .1> 6 0 = 5 0 8 = .1> 0 * * = 5 0 8 = .1> * 0 * 606 * 06 * 6 * * 0 66 6 F-.D 23 GH -;? @ 0 8 = .1> A = 0- * 0 6 C 0 8 = .1> A = 0- * 6 - 0 8 = .1>. 6 A 0 8 = .1>. 6;? @;? @ - 0 8 = .1>. *? 0 8 = .1> 6 0 = 5 0 8 = .1> 6 0 = * 0 8 = .1> = 0 * =, 0 8 = .1> & 0 A 6 0 0 C 0 8 = .1> 2A * 0 6 0 & 2 0 8 = .1> 17I, * = B 0 8 = .1> .61 = * & 0 8 = .1> * 3A 06 0 =,

0 8 = .1> 161 = 0 * A 0 8 = .1>. ***;? @ 0 8 = .1>. * = 5 0 8 = .1>. * = 5 0 8 = .1> * 7 * 0 - E 0 8 = .1> 06 * 06 * 6 * * 0 66 666 F-.D 23 GH -;? @ 0 8 = .1> = 0 * 6 = 0 8 = .1>. 0 *;? @ 0 8 = .1> 0 * 0;? @ 0 8 = .1> 2A = * 60 6 0 E;? @ 0 8 = .1>. 6 0 = A 0 8 = .1>. * = * 0 8 = .1> @ ?. 6 * = 0 8 = .1> 2A * 0 6 & 2 0 8 = .1> * 7 * - 1 0 8 = .1> 2A = * = 5 0 8 = .1>. * 6 = 6 0 8 = .1> * = B 0 8 = .1> 2A * 0 0 6 B = 0 8 = .1> 161 = 6 * -, 0 8 = .1> * 7 6 0 3 0 8 = .1>. 6 0 = D 0 8 = .1> * 6 - 0 8 = .1> 2A * 0 0 6 B = 0 8 = .1> 17I, * 6 - 0 8 = .1> 0 * * D 5 0 8 = .1> 0 * 6. 5 0 8 = .1>. * = 5 0 8 = .1> A = 0- * 0 6 C 0 8 = .1> 2A * 0 0 *. 0 0 8 = .1>. 6. 5 0 8 = .1> @ ?. 6 * = 0 8 = .1> 6 0 = 5 0 8 = .1> 2A * 0 6 0. A 0 8 = .1> 17I, 6 0 = 5 0 8 = .1> 0 * * 0 AND 5 0 8 = .1> 0 * 6. 5 0 8 = .1> 2A = * = 5 0 8 = .1> A = 0- * 0 6 C 0 8 = .1>. E;? @ 0 8 = .1> A = 0- * 6 3 0 8 = .1> * 0 =? 0 8 = .1> = 0 * 6 = 0 8 = .1>. * 6 = 6 0 8 = .1> * 7 * 2 0 8 = .1> 2A = 6 3;? @ 0 8 = .1> * C & 0 8 = .1> 161 = 0 A 0 8 =. 1> 161 = 6 * -, 0 8 = .1>. * * = 0 8 = .1>. 6 E 5 0 8 = .1> A;? @ 0 8 = .1> 6 0 = 5 0 8 = .1>. 00;? @ 0 8 = .1> * 0 * 1 0 8 = .1> 6 *;? @ 0 8 = .1> A = 0- * 6 3 0 8 = .1>. 6 0 = D 0 8 = .1>. * = * 0 8 = .1> @ ?. * 0 C? 0 8 = .1>. 6 6 C 0 8 = .1> 2A = 0 0. ;? @ 0 8 = .1>. * 6 = 6 0 8 = .1>. 0? 5 0 8 = .1> 161 = * A. 0 8 = .1>. 00 66 * D;? @

0 8 = .1>. * = 5 0 8 = .1>. 6 * = 0 8 = .1>. 0;? @ 0 8 = .1> 2A * 0 6 0 6 - 0 8 = .1>. 0;? @;? @ - 0 8 = .1> * * E 0 8 = .1> 2A * 0 0 6 B = 0 8 = .1>. 0? ;? @ 0 8 = .1> A = 0- * 0 6 * 0 8 = .1> 2A = 6 = 2 0 8 = .1> * = D 0 8 = .1> 161 = 6 * -, 0 8 = .1>. 6 * = 0 8 = .1>. 0;? @ 0 8 = .1> 2A = 60. ;? @ 0 8 = .1> * = B 0 8 = .1> 2A * 0 0 6 B = 0 8 = .1>. 6. 5 0 8 = .1> A = 0- * 6 3 0 8 = .1>. 60 &;? @ 0 8 = .1> 0 * 0 60 60006 F-.D 23 G H 3;? @ 0 8 = .1> 0 * * D 5 0 8 = .1>. 6 0 = D 0 8 = .1>. 6 0 6 = 0 8 = .1> A = 0- * 0 6 C 0 8 = .1>. 0 E;? @ 0 8 = .1>. * = * 0 8 = .1>. 0 A;? @ 0 8 = .1> 0 * -;? @ 0 8 = .1> 0 * 0;? @ 0 8 = .1> A = 0- * 0 6 C 0 8 = .1> 6 0 = 5 0 8 = .1>;? @ 0 8 = .1> 161 = 6 * -, 0 8 = .1> A = 0- * 0 6 * 0 8 = .1> * B = 0 8 = .1>. 0 A;? @ 0 8 = .1>. 0 A;? @ 0 8 = .1>. 0 =;? @ 0 8 = .1>. 0;? @ 0 8 = .1> A = 0- * 0 6 C 0 8 = .1> 2A =;? @ 0 8 = .1> * D;? @ 0 8 = .1> * B = 0 8 = .1> 2A * 0 0 6 B = 0 8 = .1>. 0 A;? @ 0 8 = .1> 2A * 0 6 0 = D 0 8 = .1> * 7 * - 1 0 8 = .1> 0 6 2 0 8 = .1> * E * 0 8 = .1> A = 0- * 6 3 0 8 = .1> 2A = 0 0 3;? @ 0 8 = .1> 2A = 060 0 0 60 F-.D 23 GH 3;? @ 0 8 =. 1>. 0 * 006066 F-.D 23 G H E;? @ 0 8 = .1>. * D 5 0 8 = .1>. * = * 0 8 = .1> 6 * = 0 8 = .1> 6 *;? @ 0 8 = .1> 2A * 0 6 DB 0 8 = .1> 161 = 6 * -, 0 8 =. 1>. 6 E 5 0 8 = .1> 2A = *? 0 8 = .1>. * = 5 0 8 = .1>. 0 A;? @ 0 8 = .1> 2A = 60 3;? @ 0 8 = .1> 7 = 01 0 C.

0 8 = .1> * = D 0 8 = .1> 6 0 = * 0 8 = .1> = 0 * 6 = 0 8 = .1>. 0 0;? @ 0 8 = .1>. 0;? @ 0 8 = .1>. * 6 = 6 0 8 = .1> * 7 * 6 - * 0 8 = .1> 2A = * 60;? @ 0 8 = .1> = 0 * =, 0 8 = .1> 2A * 0 6 0. A 0 8 = .1> 2A = * = 5 0 8 = .1> * 6 C? 0 8 = .1> 2A * 0 6 0. A 0 8 = .1> 6 0 = 5 0 8 = .1> 0;? @ 0 8 = .1> 6 0 =, 0 8 = .1> 161 = 6 * -, 0 8 = .1>. * 6 = 6 0 8 = .1>. 6 =? 0 8 = .1> 2A = ** 06 * 60 2;? @ 0 8 = .1>. 06 -;? @ 0 8 = .1> A = 0- * 6 6 0 0 8 = .1> 2A = 0 0 00. ;? @ 0 8 = .1> * 0. 5 0 8 = .1> 2A * 0 0 6 B = 0 8 = .1> * 7 * 6 - * 0 8 = .1> A = 0- * 0 6 * 0 8 = .1> = 1 * = ;? @ 0 8 = .1> A = 0- 6 DA 0 8 = .1>. * 6 = 6 0 8 = .1>. 6 =;? @ 0 8 = .1> * 7 6 3 AND 0 8 = .1>. * = * 0 8 = .1>. 6;? @;? @ - 0 8 = .1> * 6 6 0 0 8 = .1> 2A * 0 6 0. A 0 8 = .1> * 7 0 3 C 0 8 = .1> 161 = 6 * -, 0 8 = .1>. 6 0 = D 0 8 = .1>. 6. 5 0 8 = .1> @ ?. 6 * = 0 8 = .1> 6 AND 5 0 8 = .1> 2A * 0 6 & 2 0 8 = .1> 0 * 6. 5 0 8 = .1> 0 * * = 5 0 8 = .1> * * E 0 8 = .1> 2A * 0 * 6 = 0 8 = .1>. * 6 = 6 0 8 = .1> 2A * 0 0 6 B = 0 8 = .1> * 7 * - 1 0 8 = .1> A = 0- * 0 6 C 0 8 = .1> * 7 * - 1 0 8 = .1>. 6 =;? @ 0 8 = .1> A = 0- * 6 3 0 8 = .1> 2A = 0 E 5 0 8 = .1> 2A = 000. ;? @ 0 8 = .1> 0A *. A 0 8 = .1> * 7 * 6 - * 0 8 = .1> 161 = 6 * -, 0 8 = .1>. 6 3 E 0 8 = .1>. * 6 = 6 0 8 = .1> 2A * 0 0 6 B = 0 8 = .1>. * 2 0 8 = .1>. 6 0 - 0 8 = .1> @ ?. 6 * = 0 8 = .1> 2A * 0 6 0. THE

0 8 = .1> 2A * 0 6 0 = D 0 8 = .1> * 7 * - 1 0 8 = .1> * = D 0 8 = .1> = 0 * - 1 0 8 = .1> ;? @ 0 8 = .1> 6 0 =, 0 8 = .1> 2A * 0 6 0. A 0 8 = .1>. 6 * = 0 8 = .1> * 6 3 0 8 = .1> 2A * 0 6 0. A 0 8 = .1>. * 6 = 6 0 8 = .1> = 1 * =;? @ 0 8 = .1>. 0 2;? @ 0 8 = .1> 2A = 0 6 3 0 8 = .1> 6 0 = 5 0 8 = .1> * 0 =? 0 8 = .1>. 0;? @ 0 8 = .1> A = 0- 6 D B 0 8 = .1> 2A = * = * 0 8 = .1>. * 6;? @ 0 8 = .1>. 0 =;? @ 0 8 = .1> * = D 0 8 = .1> 6 * 5 0 8 = .1> * 7 0 6 0 0 8 = .1> 17I, 6 * = 6 0 8 =. 1>. * = * 0 8 = .1> 2A * 0 0 6 B = 0 8 = .1> * 7 * 6 - * 8 = .1> 0 *;? @;? @ - 8 = .1> 2A * 0 0 6 & C 0 8 = .1> = 1 * =;? @ 0 8 = .1> 2A * 0 6 0. A 0 8 = .1> * 7 * - 1 0 8 = .1> 6 0 = 5 0 8 = .1> 6 0 = * 0 8 = .1> 6 0? 0 8 = .1>. * = 5 0 8 = .1> A = 0- 6 D B 0 8 = .1> = 1 * =;? @ 0 8 = .1>. 0 A 5 0 8 = .1>. 0 *;? @ 0 8 = .1> A = 0- * 0 6 C 0 8 = .1> 2A = 00 A;? @ 0 8 = .1> 0 * - = 0 8 = .1> 660 66 0;? @ 0 8 = .1> 2A * 0 0 B? 0 8 = .1> 161 = 0 A 0 8 = .1>. * = 5 0 8 = .1> 2A = * 6 0;? @ 0 8 = .1> A = 0- * 6 6 0 0 8 = .1> = 1 6 =;? @ 0 8 = .1> 2A = 6. 5 0 8 = .1>. 6 0 = B 0 8 = .1>. 6 3 E 0 8 = .1> A = 0- * 6 3 0 8 = .1> 2A = 660 D;? @ 0 8 = .1> 2A = 606 D;? @ 0 8 = .1>. 6;? @;? @ - 0 8 = .1>. 6 =;? @ 0 8 = .1>. 6006 -;? @ 0 8 = .1> = 1 * =;? @ 0 8 = .1> A;? @ 0 8 = .1>. 0 6 E * 0 8 = .1> A = 0- * 0 6 * 0 8 = .1> = 1 * =;? @ 0 8 = .1>. 0 2 A

0 8 = .1>. 0 A;? @ 0 8 = .1> A = 0- * 6 3 0 8 = .1> 2A * 0 6 D B 0 8 = .1> 161 = 6 * -, 0 8 = .1>. 0;? @ 0 8 = .1>. 1;? @ 0 8 = .1> * = B 0 8 = .1> 2A = 00 0 6006. ;? @ 0 8 = .1> 2A = 600600 0 E;? @ 0 8 = .1>. 6 *;? @;? @ - 0 8 = .1> A = 0- * 6 3 0 8 = .1> 2A = * 00600000 D;? @ 0 8 = .1> 0 *? = 0 8 = .1> = 0 * 0 - E 0 8 = .1> 2A * 0 6 0. A 0 8 = .1> * 6 = 0 8 = .1>. * =;? @ 0 8 = .1> 2A = 600600 0. ;? @ 0 8 = .1>. 0;? @ 0 8 = .1> 2A * 0 6 0. A 0 8 = .1>. 0 A;? @ 0 8 = .1> A = 0- * 6 3 0 8 = .1> = 1 * =;? @ 0 8 = .1> 2A * 0 0 6 E * 0 8 = .1> A = 0- * 6 3 0 8 = .1> = 1 * =;? @ 0 8 = .1> 2A = 00 A;? @ 0 8 = .1>. * 6 = 6 0 8 = .1>. 0 *;? @ 0 8 = .1> = 1 * =;? @ 0 8 = .1> = 0 * =, 0 8 = .1>. 0 E - 0 8 = .1> * 0;? @ 0 8 = .1> *? ;? @ 0 8 = .1> 2A * 0 0 6 0 0 8 = .1> = 0- * 0 6 * 0 8 = .1>. 6006 6;? @ 0 8 = .1>. 0066 6;? @ 0 8 = .1> = 1 * =;? @ 0 8 = .1> = 1 6 0;? @ 0 8 = .1> 2A * 0 6 0 = D 0 8 = .1> 161 = 6 * -, 0 8 = .1>. *, = 0 8 = .1> 2A * 0 0 6 B = 0 8 = .1> 6 0 = 5 0 8 = .1>. * = 5 0 8 = .1> * 7 6 3 E 0 8 = .1> 2A = 000 A;? @ 0 8 = .1> * 0 6 * 0 8 = .1>. 6 0 = D 0 8 = .1>. * = 5 0 8 = .1> @ ?. * 6 C? 0 8 = .1>. * 6 B 3 0 8 = .1> *;? @;? @ - 0 8 = .1>. 6 A;? @ 0 8 = .1> * 7 * - 1 0 8 = .1>. * = 5 0 8 = .1> A = 0- * 0 6 C 0 8 = .1> 6 0 B E 0 8 = .1> 2A * 0 6 0. A 0 8 = .1> 6 0 = * 0 8 = .1> 2A * 0 6 0. A 0 8 = .1> = 0- * 0 6 C 0 8 = .1>. * = 5 0 8 = .1> = 1 * =;? @ 0 8 = .1> = 1 6 0;? @

0 8 = .1> = 1 6 0 = 5 0 8 = .1>. 6 6 C 0 8 = .1>. 6. ;? @ 0 8 = .1> 2A = * 60;? @ 0 8 = .1> * * E 0 8 = .1> 161 = 6 * -, 0 8 = .1>. 0 2;? @ 0 8 = .1>. 0 *;? @ 0 8 = .1> * 6 = 0 8 = .1> 161 = 6 * -, 0 8 = .1>. 6;? @;? @ - 0 8 = .1>. 0 6;? @;? @ - 0 8 = .1> 2A * 0 0 6 D = 0 8 = .1> * 7 * - 1 0 8 = .1>. 0;? @ 0 8 = .1> A = 0- 0 DB 0 8 = .1> * 3A 0 666 * 6 * 6 *** 0 * F-.D 23 GH =;? @ 0 8 = .1 > A = 0- * 6 3 0 8 = .1> * = B 0 8 = .1> = 0 * 6 = 0 0 8 = .1>. 6 E 3 - 0 0 8 = .1> A = 0- * 0 6 C 0 0 8 = .1> 2A = 00 * 6 0 &;? @ 0 0 8 = .1>. 6 0 = D 0 0 8 = .1> 2A * 0 6 0 = D 0 0 8 = .1> 2A * 0 6 A 0 0 8 = .1> 0A *. A 0 0 8 = .1> * 7 0 6 3 6 0 0 8 = .1> A = 0- * 6 3 0 0 8 = .1> * 0 C? 0 0 8 = .1> 17I, * = B 0 0 8 = .1> 2A = 0060 * 6060 A;? @ 0 0 8 = .1> 161 = 6 B 0 0 8 = .1>. 6 0 = D 0 0 8 = .1> 2A * 0 6 0 = D 0 0 8 = .1> 0 * * = 5 0 0 8 = .1> 0 * * 0 - 5 0 0 8 = .1> . 0 A;? @ 0 0 8 = .1>. 0 A;? @ 0 0 8 = .1> A = 0- * 6 3 0 0 8 = .1> 2A * 0 6 0 = D 0 0 8 = .1> * 6. 0 0 8 = .1>. * D 5 0 0 8 = .1>. * = 5 0 0 8 = .1> = 1 * =;? @ 0 0 8 = .1> * = B 0 0 8 = .1> 17I, * = D 0 0 8 = .1> 2A = 60 3 ;? @ 0 0 8 = .1> 161 = 6 * -, 0 0 8 = .1>. * 6 = 6 0 0 8 = .1> 2A * 0 * 6 D. 0 0 8 = .1> 2A =? ;? @ 0 0 8 = .1> 2A = * 0;? @ 0 0 8 = .1> A = 0- * 0 6 C 0 0 8 = .1> 2A * 0 0 6 & C 0 0 8 = .1> * 0. 5 0 0 8 = .1> = 0 * 6 = 0 0 8 = .1> 2A = 6 * 0 D;? @ 0 0 8 = .1> 0 * C 6 0 0 8 = .1> 6 0 = , 0 0 8 = .1> = 0- * 6 3 0 0 8 = .1> * = B 0 0 8 = .1> 2A * 0 6 0. A 0 0 8 = .1> @ ?. * 0 C? 0 0 8 = .1> 2A * 0 6 0 = D 0 0 8 = .1> * 7 0 6 0

0 0 8 = .1> A = 0- * 6 3 0 0 8 = .1> 0;? @ 0 0 8 = .1>. 6. 5 0 0 8 = .1>. 6 =? 0 0 8 = .1> A = 0- * 6 3 0 0 8 = .1> 17I, * =;? @ 0 0 8 = .1> 2A = 66 * 00 60600 F-.D 23 GH 6; @ 0 0 8 = .1> 2A = ** 00 0 * 60 F-.D 23 GH 3;? @ 0 0 8 = .1> * 6;? @;? @ - 0 0 8 = .1>. 0 A;? @ 0 0 8 = .1> 2A = 0 60;? @ 0 0 8 = .1> 161 = 6? 2 0 0 8 = .1>. 6. 5 0 0 8 = .1>. 6 0 = B 0 0 8 = .1> 2A * 0 6 0 = D 0 0 8 = .1> 2A * 0 6 & 2 0 0 8 = .1> * 7 0 6 0 0 0 8 = .1> 2A = * = 5 0 0 8 = .1> = 06 0 = D 0 0 8 = .1> * 7 6 6 C 0 0 8 = .1>. * =;? @ 0 0 8 = .1>. * 6 = 6 0 0 8 = .1> * 0 *;? @ 0 0 8 = .1>. 6. 5 0 0 8 = .1>. ? ;? @ 0 0 8 = .1> * 0 0 0 8 = .1> 17I, 6 0 = * 0 0 8 = .1>. 6 *;? @;? @ - 0 0 8 = .1>. ;? @ 0 0 8 = .1> 2A * 0 * 6 D. 0 0 8 = .1> * 7 * 6 - * 0 0 8 = .1> 0A *. A 0 0 8 = .1> * 7 * 2 0 0 8 = .1>. 0 A;? @ 0 0 8 = .1> = 1 * =;? @ 0 0 8 = .1> 2A * 0 0 6 B = 0 0 8 = .1> 0 * C 6 0 0 8 = .1 > A = 0- * 0 6 C 0 0 8 = .1> * B = 0 0 8 = .1> * 3A 06 0 = 5 0 0 8 = .1> * 6 * - 0 0 8 = .1> 6 0 = 5 0 0 8 = .1> A = 0- * 6 6 0 0 0 8 = .1> 2A * 0 0 6 B = 0 0 8 = .1> 6 0 = 5 0 0 8 = .1 > 0 * * 0 - 5 0 0 8 = .1> 6;? @ 0 0 8 = .1> * 3A 0 06;? @ 0 0 8 = .1> * C & 0 0 8 = .1> 2A * 0 * 0 DA 0 0 8 = .1>. * 6 = 6 0 0 8 = .1> = 0- * 0 0 0 8 = .1>. ? ;? @ 0 0 8 = .1> 2A * 0 6 *. D 0 0 8 = .1> * 7 6 0 6. 0 0 8 = .1>. * 0 - 5 0 0 8 = .1>. 6 1 C 0 0 8 = .1> @ ?. 6 * = 0 0 8 = .1> 0 * 6. 5 0 0 8 = .1> 2A = * = 5 0 0 8 = .1> 161 = 6 * -, 0 0 8 = .1>. 0 6;? @;? @ - 0 0 8 = .1> * 7 * 0 - E 0 0 8 = .1> 2A = 0 6 A. 0 0 8 = .1> 161 = 6 * -, 0 0 8 = .1>. 6 * 3 B

0 0 8 = .1> 2A = 6 0 600 * 06 * 0 000 * F-.D 23 G H =;? @ 0 0 8 = .1> * 3A 06 0 = 5 0 0 8 = .1>. 6 *;? @;? @ - 0 0 8 = .1>. 0 A;? @ 0 0 8 = .1> = 1 * =;? @ 0 0 8 = .1> 2A = 0 6 A. 0 0 8 = .1> 2A * 0 6 0. A 0 0 8 = .1> * 7 * 2 0 0 8 = .1> * = B 0 0 8 = .1> * * E 0 0 8 = .1>. 6. 5 0 0 8 = .1>. 6 0 = D 0 0 8 = .1> @ ?. * 0 C? 0 0 8 = .1> 17I, 6 0 = 5 0 0 8 = .1> 0 * * 0 - 5 0 0 8 = .1> 2A * 0 0 6 B = 0 0 8 = .1> * = B 0 0 8 = .1>. * 0 E 5 0 0 8 = .1> 6 0 = * 0 0 8 = .1> 0 6;? @;? @ - 0 0 8 = .1> 6;? @ 0 0 8 = .1> * * &;? @ 0 0 8 = .1> 2A * 0 0 6 B = 0 0 8 = .1> * 7 0 6 3 6 0 0 8 = .1>. * 6 = 6 0 0 8 = .1>. 6 =? 0 0 8 = .1> A = 0- * 6 3 0 0 8 = .1> 2A * 0 6 & 2 0 0 8 = .1> 2A = 6 * 606 0 * 606000 60 * F-.D 23 GH 0;? @ 0 0 8 = .1>. 6 A 0 0 8 = .1>. 6 0 = D 0 0 8 = .1> @ ?. 6 * = 0 0 8 = .1> * - 0 0 8 = .1> 6 0 = 5 0 0 8 = .1> = 0 * =, 0 0 8 = .1> 161 = * A. 0 0 8 = .1>. * = 5 0 0 8 = .1>. 6 0 = D 0 0 8 = .1>. 0 A;? @ 0 0 8 = .1> @ ?. * 0 C? 0 0 8 = .1> 0 6 * = 0 0 8 = .1> 2A * 0 0 6 E * 0 0 8 = .1> 17I, 6 0 = * 0 0 8 = .1>. 6? = 0 0 8 = .1>. * 6 = 6 0 0 8 = .1> * = D 0 0 8 = .1> * 7 0 6 0 0 0 8 = .1> 161 = 0 A 0 0 8 = .1> 0A *. A 0 0 8 = .1> 17I, * = B 0 0 8 = .1> 0 * 0;? @ 0 0 8 = .1> 2A = 0 6 A. 0 0 8 = .1>. 6. 5 0 0 8 = .1> = 1 * =;? @ 0 0 8 = .1> 2A * 0 0 6 B = 0 0 8 = .1> * 7 6 - 0 0 0 8 = .1> 161 = 6 * -, 0 0 8 = .1>. 0;? @ 0 0 8 = .1> 2A = 6. 5 0 0 8 = .1> 2A = 00 * 6 0 600 * 06 * 0 F-.D 23 G H 2;? @ 0 0 8 = .1> 161 = 6 * -, 0 0 8 = .1>. 0 * - 0 0 8 = .1>. 6 0 = D 0 0 8 = .1> 6 * = 6 0 0 8 = .1> 2A * 0 0 6 B = 0 0 8 = .1> * = D

0 0 8 = .1> 2A * 0 0 B? 0 0 8 = .1>. 6. 5 0 0 8 = .1>. 6 0 = D 0 0 8 = .1> @ ?. * 0 C? 0 0 8 = .1> 2A * 0 6 0 = D 0 0 8 = .1> 2A * 0 0 6 E * 0 0 8 = .1> 17I, * = D 0 0 8 = .1> A = 0 - * 6 6 0 0 0 8 = .1> = 0 * =, 0 0 8 = .1>. 6 0 = D 0 0 8 = .1>. * = * 0 0 8 = .1> @ ?. * 0 C? 0 0 8 = .1> 6 0 = 5 0 0 8 = .1> 17I, * = D 0 0 8 = .1> 2A = 0 6 A. 0 0 8 = .1>. 0;? @ 0 0 8 = .1> = 1 * =;? @ 0 0 8 = .1> 2A = *? ;? @ 0 0 8 = .1>. 6;? @;? @ - 0 0 8 = .1>. 0 *;? @ 0 0 8 = .1> A = 0- * 6 3 0 0 8 = .1> 6 0 = 5 0 0 8 = .1> 0 E 0 0 8 = .1> * 0 C? 0 0 8 = .1> 0 E;? @ 0 0 8 = .1> 161 = * A. 0 0 8 = .1>. * 6 = 6 0 0 8 = .1> 2A * 0 0 B? 0 0 8 = .1> * 6 3 B 0 0 8 = .1> A = 0- * 0 6 C 0 0 8 = .1> 6 0 =, 0 0 8 = .1> * = B 0 0 8 = .1>. 6 0;? @;? @ - 0 0 8 = .1> 2A * 0 0 6 D = 0 0 8 = .1>. * = 5 0 0 8 = .1>. ,;? @ 0 0 8 = .1> * 0 =? 0 0 8 = .1> * * E 0 0 8 = .1> 2A = * 60606;? @ 0 0 8 = .1>. * D 5 0 0 8 = .1>. 006 60060 60 * 00 F-.D 23 G H? ;? @ 0 0 8 = .1> * 7 6 3 E 0 0 8 = .1>. 6 E 5 0 0 8 = .1> A = 0- * 0 6 * 0 0 8 = .1> A = 0- * 6 3 0 0 8 = .1> = 1 * =;? @ 0 0 8 = .1> 2A * 0 * BE 0 0 8 = .1> * * E 0 0 8 = .1>. 6 0 6. 0 0 8 = .1>. 0 A 5 0 0 8 = .1> * 7 6 * 6 = 0 0 8 = .1> * = B 0 0 8 = .1> = 0 * =, 0 0 8 = .1> = 06 0 = D 0 0 8 = .1> 0 * 0 * 5 0 0 8 = .1>. * = 5 0 0 8 = .1>. 0 A;? @ 0 0 8 = .1> A = 0- * 6 3 0 0 8 = .1>. 6. 5 0 0 8 = .1>. 0 2;? @ 0 0 8 = .1> = 1 * =;? @ 0 0 8 = .1> 6 0 = 5 0 0 8 = .1> * E * 0 0 8 = .1>. * 66 6 00;? @ 0 0 8 = .1> A = 0- * 6 3 0 0 8 = .1> 17I, * = B

0 0 8 = .1> 2A = * 60;? @ 0 0 8 = .1> * 6 = 0 0 8 = .1> = 0- * 6 3 0 0 8 = .1>. 0 A;? @ 0 0 8 = .1> 2A * 0 0 6 B = 0 0 8 = .1> * E * 0 0 8 = .1>. 0 *;? @ 0 0 8 = .1>. 0 *;? @;? @ - 0 0 8 = .1> = 1 * =;? @ 0 0 8 = .1> * 0 - 0 0 8 = .1> 161 = 6 * -, 0 0 8 = .1>. 6 0 6 = 0 0 8 = .1>. 006 0 A;? @ 0 0 8 = .1>. 6 6 5 0 0 8 = .1> 2A = 00 E;? @ 0 0 8 = .1> 161 = 6 B 0 0 8 = .1>. 6 0 = D 0 0 8 = .1> @ ?. * 6 C? 0 0 8 = .1> 2A * 0 * 6 D. 0 0 8 = .1> 2A * 0 0 6 B = 0 0 8 = .1> 0A *. A 0 0 8 = .1> * 7 * 0 - E 0 0 8 = .1>. 0 * 0006060 * 6 F-.D 23 G H 2;? @ 0 0 8 = .1> = 1 * =;? @ 0 0 8 = .1> 2A * 0 6 A 0 0 8 = .1>. * D 5 0 0 8 = .1>. 0;? @ 0 0 8 = .1> * 6 - 0 0 8 = .1> * - 0 0 8 = .1> 2A * 0 0 6. 6 0 0 8 = .1> * 7 * - 1 0 0 8 = .1> * 0 C? 0 0 8 = .1> A = 0- * 6 3 0 0 8 = .1> * 6 3 0 0 8 = .1> A = 0- * 0 6 C 0 0 8 = .1>. 6 0, 5 0 0 8 = .1>. 00 6;? @ 0 0 8 = .1>. 0 A 5 0 0 8 = .1> 0 6 * = 0 0 8 = .1> 0 * 1 * 0 0 8 = .1>. * 6 = 6 0 0 8 = .1> A = 0- * 6 3 0 0 8 = .1> 2A * 0 6 *. D 0 0 8 = .1> 2A * 0 0 A 0 0 8 = .1>. 60 6;? @ 0 0 8 = .1> * B = 0 0 8 = .1> * 6. 0 0 8 = .1> * E * 0 0 8 = .1> 6 0 = 5 0 0 8 = .1> A = 0- * 0 6 C 0 0 8 = .1> 2A * 0 0 6 0 0 0 8 = .1> A = 0- * 6 3 0 0 8 = .1> 6 * *, 0 0 8 = .1> A;? @ 0 0 8 = .1> 161 = 6 * -, 0 0 8 = .1>. * 6 = 6 0 0 8 = .1> 2A * 0 0 6 B = 0 0 8 = .1> 2A = 6;? @ 0 0 8 = .1> * 7 * 6 - * 0 0 8 = .1 > A = 0- * 0 6 * 0 0 8 = .1> 2A * 0 6 *. D 0 0 8 = .1> 2A * 0 0 B? 0 0 8 = .1>. * 6 = 6 0 0 8 = .1>. 000 * 2;? @ 0 0 8 = .1> = 1 * =;? @ 0 0 8 = .1> * 7 * 2

0 0 8 = .1>. * 6 = 6 0 0 8 = .1> A = 0- 6 D B 0 0 8 = .1> 2A * 0 6 0. A 0 0 8 = .1> * 6 6 * 006 * 0 * 060 * F-.D 23 G H;? @ - 0 0 8 = .1> * 6 * - 0 0 8 = .1> * 0 C? 0 0 8 = .1>. * 6 0 0 0 8 = .1>. * B;? @ 0 0 8 = .1>. * D 5 0 0 8 = .1> * - 6 0 0 8 = .1> 2A = 0 0 600 600 F-.D 23 G H? ;? @ 0 0 8 = .1>. * 6 = 6 0 0 8 = .1>. *? 0 0 8 = .1> A = 0- * 6 3 0 0 8 = .1> 2A * 0 0 B? 0 0 8 = .1> * 7 6 3 E 0 0 8 = .1> 6 0 = 5 0 0 8 = .1> 0 * 060. ;? @ 0 0 8 = .1> 161 = * - 0 0 8 = .1> 161 = 6 * -, 0 0 8 = .1>. 0 * 3;? @ 0 0 8 = .1>. * -;? @ 0 0 8 = .1> A = 0- * 0 6 C 0 0 8 = .1> * = B 0 0 8 = .1> 6 0 =, 0 0 8 = .1> 2A * 0 * 0 DA 0 0 8 = .1> * 7 6 0 3 0 0 8 = .1>. 6. 5 0 0 8 = .1> @ ?. 6 * = 0 0 8 = .1> *. A 0 0 8 = .1> *, 5 0 0 8 = .1> * B = 0 0 8 = .1> 6 0 =, 0 0 8 = .1> * = B 0 0 8 = .1> A = 0- * 6 3 0 0 8 = .1> 6666;? @ 0 0 8 = .1> 0 * * = 5 0 0 8 = .1>. 6 0 = D 0 0 8 = .1> *, 5 0 0 8 = .1> 2A * 0 6 0. A 0 0 8 = .1> 17I, * = B 0 0 8 = .1>. 6. 5 0 0 8 = .1>. 6 0 6. 0 0 8 = .1>. 6 0 = D 0 0 8 = .1> @ ?. 6 * = 0 0 8 = .1> 2A * 0 6 0. A 0 0 8 = .1> 0 * * 0 - 5 0 0 8 = .1> 2A = * = 5 0 0 8 = .1>. 6 6 5 0 0 8 = .1> 2A * 0 6 0. A 0 0 8 = .1>. 6. 5 0 0 8 = .1> @ ?. 6 * = 0 0 8 = .1> A = 0- * 6 3 0 0 8 = .1> 6 0 = 5 0 0 8 = .1> * 0 =? 0 0 8 = .1> 6 0 0 8 = .1> * = B 0 0 8 = .1> A = 0- * 0 6 C 0 0 8 = .1> 6 0 =, 0 0 8 = .1 > 0 * - 0 0 0 8 = .1> * = B 0 0 8 = .1> 0 6 1 - 0 0 8 = .1>. * 3;? @ 0 0 8 = .1> 6 0? 0 0 8 = .1> 2A = 00 2;? @ 0 0 8 = .1> 6 0 =,

0 0 8 = .1> *;? @;? @ - 0 0 8 = .1> 6 D 0 0 8 = .1> ** 0 * 6;? @ 0 0 8 = .1> 6;? @ 0 0 8 = .1> 0? 5 0 0 8 = .1> * 0 6 * 0 0 8 = .1> * 6 - 0 0 8 = .1> * 6 3 0 0 8 = .1> = 0 * = * 0 0 8 = .1 > 6 0 = 5 0 0 8 = .1>. 0? ;? @ 0 0 8 = .1> *;? @;? @ - 0 0 8 = .1> * 0 * 1 0 0 8 = .1> 6 * = 0 0 8 = .1> 2A = * = 5 0 0 8 = .1> @ ?. * 0 C? 0 0 8 = .1> 2A * 0 6 & 2 0 0 8 = .1> * = 6 0 0 8 = .1> * = B 0 0 8 = .1> 6 0 D 5 0 0 8 = .1 > A = 0- * 0 6 C 0 0 8 = .1> * A = 0 0 8 = .1> * 0 A 0 0 8 = .1> 0 * - 0 0 0 8 = .1> A = 0 - * 0 6 C 0 0 8 = .1> = 06 0 = D 0 0 8 = .1> * 7 * - 1 0 0 8 = .1>. 6 3 E 0 0 8 = .1> 0 6? 0 0 8 = .1> * 0 C? 0 0 8 = .1> * 3A 0 ** 06 0;? @ 0 0 8 = .1> * 3A 0 ** 06,;? @ 0 0 8 = .1>. * 6 = 6 0 0 8 = .1>. 6 0 = D 0 0 8 = .1> 2A * 0 * 6 = 0 0 8 = .1> 2A * 0 6 0. A 0 0 8 = .1> 660? ;? @ 0 0 8 = .1> A = 0- * 0 6 C 0 0 8 = .1> 0 B? 0 0 8 = .1> * 0 = - 0 0 8 = .1> * C & 0 0 8 = .1> 2A = * 60;? @ 0 0 8 = .1> * E * 0 0 8 =. 1> = 0- * D = 0 0 8 = .1>. *;? @;? @ - 0 0 8 = .1>. 0 6 B = 0 0 8 = .1> 2A * 0 0 B? 0 0 8 = .1> * 7 * - 1 0 0 8 = .1> = 1 * =;? @ 0 0 8 = .1> 2A * 0 0 6 B = 0 0 8 = .1> 161 = 6 * -, 0 0 8 = .1>. * D 5 0 0 8 = .1> = 1 * =;? @ 0 0 8 = .1> 2A = 6,;? @ 0 0 8 = .1>. * D 5 0 0 8 = .1> 0 * 0;? @ 0 0 8 = .1>. 6 A - 0 0 8 = .1> = 0 * =, 0 0 8 = .1> * 7 * - 1 0 0 8 = .1> 2A = 0060 * 6060 A;? @ 0 0 8 = .1> . * 6 = 6 0 0 8 = .1>. 600 * 66 6 00 60 0 6 6 F-.D 23 G H =;? @ 0 0 8 = .1> 6 0 D 5 0 0 8 = .1> * =;? @ 0 0 8 = .1>. 6 * = 0 0 8 = .1> A = 0- * 6 6 0

0 0 8 = .1>. 0? ;? @ 0 0 8 = .1> = 1 * =;? @ 0 0 8 = .1> 0A *. A 0 0 8 = .1> *. A 0 0 8 = .1> = 06 0 = D 0 0 8 = .1> 6 0;? @ 0 0 8 = .1>. * 6 = 6 0 0 8 = .1> 2A * 0 6 0 & 2 0 0 8 = .1>. * 6 = 6 0 0 8 = .1> A = 0- * 0 6 C 0 0 8 = .1> 2A * 0 6 0 = D 0 0 8 = .1> 6 -;? @ 0 0 8 =. 1> 0 6 - 0 0 8 = .1> = 0 * 6 = 0 0 8 = .1> 2A * 0 6 & 2 0 0 8 = .1>. *? 0 0 8 = .1>. 0 6 E * 0 0 8 = .1> 2A * 0 0 B? 0 0 8 = .1> 161 = 0 A 0 0 8 = .1>. 0 * 5 0 0 8 = .1> * = D 0 0 8 = .1> 2A * 0 6 0 = D 0 0 8 = .1>. 6 0 = D 0 0 8 = .1> * 7 * - 1 0 0 8 = .1>. 66 * 06 000 F-.D 23 G H A;? @ - 0 0 8 = .1>. 0 A;? @ 0 0 8 = .1> 6 0 = * 0 0 8 = .1>. 6? 5 0 0 8 = .1> * = B 0 0 8 = .1> 2A = 0 0. ;? @ 0 0 8 = .1> A = 0- * 0 6 C

5 * J # $ '4 KL LB F6 = * 6 = * K 1 "H $ #% &' (# +" 0, & & 01, & M / & '4 23 *' 4 5 & 7 7 8 - &8; 23 <6 #! * 9 * 9 * 9) 0 @ "-7- * * 7 - 1 0 @" -7- 0 6 2A * 0 B = 0 @ "2 # 6 -;? @ 0 @" -7- 6 0 6 0- = B * $ '4

0 @ "-7- * 0;? @;? @ 0 @" -7- * E * 0 @ "2 # * 6;? @ 0 @" -7- 0 6 2 0 @ "-7- * 0 A = 0- 6 C 0 @ "-7- 6 0 = 5 0 @" -7- * * E 0 @ "-7- * 6. 0 @ "-7- 6 0 & 0 @" -7- * = B 0 @ "-7- 0 6 1? 0 @" 2 # 6 1;? @ 0 @ "-7- 6 0 2A * 0. A 0 @ "-7- * = 0 =, 0 @" -7- 6 0 = 5 0 @ "-7- * E * 0 @" -7- * A = 0- 6 - 0 @ "-7- * B = 0 @ "2 # * D;? @

0 0 @ "-7- 0 * C 6 0 0 @" 2 # * 0 *;? @ 0 0 @ "-7- 0 6 2A * 0 B = 0 0 @" -7- 6 0 = 5 0 0 @ "-7- 6 0 = 5 0 0 @" -7- * 0 2A * 0 DA 0 0 @ "-7- * 6 = 0 0 @" -7- *. . - N # # #) 0 0 @ "-7- * 0 C? 0 0 @" -7- * = 6 0 0 @ "-7- * 6 * - 0 0 @" -7- 0 6 2A * 0 . 6 0 0 @ "-7- * = B 0 0 @" -7- * 0 2A * 0 DAN # # #) 0 0 @ "-7- 6 0 = 5 N # # #) 0 0 @" -7 - *, 5 0 0 @ "-7- * * E 0 0 @" -7- * B = 0 0 @ "-7- * 0 * = 5 0 0 @" -7- 6 0 2A * 0. A 0 0 @ "-7- 6 0. = D 0 0 @" -7- *, 5

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7? 30 7 # 0 0 BQ 0 0 0 0 0 7? 30 7 # 0 0 BQ 0 0 0 0 0 7? 30 7 # 0 0 BQ 0 0 0 0 0 7? 30 PBB & BQ B6 ** B6 7 "30 PBB 0"; - 0 0 0 0 7? 30 PBB & BQ 0 0 0 0 7? 30 PBB 1 BQ 0 0 0 0 7? 30 PBB & BQ 0 0 0 0 7? 30 PBB & BQ 0 0 0 0 7? 30 PBB & BQ 0 0 0 0 0 7 - 30 PBB 1 @ 0 0 0 0 7 - 30 PBB 1 '; - * 2- B6 7? 30 PBB 0 0 BQ 0 0 0 0 0 7? 30 PBB 1 BQ 0 0 0 0 7? 30 PBB & BQ 0 0 0 0 7? 30 PBB & BQ 0 0 0 0 7? 30 PBB 0 0 BQ 0 0 0 0 7? 30 PBB & BQ 0 0 0 0 7? 30 PBB & BQ 0 0 0 0 7? 30 PBB & @ 0 0 0 0 7? 30 PBB & BQ 0 0 0 0 7? 30 PBB & BQ 0 0 0 0 7? 30 PBB & BQ 0 0 0 0 7? 30 PBB 1 BQ 0 0 0 0 7? 30 PBB & BQ 0 0 0 0 7? 30 PBB 1 BQ 0 0 0 0 7? 30 PBB 0 0 BQ 0 0 0 0 7? 30 PBB 1 BQ 0 0 0 0 7? 30 PBB 1 BQ 0 0 0 0 0 7? 30 PBB 1 BQ 0 0 0 0 7? 30 PBB 1 BQ 0 0 0 0 7 7 ? 30 PBB & BQ 0 0 0 0 7? 30 PBB 0 0 BQ 0 0 0 0 7? 30 PBB & BQ 0 0 0 0 0 7? 30 PBB 1 BQ 0 0 0 0 7? 30 PBB & BQ 0 0 0 0 0 7? 30 PBB & BQ 0 0 0 0 7? 30 PBB & BQ 0 0 0 0 7? 30 PBB & BQ 0 0 0 0 0 7? 30 PBB 0 0 BQ 0 0 0 0 7? 30 PBB & BQ 0 0 0 0 7? 30 PBB 0 0 BQ 0 0 0 0 7? 30 PBB 1 BQ 0 0 0 0 7? 30 PBB 0 0 BQ 0 0 0 0 7? 30 PBB & BQ 0 0 0 0 7? 30 PBB 0 0 BQ 0 0 0 0 7? 30 PBB 1 BQ 0 0 0 0 7? 30 PBB 1 BQ 0 0 0 0 7? 30 PBB & BQ 2 ** B6 7? 30 PBB & @ 0 0 0 0 7? 30 PBB & @ 0 0 0 0 7? 30 PBB 0 0 BQ 0 0 0 0 7? 30 PBB 1 BQ 0 0 0 0 7? 30 PBB 0 0 BQ 0 0 0 0 7? 30 PBB 1 BQ 0 0 0 0 7? 30 PBB 1 BQ 0 0 0 0 7? 30 PBB & BQ 0 0 0 0 7? 30 PBB & BQ 0 0 0 0 7? 30 PBB & BQ 0 0 0 0 7 7 30 PBB & BQ 0 0 0 0 7? 30 PBB & BQ 0 0 0 0 7? 30 PBB & BQ 0 0 0 0 7? 30 PBB 0 0 BQ 0 0 0 0 7? 30 PBB 0 0 BQ 0 0 0 0 7? 30 PBB 0 0 BQ 0 0 0 0 7? 30 PBB & BQ 0 0 0 0

7? 30 PBB 1 BQ 0 0 0 0 7? 30 PBB & BQ 0 0 0 0 7? 30 PBB & BQ 0 0 0 0 0 7? 30 PBB 1 BQ 0 0 0 0 7? 30 PBB & BQ 0 0 0 0 0 7? 30 PBB 1 BQ 0 0 0 0 7? 30 PBB & BQ 0 0 0 0 7? 30 PBB & BQ 0 0 0 0 7? 30 PBB & BQ * 2- B6 7? 30 PBB & BQ 0 0 0 0 0 7? 30 PBB & BQ 0 0 0 0 7? 30 PBB & BQ 0 0 0 0 7? 30 PBB & BQ 0 0 0 0 0 7? 30 PBB & BQ 0 0 0 0 7? 30 PBB & BQ 0 0 0 0 0 7? 30 PBB 1 BQ 0 0 0 0 7? 30 PBB 0 0 BQ 0 0 0 0 7? 30 PBB 0 0 BQ 0 0 0 0 7? 30 PBB 0 0 BQ 0 0 0 0 7? 30 PBB & BQ 0 0 0 0 7? 30 PBB 1 BQ? 6 **? 6 7? 30 PBB & BQ 0 0 0 0 7 7 30 PBB & BQ 0 0 0 0 7? 30 PBB 1 BQ 0 0 0 0 7? 30 PBB 1 BQ 0 0 0 0 7? 30 PBB & BQ 0 0 0 0 7? 30 PBB & BQ 0 0 0 0 7? 30 PBB 1 BQ 0 0 0 0 7? 30 PBB & BQ 0 0 0 0 7? 30 PBB & BQ 0 0 0 0 7? 30 PBB & BQ 0 0 0 0 7? 30 PBB & BQ 0 0 0 0 7? 30 PBB & BQ 0 0 0 0 7? 30 PBB & BQ 0 0 0 0 7? 30 PBB 1 BQ 0 0 0 0 7? 30 PBB & BQ 0 0 0 0 7? 30 PBB & BQ 0 0 0 0 7? 30 PBB & BQ 0 0 0 0 7? 30 PBB 0 0 BQ 0 0 0 0 7? 30 PBB 0 0 BQ 0 0 0 0 7? 30 PBB 0 0 BQ 0 0 0 0 7? 30 PBB 0 0 BQ 0 0 0 0 7? 30 PBB 0 0 BQ 0 0 0 0 7? 30 PBB 0 0 BQ 0 0 0 0 7? 30 PBB & BQ 0 0 0 0 7 7 30 PBB 0 0 BQ 0 0 0 0 7? 30 PBB & BQ 0 0 0 0 7? 30 PBB 1 BQ 0 0 0 0 7? 30 PBB 1 BQ 0 0 0 0 7? 30 PBB 0 0 BQ 0 0 0 0 7? 30 PBB & @ 0 0 0 0 7? 30 PBB 1 @ 0 0 0 0 7? 30 PBB 1 @ 0 0 0 0 7? 30 PBB & @ 0 0 0 0 7? 30 PBB & @ 0 0 0 0 7? 30 PBB 1 @ 0 0 0 0 7? 30 PBB 1 @ 0 0 0 0 0 7? 30 PBB 1 @ 0 0 0 0 7? 30 PBB 0 0 BQ 0 0 0 0 7? 30 PBB 0 0 @ 0 0 0 0 7? 30 PBB 0 0 @ 0 0 0 0 7? 30 PBB 0 0 @ 0 0 0 0 7 ? 30 P 4 0 0 BQ 0 0 0 0 0 7? 30 P 4 0 0 BQ 0 B6 ** B6 7? 30 P 4 0 0 BQ 0? 6 **? 6 7? 30 P 4 0 0 BQ 0 B6 ** B6

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7? 30 PBB & @ B6 ** B6 7? 30 PBB & BQ 2 ** B6 7? 30 PBB & BQ 0 0 0 0 7? 30 PBB & @ 2 ** B6 7? 30 PBB 1 BQ? 6 ** ? 6 7? 30 PBB & BQ 0 0 0 7? 30 PBB 0 0 @ * 2- B6 7? 30 PBB & BQ 2 ** B6 7? 30 PBB & @ 2 ** B6 7? 30 PBB & @ 0 0 0 7? 30 P 4 0 0 BQ 0 2 ** B6 7? 30 P 4 0 0 BQ 0 B6 ** B6 7? 30 P 4 0 0 BQ 0 B6 ** B6 7? 30 P 4 0 0 BQ 0 2 ** B6 7? 30 P 4 0 0 BQ 0 0 0 0 0 7? 30 P 4 0 0 BQ 0 0 0 0 0 7? 30 P 4 0 0 BQ 0? 6 **? 6 7? 30 P 4 0 0 BQ 0 B6 ** B6 7? 30 P 4 0 0 BQ 0 2 ** B6 7? 30 P 4 0 0 BQ 0 2 ** B6 7? 30 P 4 0 0 BQ 0 2 ** B6 7? 30 P 4 0 0 BQ 0 2 ** B6 7? 30 P 4 0 0 BQ 0 B6 ** B6 7? 30 P 4 0 0 BQ 0 B6 ** B6 7? 30 P 4 0 0 BQ 0 B6 ** B6 7? 30 P 4 0 0 BQ 0 B6 ** B6 7? 30 P 4 0 0 BQ 0 * 2- B6 7? 30 P 4 0 0 BQ 0 2 ** B6 7? 30 P 4 0 0 BQ 0 2 ** B6 7? 30 P 4 0 0 BQ 0 2 ** B6 7? 30 P 4 0 0 BQ 0 2 ** B6 7? 30 P 4 0 0 BQ 0 2 ** B6 7? 30 P 4 0 0 BQ 0 B6 * * B6 7? 30 P 4 0 0 BQ 0 B6 ** B6 7? 30 P 4 0 0 BQ 0 2 ** B6 7? 30 P 4 0 0 BQ 0 B6 ** B6 7? 30 P 4 0 0 BQ 0 2 ** B6 7? 30 P 4 0 0 BQ 0 2 ** B6 7? 30 P 4 0 0 B Q 0 2 ** B6 7? 30 P 4 0 0 BQ 0 2 ** B6 7? 30 P 4 0 0 BQ 0 B6 ** B6 7? 30 P 4 0 0 BQ 0 B6 ** B6 7? 30 P 4 0 0 BQ 0? 6 **? 6 7? 30 PBB & BQ B6 ** B6 7? 30 PBB &?; - 0 0 0 7? 30 PBB & BQ 2 ** B6 7? 30 PBB & BQ B6 ** B6 7? 30 PBB & BQ 0 0 0 7? 30 PBB 1 BQ 2 ** B6 7? 30 PBB 0 0 BQ * 2- B6 7? 30 0 0 BQ 0 0 0 0 7? 30 0 0 BQ 0 0 0 0 0 7? 30 0 0 BQ 0 0 0 0 0 7? 30 0 0 BQ 0 0 0 0 0 7? 30 0 0 BQ 0 2 ** B6 7? 30 0 0 BQ 0 0 0 0 0 7? 30 0 0 BQ 0 B6 ** B6 7? 30 0 0 BQ 0 0 0 0 0 7? 30 0 0 BQ 0? 6 **? 6 7? 30 0 0 BQ 0 2 ** B6 7? 30 0 0 BQ 0? 6 **? 6 7? 30 0 0 BQ 0? 6 **? 6 7? 30 0 0 BQ 0 2 * * B6 7? 30 0 0 BQ 0 2 ** B6 7? 30 0 0 BQ 0 0 0 0 0 7? 30 0 0 BQ 0 0 0 0 0

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$ #% &'(#. &## / L 3' 4% 0 0 1% 8 #.; KL = C)) # # Q) 0 0 6 * '4 $ $ * 9% "# $ 7? 3, 7? 3 7? 3 6 0 = 5 <- 7? 3; 7? 3 7? 3 6 0 = * <- 7? 3; 7? 3 7? 3. = 77 * - 1 <- 7? 3; 7? 3 7? 3 2A * 0 6 0. A <- 7? 3; 7? 3 7? 30 0 6 06 6 06 6 F-.D 23 GH;? @ &;? @ <- 7? 3; 7? 3;? @ * 3A 0 * 6 3 B <;? @ 7? 3; 7? 3;? @ * 0 6 C <;? @ 7? 3; 7? 3 7? 3 * 0. 5 <7? 3; 7? 3;? @ 161 = * 6 - * <;? @ 7? 3; 7? 3 7? 3 *. A <7? 3; 7? 3 7? 3 * 6 3 B <7? 3; 7? 3 7? 3. = 77 * 0 - E <7? 3; 7? 3 7? 3. = 77 * - 1 <7? 3; 7? 3;? @ A = 0- * 6 3 <;? @ 7? 3; 7? 3 7? 3 6 0 - 1 <- 7? 3; 7? 3 7? 30 2A * 0 6 0. A <- 7? 3; 7? 3 7 ? 30 161 = * 6 - * <- 7? 3; 7? 3;? @ 6 - E <;? @ 7? 3; 7? 3;? @ 6 0 D 5 <;? @ 7? 3; 7 ? 3 7? 30 * 0 A <- 7? 3; 7? 3 7? 30 A = 0- * 6 3 <- 7? 3; 7? 3 7? 30 * E * <- 7? 3; 7? 3;? @ 2A * 0 6 0. A <;? @ 7? 3; 7? 3 7? 30 6 * - 5 <- 7? 3; 7? 3 7? 30 CB? 6 0. A <- 7 ? 3; 7? 3 7? 30 6 * - * <- 7? 3; 7? 3 7? 30 * 0. 5 <- 7? 3; 7? 3 7? 30 A = 0- * 0 6 C < - 7? 3; 7? 3 7? 30 2A * 0 6 0. A <- 7? 3; 7? 3 7? 30 2A * 0 6 0. A <- 7? 3; 7? 3 7? 30 161 = 0 6 E * <- 7? 3; 7? 3 7? 30 * 3A 0 6 0 = 5 <7? 3; 7? 3 7? 30 161 = 0 6 E * <- 7? 3; 7? 3 7? 30 B = 0- * 0 6 C <- 7? 3; 7? 3;? @ 2A * 0 6 0. A <;? @ 7? 3; 7? 3;? @ * 3A 0 6 0 = 5 <;? @ 7? 3; 7? 3 7? 30 6 0 - 1 <- 7? 3; 7? 3 7? 30 161 = 0 6 E * <- 7? 3; 7? 3 7? 30 * 6 & 2 <- 7? 3; 7? 3 7? 30 * 3 <- 7? 3; 7? 3 7? 30 B = 0- 0 D? <- 7? 3; 7? 3 7? 30 0 6 * = <- 7? 3; 7? 3 7? 30 * 6 3 B <7? 3; 7? 3 7? 30 6 0 = * <- 7? 3; 7? 3 7? 30 161 = * 6 - * <- 7? 3; 7? 3 7? 30 * 3A 0 * 6 3 B <- 7? 3; 7? 3 7? 3 2A * 0 6 0. A <- 7? 3; 7? 3;? @ 161 = * 6 - * <;? @ 7? 3; 7? 3 7? 3 * 0. 5 <7? 3; 7? 3;? @ * 3A 0 6 0 = 5 <;? @ 7? 3; 7? 3 7? 30 161 = * 6 - * <- 7? 3; 7? 3 7? 3 A = 0- * 0 6 C <

7-3; 7? 3 7? 3;? @ A;? @ <7? 3; 7? 3 7? 3 0 * 1 C <7? 3; 7? 3 7? 3 * 3A 0 *. A <7? 3; 7? 3 7? 3 * 3A 0 * 0 3 E <7? 3; 7? 3 7? 3 * 3A 0;? @ * 0;? @ <7? 3; 7? 3 7? 3 0 6 1? <7? 3; 7? 3 7? 3 6 0 D 5 <7? 3; 7? 3 7? 3 2A * 0 6 0. A <7? 3; 7? 3 7? 3 161 = * 6 - * <- 7? 3; 7? 3 7? 3. = 77 * - 1 <7? 3; 7? 3;? @ 2A * 0 0 B? <;? @ 7? 3; 7? 3;? @ 161 = 6 6 * <;? @ 7? 3; 7? 3 7? 3 6 0 D 5 <- 7? 3; 7? 3;? @ 161 = 0 6 E * <;? @ 7? 3; 7? 3 7? 3 * 3 <- 7? 3; 7? 3 7? 3;? @ A;? @ <- 7? 3; 7? 3 7? 3 * - <7? 3; 7? 3 7? 3 161 = * 6 - * <- 7? 3; 7? 3;? @ A = 0- * 0 6 * <;? @ 7? 3; 7 "3;" @ 0; "@ C;" @ <; "@ 7" 30; 7? 30;? @ 161 = 0 6 E * <;? @ 7? 30; 7? 30 7? 30 * = B <- 7? 30; 7? 30;? @ A = 0- * 6 3 <;? @ 7? 30; 7? 30;? @ * 6 = <;? @ 7? 30; 7 "30;" @. = 77 * - 1 <; "@ 7" 30; 7? 30;? @ 2A * 0 6 0. A <;? @ 7? 30; 7 - 30 7 - 30 * - <- 7 - 30; 7? 30 7? 30 A = 0- * 6 3 <- 7? 30; 7? 30 7? 30 0 * 000 * 0 ** 0 6 * 6 F-.D 23 G H;? @ &;? @ <7? 30; 7 - 30 7 - 30 2A * 0 6 0. A <- 7 - 30; 7 - 30 7 - 30 161 = 0 6 E * <- 7 - 30; 7 - 30 7 - 30 2A * 0 6 0. A <7 - 30; 7 - 30 7 - 30 2A * 0 6 0. A <7 - 30; 7 - 30 7 - 30 161 = * 6 - * <7 - 30; 7? 30;? @ * 0. 5 <;? @ 7? 30; 7? 30;? @ * 6 = <;? @ 7? 30; 7? 30;? @ 6 B <;? @ 7? 30; 7 - 30 7 - 30 6 0 D 5 <- 7 - 30; 7 - 30 7 - 30 * 6 = <7 - 30; 7 "30 7" 30 *; "@;" @ <7 - 30; 7 - 30 7 - 30 2A * 0 0 6 B = <- 7 - 30; 7 - 30 7 - 30 161 = * = * <- 7 - 30; 7 - 30 7 - 30 2A * 0 6 *. D <- 7 - 30; 7 - 30 7 - 30 161 = 6 6 * <- 7 - 30; 7? 30 7? 30 A = 0- 6 * D. <7-30; 7? 30 7? 30 *. A <- 7 - 30; 7 - 30 7 - 30 6 0 D 5 <7 - 30; 7? 30 7? 30 * = B <7? 30; 7 - 30 7 - 30 2A * 0 6 0. A <7 - 30; 7 - 30 7 - 30 * 6 A <7 - 30; 7 - 30 7 - 30 6 0 D 5 <- 7 - 30; 7 - 30 7 - 30 6 0 D 5 <- 7 - 30; 7 - 30 7 - 30 0 E 1 <- 7 - 30; 7 - 30 7 - 30 2A * 0 6 0. A <- 7 - 30; 7? 30 7? 30 * 3A 0 * 0. 5 <- 7 - 30; 7 - 30 7 - 30 161 = 0 6 E * <- 7 - 30; 7 - 30 7 - 30 6 * 3. <7-30; 7 - 30 7 - 30 B = 0 - 6 D A <7 - 30; 7 - 30 7 - 30 161 = * 6 - * <- 7 - 30; 7 - 30 7 - 30 6 * = 6 <7 - 30; 7? 30 7? 30 6;? @ -;? @ <- 7? 30; 7 - 30 7 - 30 2A * 0 6 0 6 - <- 7 - 30; 7 - 30 7 - 30 0 2 <- 7 - 30; 7? 30 7? 30 * 6. D <- 7 - 30; 7? 30 7? 30 * 0. 5 <7-30; 7 - 30 7 - 30 * 6 = <7 - 30; 7? 30;? @ 6 0 =, <;? @ 7? 30; 7? 30 7? 30 * 0. 5 <- 7 - 30; 7? 30 7? 30 * 6. D <- 7 - 30; 7 - 30 7 - 30 161 = * 6 - * <- 7 - 30; 7 - 30 7 - 30 * - <- 7 - 30; 7 - 30 7 - 30 161 = 0 6 E * <- 7 - 30; 7 - 30 7 - 30 161 = * 6 - * <- 7 - 30; 7? 30;? @ * C &<;? @ 7? 30; 7? 30;? @ 161 = 6 0 6 = <;? @ 7? 30; 7? 30;? @ 6 0 D 5 <;? @ 7? 30; 7? 30;? @ * 6 A <;? @ 7? 30; 7? 30;? @ 6 * - 5 <;? @ 7? 30; 7? 30;? @ * = A <;? @

7-30; 7 "30;" @ 6; "@ -;" @ <; "@ 7" 30; 7 "30;" @. = 77 * - 1 <; "@ 7" 30; 7? 30;? @ 2A * 0 * 6 D. <;? @ 7? 30; 7? 30;? @ 6 0 D 5 <;? @ 7? 30; 7 "30;" @. = 77 * - 1 <; "@ 7" 30; 7? 30;? @ 161 = * 6 - * <;? @ 7? 30; 7? 30;? @ * = A <;? @ 7? 30; 7? 30;? @ 2A * 0 6 0. A <;? @ 7? 30; 7? 30;? @ 2A * 0 6 0. A <;? @ 7? 30; 7? 30;? @ 161 = * 6 - * <;? @ 7? 30; 7? 30;? @ 2A * 0 0 B? <;? @ 7? 30; 7? 30;? @ 161 = * 6 - * <;? @ 7? 30; 7? 30;? @ * = B <;? @ 7? 30; 7? 30;? @ 161 = * 6 - * <;? @ 7? 30; 7? 30;? @ 6 0 =, <;? @ 7? 30; 7? 30;? @ 6 0 &<;? @ 7? 30; 7 - 30 7 - 30 161 = * 6 - * <- 7 - 30; 7 - 30 7 - 30 6 0 D 5 <- 7 - 30; 7 - 30 7 - 30 * 6 = <- 7 - 30; 7? 30 7? 30 * = A <- 7? 30; 7 - 30 7 - 30 * 6 3 B <- 7 - 30; 7 - 30 7 - 30 6 0 D 5 <- 7 - 30; 7? 30 7? 30 CB? * 6 -, <- 7 - 30; 7 - 30 7 - 30 6; "@;" @ <- 7 - 30; 7? 30;? @ * 6 = <;? @ 7? 30; 7? 30;? @ * B = <;? @ 7? 30; 7? 30;? @ * 0 A <;? @ 7? 30; 7? 30 7? 30. = 77 * 0 - E <- 7 - 30; 7 - 30 7 - 30 2A * 0 6 0. A <7 - 30; 7 - 30 7 - 30 161 = * 6 - * <- 7 - 30; 7? 30;? @ A = 0- * 6 6 = <;? @ 7? 30; 7? 30;? @ 6;? @ 2;? @ <;? @ 7? 30; 7? 30;? @ 2A * 0 0 6 B = <;? @ 7? 30; 7? 30;? @ A = 0- * 6 - <;? @ 7? 30; 7? 30;? @ 6 0 - <;? @ 7? 30; 7 "30;" @ *; "@;" @ <; "@ 7" 30; 7 "30;" @ * 3A 0; "@;" @ <; "@ 7" 30; 7? 30;? @ 2A * 0 6 0. A <;? @ 7? 30; 7? 30;? @ 161 = * 6 - * <;? @ 7? 30; 7? 30;? @ 161 = 6 6 * <;? @ 7? 30; 7? 30 7? 30 * = A% <- 7? 30; 7 - 30 7 - 30 * E * <- 7 - 30; 7? 30;? @ 161 = * 6 - * <;? @ 7? 30; 7 - 30 7 - 30 161 = * 6 - * <- 7 - 30; 7? 30 7? 30 0;? @? ;? @ <7? 30; 7 - 30 7 - 30. = 77 * 0 - E <7 - 30; 7 - 30 7 - 30 161 = * 6 - * <- 7 - 30; 7? 30;? @ *. A <;? @ 7? 30; 7? 30 7? 30 * 3A 0 * 0. 5 <- 7 - 30; 7 - 30 7 - 30 2A * 0 6 0. A <7 - 30; 7 - 30 7 - 30. = 77 * - 1 <7 - 30; 7? 30 7? 30 *. A <- 7 - 30; 7 "30 7" 30 * 6; "@;" @ <- 7 - 30; 7? 30;? @ * 6 A <;? @ 7? 30; 7? 30;? @ * 6. 3 <;? @ 7? 30; 7 - 30 7 - 30 161 = * 6 - * <7 - 30; 7 - 30 7 - 30 2A * 0 6 0. A <- 7 - 30; 7 - 30 7 - 30 2A * 0 6 0 & 2 <7 - 30; 7 - 30 7 - 30 2A * 0 6 0. A <7 - 30; 7 - 30 7 - 30 161 = * 6 - * <7 - 30; 7 - 30 7 - 30 A = 0 - 6 * 0 6 <7 - 30; 7 - 30 7 - 30. = 77 * 0 - E <7 - 30; 7 - 30 7 - 30 161 = 6 6 * <- 7 - 30; 7 - 30 7 - 30 161 = * = *% <- 7 - 30; 7 - 30 7 - 30 161 = * = * <- 7 - 30; 7 - 30 7 - 30 * 0 <- 7 - 30; 7? 30;? @ * 3A 0 * 6 3 B <;? @ 7? 30; 7? 30;? @ 2A * 0 0 6 B = <;? @ 7? 30; 7? 30;? @ A = 0- * 6 6 0 <;? @ 7? 30; 7? 30;? @ * 6. D <;? @ 7? 30; 7? 30;? @ 6 - E <;? @ 7? 30; 7? 30;? @ *. A <;? @ 7? 30; 7? 30;? @ 161 = * 6 - * <;? @ 7? 30; 7 "30;" @. = 77 * - 1 <; "@ 7" 30; 7 "30;" @. = 77 * 0 - E <; "@ 7" 30; 7? 30;? @ 6 0 B E <;? @ 7? 30; 7? 30;? @ * = A <;? @ 7? 30; 7? 30;? @ * 6 - <;? @ 7? 30; 7? 30;? @ 6 0 = * <;? @

7-30; 7? 30 7? 30 * 0. 5 <- 7 - 30; 7? 30;? @ * = D <;? @ 7? 30; 7? 30;? @ 2A * 0 6 0. A <;? @ 7? 30; 7? 30;? @ 161 = * 6 - * <;? @ 7? 30; 7 - 30 7 - 30 0; "@;" @ <- 7 - 30; 7? 30;? @ 2A * 0 6 0. A <;? @ 7? 30; 7? 30;? @ 161 = 0 * A <;? @ 7? 30; 7? 30;? @ 161 = * 6 - * <;? @ 7? 30; 7? 30;? @ 2A * 0 6 0. A <;? @ 7? 30; 7? 30;? @ 161 = * = * <;? @ 7? 30; 7? 30;? @ * 3A 0 06;? @;? @ <;? @ 7? 30; 7? 30;? @ * 3A 0 6 *** 0 * 0 * 0 60 ** 6 ** 0606 F-.D 23 G H;? @ -;? @ <;? @ 7? 30; 7? 30;? @ A = 0- * 6 3 <;? @ 7? 30; 7? 30;? @ * C &<;? @ 7? 30; 7? 30;? @ * 3A 0 *;? @;? @ <;? @ 7? 30; 7? 30;? @ * 0 =? <;? @ 7? 30; 7? 30;? @ 2A * 0 0. C <;? @ 7? 30; 7? 30;? @ 2A * 0 6 0. A <;? @ 7? 30; 7? 30;? @ 161 = * 6 - * <;? @ 7? 30; 7? 30;? @ 2A * 0 6 0. A <;? @ 7? 30; 7? 30;? @ 161 = * 6 - * <;? @

$ #% &'(#.. = 3' 4% 1% 8 #.; KL 7 7 # # Q) 0 * $ 6 * '4% "# $ 7? 3; 7? 3;? @. = * <0 7? 3; 7? 3 7? 3. = * <- 7? 3; 7? 3 7? 3. = * <- 7? 3; 7? 3;? @. = * <0 7? 3, 7? 3;? @. = * <0 7? 3; 7? 3;? @. = * <0 7? 3; 7? 3 7? 3. = * <- 7? 3; 7? 3 7? 3. = * <- 7? 3; 7? 3;? @. = * <0 7? 3; 7? 3 7? 3. = * <- 7? 3; 7? 3 7? 3. = * <7? 3; 7? 3 7? 3. = * <- 7? 3; 7? 3 7? 3. = * <- 7? 3; 7? 3 7? 30. = * <- 7? 3 ; 7? 3 7? 30. = * <- 7? 3; 7? 3;? @. = * <0 7? 3; 7? 3 7? 30. = * <- 7? 3; 7? 3 7 ? 30. = * <- 7? 3; 7? 3 7? 30. = * <- 7? 3; 7? 3 7? 30. = * <- 7? 3; 7? 3 7? 30. = * <- 7? 3; 7? 3 7? 30. = * <- 7? 3; 7? 3 7? 30. = * <- 7? 3; 7? 3 7? 30. = * <- 7? 3 ; 7? 3;? @. = * <0 7? 3; 7? 3 7? 30. = * <- 7? 3; 7? 3 7? 30. = * <- 7? 3; 7? 3 7 ? 30. = * <- 7? 3; 7? 3 7? 3. = * <- 7? 3; 7? 3;? @. = * <0 7? 3; 7? 3 7? 3. = * <- 7? 3; 7? 3;? @. = * <0 7? 3; 7? 3 7? 3. = * <- 7? 3; 7? 3;? @. = * <0 7? 3 ; 7? 3;? @. = * <0 7? 3; 7? 3;? @. = * <0 7? 3; 7? 3 7? 30. = * <- 7? 3; 7? 3 7 ? 3. = * <- 7? 3; 7? 3 7? 3. = * <- 7? 3; 7? 3;? @. = * <0 7-3; 7? 3;? @. = * <0

7-3; 7? 3 7? 3. = * <7? 3; 7? 3;? @. = * <0 7? 3; 7? 3 7? 3. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30;? @. = * <0 7? 30; 7? 30;? @. = * <0 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30;? @. = * <0 7? 30; 7? 30;? @. = * <0 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30;? @. = * <0 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30;? @. = * <0 7? 30; 7? 30;? @. = * <0 7? 30; 7? 30;? @. = * <0 7? 30; 7? 30;? @. = * <0 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30;? @. = * <0 7? 30; 7? 30;? @. = * <0 7? 30; 7? 30;? @. = * <0 7? 30; 7? 30;? @. = * <0 7? 30; 7? 30;? @. = * <0 7? 30; 7? 30;? @. = * <0 7? 30; 7? 30;? @. = * <0 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30;? @. = * <0 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30;? @. = * <0 7? 30; 7? 30;? @. = * <0 7? 30; 7? 30;? @. = * <0 7? 30; 7? 30;? @. = * <0 7? 30; 7? 30 7? 30. = * <7? 30; 7? 30;? @. = * <0 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30;? @. = * <0 7? 30; 7? 30;? @. = * <0 7? 30; 7? 30;? @. = * <0 7? 30; 7? 30;? @. = * <0

7-30; 7? 30;? @. = * <0 7? 30; 7? 30;? @. = * <0 7? 30; 7? 30;? @. = * <0 7? 30; 7? 30;? @. = * <0 7? 30; 7? 30;? @. = * <0 7? 30; 7? 30;? @. = * <0 7? 30; 7? 30;? @. = * <0 7? 30; 7? 30;? @. = * <0 7? 30; 7? 30;? @. = * <0 7? 30; 7? 30;? @. = * <0 7? 30; 7 "30;" @. = * 0 <0 7 - 30; 7? 30;? @. = * <0 7? 30; 7? 30;? @. = * <0 7? 30; 7? 30;? @. = * <0 7? 30; 7? 30;? @. = * <0 7? 30; 7? 30;? @. = * <0 7? 30; 7? 30;? @. = * <0 0 4 3! "#

$ #% 6 K $ '. 0 # 8 #; 0 # Q 0 '4 6 K # 7? 3; 7? 3 7? 3; 7? 3 7? 3; 7? 3 7? 3; 7? 3 7? 3; 7? 3 7? 3; 7? 3 7? 3; 7? 3 7? 3; 7? 3 7? 3; 7? 3 7? 3; 7? 3 7? 3; 7? 3 7? 3; 7? 3 7? 3; 7? 3 7? 3; 7? 3 7? 3; 7? 3 7? 3; 7? 3 7? 3; 7? 3 7? 3; 7? 3 7? 3; 7? 3 7? 3; 7? 3 7? 3; 7? 3 7? 3; 7? 3 7? 3; 7? 3 7? 3; 7? 3

7-3; 7? 3 7? 3; 7? 3 7? 3; 7-3 7-30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7-30

7-30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7-30

$ #% & '(. &## /) 3' 4% C = & 8 #) "KL 7 7% Q 0 6 '4 C * 9)) # # # 7? 3 7? 3 6 0 = 5 <- 7? 3 7? 3 6 0 = * <- 7? 3 7? 3 6 0 - <7? 3 7? 3 2A * 0 6 0. A <- 7? 3 7? 3. = 77 * - 1 <- 7? 3 7? 3 * 6 = <7? 3 7? 3 6 0 = * <7? 3 7? 3 6 B <7? 3 7? 3 6 0 - 1 <- 7? 3 7? 3 161 = 0 6 A. <7? 3 7? 3 2A * 0 6 0. A <- 7? 3 7? 3 * 3A 0 6 B <7? 3 7? 3 * 6 = - <7? 3 7 ? 3 B = 0- * D = <7? 3 7? 3 161 = * 6 - * <- 7? 3 7? 3 * = B <7? 3 7? 3 * 3A 0 6 B <7? 3 7 ? 3 6 0 D 5 <- 7? 3 7? 3 161 = * 6 - * <- 7? 3 7? 3 A;? @ <

7? 3 7? 3 * 3 <- 7? 3 7? 3 * 0, * <7? 30 7? 30 161 = * 6 - * <7? 30 7? 30 * 3A 0 6 B <7? 30 7 ? 3 A = 0- * 6 3 <- 7? 30 7? 3 * 0 A <- 7? 30 7? 3 161 = * 6 - * <7? 30 7? 3 * 0. 5 <- 7? 30 7? 3. = 77 * - 1 <7? 30 7? 3 6 * - 5 <- 7? 30 7? 3 6 * - * <- 7? 30 7? 3 * E * < 7? 30 7? 3 A = 0- * 0 6 C <- 7? 30 7? 3 2A * 0 6 0. A <- 7? 30 7? 3 161 = * 6 - * <7? 30 7? 3 2A * 0 6 0. A <7? 30 7? 3 161 = 0 6 E * <- 7? 30 7? 3 2A * 0 6 0. A <- 7? 30 7? 3 161 = 0 6 E * <- 7? 30 7? 3 B = 0- * 0 6 C <- 7? 30 7? 3 B = 0- 0 D? <7? 30 7? 3 6 0 - 1 <- 7? 30 7? 3 161 = 0 6 E * <7? 30 7? 3 161 = * 6 - * <7? 30 7? 3 * 6 & 2 < - 7? 30 7? 3 * 3 <- 7? 30 7? 3 * E * <- 7? 30 7? 3 161 = 0 6 E * <- 7? 30 7? 3 B = 0- 0 D? <- 7? 30 7? 3 0 6 * = <- 7? 30 7? 3 * 6 3 B <- 7? 30 7? 3 161 = * 6 - * <7? 30 7? 3 2A * 0 0 B ? <7? 30 7? 3 * C 2 <7? 30 7? 3 161 = * 6 - * <- 7? 30 7? 3 * 3A 0 * 6 3 B <- 7? 30 7? 3 6 0 = * <- 7? 30 7? 30 6 0 = 5 <7? 30 7? 30 161 = * 6 - * <7? 30 7? 30 * - <- 7? 30 7? 30 2A * 0 6 0. A <- 7? 30 7? 30 * 6 - <7? 30 7? 30 161 = 0 6 E * <- 7? 30 7? 30 6 0 D 5 <- 7? 30 7? 30 161 = * = * <- 7? 30 7? 30 2A * 0 0 6 B = <- 7? 30 7? 30 161 = 6 6 * <- 7? 30 7? 30 161 = * 6 - * <7? 30 7? 30 2A * 0 6 0. A <7? 30 7? 30 6 0 D 5 <- 7? 30 7? 30 6 0 D 5 <- 7? 30 7? 30. = 77 * - 1 <7? 30 7? 30 6 0 =, < 7? 30 7? 30 0 E 1 <- 7? 30 7? 30 2A * 0 6 0. A <- 7? 30 7? 30 161 = 0 6 E * <- 7? 30 7? 30 161 = * 6 - * <- 7? 30 7? 30 161 = * = * <- 7? 30 7? 30 6 -;? @ <7? 30 7? 30 0 2 <- 7? 30 7? 30 * 6. D <- 7? 30 7? 30;? @ <7? 30 7? 30 * 0. 5 <- 7? 30 7? 30 * 6. D <- 7? 30 7? 30 161 = * 6 - * <- 7? 30 7? 30 * - <-

7? 30 7? 30 161 = 0 6 E * <- 7? 30 7? 30 * = A <- 7? 30 7? 30 * E * <- 7? 30 7? 30 161 = * 6 - * <- 7? 30 7? 30 161 = * 6 - * <- 7? 30 7? 30 161 = * 6 - * <- 7? 30 7? 30 *. A <- 7? 30 7? 30 * 6 A <- 7? 30 7? 30 2A * 0 6 0. A <- 7? 30 7? 30 161 = 6 6 * <- 7? 30 7? 30 * & 2 <7? 30 7? 30 161 = * = * <- 7? 30 7? 30 161 = * 6 - * <7? 30 7? 30 6 0 D 5 <- 7? 30 7? 30 6 0 D 5 <- 7? 30 7? 30 CB? * 6 -, <- 7? 30 7? 30 * 6 = <- 7? 30 7? 30 * = A <- 7? 30 7? 30 * 6 3 B <- 7? 30 7? 3 0 6 06 6 06 6 F-.D 23 GH &;? @ <7? 30 7? 3 161 = * 6 - * <- 7? 30 7? 30 161 = * 6 - * <- 7? 30 7? 3 161 = * 6 - * <- 7? 30 7? 3 2A * 0 6 0. A <- 7? 30 7? 30 * = B <- 7? 30 7? 30 0;? @ <7? 30 7? 30 * 3A 0 6 B <7? 30 7? 30. = 77 * 0 - E <- 7? 30 7? 30 161 = * 6 - * <- 7? 30 7? 30 * 0. 5 <- 7? 30 7? 30 * 3A 0 6 B <

$ #% & '(.. = O) 3' 4% 1% 8 #) "KL C =% # # Q 0 6 '4 $ $ # 7? 3 7? 3. = * <- 7? 3 7 ? 3. = * <- 7? 3 7? 3. = * <- 7? 3 7? 3. = *% <- 7? 3 7? 3. = * <- 7? 3 7? 3. = * <- 7? 30 7? 3. = * <- 7? 30 7? 3. = * <- 7? 30 7? 3. = * <- 7? 30 7? 3. = * <- 7? 30 7 ? 3. = * <- 7? 30 7? 3. = * <- 7? 30 7? 3. = *% <- 7? 30 7? 3. = * <- 7? 30 7? 3. = * <- 7? 30 7? 3. = * <- 7? 30 7? 3. = * <7? 30 7? 3. = * <7? 30 7? 3. = * <- 7? 3 7? 3 . = * <- 7? 3 7? 3. = * <- 7? 3 7? 3. = * <- 7? 30 7? 3. = * <- 7? 3 7? 3. = * <- 7 ? 3 7? 3. = * <- 7? 3 7? 3. = * 0 <7? 3 7? 3. = * <- 7? 30 7? 30. = * <-

7? 30 7? 30. = * <- 7? 30 7? 30. = * <- 7? 30 7? 30. = * <- 7? 30 7? 30. = * <- 7? 30 7? 30 . = * <- 7? 30 7? 30. = * <- 7? 30 7? 30. = * <- 7? 30 7? 30. = * <- 7? 30 7? 30. = * <- 7 ? 30 7? 30. = * <- 7? 30 7? 30. = * <- 7? 30 7? 30. = * <- 7? 30 7? 30. = * <- 7? 30 7? 30. = * <- 7? 30 7? 30. = * <- 7? 30 7? 30. = * <- 7? 30 7? 30. = * <- 7? 30 7? 30. = * <- 7? 30 7? 30. = * <- 7? 30 7? 30. = *% <- 7? 30 7? 30. = * <- 7? 30 7? 30. = * <- 7? 30 7? 30. = *% <- 7? 30 7? 30. = * <- 7? 30 7? 30. = * <- 7? 30 7? 30. = * <- 7? 30 7? 30. = * <- 7 ? 30 7? 30. = * <- 7? 30 7? 30. = * <-

$ #% *! L) #! * 2 "O L '4

2 # 2 # 2 # = = = # * # L '4 7C 8 1 - / 2 3 L 9 3 L 9. ) L 6 3 L 9. ) L 6 F H 7? 3; PBB 1 0 B6 ** B6 B6 ** B6 7? 3; PBB 1 0 B6 ** B6 2 ** B6 7? 3; PBB & 0 2 ** B6 2 ** B6 7? 3; PBB & 0 2 ** B6 * 2- B6 7? 3; PBB & 0 2 ** B6 2 ** B6 7? 3; PBB & 0 2 ** B6? 6 **? 6 7? 3; PBB & 0 2 ** B6 B6 ** B6 7? 3; PBB & 0 B6 ** B6 B6 ** B6 7? 3; PBB 1 0 2 ** B6 2 ** B6 7? 3; PBB & 0 2 ** B6 0 0 0 0 7? 3; PBB & 0 2 ** B6 0 0 0 0 7? 3; PBB & 0 2 ** B6 * 2- B6 7? 3; PBB & 0 B6 ** B6? 6 **? 6 7? 3; PBB & 0 B6 ** B6 0 0 0 0 7? 3; PBB 1 0 2 ** B6 2 ** B6 7? 3; PBB 1 0 B6 ** B6 B6 ** B6 7? 3; PBB & 2 ** B6 2 ** B6 7? 3; PBB & 2 ** B6 2 ** B6 7? 3; PBB & B6 ** B6 B6 ** B6 7-3; PBB 1 2 ** B6 0 0 0 0 7? 3; PBB 1 2 ** B6 0 0 0 0 7? 3; PBB & 2 ** B6 2 ** B6 7? 3; PBB &? 6 **? 6 0 0 0 7? 3; PBB 1? 6 **? 6? 6 **? 6 7? 3; PBB &? 6 **? 6? 6 **? 6 7? 3; PBB & B6 ** B6 B6 ** B6 7-3; PBB 1 2 ** B6 2 ** B6 7? 3; PBB 1 B6 ** B6 0 0 0 0 7? 3; PBB & B6 ** B6 0 0 0 0

7-3; PBB & 2 ** B6 B6 ** B6 7? 3; PBB & 2 ** B6 B6 ** B6 7? 3; PBB &? 6 **? 6 0 0 0 0 7? 3; PBB &? 6 **? 6? 6 **? 6 7? 3; PBB & B6 ** B6 0 0 0 0 7? 3; PBB & 2 ** B6 0 0 0 7? 3; PBB 1 B6 ** B6 0 0 0 0 7? 3; PBB &? 6 **? 6? 6 **? 6 7? 3; PBB & B6 ** B6 0 0 0 7? 3; PBB 1 2 ** B6 2 ** B6 7? 3; PBB & * 2- B6 0 0 0 0 7? 3; PBB & 2 ** B6 0 0 0 0 7? 3; PBB & 2 ** B6 0 0 0 0 7? 3; PBB &? 6 **? 6? 6 ** B6 7? 3; PBB &? 6 **? 6 0 0 0 0 7? 3; PBB & 2 ** B6 0 0 0 0 7? 3; PBB 1? 6 **? 6? 6 **? 6 7? 3; PBB 1 2 ** B6 B6 ** B6 7? 3; PBB & 0 0 0? 6 **? 6 7? 3; PBB 1 2 ** B6 2 ** B6 7? 3; PBB 1? 6 **? 6 0 0 0 0 7? 3; PBB & B6 ** B6 * 2- B6 7? 3; PBB &? 6 **? 6 0 0 0 0 7? 3; PBB & 2 ** B6 2 ** B6 7? 3; PBB 1 2 ** B6 0 0 0 0 7? 3; PBB 1 B6 ** B6 0 0 0 0 7? 3; PBB &; ? &; ? & 0 0 0 0 7? 3; PBB &; ? &; ? & 0 0 0 0 7? 3; PBB & 2 ** B6 B6 ** B6 7? 3; PBB 1 2 ** B6 0 0 0 0 7? 3; PBB & * 2- B6 0 0 0 0 7? 3; PBB &? 6 **? 6 0 0 0 0 7? 3; PBB 0 0 2 ** B6 0 0 0 0 7? 3; PBB 0 0 2 ** B6 B6 ** B6 7? 3; PBB 0 0? 6 **? 6 0 0 0 0 7? 3; PBB 1 2 ** B6 0 0 0 0 7? 3; PBB &? 6 **? 6 0 0 0 0 7? 3; PBB & B6 ** B6 B6 ** B6 7-3; PBB & * 2- B6 0 0 0 0 7? 3; PBB 0 0 B6 ** B6? 6 **? 6 7? 3; PBB 0 0? 6 **? 6 0 0 0 7? 3; PBB & 2 ** B6 0 0 0 0 7? 3; PBB &? 6 **? 6 0 0 0 0 7? 3; PBB & 2 ** B6 0 0 0 7? 3; PBB 1 2 ** B6 0 0 0 0 7? 3; PBB & * 2- B6 B6 ** B6 7? 3; PBB & 2 ** B6 0 0 0 0 7? 3; PBB & B6 ** B6 0 0 0 0 7? 3; PBB & 0 0 0? 6 **? 6 7? 3; PBB 1 2 ** B6 2 ** B6 7? 3; PBB &? 6 **? 6 0 0 0 0 7? 3; PBB & 2 ** B6 * 2- B6 7? 3; PBB & 2 ** B6 0 0 0 0 7? 3; PBB &? 6 **? 6 0 0 0 0 7? 3; PBB 1? 6 **? 6; ? & 0 7? 3; PBB & B6 ** B6 2 ** B6 7? 3; PBB & 0 0 0 0 0 0 0 7? 3; PBB & * 2- B6 * 2- B6 7? 3; PBB 1 2 ** B6 0 0 0 0 7? 3; PBB & 2 ** B6 0 0 0 0 7? 3; PBB & * 2- B6 0 0 0 0 7? 3; PBB 1 2 ** B6 2 ** B6 7? 3; PBB & 2 ** B6 0 0 0 0 7? 3; PBB & B6 ** B6 0 0 0 0 7? 3; PBB & 2 ** B6 0 0 0 0 7? 3; PBB 1 2 ** B6 * 2- B6

7-3; PBB & 2 ** B6 0 0 0 0 7? 3; PBB & B6 ** B6 B6 ** B6 7-3; PBB &? 6 **? 6? 6 **? 6 7? 3; PBB &; ? &; ? & 0 0 0 0 7? 3; PBB 0 0? 6 **? 6? 6 **? 6 7? 3; PBB 1 2 ** B6 * 2- B6 7? 3; PBB & B6 ** B6 B6 ** B6 7-3; PBB &; ? &; ? &? 6 **? 6 7? 3; PBB & 2 ** B6 2 ** B6 7? 3; PBB &? 6 **? 6? 6 **? 6 7? 3; PBB & 0 0 0? 6 **? 6 7? 3; PBB & 2 ** B6 0 0 0 0 7? 3; PBB & 2 ** B6 0 0 0 0 7? 3; PBB & 2 ** B6 0 0 0 0 7? 3; PBB & 2 ** B6 B6 ** B6 7? 3; PBB 0 0 2 ** B6 0 0 0 0 7? 3; PBB & 2 ** B6 B6 ** B6 7? 3; PBB & 2 ** B6 0 0 0 0 7? 3; PBB &; ? &; ? &? 6 **? 6 7? 3; PBB &? 6 **? 6? 6 **? 6 7? 3; PBB 1? 6 **? 6 0 0 0 0 7? 3; PBB & 0 B6 ** B6? 6 **? 6 7? 3; PBB & 2 ** B6 0 0 0 0 7? 3; PBB & 0 2 ** B6 2 ** B6 7? 3; PBB 1 B6 ** B6 B6 ** B6 7? 3; PBB 0 0 0 * 2- B6 B6 ** B6 7? 3; PBB & 0? 6 **? 6? 6 **? 6 7? 3; PBB & 0 * 2- B6 * 2- B6 7-3; PBB & 0 0 0 0? 6 **? 6 7? 3; PBB & 0 0 0 * 2- B6 7? 3; PBB & 0 2 ** B6 2 ** B6 7? 3; PBB &; ? &; ? & 0 0 0 0 7? 3; PBB 1? 6 **? 6? 6 **? 6 7? 3; PBB & * 2- B6 0 0 0 0 7? 3; PBB & 2 ** B6 0 0 0 0 7? 3; PBB & 2 ** B6 B6 ** B6 7? 3; PBB 0 0? 6 **? 6? 6 **? 6 7? 3; PBB & B6 ** B6 * 2- B6 7? 3; PBB 1? 6 **? 6? 6 **? 6 7? 3; PBB &? 6 **? 6 0 0 0 0 7? 3; PBB & 2 ** B6 0 0 0 0 7? 3; PBB & 2 ** B6 0 0 0 0 7? 3; PBB 1? 6 **? 6? 6 **? 6 7? 3; PBB & 2 ** B6 0 0 0 0 7? 3; PBB &; ? &; ? & 0 0 0 0 7? 3; PBB & 2 ** B6 0 0 0 0 7? 3; PBB 1? 6 **? 6 0 0 0 0 7? 3; PBB & 0 0 0 0 0 0 0 7? 3; PBB &? 6 **? 6? 6 **? 6 7? 3; PBB & B6 ** B6 * 2- B6 7? 3; PBB & 2 ** B6 0 0 0 0 7? 3; PBB &? 6 **? 6? 6 **? 6 7? 3; PBB 1? 6 **? 6? 6 **? 6 7? 3; PBB &? 6 **? 6 0 0 0 0 7? 3; PBB & B6 ** B6 0 0 0 0 7? 3; PBB 1? 6 **? 6? 6 **? 6 7? 3; PBB & 2 ** B6 B6 ** B6 7? 3; PBB & 2 ** B6 0 0 0 0 7? 3; PBB &? 6 **? 6? 6 **? 6 7? 3; PBB 1? 6 **? 6 0 0 0 7? 3; PBB &? 6 **? 6? 6 **? 6 7? 3; PBB &? 6 **? 6 0 0 0 0 7? 3; PBB & B6 ** B6 0 0 0 0 7? 3; PBB & B6 ** B6 0 0 0 0 7? 3; PBB 1? 6 **? 6 0 0 0 0 7? 3; 7 R # 0 0 0? 6 **? 6; ? & 0

7-3; 7 R # 0 0? 6 **? 6 0 0 0 0 7? 3; 7 R # 0 0 2 ** B6 0 0 0 0 7? 3; 7 R # 0 0? 6 **? 6 0 0 0 0 7? 3; 7 R # 0 0 0? 6 **? 6 2 ** B6 7? 3; 7 R # 0 0 0 B6 ** B6 2 ** B6 7? 3; 7 R # 0 0 0 B6 ** B6 B6 ** B6 7? 3; 7 R # 0 0 B6 ** B6 B6 ** B6 7? 3; 7 R # 0 0 2 ** B6 0 0 0 0 7? 3; 7 R # 0 0? 6 **? 6 0 0 0 0 7? 3; 7 R # 0 0? 6 **? 6 0 0 0 0 7? 3; 7 R # 0 0 0? 6 **? 6 0 0 0 0 7? 3; 7 R # 0 0 0 0 0 0 0 0 0 7 7? 3; 7 R # 0 0 0 2 ** B6 2 ** B6 7? 3; 7 R # 0 0 0 B6 ** B6 B6 ** B6 7? 3; 7 R # 0 0 2 ** B6 0 0 0 0 7? 3; 7 R # 0 0 0 B6 ** B6 B6 ** B6 7? 3; 7 R # 0 0 0? 6 **? 6 0 0 0 0 7? 3; 7 R # 0 0? 6 **? 6 0 0 0 0 7? 3; 7 R # 0 0 * 2- B6 0 0 0 0 7? 3; 7 R # 0 0 0 2 ** B6 2 ** B6 7? 3; 7 R # 0 0 0? 6 **? 6 0 0 0 0 7? 3; 7 R # 0 0 0 0 0 0 0 0 0 0 7? 3; 7 R # 0 0? 6 **? 6? 6 **? 6 7? 3; 7 R # 0 0? 6 **? 6 0 0 0 0 7? 3; 7 R # 0 0 0 2 ** B6 0 0 0 0 7? 3; 7 R # 0 0 0 0 0 0 2 ** B6 7? 3; 7 R # 0 0 * 2- B6 0 0 0 0 7 - 30; 7 R # 0 0 0? 6 **? 6 0 0 0 0 7? 30; 7 R # 0 0 0 B6 ** B6 B6 ** B6 7 - 30; 7 R # 0 0 * 2- B6 0 0 0 0 7 - 30; 7 R # 0 0 0? 6 **? 6? 6 **? 6 7? 30; 7 R # 0 0? 6 **? 6 0 0 0 0 7? 30; 7 R # 0 0 2 ** B6 0 0 0 0 7 - 30; 7 R # 0 0 0 2 ** B6 0 0 0 0 7 - 30; 7 R # 0 0 B6 ** B6 B6 ** B6 7 - 30; 7 R # 0 0 0; ? &; ? &; ? & 0 7? 30; 7 R # 0 0 B6 ** B6 0 0 0 0 7 - 30; 7 R # 0 0? 6 **? 6 0 0 0 0 7? 30; 7 R # 0 0 B6 ** B6 0 0 0 0 7 - 30; 7 R # 0 0? 6 **? 6 0 0 0 0 7? 30; 7 R # 0 0? 6 **? 6? 6 **? 6 7? 30; 7 R # 0 0? 6 **? 6 0 0 0 0 7? 30; 7 R # 0 0? 6 **? 6 0 0 0 0 7? 30; 7 R # 0 0 B6 ** B6 0 0 0 0 7 - 30; 7 R # 0 0 2 ** B6 2 ** B6 7 - 30; 7 R # 0 0 0? 6 **? 6 0 0 0 0 7? 30; 7 R # 0 0 B6 ** B6 2 ** B6 7 - 30; 7 R # 0 0 0? 6 **? 6? 6 **? 6 7? 30; 7 R # 0 0 0 2 ** B6 2 ** B6 7 - 30; 7 R # 0 0 0 0 0 0 0 0 0 7? 30; 7 R # 0 0 B6 ** B6 0 0 0 0 7 - 30; 7 R # 0 0; ? &; ? & 0 0 0 0 7? 30; 7 R # 0 0? 6 **? 6 B6 ** B6 7? 30; 7 R # 0 0? 6 **? 6 0 0 0 0 7? 30; 7 R # 0 0? 6 **? 6 0 0 0 7? 30; 7 R # 0 0? 6 **? 6 0 0 0 0 7? 30; 7 R # 0 0 0? 6 **? 6? 6 **? 6 7? 30; 7 R # 0 0? 6 **? 6? 6 **? 6 7? 30; 7 R # 0 0? 6 **? 6? 6 **? 6 7? 30; 7 R # 0 0 0 0 0 0 0 0 0 7? 30; 7 R # 0 0 0? 6 **? 6? 6 **? 6 7? 30; 7 R # 0 0 0 2 ** B6 2 ** B6 7 - 30; 7 R # 0 0 * 2- B6 * 2- B6 7 - 30; 7 R # 0 0 0 B6 ** B6 0 0 0 0 7? 30; 7 R # 0 0 2 ** B6 * 2- B6 7 - 30; 7 R # 0 0? 6 **? 6 0 0 0 0

7-30; 7 R # 0 0 0 B6 ** B6 B6 ** B6 7 - 30; 7 R # 0 0 0 2 ** B6 2 ** B6 7 - 30; 7 R # 0 0 * 2- B6 * 2- B6 7 - 30; 7 R # 0 0 B6 ** B6 0 0 0 0 7 - 30; 7 R # 0 0 0 B6 ** B6 0 0 0 0 7? 30; 7 R # 0 0; ? &; ? & 0 0 0 7? 30; 7 R # 0 0 B6 ** B6 0 0 0 0 7 - 30; 7 R # 0 0 B6 ** B6 0 0 0 0 7 - 30; 7 R # 0 0? 6 **? 6? 6 **? 6 7? 30; 7 R # 0 0 B6 ** B6 2 ** B6 7 - 30; 7 R # 0 0 * 2- B6 0 0 0 0 7 - 30; 7 R # 0 0? 6 **? 6 0 0 0 0 7? 30; 7 R # 0 0? 6 **? 6 0 0 0 0 7? 30; 7 R # 0 0? 6 **? 6? 6 **? 6 7? 30; 7 R # 0 0? 6 **? 6 0 0 0 0 7? 30; 7 R # 0 0? 6 **? 6? 6 **? 6 7? 30; 7 R # 0 0 0? 6 **? 6? 6 **? 6 7? 30; 7 R # 0 0 B6 ** B6 0 0 0 0 7 - 30; 7 R # 0 0? 6 **? 6 0 0 0 0 7? 30; 7 R # 0 0? 6 **? 6? 6 **? 6 7? 30; 7 R # 0 0 B6 ** B6 0 0 0 0 7 - 30; 7 R # 0 0? 6 **? 6 0 0 0 0 7? 30; 7 R # 0 0 0 2 ** B6? 6 **? 6 7? 30; 7 R # 0 0? 6 **? 6 0 0 0 0 7? 30; 7 R # 0 0? 6 **? 6 0 0 0 0 7? 30; 7 R # 0 0 2 ** B6 B6 ** B6 7 - 30; 7 R # 0 0 B6 ** B6 B6 ** B6 7 - 30; 7 R # 0 0 0 2 ** B6 2 ** B6 7 - 30; 7 R # 0 0 0? 6 **? 6 0 0 0 0 7? 30; 7 R # 0 0 B6 ** B6 B6 ** B6 7 - 30; 7 R # 0 0; ? &; ? & 0 0 0 0 7? 30; 7 R # 0 0 0 2 ** B6 0 0 0 7 - 30; 7 R # 0 0? 6 **? 6 0 0 0 0 7? 30; 7 R # 0 0 0? 6 **? 6? 6 **? 6 7? 30; 7 R # 0 0 0 B6 ** B6 B6 ** B6 7 - 30; 7 R # 0 0 0? 6 **? 6 0 0 0 0 7? 30; PBB 1; ? &; ? & 0 0 0 0 7? 30; PBB & * 2- B6 0 0 0 7 - 30; PBB 1? 6 **? 6 0 0 0 0 7? 30; PBB &? 6 **? 6 0 0 0 0 7? 30; PBB &? 6 **? 6? 6 **? 6 7? 30; PBB &? 6 **? 6 0 0 0 0 7? 30; PBB & 2 ** B6 0 0 0 0 7 - 30; PBB & 2 ** B6 2 ** B6 7-30; PBB & 2 ** B6 0 0 0 7 - 30; PBB 1? 6 **? 6 0 0 0 0 7? 30; PBB & 2 ** B6 0 0 0 0 7 - 30; PBB 0 0 2 ** B6 2 ** B6 7 - 30; PBB & 2 ** B6 B6 ** B6 7 - 30; PBB 1? 6 **? 6? 6 **? 6 7? 30; PBB 0 0? 6 **? 6? 6 **? 6 7? 30; PBB & 2 ** B6? 6 **? 6 7? 30; PBB & * 2- B6 * 2- B6 7-30; PBB 0 0 2 ** B6 2 ** B6 7 - 30; PBB & B6 ** B6 * 2- B6 7-30; PBB 0 0 2 ** B6 2 ** B6 7 - 30; PBB 1? 6 **? 6? 6 **? 6 7? 30; PBB 1 B6 ** B6 B6 ** B6 7 - 30; PBB & B6 ** B6? 6 **? 6 7? 30; PBB 1? 6 **? 6; ? & 0 7? 30; PBB 1 2 ** B6 B6 ** B6 7 - 30; PBB & * 2- B6 * 2- B6 7-30; PBB & * 2- B6 2 ** B6 7-30; PBB &? 6 **? 6? 6 **? 6 7? 30; PBB & B6 ** B6 B6 ** B6 7 - 30; PBB &? 6 **? 6? 6 ** B6

7-30; PBB &? 6 **? 6? 6 **? 6 7? 30; PBB & * 2- B6 * 2- B6% 7-30; PBB 1? 6 **? 6? 6 **? 6 7? 30; PBB 0 0? 6 **? 6? 6 **? 6 7? 30; PBB & * 2- B6 * 2- B6 7-30; PBB 0 0 B6 ** B6 * 2- B6 7 - 30; PBB & 2 ** B6 * 2- B6 7-30; PBB & * 2- B6 B6 ** B6 7-30; PBB & * 2- B6 * 2- B6 7-30; PBB & * 2- B6 * 2- B6 7-30; PBB 1? 6 **? 6 2 ** B6 7? 30; PBB 0 0? 6 **? 6? 6 **? 6 7? 30; PBB & B6 ** B6 2 ** B6 7 - 30; PBB & 2 ** B6 2 ** B6 7-30; PBB 0 0 2 ** B6 * 2- B6 7 - 30; PBB &? 6 **? 6? 6 **? 6 7? 30; PBB 0 0 B6 ** B6 * 2- B6 7 - 30; PBB 1? 6 **? 6? 6 **? 6 7? 30; PBB & * 2- B6 * 2- B6 7-30; PBB & 2 ** B6 2 ** B6 7-30; PBB 1 2 ** B6 2 ** B6 7 - 30; PBB 0 0 2 ** B6 2 ** B6 7 - 30; PBB 0 0? 6 **? 6? 6 **? 6 7? 30; PBB & 2 ** B6 B6 ** B6 7 - 30; PBB 1 B6 ** B6 B6 ** B6 7 - 30; PBB & 2 ** B6 B6 ** B6 7 - 30; PBB & 2 ** B6 2 ** B6 7-30; PBB & B6 ** B6 B6 ** B6 7 - 30; PBB & 2 ** B6 B6 ** B6 0 4 3! "# 3 L 9 * # L G L" 0! "

$ #% &'(#. &## / L 2 "L' 4% = C & 8 #.; KL = CQ) 0 0 6 * '4 $ $ * 9)) # # # $ 7? 3; 7? 3 7? 3 161 = * 6 - * <- 7? 3; 7? 3 7? 3 161 = * 6 - * <- 7? 3; 7? 3 7? 3 6 0 =, <- 7? 3, 7? 3 7? 3 161 = 0 6 A. <- 7? 3; 7? 3 7? 3 161 = 0 6 E * <- 7? 3; 7? 3 7? 3 6 * 1? <- 7? 3; 7? 3 7? 3 161 = * 6 - * <- 7? 3; 7? 3 7? 3 2A * 0 6 0 & 2 <- 7? 3; 7? 3 7? 3 161 = * 6 - * <- 7? 3; 7? 3 7? 3 161 = * 6 - * <- 7? 3; 7? 3 7? 3 161 = 0 6 E * <- 7? 3; 7? 3 7? 3 161 = * 6 - * <- 7? 3; 7? 3 7? 3 0 * 2 - <- 7? 3; 7? 3 7? 3 A = 0- * 6 3 <- 7? 3; 7? 3 7? 3 2A * 0 6 0. A <- 7? 3; 7? 3 7? 3 161 = * 6 - * <- 7? 3; 7? 3 7? 3 161 = * 6 - * <- 7 ? 3; 7? 3 7? 3. = 77 * - 1 <- 7? 3; 7? 3 7? 3 * 6. D <- 7? 3; 7? 3 7? 3 *, 5 <- 7? 3, 7? 3 7? 3 0 6 * = <- 7? 3; 7? 3 7? 3 161 = * 6 - * <- 7? 3; 7? 3 7? 3 A = 0- * 0 6 C <- 7? 3; 7? 3 7? 3 B = 0- * D = <- 7? 3; 7? 3 7? 3 161 = * 6 - * <-

7-3; 7? 3 7? 3 2A * 0 0 6 B = <- 7? 3; 7? 3 7? 30 161 = * 6 - * <- 7? 3; 7? 3 7? 3 161 = * 6 - * <- 7? 3; 7? 3 7? 3 161 = * 6 - * <- 7? 3; 7? 3 7? 3 2A * 0 0 6 B = <- 7? 3; 7? 3 7? 3 A = 0- * 0 6 C <- 7? 3; 7? 3 7? 3 2A * 0 6 0. A <- 7? 3; 7? 3 7? 3 161 = * 6 - * <- 7? 3; 7? 3 7? 3 161 = * 6 - * <- 7? 3; 7? 3 7? 3 006;? @ *;? @ <- 7? 3; 7? 3 7? 3 161 = * 6 - * <- 7? 3; 7? 3 7? 3 * 6 = <- 7? 3; 7? 3 7? 3 * 0;? @;? @ <- 7? 3; 7? 3 7? 3 161 = * 6 - * <- 7? 3; 7? 3 7? 3 * 3A 0 6 0 - <- 7? 3; 7? 3 7? 3 161 = * 6 - * <- 7? 3; 7? 3 7? 3 2A * 0 6 0. A <- 7? 3; 7? 3 7? 30 161 = 0 - * <- 7? 3; 7? 3 7? 3 161 = * 6 - * <- 7? 3; 7? 3 7? 3 161 = * 6 - *% <- 7? 3; 7? 3 7? 3 161 = 0 6 E * <- 7? 3; 7? 3 7? 3 2A * 0 6 0. A <- 7? 3; 7? 3 7? 3 161 = * 6 - * <- 7? 3; 7? 3 7? 3 2A * 0 6 0. A <- 7? 3; 7? 3 7? 3 * = D <- 7? 3; 7? 3 7? 3 161 = * 6 - * <- 7? 3; 7? 3 7? 3 * = D <- 7? 3; 7? 3 7? 3 161 = * 6 - * <- 7? 3; 7? 3 7? 3 161 = * 6 - * <- 7? 3; 7? 3 7? 3 2A * 0 6 0. A <- 7? 3; 7? 3 7? 3 161 = * 6 - * <- 7? 3; 7? 3 7? 3 * 0 = - <- 7? 3; 7? 3 7? 3 161 = * 6 - * <- 7? 3; 7? 3 7? 3 A = 0- * 6 3 <- 7? 3; 7? 3 7? 3 * 6. 3 <- 7? 3; 7? 3 7? 3 *. A <- 7? 3; 7? 3 7? 3 6 6;? @;? @ <- 7? 3; 7? 3 7? 3. = 77 * - 1 <- 7? 3; 7? 3 7? 3 161 = * 6 - * <- 7? 3; 7? 3 7? 3 161 = 0 6 E * <- 7 - 30; 7? 30 7? 30 A = 0- * 0 6 C <- 7? 30; 7 - 30 7 - 30 161 = * 6 - * <- 7 - 30; 7 - 30 7 - 30 * 3A 0 * 0 3 E <- 7 - 30; 7 - 30 7 - 30 161 = * 6 - * <- 7 - 30; 7 - 30 7 - 30 161 = 0 6 E * <- 7 - 30; 7? 30 7? 30 A = 0- * 0? 1 <- 7 - 30; 7 - 30 7 - 30 161 = 6 6 * <- 7 - 30; 7 - 30 7 - 30 2A * 0 * 0 D A <- 7 - 30; 7 - 30 7 - 30 161 = 6 6 * <- 7 - 30; 7 - 30 7 - 30 2A * 0 6 0. A <- 7 - 30; 7 - 30 7 - 30 161 = 0 6 E * <- 7 - 30; 7 - 30 7 - 30 6 0 D 5 <- 7 - 30; 7? 30 7? 30 * 0. 5 <- 7 - 30; 7 - 30 7 - 30 * 6 A <- 7 - 30; 7 - 30 7 - 30 6 0 =, <- 7 - 30; 7 - 30 7 - 30 2A * 0 6 0. A <- 7 - 30; 7? 30 7? 30 * = 6 <- 7? 30; 7 - 30 7 - 30 161 = * = * <- 7 - 30; 7? 30 7? 30 * 0 66 66666 * 06 * 6 * F-.D 23 @G H;? @. ;? @ <- 7? 30; 7 - 30 7 - 30 6 0 D 5 <- 7 - 30; 7 - 30 7 - 30 161 = * 6 - * <- 7 - 30; 7 - 30 7 - 30 161 = * 6 - * <- 7 - 30; 7? 30 7? 30 * 0 = 2 <- 7? 30; 7? 30 7? 30 6 0 & <- 7? 30; 7? 30 7? 30 A = 0- * 0 6 C <-

7-30; 7 - 30 7 - 30 161 = * 6 - * <- 7 - 30; 7 - 30 7 - 30 2A * 0 6 0. A <- 7 - 30; 7 - 30 7 - 30 * 6 = <- 7 - 30; 7 - 30 7 - 30 6 * 5 <- 7 - 30; 7 - 30 7 - 30 161 = * = * <- 7 - 30; 7 - 30 7 - 30 161 = * 6 - * <- 7 - 30; 7? 30 7? 30 2A * 0 0 B? <- 7 - 30; 7 - 30 7 - 30 161 = * 6 - * <- 7 - 30; 7 - 30 7 - 30 2A * 0 6 0. A <- 7 - 30; 7 - 30 7 - 30 * 6 = <- 7 - 30; 7 - 30 7 - 30 161 = * = * <- 7 - 30; 7 - 30 7 - 30 * 0 A <- 7 - 30; 7 - 30 7 - 30 161 = * 6 - * <- 7 - 30; 7 - 30 7 - 30 161 = 0 6 E * <- 7 - 30; 7? 30 7? 30;? @ *;? @ <- 7? 30; 7 - 30 7 - 30 161 = * 6 - * <- 7 - 30; 7? 30 7? 30 2A * 0 0 B? <- 7 - 30; 7? 30 7? 30 * = D <- 7? 30; 7? 30 7? 30 6 0 & <- 7? 30; 7 - 30 7 - 30 * 6 =% <- 7 - 30; 7 - 30 7 - 30 161 = * 6 - * <- 7 - 30; 7 - 30 7 - 30 161 = 0 6 E * <- 7 - 30; 7? 30 7? 30 * C & <- 7? 30; 7? 30 7? 30 * & 2 <- 7? 30; 7 - 30 7 - 30 2A * 0 6 0. A <- 7 - 30; 7? 30 7? 30 0 & A <- 7? 30; 7 - 30 7 - 30 161 = * = * <- 7 - 30; 7? 30 7? 30 B = 0- * 6 D B <- 7? 30; 7? 30 7? 30 * 066006 * 6 F-.D 23 @G H;? @;? @ <- 7 - 30; 7? 30 7? 30. = 77 * - 1 <- 7 - 30; 7 - 30 7 - 30 161 = * 6 - * <- 7 - 30; 7 - 30 7 - 30 2A * 0 6 0. A <- 7 - 30; 7 - 30 7 - 30 161 = * = * <- 7 - 30; 7 - 30 7 - 30 2A * 0 6 0. A <- 7 - 30; 7 - 30 7 - 30 * 3A 0 * 3 <- 7 - 30; 7? 30 7? 30 * B = <- 7? 30; 7 - 30 7 - 30 161 = * 6 - * <- 7 - 30; 7? 30 7? 30 6 0 & <- 7? 30; 7 - 30 7 - 30 161 = * 6 - * <- 7 - 30; 7? 30 7? 30 * B = <- 7? 30; 7 "30 7" 30 * 0; "@;" @ <- 7 - 30; 7? 30 7? 30 B = 0- 0 D? <-

$ #% &'(#.. = 2 "L' 4 7 *% & 8 #.; KL C = Q) 0 $ 6 * '4 $ $ # # # $ 7? 3; 7? 3 7? 3 . = * <- 7? 3; 7? 3 7? 3. = * <- 7? 3; 7? 3 7? 3. = * <- 7? 3; 7? 3 7? 3. = * <- 7? 3; 7? 3 7? 3. = * <- 7? 3; 7? 3 7? 3. = * <- 7? 3; 7? 3 7? 3. = * <- 7? 3; 7 ? 3 7? 3. = * <- 7? 3; 7? 3 7? 3. = * <- 7? 3; 7? 3 7? 3. = * <- 7? 3; 7? 3 7? 3 . = * <- 7? 3; 7? 3 7? 3. = * <- 7? 3; 7? 3 7? 3. = * <- 7? 3; 7? 3 7? 3. = * <- 7? 3; 7? 3 7? 3. = * <- 7? 3; 7? 3 7? 3. = * <-

7-3; 7? 3 7? 3. = * <- 7? 3; 7? 3 7? 3. = * <- 7? 3; 7? 3 7? 3. = * <- 7? 3; 7? 3 7? 3. = * <- 7? 3; 7? 3 7? 3. = * <- 7? 3; 7? 3 7? 3. = * <- 7? 3; 7? 3 7? 3. = * <- 7? 3; 7? 3 7? 3. = * <- 7? 3; 7? 3 7? 3. = * <- 7? 3; 7? 3 7? 3. = * <- 7? 3; 7? 3 7? 3. = * <- 7? 3; 7? 3 7? 30. = * <- 7? 3; 7? 3 7? 3. = * <- 7? 3; 7? 3 7? 30. = * <- 7? 3; 7? 3 7? 3. = * <- 7? 3; 7? 3 7? 3. = * <- 7? 3; 7? 3 7? 3. = * <- 7? 3; 7? 3 7? 3. = * <- 7? 3; 7? 3 7? 3. = * <- 7? 3; 7? 3 7? 3. = * <- 7? 3; 7? 3 7? 3. = * <- 7? 3; 7? 3 7? 3. = * <- 7? 3; 7? 3 7? 3. = * <- 7? 3; 7? 3 7? 3. = * <- 7? 3; 7? 3 7? 3. = * <- 7? 3; 7? 3 7? 3. = * <- 7? 3; 7? 3 7? 3. = * <- 7? 3; 7? 3 7? 3. = *% <- 7? 3; 7? 3 7? 3. = * <- 7? 3; 7? 3 7? 3. = * <- 7? 3; 7? 3 7? 3. = * <- 7? 3; 7? 3 7? 3. = * <- 7? 3; 7? 3 7? 3. = * <- 7? 3; 7? 3 7? 3. = * <- 7? 3; 7? 3 7? 3. = * <- 7? 3; 7? 3 7? 3. = * <- 7? 3; 7? 3 7? 3. = * <- 7? 3; 7? 3 7? 3. = * <- 7? 3; 7? 3 7? 3. = * <- 7? 3; 7? 3 7? 3. = * <- 7? 3; 7? 3 7? 3. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <-

7-30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <- 7? 30; 7? 30 7? 30. = * <-

$ #% 6 K $ '#. 0 # 2 "OL '4%;' 4 8 # 0 0 # 6 KQ # 7? 3; 7? 3 7? 3; 7? 3 7? 3; 7? 3 7? 3; 7? 3 7? 3 ; 7? 3 7? 3; 7? 3 7? 3; 7? 3 7? 3; 7? 3 7? 3; 7? 3 7? 3; 7? 3 7? 3; 7? 3 7? 3; 7? 3 7? 3; 7? 3 7? 3; 7? 3 7? 3; 7? 3 7? 3; 7? 3 7? 3; 7? 3 7? 3; 7? 3 7? 3; 7 ? 3 7? 3; 7? 3 7? 3; 7? 3 7? 3; 7? 3

7-3; 7? 3 7? 3; 7-3 7-30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7 - 30 7 - 30; 7-30

$ #% 3 #! ";; *%!;; * 0? O 0 #" # - 7 * - 7 * - 7 *; ;; ; - - NO L NO L NO L * # L 0L 1 * A3?; ; ; *; ; * # - 7 * 7 * 8 - / FHK 9) L; CP "#: L) L% 7 * L) L)% -7 # 1 0 7 / & K - 0 # KKK -7 - & SO OT ;; 0 # & K -7 & SO OT 0 # & K -7 & SO # OT 0 # & K -7 1 7 T?; 0 # & K - 0 # - 6 $ U # / -7 1 O 7 / & K -7 # &0?; 0 # & K - 0 # 2 "# & /

-7 & SO # 0 0 # & KU -7 - & SO # 7?; O; CP 0 # & KU - 0 # -7 - 1 0 ;; ?; 7 / & K -7 & 0 V? @D 0 # & K -7 &O;;?; 0 # & K - 0 # -7 # & 0 0 # & KU -7 # &;;?; 0 # & 7 / L - 0 # -7 - &O?; 0 # & KU - 0 # -7 # &O;;?; 0 # & - 0 # -7 0 1 O ;; 0 # & -7 0 - 17?; 0 # & -7 &?; 0 # & - 0 # 2 "# & / -7 0 1 7 0 # & -7 - 1 O?; O; CP 0 # & - 0 # -7 1 O?; 0 # & -7 1 ;;? ; 0 # & - 0 # -7 1 0 # & - 7 / -7 0 1 O 0 # & -7 & O 0 # & -7 # 1 O ;; 0 # & -7 # 1 7?; 0 # & - 0 # -7 1 O ;; 0 # & -7 0 1 O 0 # & -7 0 &O?; 0 # & -7 1 7?; 0 # & - 7/2 "# & / -7 - & O ?; 0 # & -7 1 O 0 # & - 0 # 2 "# & / -7 1 O 0 # & -7 # 1 O 0 # & -7 # 1 7?; 0 # & -7 0 1 O 0 # & -7 # 1 7?; 0 # & -7 0 & 7 7 / & -7 & SO # 7?; 0 # & - 0 # -7 0 1?; 0 # & -7 1 O?; 0 # & -7 1 0?; 7 / & - 0 # 2 "# & / -7 # &0?; 0 # & -7 1 0 ;; 0 # & -7 1 O 0 # & -7 0 & SO # 0 # & - 0 # 2 "# @ F7 * 6H / -7 # &O?; 0 # & -7 1 0 V? @D ;; 0 # & -7 # 1 0 # & - 7 / - 6 $ U # / -7 0 1 0?; 0 # & -7 # & SO # 0?; 0 # & -7 0 & SO # 0 0 # & -7 & SO # 0 ;;?; 7 / & - 7 / -7 1 O?; 0 # & - 0 # 2 "# @ F7 * 6H /

; '4 # L G F L "HW 0 F HW - F HW F" HW # # L! "W Q O O Q # L O

SOX%. ) L * A3) L '# Y F # #' 4 & 3 = 3 61 = "& N H%

0? O 0 # 2O 0! "9 / #% #!%;; * # #% - 7 * + $ / L! L 9 $ / L% 7 * 6 7 ## * # Z6 Z% /% $ #% & '(3 0 N # #) O;; * # # / LC = 8 #; BL = CQ 6 * K' 4 $ $ * 9)) & 01 # -7; K 0 - = <- -7 ; 2A * 0 K 6 0. A <- -7; 161 = K * 6 - * <- -7; K 6 0 &% <- -7; 161 = K * 6 - * <- -7; K * E * <-7; 161 = K * 6 - * <- -7; 161 = K * 6 - * <-7; 55 K 0 = - <- -7; 55 K 0 = - <- -7; 55 K 0 B? <- -7; 55 K 0 B? <- -7; K 0 A 5 <- -7; K 0 A 5 <- -7; 55 A = 0- K * 6? 1 <- - 7; 55 A = 0- K * 6? 1 <- -7; K 0 2 1 <- -7;. = 77 K * - 1 <- -7; 2A * 0 K 6 0. A <- -7 ; 2A * 0 K 0 6 B = <-7; K 0;? @;? @ <- -7; 2A * 0 K 6 0. A <- -7; 2A * 0 K 0 6 B = <-7 ; K 0 E 1 <- -7; K 0 = 5 <-7; K 0 2 1 <-7; K 6 0? <-7; K 0;? @;? @ <-7; K 6;? @;? @ <-7; * 3A 0 K 0 &? <-7; & ?? K 0 A 5 <-7; K 0 A & <- -7; K 0. C <- -7; K * * 0660 * 066 * 0 * 000 * 0 * 6 * F-.D 23 GH;? @ 6;? @ <- -7; K * E * <- -7; K 0 E <-7; K * A . <-7; K 0 A 5 <- -7; K 0 E <-7; 161 = K * 6 - * <- -7; K 0 . C <- -7; B = 0- K 0 D? <- -7; B = 0- K 0 D? <- -7; 161 = K * = * <-

-7; * 3A 0 K 6 * 0 **;? @? ;? @ <-7; K 6 0 = 5 <- -7; K;? @ * 6;? @ <- -7; K;? @ 6 0;? @ <- -7; K * 6. D <- -7; K *. A <-7; K * 6. D <- -7; 161 = K * 6 - * <- -7; K * E * <- -7; 161 = K 0 6 E * <-7; 161 = K 6 0 6 = <- -7; K 6 0 =, <- -7; K 0 - * <- -7; CB? K 6 0 = B <- -7; * 3A 0 K *, 5 <- -7; * 3A 0 K * 0. 5 <-

$ #% & '(. = 3 0 NO # #); ; *% 8 #; BL C = Q 0 6 * '4 $ $ & 01 #) -7; . = * <- -7; . = * <- -7; 5. = * <-7; 5. = * <-7; . = * <- -7; . = * <-7; 5. = * <- -7; 5. = * <- -7; . = * <- -7; . = * <- -7; . = * 6 <- -7; . = * <- -7; . = * <- -7; . = * <- -7; . = * <- -7; . = * <- -7; . = * <- -7; . = * <- -7; . = * <- -7; . = * <-

W BL L K W & 01 # #% 50 K $ # K $% $ #% 0 3 0 N # #); ; * "#% 8 #; Q 0 '4 6 K # -7;

-7; -7; -7; -7; -7; -7; -7; -7; 5 -7; 5 -7; -7; -7; -7; -7; -7; -7; -7; -7; -7; -7; -7; -7; 6? L K T W 02 #! $ # N # W 6? O02 [L K T O

The 50 K $ #

K $ $ #% & '(3 0) O; ; * # # # / L% = C 8 # 0 BL = CQN # # 6 * '4 $ $ * 9)) & 01 #) -7 2A * 0 0 6 B = <-7 161 = * 6 - * < -7 0 - = <- -7 2A * 0 6 0. A <- -7 161 = * 6 - * <- -7 6 0 & <- -7 161 = * 6 - * <- -7 6;? @ <-7 * 6 C? <-7 161 = * 6 - * <- -7 0 = - <- -7 0 B? <- -7 0 A 5 <- -7 0 A 5 <- -7 A = 0- * 6? 1 <-

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

W BL L K W & 01 # # W; 23 Q% 0 \ "(! L R) # 9 4" #K% $ #% & '(. = 3 0); ; *% 8 # 0 BL = C Q N # # 6 * '4 $ $ & 01 #%) -7. = * <

-7. = * <- -7. = * <- -7. = * <- -7. = * <- -7. = * <- -7. = * <- -7. = * 6 <- -7. = * <-7. = * <- -7. = * <- -7. = * <-7. = * <- -7. = * <-7. = * <-7. = * <-7. = * <- -7. = * <-7. = * <- -7. = * <-7. = * <- -7. = * <- -7. = * <- -7 . = * <-

W BL L K W & 01 # #% $ #%. $ #, 0? 3 @ - $ # - $ # - $ # 7 'F <1' 4 F <1 '4 F <1' 4 * 0 # '4? * N # # H * N # # H * N # # H. \ VO \ VO \ VO L K \ VO L K \ VO L K \ VO \ VO \ VO \ VO L K \ VO L K \ VO L K \ VO \ VO \ VO \ VO L K \ VO L K \ VO L K \ VO \ VO

\ VO \ VO LK \ VO LK \ VO LK \ VO \ VO \ VO \ VO LK \ VO LK \ VO LK \ VO \ VO \ VO \ VO LK \ VO LK \ VO LK \ VO \ VO \ VO \ VO LK \ VO LK \ VO LK \ VO \ VO \ VO \ VO LK \ VO LK \ VO LK \ VO \ VO \ VO \ VO LK \ VO LK \ VO LK \ VO \ VO \ VO \ VO LK \ VO LK \ VO LK \ VO \ VO \ VO \ VO LK \ VO LK \ VO LK \ VO \ VO \ VO \ VO LK \ VO LK \ VO LK \ VO \ VO \ VO \ VO LK \ VO LK \ VO LK \ VO \ VO \ VO \ VO LK \ VO LK \ VO LK \ VO \ VO \ VO \ VO LK \ VO \ VO \ VO \ VO LK \ VO \ VO \ VO \ VO LK \ VO LK \ VO LK \ VO \ VO \ VO \ VO LK \ VO LK \ VO LK \ VO \ VO \ VO \ VO LK \ VO LK \ VO LK \ VO \ VO \ VO \ VO LK \ VO LK \ VO LK \ VO \ VO \ VO \ VO LK \ VO LK \ VO LK \ VO \ VO \ VO \ VO LK \ VO LK \ VO LK \ VO \ VO \ VO \ VO LK \ VO LK \ VO LK \ VO \ VO \ VO \ VO LK \ VO LK \ VO LK \ VO \ VO \ VO \ VO LK \ VO LK \ VO LK \ VO \ VO \ VO \ VO LK \ VO LK \ VO LK \ VO \ VO \ VO \ VO LK \ VO LK \ VO LK \ VO \ VO \ VO \ VO LK \ VO LK \ VO LK \ VO \ VO \ VO \ VO LK \ VO LK \ VO LK \ VO \ VO \ VO \ VO LK \ VO LK \ VO LK \ VO \ VO \ VO \ VO LK \ VO LK \ VO LK \ VO \ VO \ VO \ VO LK \ VO LK \ VO LK \ VO \ VO \ VO \ VO LK \ VO LK \ VO LK \ VO \ VO \ VO \ VO LK \ VO LK \ VO LK \ VO \ VO \ VO \ VO LK \ VO \ VO \ VO \ VO LK \ VO \ VO \ VO \ VO LK \ VO LK \ VO LK \ VO \ VO \ VO \ VO LK \ VO LK \ VO LK \ VO \ VO \ VO \ VO LK \ VO LK \ VO LK \ VO \ VO \ VO \ VO LK \ VO LK \ VO LK \ VO \ VO \ VO \ VO LK \ VO LK \ VO LK \ VO \ VO \ VO \ VO

$ #%) * 9 L 7 &##? ] * = * O O O O O O O O O O O * = * O O O O O O O O O O O * O * O O O O O O O O O O O O O O * = * O O O O O O O * = * O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

* = * OOOOOOOOO * = * OOOOOOOOO * = * O? * = OOOOOOOO? * = OOOOOOO * = * OOOOOOOOOO * = * OOOOOOOOOO * = * OOOOOOOO * = * OOOOOO * = * OOOOOOO * = * OOOOOOOO * = * OOOOOOOOO * = * OOOOOOOOOOOO * = * OOOOOOOOOOOO * = * OOOOOOOOOOOOOO * = * OOOOOOOOOOO * = * OOOOOO * = * OOOOOO * = * OOOOOO * = * OOOOOOOOOOOOOO * = * OOOOOOOOOOO * = * OOOOO * = * OOOOOOOOOOOOOOOOO * = * OOOOOOOOOOOOOOO * = * = * OOOOOOOO * = * OOOOOOOOOOOOOO * = * OOOOOOOOOO * = * OOOOOOOOOO * = = OOOOOOOO? * = OOOOOOOOO? * = OOOOOOOOO? * = OOOOOOOOO? * = OOOOO? * = OOOOOOOOOOOO? * = OOOOOOOOOO? * = OOOOOOOOOOO? * = OOOOOOOOOOO? * * = OOOOOOOOOO? * = OOOOOOO? * = OOOOOO? * = OOOOOO? * = OOOOOOOOOOO? * = OOOOOOOOOOOOOOO? * = OOOOOOOOOOO? * = OOOOOOOOO ? * = OOOOOOOOOOO? * = OOOOOOO? * = OOOOOOOOOOO? * = OOOOOOOOO? * = OOOOOO? * = OOOOOOO? * = OOOOOOOOOOO? * = OOOOOOOOOOO? * = OOOOOOO? * = OOOOOO? * = OOOOOOOO? * = OOOOOOOO? * = OOOOOOOO? * = OOOOOOOO? * = OOOOOOOO? * = OOOOOOOO? * = OOOOOOOO? * = OOOOOOO? * = OOOOOOOOOOOOO? * = OOOOOOOOOOOO? * = OOOOOOOOOO? * = OOOOOOOOOOO? * = OOOOOOOOOOO? * = OOOOOOOO? * = OOOOOOOOO? * = OOOOOOOOO? * = OOOOOOOOOOO? * = OOOOOOOOO? * = OOOOOOOOOO * * = OOOOOOOOOO * * = OOOOOOOOOO ? * = OOOOOOOOOOOOO? * = OOOOOOOOOOOO? * = OOOOOOOOOOO? * = OOOOOOOOOO? * = OOOOOOOO? * = OOOOOOOOOOO? * = OOOOOOOOOOOOOOO * * = OOOOOOO? * = OOOOOOO? * = OOOOOOO? * = OOOOOOOOOOO? * * = OOOOOO? * * = OOOOOOO? * = OOOOOOOO? * = OOOOOOOOO? * = OOOOOOO? * = OOOOOOO? * = OOOOOOOOOOO

? * = OOOOOOOOOOOOO? * = OOOOOOOOOO? * = OOOOOOOOOOOOO? * = OOOOOOOOOOO? * = OOOOOOOOOOOO? * = OOOOOOOOO * * = OOOOOOOOO? * = OOOOOOOO? * = OOOOOOOOO? * = OOOOOOOOO * * = OOOOOOOOO? * = OOOOOOOOO? * = OOOOOOOO? * = = OOO? * = OOOOOOOOO? * = OOOOOOOOOOOOOOOOO? * = OOOOOOOOO? * = OOOOOOOOO? * = OOOOOOOO? * = OOOO? * = OOOOOOOOOOOOOOOOO? * = OOOOOOOOOOO? * = OOOOOOOOOOOOOOOO? * = OOOOOOOOO? * = OOO? * = OOOOOOOOOO ? * = OOOOOO? * = OOOOOOOOOOOO? * = OOOOOOOOOOO? * = OOOOOOOOOOOOOO? * = OOOOOOOOOOOOOO? * = OOOOOOOOOOOO? * = OOOOOOOOOOOO? * = OOOOOOOOOO? * = OOOOOOOOO? * = OOOOOOOOOOO? * = OOOO? * = OOOOOOOO? * = OOOOOOOO? * = OOOOOOOOOOOOOO? * = OOOOO? * = OOOOOOOOOOO? * = OOOOO? * = OOOOOOOOOOO? * = OOOOOOOOO? * = OOOOOOOO? * = OOOO OOOOOO? * = OOOOOOOOOOOOO? * = OOOOOOOOOO? * = OOOOOOO? * = OOOOOOO * = * OOOOOOOOO * = * OOOOOOOOOOO * = * OOOOOOOOOOOOOO * = * OOOOOOOOOOOOOOOOOOO * = * OOOOOOOOOOOOO * = * OOOOOOOOOOOOOOO * * = OOOOOOOOO * = * OOOOOOOOOO * = * OOOOOOOO * = * OOOOOOOOOOO * = * OOOOOOOOOOO * = * OOOOOOOOOOO * = * OOOOO * = * OOOOOOOO * = * OOOOOOO * = * OOOOOOOOOOOOOO * = * OOOOOOOOO * = * OOOOOOOOOOO * = * OOOOOOOOOOO * * = * OOOOOOOOOOOOOOO * = * OOOOOOOOOOOOO * = * OOOOOOOOOOOOO * = * OOOOOOOOOOO * = * OOOOOOOO * = * OOOOOOOOO * = * OOOOOOOOOOO * = * OOOOOOOOOOO * = * OOOOOOOOOOO * = * OOOOOOOOOOOOOOOOO * = * OOOOOOOOOOOOOO * = * OOOOO * = * OOOOOOOOOOO O * = * O O O O O O O O O O O O * = * O O O O O O O O O O * O * O O O O O O O O O O O O * = * O O O O O O O * = * O O O O O O O O

* = * OOOOOO * = * OOOOOOO * = * OOOOOOOO * = * OOOOOO * = * OOOOOOOOOOOOO * = * OOOOO * = * OOOOOOOOOO * = * OOOOOOOO * = * OOOOOO * = * OOOOOOOOOOOOOO * = * OOOOOOOOOOO * = * OOOOOOOO * = = OOOOOOOOOO * = * OOOOO * = * OOOOOOOOOOOO * = * OOOOOOOOOOO * = * OOOOOOO * = * OOOOOO * = * OOOOOOOOOOOOOOO * = * OOOOOOOOOOOOO * = * OOOOOOOOOOOOO * = * OOOOO * = * OOOOOOOOOOOOO * = * OOOOOOOOOOOOOOOOO * = * OOOOOOO * = * OOO * = * OOOOOOOOOOOO * = * OOOOOOOOOO * = * OOOOOOOOO 6 OOOOOOOO 6 OOOOO 6 OOOOOOOO 6 OOOOOOOOOOO 6 OOOOOOOOOOOOO 6 OOOOOOOO 6 OOOOOOOOO 6 OOOOOOOOOO 6 OOOOOOOOOOO 6 OOOOOOOO 6 OOOOOOOOO 6OOOO 6OOOOOOOOO OOO 6 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B ** O O O O O O O O O O O B ** O O O O O O B O O O B O O O O O O O O

B O O O O O O O O O B O O O O O O O O B O O O O O B O O O O O O O O B O O O O O O B O O O O O O O O B O O O O O O O O O O B O O O O O O O B O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O B O O O O O O B O O O O O O O B O O O O B O O O O O O O O B O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O B O O O O O O O O O O O B O O O O O O O O O O O B O O O O O O O O B O O O B O O O O O O O O B O O O O O O O O B O O O O O O O O O B O O O O O O O O B O O O O O O O B O O O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O B O O O O O O O O O B O O O O O O B O O O O O O O O B O O O O O O O O O O O B O O O O B O O O O O O O O O O B O O O O O O O O O O O B O O O O O O O O O O B O O O O O B O O O O O O O O O O B O O O O O O B O O O O O B O O O O O O O O O O B O O O O O O B O O O O O O O O B O O O O O O B O O O O O O O O O O O O B O O O O O O O O O B O O O O O O B O O O O O O B O O O O O O B O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O B O O O O O O O O O B O O O O O O O B O O O O O O O O O B O O O O O O O B O O O O O O B O O O O B O O O O O O O O O O O B O O O O O O O O B O O O O O O O O B O O O O O O O O O O O B O O O O B O O O O O O O O O O O B O O O O O B O O O O O O B O O O O O O B O O O O O B O O O O O B O O O O O O O O B O O O O O O O B O O O O O O O O O B O O O O O O O B O O O O B O O O O O O O O B O O O O O O O O O B O O O O O O B O O O O O O O O B O O O O O B O O O O O O O O B O O O O B O O O O O O O O B O O O O O O B O O O O O O O O O O B O O O O O O O B O O O O B O O O O O O O O B O O O O B O O O O O B O O O O O O O O O O O O O

BOOOOOO 2 32 OOOOOOOOOOOOO 2 32 OOOOOOOOOOOO 2 32 OOOOOOOOOOOOO 2 32 OOOOOOOOOO 2 32 OOOOOOOOOO 2 32 OOOOOOOOOOO 2 32 OOOOOOO 2 32 OOOOOOOOOOO 2 32 OOOOOOOO 2 32 OOO 2 32 OOOOOOOOOOO 2 32 OOOOOOOO 32OOOOOO 2 32 OOOOOOOOO 2 32 OOOOOOOOO 2 32 OOOOOOOOOOO 32 32 32 OOOOOO 2 32 OOOOOOO 2 32 OOOOOOOOOOOO 2 32 OOOOOOOOO 2 32 OOOOOOOO 2 32 OOOOOOOOOOO 2 32 OOOOO 2 32 OOOOOOOOOOOO 2 32 OOOOOOOOO

2 32 OOOOO 2 32 OOOOOOO 2 32 OOOOO 2 32 OOOOOOOO 2 32 OOOOOOOOOO 2 32 OOOOOOOOOOO 2 32 OOOOOOOOOO 2 32 OOOOOOOOOOOOOOO 2 32 OOOOOOOOOOO 32 32OOOOOOOOOO 2 32 OOOOOOOOOOO 2 32 OOOOOOOOOOOOO 32 32OOOOOOOOOOO 32 32 OOOO 2 32 OOOOO 2 32 OOOOOOOO 2 32 OOOOOOOOOOO 2 32 OOOOOOOOO 2 32 OOOOOO 2 32 OOOOOOO 2 32 OOOOOOOO 2 32 OOOOOOOOO 2 32 OOOOOO 2 32 OOOOOOO 2 32 OOOOOOOOOO 2 32 OOOOOOOOOOO 2 32 OOOOOOO 2 32 OOOOOO 2 32 OOOOOOOOOOOOO 2 32 OOOOOOOOO 2 32 OOOOOOOOOOOOOOO 2 32 OOOOOOOOOOOOOOO 2 32 OOOOOOOOOOOOO 2 32 OOOOOO 2 32 OOOOOOOOO 2 32 OOOOO 2 32 OOOOOOOOOOOO 2 32 OOOOOOOOOOOOOOO 2 32 OOOOOOOO 2 32 OOO OOOOOOO 2 32 OOOOOOOOOOOO 2 32 OOOOOOOOOOOOOOOO 2 32 OOOOOOOOOOO 2 32 OOOOOOOOOOOO 2 32 OOOOOOOOOOOOOOO 2 32 OOOOOOOOOOOOOOOOO 2 32 OOOOOOOOOOOOO 2 32 OOOOOOOOOOOO 2 32 OOOOOOOOOOO 2 32 OOOOOOOOO 2 32 OOOOOOOOO 2 32 OOOOOOOOOO 2 32 32 OOOOOOOOOOOOO 2 32 OOOOOOOOOO 2 32 OOOOOOOO 2 32 OOOOOO 2 32 OOOOOOOOOOO 2 32 OOOOOOOOOO 2 32 OOOOOOOOO 2 32 OOOOOOOOOOO 2 32 OOOOOOOO 2 32 OOOOOOOOOO 2 32 OOOOOOOOOOO 2 32 OOOOOOOOOOO 2 32 OOOOOOOOOOO 2 32 OOOOOOOOOOO 2 32 2 32 OOOOOOOO 2 32 OOOOOOOOOOO 2 32 OOOOOOOOOOOOOO 2 32 OOOOOOOOOO O O 2 32 O O O O O O O 2 32 O O O O O O O O O O O O O O 2 32 O O O O O O O O 2 32 O O O O O O O 2 32 O O O O O O O O 2 32 O O O O 2 32 O O O O O O O 2 32 O O O O O O O O O O O O O

2 32 OOOOOOOOO 2 32 OOOOOOO 2 32 OOOOOOOO 2 32 OOOOOOOOOOOOO 2 32 OOOOOOOOO 2 32 OOOOOOOOOOOOOO 2 32 OOOOOOOOO 2 32 OOOOOOOOOOOO 2 32 OOOOOOOOOOOOO 2 32 OOOOOOOOO 2 32 OOOOOOO 2 32 OOOOOOO 2 32 OOOOOOO 2 32 OOOOO 2 32 OOOOOOOO 2 32 OOOOOOOOOO 2 32 OOOOOOOOO 2 32 OOOOOOOOO 2 32 OOOOOOOOOOO 2 32 OOOOOOOOOO 2 32 OOOOOOO 2 32 OOOOOOOOOOOO 2 32 OOOOOOOO 2 32 OOOOOOOOOOO 2 32 OOOOOOOOOOOOO 2 32 OOOOOOOOOOO 2 32 OOOOOOOOOOO 2 32 OOOOOOO 2 32 OOOOOOOOOOO 2 32 OOOOOOOOOOO 2 32 OOOOOOOOOOO 2 32 OOOOOOOOOOO 2 32 32 OOOOOOOOOOOOOO 2 32 OOOOOO 2 32 OOOOOOOOO 2 32 OOOOOOOOOO 2 32 OOOOOOOOOO 2 32 OOOOOOOOO 2 32 OOOOOOOOOOOOOOO 2 32 OOOOOOOOOO The second 32 ooooooooooooo 2 32 OOOOOOOOOO 2 32 OOOOOOOOOO 2 32 OoOoOoOoOoOoOoO 2 32 OOOOOOOOO 2 32 ooooooooOOo 2 32 OOOOOOOO 2 32 OoOoOoOoOoOoOoO 2 32 OOOOOOOO 2 32 OOOOOOOOO 2 32 oooooooooooooo 2 32 ooooooooooooo 2 32 oooooooooooooooo 2 32 oooooooooooo 2 32 oooooooooooooo 2 32 oooooooooooooo 2 32 OOOOOOOOOOOOOO 2 32 OOOOOOOOOOOO 2 32 OOOOOOOOOOOOOO 2 32 OOOOOOOO 2 32 OOOOOO 2 32 OOOOOOOOOOOOOOOO 2 32 OOOOOOOOOOOOOOO 2 32 OOOOOOOOOOOOOOOO 32 32OOOOOOOOOOOO 2 32 OOOOOOOOOO 2 32 OOOOOOOOOO 2 32 OOOOOOOOO 2 2 OOO 2 32 OOOO 2 32 OOOOOOOOOO 2 32 OOOOO O O O O O O 2 32 O O O O O O O O O O O O 2 32 O O O O O O O O O O O O O 2 32 O O O O O O O O O O 2 32 O O O O O O O O O O O O 2 32 O O O O O O O O O O O O O 2 32 O O O O O O O 2 32 O O O O O O O O O O O O O O 2 32 O O O O O O O O O O O 2 32 O O O O O O O O O O O 2 32 O O O O O O O O O O O O O O O 2 32 O O O O O O O O O O O O O O

2 32 OOOOOOOOOOOOOOO 2 32 OOOOOOOOO 2 32 OOOOOOO 2 32 OOOOOO 2 32 OOOOOOOOO 2 32 OOOOOOOO 2 32 OOOOOOOOOOO 2 32 OOOOOOOOOOOO 2 32 OOOOOOOO 2 32 OOOOOOOOOO 2 32 OOOOOOOOOOO 2 32 OOOOOO 2 32 OOOOOOOOOO 32 32 OOOOO 2 32 OOOOOOOO 2 32 OOOOOOOOO 2 32 OOOOOOOO 2 32 OOOOO 2 32 OOOOOOOOOOOOO 2 32 OOOOOOOOOOO 2 32 OOOOOOOOOOO 2 32 OOOOOOO 2 32 OOOOOOOOOOOOO 2 32 OOOOOOOOO 2 32 OOOOOOOOOOOOOOO 2 32 OOOOOOOOOOO 2 32 OOOOOOOOOOO 2 32 OOOOOOOOOOO 32 OOOOOOOOOOO 2 32 OOOOOOOOOOO 2 32 OOOOOOOOO 2 32 OOOOOOOOO 2 32 OOOOOOOOO 2 32 OOOOOO 2 32 OOOOOOOOOOO 2 32 OOOOOOOOO OOO 2 32 OOOOOOOOO 2 32 OOOOOOOOOOOOOOO 2 32 OOOOOOOOOOOO 2 32 OOOOOOOOOOO 2 32 OOOOO 2 32 OOOOOOOOOOOOO 2 32 OOOOOOOOOOO 2 32 OOOOOOOOOO 2 32 OOOOOOOOOO 2 32 OOOOOOOOO 2 32 OOOOOOOOO 2 32 OOOOOOOOOOOOO 32 32OOOOOOOOOO 2 32 OOOOOOOOOOO 2 32 32 OOOOOOOOOOO 2 32 OOOOOOOOOOOOOOOO 2 32 OOOOOOOOOO 2 32 OOOOOOOOO 2 32 OOOOOOOOOOOOOO 2 32 OOOOOOOOOO 2 32 OOOOOOOOOOOOOOOOO 2 32 OOOOOOOOOOOOO 2 32 OOO 2 32 OOOOOOO 2 32 OOOOOOOOOO 2 32 OOOOOOOO 32OOOOO 32OOOOO 32OO 2OO 2 32 OOOOOOOOO 2 32 OOOOOOOOOOO 2 32 OOOOOOO 2 32 OOOOOOO 2 32 OOOOOOOOO 2 32 OOOOOOOOOOOO 2 32 OOOOOO O O O O O O O O O O O O 2 32 O O O O O O O O 2 32 O O O O O O O O O 2 32 O O O O O O O O O O O O 2 32 O O O O O O 2 32 O O O O O O 2 32 O O O O O O O O O O O O O O O O O O O O O O

2 32 OOOOOOOOOOOO 2 32 OOOOOOOOOOO 2 32 OOOOOOOOOOOOO 2 32 OOOOOOOOOOO 2 32 OOOOOO 2 32 OOOOOOO 2 32 OOOOOOOOOO 2 32 OOOOOOOOOOOOOOOO 2 32 OOOOOOOOOO 32 32OOOO 2 32 OOOOOOOOO 2 32 OOOOOOOO 32 32OOOOOOO 2 32 OOOOOOOOO 2 32 OOOOOOOOOOO 2 32 OOOOOOOOOOOOO 2 32 OOOOOOO 2 32 OOOOOOOO 2 32 OOOOOOOOOOOOO 2 32 OOOOOOOOOO 2 32 OOOOOOOO 2 32 OOOOOOOO 2 32 OOOOOOOOO 2 32 OOOOO 2 32 OOOOO 2 32 OOO 2 32 OOOOOOOOOO 2 32 OOOOOOOOOOOOOO 2 32 OOOOOO 2 32 OOOOOOOOO 2 O 32 OOOOO 2 32 OOOOO 2 32 OOOOOOOOO 2 32 OOOOOOOOOOOOO 2 32 OOOOOOOOOOO 2 32 OOOOOOOOO 2 32 OOOOOOO 2 32 OOOOO 2 32 OOOOOOO 2 32 OOOOOOOO 2 32 OOOOOOOOOO 2 32 OOOOOOO 2 32 OOOOOO 2 32 OOOOOO 2 32 OOOOO 2 32 OOOOOO 2 32 OOOOOOO 2 32 OOOOOOOOO 2 32 OOOOOOOOO 2 32 OOOOOOOOO 2 32 OOOOOOOOOOOOO 2 32 OOOOOOOOOOOOO 2 32 OOOOO 2 32 OOOOOOOOOOOOOOO 2 32 OOOOOOOOOOO 2 32 OOOOOOOOOOOOO 2 32 OOOOOOOOOOO 2 32 OOOOOOOOOO 2 32 OOOOO 2 32 OOOOOOOOOOOO 2 32 OOOOOO 2 32 OOOOOOOO 2 32 OOOOOOOO 2 32 OOOOOOOOOOOOOO 2 32 OOOOOOOOOO 2 32 OOOOOOOOO 2 32 OOOOOOO 2 32 OOOOOO 2 32 OOOOOOOOOO 2 32 OOOOOOOO 2 32 OOOOOOOOOOO 32 32 OOOOOOOOOO 32 32 2 32 OOOOOOOOO 2 32 OOOOOOOOO 2 32 OOOOOOOOOOO 2 32 OOOOOOO 2 32 OOOOOOO 2 32 OOOOOO 2 32 OOOOOOO 2 32 OOOOOOOO 2 32 OOOOOOOOOOOO 2 32 OOOOOOOOO

2 32 OOOOOO 2 32 OOOOOO 2 32 OOOOOOOOOOOO 2 32 OOOOOOOO 2 32 OOOOOOOOO 2 32 OOOOOOOOOO 2 32 OOOOOOOOO 2 32 OOOOO 2 32 OOOOOOOOO 2 32 OOOOOOOOOOO 2 32 OOOOOOO 2 32 OOOOOO 2 32 OOOOOOOOO 2 32 OOOOOO 2 32 OOOOOOOOOOOO 2 32 OOOOOOOOOO 2 32 OOOOOOOOOO OOOOOOOOOOO 2 32 OOOO 2 32 OOOOOOOOOOOOO 2 32 OOOOOOOOO 2 32 OOOOOO 2 32 OOOOOOO 2 32 OOOOOOOOOOOO 2 32 OOOOOOOOOOOOOOOOO 2 32 OOOOOOOOOOO 2 32 OOOOOOOOOOOO 2 32 OOOOOOOOOOOOO 2 32 OOOOOOOOOOO 2 32 OOOOOOOOOOO 2 32 OOOOOOOOOOO 2 32 OOOOOOOOOOO 32 OOOOOO 2 32 OOOOOOOO 2 32 OOOOOOO 2 32 OOOOOOOOOOOOO 2 32 OOOOOOOOOOO 2 32 OOOOOOO 2 32 OOOOOOOOOOOOOO 2 32 OOOOOOOOOOO 2 32 OOOOOO 2 32 OOOOOOOOO 2 32 OOOOOOOOOOOO 2 32 OOOOOOOO 2 32 OOOOOOOOO 2 32 OOOO 2 32 OOOOOO 2 32 OOOOOOOOO 2 32 OOOOOOOO 2 32 OOO 2 32 OOOOOOO 2 32 OOOOOOOOOOOOOOO 2 32 OOOOOOOOO 2 32 OOOOOOOOOOOO 32 32OOOOOOO 2 32 OOOOOOO 2 32 OOOOOO 32 32 OOOOOOOOOOOOO 2 32 OOOOO 2 32 OOOOOOOOO 2 32 OOOOOO 2 32 OOOOOOO 2 32 OOOOOOOO 2 32 OOOO 2 32 OOOOOOOOOOO 2 32 OOOOOOOOO 2 32 OOOOO 2 32 OOOOOOOOO 2 32 OOOOOO 2 32 OOOOOOOOO 2 32 OOOOOOOOOOO 2 32 OOOOOOOOOOO 32 32 32 OOOOOOOOOOOOO 2 32 OOOOOOOOO 2 32 OOOOOOOOO 2 32 OOOOOOOOOOO 2 32 OOOOOOOOOO 2 32 OOOOOOOOO 2 32 OOOOOOOOOO 2 32 OOOOOOOO 2 32 OOOOOOO 2 32 OOOOOOOOOOOOO 2 32 OOOOOOOOOOOOOO 2 32 O O O O O O O 2 32 O O O O O O O O

2 32 OOOOOOOOOOOO 2 32 OOOOOOO 2 32 OOOOOOOOO 2 32 OOOOOOOOO 2 32 OOOOOOOOOOO 2 32 OOOOOOOOOOOO 2 32 OOOOOOOOOOOOO 2 32 OOOOOOOOOO 2 32 OOOOOOOOO 2 32 OOOO 2 32 OOOOOOOO 2 32 OOOOOOOO 2 32 OOOOOO 2 32 OOOOOOO 2 32 OOOOOOOOO 2 32 OOOOOOOOOOOO 2 32 OOOOOOO 2 32 OOOOOOOOOOOO 2 32 OOOOOOOOO 2 32 OOOOOOOOOOOOO 2 32 OOOOOOOOO 2 32 OOOOOO 2 32 OOOOOOOOOOOO 2 32 OOOOOOOOOOOOOO 2 32 OOOOOOOOOOO 2 32 OOOOOOOOOOOOO 32 32OOOOOOOO 2 32 OOOOOOOOO 32OOOOOO 32OOOOOO 32 32 32 OOOOOOO 2 32 OOOOOOOOOO 2 32 OOOOOOOOOOOOO 2 32 OOOOOOOO 2 32 OOOOOOOOOO 2 32 OOOOOOOOOOO 2 32 OOOOOOOOO 2 32 OOOOOOO 2 32 OOO OOOOOOOOO 2 32 OOOOOOOOOOOO 2 32 OOOOOOOOOOO? * = OOOOOOOOOOOO? * = OOOOOOOOOO? * = OOOOOOOOOOOOO? * = OOOOOOOOOOOOOB ** OOOOOOOOOOOOOB ** OOOOOB ** OOOOOOB ** OOOOOB ** OOOOOO 0 * OOOOOOOOOO 0 * OOOOOOO 0 * OOOOO 0 * OOOOOOOOOOOOO * * 0 * OOOOOOOOOOOO 0 * OOOOO 0 * OOOOOO 0 * OOOOOOOOOOO 0 * OOOOOOOOO 0 * OOOOOOOOO 0 * OOOOOOOOOOOO 0 * OOOOOOO 0 * OOOOOOOOOO 0 * OOOOOOOO 0 * OOOOOOOOO 0 * OOOOOOOOOOO 0 * OOOOOOOOOOO 0 * OOOOOOOO 0 * OOOOOOOOOO * * OOOOOO 0 * OOOOOOOOO 0 * OOOOOOO 0 * OOOO 0 * OOOOOOOOOO 0 * OOOOOOOOO 0 * OOOOOOOOOO 0 * OOOO 0 * OOOOOO 0 * OOOOOOOO 0 * OOOOOOOOOOO 0 * OOOOOOOO 0 * OOOOOO 0 * OOOOOOOOO 0 * OOOO

0 * OOOOOOOOOOOOO 0 * OOOOOOOOOOOO 0 * OOOOOOOOOOO 0 * OOOOOOOOOO 0 * OOOOOOOOOOO 0 * OOOOOOOOO 0 * OOOOOO 0 * OOOOOOOO 0 * OOOOOOO 0 * OOOOOOOO 0 * OOOOOOOOO 0 * OOOOOOOOOOO 0 * OOOOOOOOOOO 0 * OOOOOOOOOOO 0 * OOOOOOOOO 0 * OOOOOOOOO 0 * OOOOOOOOOO 0 * OOOOOOOOOOOO 0 * OOOOOOOO 0 * OOOOOOOO 0 * OOOOOOOOOO 0 * OOOOOOOO 0 * OOOOOOO 0 * OOOOOOO 0 * OOOOOOOOOOO 0 * OOOOOOOOOOO 0 * OOOOOOOO 0 * OOOOOO 0 * OOOOOOO * OOOOOOO 0 * OOOOOOOOOO 0 * OOOOOOOOOOO 0 * OOOOOOOO 0 * OOOOOOOOOO 0 * OOOOOOOO 0 * OOOOOO 0 * OOOOOOOOOOO 0 * OOOOOOOOOOO 0 * OOOOOOOOOOOO 0 * OOOOOOOOO 0 * OOOOOOOOO OOOO 0 * OOOOOOOOOOOOO 0 * OOOOOOOO 0 * OOOOOO 0 * OOOOOOOOOOOO 0 * OOOOOOOO 0 * OOOOO 0 * OOOOOO 0 * OOOOO 0 * OOO 0 * OOOOOOOOOOOO 0 * OOOOOOO 0 * OOOOOOOO 0 * OOOOOOOO 0 * OOOOOOO 0 * OOOOOOOOO 0 * OOOOOOOOO 0 * OOOOOOOOOO 0 * OO 0 * OOOOOOOOOOOO 0 * OOOOOO 0 * OOOOOO 0 * OOOOOO 0 * OOOOOOOO 0 * OOOOOOO 0 * OOOOOOOO 0 * OOOOOOO 0 * OOOOO 0 * OOOO 0 * OOOOOOO 0 * OOOOOOOOO 0 * OOOOOO 0 * OOOOOOOOO 0 * OOOOOOOO 0 * OOOOOO 0 * OOOOOOOO 0 * OOOOO 0 * OOOOO 0 * OOOOOOO 0 * OOOOOOO 0 * OOOOOO 0 * OOOOOOOOO 0 * OOOOOOO 0 * OOOOOOOO

0 * OOOOOOOO 0 * OOOOOOOO 0 * OOOOOOOOOOO 0 * OOOOOOOOOOOO 0 * OOOOOOOOO 0 * OOOOOOOOO 0 * OOOOOOOOOO 0 * OOOOOO 0 * OOOO 0 * OOOOOOOOO 0 * OOOOOOOO 0 * OOOOOOOO 0 * OOOOOOO 0 * O 0 * OOOOOOOOO 0 * * OOOOOOOOO 0 * OOOOOOOO 0 * OOOOOOOO 0 * OOOOOOOOOO 0 * OOOOOOO 0 * OOOOO 0 * OOOOOOOOOOOO 0 * OOOOOOOO 0 * OOOOOOO 0 * OOOOOOOOOOOOO 0 * OOOOOOOOOOOOO 0 * OOOOOO 0 * OOOOO 0 * OOOO 0 * OOOOOO 0 * OOOOOO 0 * OOOOOOOOOOOO 0 * OOOOOOOOOO * OOOOOOOOO 0 * OOOOOOOOO 0 * OOOOOOO 0 * OOOOOOO 0 * OOOOOOOOOOOOOOO 0 * OOOO 0 * OOOOOOOOOO 0 * OOOOOOOOOOOO 0 * OOOOOOOOOOOOO 0 * OOOOOOOOOOOOO 0 * OOOOOOOOOO 0 * OOOOOOOOOOOO 0 * OOOOOOOOOOO 0 * OOOOOOOOOOOOO 0 * OOOOOOOOOOO 0 * OOO OOOO 0 * OOOOOOOOOOOO 0 * OOOOOOOOOOOOO 0 * OOOOOOOOOOO 0 * OOOOOOOOOOO 0 * OOOOOOOOOOO 0 * OOOOOOOOOOO 0 * OOOOOOOOOO 0 * OOOOOOOOOOO 0 * OOOOOOOOOOO 0 * OOOOOOOOOOOOO 0 * OOOOOOOOOOOOO 0 * OOOOOOOOOOOOO 0 * * OOOOOOOOOOOOO 0 * OOOOOOOOOO 0 * OOOOOOOO 0 * OOOOOOOOOO 0 * OOOOOOOOOO 0 * OOOOOOOOOOOO 0 * OOOOOOO 0 * OOOOOOOOOOOOO 0 * OOOOOOOOOOO 0 * OOOOOOOOOOO 0 * OOOOOOOOOOO 0 * OOOOOOOOOOOO 0 * OOOOOOOOOOOO 0 * OOOOOOOOOOOO 0 * OOOOOOOOOOOO 0 * OOOOOOOOOOOO 0 * OOOOOOOOOO 0 * OOOOOOOO 0 * OOOOOOOO 0 * OOOOOOOOOO 0 * OOOOOOOOOOOOOOO 0 * OOOOOOOOOOOO 0 * OOOO O O O O O O O O

0 * O O O O O 0 * O O O O O O O O O O O * O O O O O O O * O O O O O O O O * O O O O O 0 * O O O O O 0 * O O O O O 0 * O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

0 * OOOOOOOOOO 0 * OOOOOOOOO 0 * OOOOOOOOOOOOO 0 * OOOOOOOOOO 0 * OOOOO 0 * OOOOOOOOO 0 * OOOOOOO 0 * OOOOOOOOO 0 * OOOOOOOO 0 * OOOOOOOOOOO 0 * OOOOOOOOOOO 0 * OOOOO 0 * OOOOO 0 * OOOOOO 0 * OOOOO 0 * OOOOOOOO 0 * OOOOOOOO 0 * OOOOOOOOO 0 * OOOOOO 0 * OOOOO 0 * OOOOOO 0 * OOOOOOOO 0 * OOOOOOO 0 * OOOOOOO 0 * OOOOOO 0 * OOOOOOOOOOO 0 * OOOOOOOOOO 0 * OOOOOOO 0 * OOOO 0 * OOOOOOOOO 0 * OOOOOOOO 0 * OOOOO 0 * OOOOOOOOOOO 0 * OOOOOOOOOOO 0 * OOOOOOOOOOO 0 * OOOOOOOOOOO 0 * OOOOOOO 0 * OOOOO 0 * OOOOOOOOO 0 * OOOOOOOOO 0 * OOOOO 0 * OOOOOOOO 0 * OOOOOOOOOOOO 0 * OOOOOOOOO 0 * OOOOOOO 0 * OOOOOOOO 0 * OOOOOOOO 0 * OOOO 0 * OOOOOOOOO 0 * OOOOOOOOOO 0 * OOOOOOOOOOOOO 0 * OOOOOOOOOOOO 0 * OOOOOOOOOOO 0 * OO OOOOOOO 0 * OOOOOOOOOOOOOO 0 * OOOOOOO 0 * OOOOOOOOOOOO 0 * OOOOOOOOO 0 * OOOOOOOOO 0 * OOOOOOOOO 0 * OOOOOOOOOOOOOO 0 * 0 OOOOOOOOO 0 * 0 OOOOOOOO 0 * 0 OOOOOOOOOO * * 0 OOOOOOOOOO * 0 * OOOOOOOOO 0 * 0 OOOOOOOOO 0 * 0 0 * 0 OOOOOO 0 * 0 OOOOOOOO 0 * 0 OOOOOOOO 0 * 0 OOOOOOOOOOOO 0 * 0 OOOOOOOOOO 0 * 0 OOOOOOO 0 * 0 OOOOOOO

0 * 0 OOOOOOOOO 0 * 0 OOOOOOOO 0 * 0 OOOOOOOO 0 * 0 OOOO 0 * 0 OOOOOO 0 * 0 OOOOOOO 0 * 0 OOOOOO 0 * 0 OOOOOOOO 0 * 0 OOOOOOOOOO 0 * 0 OOOOOOOO 0 * 0 OOOOOOOO 0 * 0 OOOOOOO 0 * 0 0 OOOOOO 0 * 0 OOOOOOOO 0 * 0 OOOOOOOOOOO 0 * 0 OOOOO 0 * 0 OOOOOOO 0 * 0 OOOOOOOOOOOOO 0 * 0 OOOOO 0 * 0 OOOOOOOOOOOOO 0 * 0 OOOOO 0 * 0 OOOOOOOOO 0 * 0 OOOOOOOOOO 0 * 0 OOOOOOOO 0 * 0 OOOOOOOO 0 * 0 0 * 0 OOOOOOOOOOOOOO 0 * 0 OOOOOOOOO 0 * 0 OOOOOOOOOO 0 * 0 OOOOOOOOOO 0 * 0 OOOOOO 0 * 0 OOOOOOO 0 * 0 OOOOOO 0 * 0 OOOO 0 * 0 OOOOO 0 * 0 OOOO 0 * 0 OOOOOO 0 * 0 OOOOOOOOO 0 * 0 OOOOOOOOOO 0 * 0 OOOOOO 0 * 0 OOOOOOOOOO 0 * 0 OOOOOOOO 0 OOOOOOOOOOOOO 0 OOOOOOOOO 0 OOOOOOOOOOO 0 OOOOOO 0 OOOOOOOOOOOO 0 OOOOOOOOOOOOO 0 OOOOOOOO OOOOOO 0 OOOOOOOOOOOOOO 0 OOOOOOOOO 0 OOOOOOOO 0 OOOOOOOOOO 0 OOOOOOOOO 0 OOOOOOOO 0 OOOOOO 0 OOOOOOOOOO 0 OOOOOOOOOOO 0 OOOOOOOOOOOOOOO 0OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO OOOOOOO 0 OOOO 0 OOOOOOOOO 0 OO 0 OOOOOOOOOOO 0 OOOOOOOOO 0 OOOOOOOO 0 OOOOOOO 0 OOOOOOO 0 OOOOOOOOO 0 OOOOOOOO 0 OOOOOO 0 OOOOOOOOOOOOOOO 0 OOOOO 0 OOOOOOOOOOOOO 0 OOOOOOOOOOOOOO

0 OOOO 0 oooooooooooooo 0 ooooooooOOo 0 oooooooooooo 0 oooooooooooooo 0 OOOOOOOO 0 ooooooooOOo 0 ooooooooOOo 0 ooooooooooooo 0 OoOoOoOoOoOoOoO 0 OOOOOOOOO 0 OOOOOOOOOO 0 OOOOOOOOOO 0 OOOOOOOOO 0 OOOOOOOOO 0 OOOOOOOOO 0 ooooooooooooo 0 OOOOOOOO 0 oooooooooooooo 0 OOOOOOOO 0 OoOoOoOoOoOoOoO 0 ooooooooOOo 0 OOOOOOOOO 0 oooooooooooo 0 OOOOOOOOOO 0 OOOOOOOO 0 OOOOOOOOO 0 OOOOOOOOOO 0 OOOOOOOOOOOOOOOO 0 OOOOOOOOOOOO 0 OOOOOOO 0 OOOOOOO 0 OOOOOOOOOOO 0 OOOOOOOOOOO 0 OOOOOOOOOO 0 OOOOOOOOOOO 0 OOOOOOOO 0 OOOOOOOOOOOOOOO 0OOOOOOOOOOOOOO 0 OOOOOO 0 OOOOOO 0 OOOOOOOO 0 OOOOO 0 OOOOOOOOO 0 OOOOOOOOOOOO 0 OOOOOOO 0 OOOOOO 0 OOOOOOOOOO 0 OOOOOOO 0 OOOOOOOOO 0 OOOOOOOOO 0 OOOOO 0 OOOO 0 OOOOOOOOOO 0 OOOOO 0 OOOOOOOOOOO 0 OOOOOO 0OOOOOOOOO 0 OOOOOOOO OOOO 0 OOOOOOOOOOO 0 OOOOOOO 0 OOOOOOOOOOO 0 OOOOO 0 OOOOOOOOO 0 OOOOOOOO 0 OOOOOOOOOOO 0 OOOOOO 0 OOOOOOOOO 0 OOOOOOOOOOOO 0 OOOOOOOO 0 OOOOOOOOOOO 0 OOOOOOOOOO 0 OOOOO 0 OOOO 0 OOOOOOO 0 OOOOOOOOOOOOOOO 0OOOOOOOOOO

0 OOOOOOOOOO 0 OOOOOOOOOO 0 OOOOOOO 0 OOOOOOOOOOO 0 OOOO 0 OOOOOOOOOOO 0 OOOOOOOOOO 0 OOOOOOOOOOO 0 OOOOOOOOO 0 OOOOO 0 OOOOOOOOOOO 0 OOOOO 0 OOOOOOOOOOO 0 OOOOOOOOOO OOOOOOO 0OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO 0 OOOOOOOOOOOO 0 OOOOOOOOOOOO 0 OOOOOOOOOOO 0 OOOOOOOO 0 OOOOOOOOO 0 OOOOOOOO 0 OOOOOOOOOOOO 0 OOOOOOOOO 0 OOOOOOO 0 OOOOOOOOOOOO 0 OOOOOOOOOOO OOOOOOOOOOOOO 0OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO OOOOOOO 0 OOOOOOOO 0 OOOOOOO 0 OOOOOOOOOOOOO 0 OOOOOOOOOOO 0 OOOOOOOOOOO 0 OOOOOOO 0 OOOOOOOO 0 OOOOOOO 0 OOOOOOOOO 0 OOOOOOOOOOOOO 0 OOOOOOOO OOOO OOOOOOOOO 0OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO OOOOOOOOOO 0 OOOOOOO 0 OOOOOOOO 0 OOOOOOOOOOOOO 0 OOOOOOOOOOOOOO 0 OOOOOOOOOOOOOOO 0 OOOOOOOO 0 OOOOOOOOOOOOOO 0 OOOOOOOOOOOO 0 OOOOOO 0 OOOOOOOOOO 0 OOOOOOOOOOOOOO 0 OOOOOOOOOO 0 OOOOOOOOOOO 0 OOOOOOOOOOO

0 OOOOOOOOOOOOOOOO 0 OOOOOOOOO 0 OOOOOOOO 0 OOOOOOOOOO 0 OOOOO 0 OOOOOOOOO 0 OOOOOOO 0 OOOOOO 0 OOOOOOOO 0 OOOOOOO 0 OOOOOOOOOO 0 OOOOOOOOOOOOO 0OOOOOOOOOOOO 0OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO 0 OOOOOOO 0 OOOOOOOOOOOO 0 OOOOOOOOOOOO 0 OOOOOOO 0 OOOOOOOOOO 0 OOOOOOOOOOOOOOO 0 OOOOOOOOOOOO 0 OOOOOOO 0 OOOO 0 OOOOOOOO 0 OOOOOOOO 0 OOOOOOOOOOOOO 0OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO 0 OOOOOOOO 0 OOOOOOOO 0 OOOOOOOOOOOO 0 OOOOOOOOOOO 0 OOOOO 0 OOOOOOOOOOO 0 OOOOOOOOOO 0 OOOOOOOOOOOO 0 OOOOOOOOO 0 OOOOOO 0 OOOOOO 0 OOOOO 0 OOOOOOOOOO OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO OOOOOOOOOOOO 0 OOOOOOOOOOOO 0 OOOOOOOOOOO 0 OOOOOOOOOOOOOO 0 OOOOOOOOOOOO 0 OOOOOOOOOOOOO 0 OOOOOOOOOOO 0 OOOOOOOOOO 0 OOOOOOOOOOOO 0 OOOOOOOO 0 OOOOOOOOOOOOOOO 0 OOOOOOOOO 0 OOOOOOOOOO 0 OOOOOOOOOOOOO

0 O O O O O O O O O O O O O O O 0 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

0 OOOOOOOO 0 OOOOOO 0 OOOOOOOOOOO 0 OOOOO 0 OOOOOOO 0 OOOOO 0 OOOOOOOOOOOOOOO 0 OOOOO 0 0 OOOOOO 0 0 OOOOOO 0 0 OOOOOOOOO 0 0 OOOOOOOOOOOO 0 0 OOOOOO 0 0 OOOOOOOO 0 0 OOOOOOO 0 0 OOOOOOOOO 0 0 OOOOOOO 0 0 OOOOOOOOO 0 0 0 OOOOOOOO 0 0 OOOOOOOO 0 0 OOOO 0 0 OOOOOOO 0 0 OOOOOOOOOOO 0 0 OOOOOOO 0 0 OOOOOOO 0 0 OOOOOOO 0 0 OOOO 0 0 OOOO 0 0 OOOOOOOOOO 0 0 OOOOOOOO 0 0 OOOOOOOOO 0 0 OOOOOOOO 0 0 OOOOO 0 0 OOOOOOOOO 0 0 OOOOOO 0 0 OOOOOOOO 0 0 OOOOOOO 0 0 OOOOOOOOOOO 0 0 OOOOOOOO 0 0 OOOOOO 0 0 OOOOOO 0 0 OOOOOOO 0 0 OOOOOOOOOOOO 0 0 OOOOOOOOOOOOO 0 0 OOOOOOOOO 0 0 OOOOO 0 0 OOOO 0 0 OOOOO 0 0 OOOOOOOOOOOOOOOO 0 0 OOOOOOOOOOO 0 0 OOO OOOOOOOOOO 0 0 OOOOOOOOOOOOO 0 0 OOOOOOOOOOOOOOOOOOO 0 0 OOOOOOOOOO 0 0 OOOOOOOOOOOOO 0 0 OOOOOOOOOO 0 0 OOOOOOO 0 0 OOOOOOOOO 0 0 OOOOOOOOOOOOOO 0 0 OOOOOOOOOOOOOOO 0 0OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO 0 OOOOOOOOOOOOOOOO 0 0 OOOOOOOOOOOOOOOO 0 0 OOOOOOOOOOOOO 0 0 OOOOOOOOOO 0 0 OOOOOOOOO 0 0 OOOOOOOO 0 0 OOOOOOOOOOO 0 0 OOOOOOOOOOOOOO 0 0 OOOOOOOOOOOOOO

0 0 O O O O O O O O O O O O 0 0 O O O O O O O O O O O O 0 0 O O O O O O O O O O O

0 0 OOOOOOOO 0 0 OOOOOOOOOOO 0 0 OOOOOOOOOO 0 0 OOOOOOOOO 0 0 OOOOOOOOOOOOOOOO 0 0 OOOOOOOOOOOOOOO 0 0 OOOOOOOOOOOOO 0 0 OOOOOOOO 0 0 OOOOOOOOOOO 0 0 OOOOOOOOOOO 0 0 OOOOOOOOOOOO 0 0 OOOOOOOOOO 0 0 OOOOOOOOOO OOOOOOOOOOOOOO 0 0 OOOOOOOOOOOO 0 0 OOOOOOOOOOOO 0 0 OOOOOOOO 0 0 OOOOOOOOOOOO 0 0 OOOOOOOOOOOO 0 0 OOOOOOOOOOOO 0 0 OOOOOOOOOOOOOOOOO 0OO OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO 0 OOOOOOOOOOOO 0 0 OOOOO 0 0 OOOOOO 0 0 OOOOOOOOOOOO O O 0 0 O O O O O O O O O

0 0 OOOOOOOOOOOOOO 0 0 OOOOOOOOOOOOO 0 0 OOOOOOOOOOOOO 0 0 OOOOOOOOOOOOO 0 0 OOOOOOOOOOOO 0 0 OOOOOOOOOOOO 0 0 OOOOOOOOOOO 0 0 OOOOOOOOOOO 0 0 OOOOOOOOOOO 0 0 OOOOOOOOOOO - OOOOOOOOOOOOO - OOOOOOOOOOOO OOOOOOOOOO - OOOOOOOOOOOOO - OOOOOOOOOO - OOOOOOOO - OOOOOOOOOOO - OOOOOOOOOOOOOOO - OOOOOOO - OOOOOO - OOOOOOOO - OOOOOOOOOOOO - OO - OOOOOOOOO - OOOOOOOOOOOOO - OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO OOOOOO - OOOOOO

^ 02 ^ 02 ^ 02 ^ 02 O O O O O O O O O O O%

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O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O% O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O% O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O% O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O% O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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O O O O O O O O O O O ^ 02 ^ 02 ^ 02 ^ 02 ^ 02 ^ 02 ^ 02 ^ 02 ^ 02 ^ 02 ^ 02 ^ 02 ^ 02 ^ 02 ^ 02 ^ 02 ^ 02 ^ 02 ^ 02 ^ 02 ^ 02 ^ 02 ^ 02 ^ 02

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O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O% O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O% O O O O O O O O O O O O O O O O O O O O O O O% O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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OOO * $ '' 0 0%% OOO * $ '' 0 0%% O * $ '' 0 0 OOOOOOOOO * $ '' 0 0 O% * $ '' 0 0 OOOOO * $ '' 0 0% * $ '' 0 0 OOO% OO * $ '' 0 0 OOOOOOO * $ '' 0 0 O * $ '' 0 0 OOOOO * $ '' 0 0 OOOO * $ '' 0 0 OOOOO * $ '' 0 0 * $ '' 0 0 OOOOOOOO * $ '' 0 0 OO * $ '' 0 0 OOOOOO * $ '' 0 0 OOO * $ '' 0 0 * $ '' 0 0 OOOOO * $ '' 0 0 OOOOO * $ ' '0 0 OOOO * $' '0 0 O% OOO * $' '0 0 OO * $' '0 0% * $' '0 0 OOOOO% * $' '0 0 OOO% * $' '0 0 OOOOOOOO * $ '' 0 0 OOOOO * $ '' 0 0% O%% * $ '' 0 0 OOOOOOOOOO * $ '' 0! * 0 O O O O O * $ '' 0! * 0 O O O O O O O O O * $ '' & * 0 O O O O O O O O O O * $ '' & * 0 O O O O O O O O O O O * $ '' 0! * 0 O O O O O O O * $ '' 9! 0 O O O O O% O O O * $ '' $ $ #! 0 O O O O O O O O O * $ '' 9! ! 0 O O O O O O O O O * $ '' $ $ #! ! 0 O O O O O O O O O * $ ''! ! 0 O O O O O O O O O O O * $ ''! 0 O O O O O O O O O * $ '' #! 0 O O O O O O O * $ '' / #! 0 O O O O O O O O O O * $ '' / #! 0 O O O O O O O O O * $ '' #! L @ -! 0 O O O O% O O O O * $ '' #! 0 O O O O O O O O O O O * $ '' / #! 0 O O O O O O O O O O * $ '' / #! 0 O O O O O O O O O * $ '' $ #! 0 O O O O O O O O O * $ '' 9! 0 O O O O O O O O O O * $ '' $ $ #! 0 O O O O O O O O O O * $ '' / #! 0 O O O O O O O O O O * $ '' / #! 0 OO% O * $ '' R '4 - ** 2C0 OOO% O * $' '- ** 2C OOO * $' '- ** 2C OOOOOOOOO * $' '- ** 2C OOO * $' '- ** 2C0 OOOOO * $ '' - ** 2 OOO * $ '' - ** 222% O * $ '' - ** 2C0 OOOOOOOOO * $ '' 0 0 O * $ '' 0 0% OO% * $ '' 0 0 O * $ '' 0 0 O%% O * $ '' 0 0 O * $ '' 0 0

OOOO * $ '' 0 0 OOOOOO * $ '' 0 0 OOOOOOO * $ '' 0 0% OO% OO * $ '' 0 0 OOOO * $ '' 0 0 * $ '' 0 0 OOOOO * $ '' 0 0 OOOO * $ '' 0 0 OO% * $ '' 0 0 OOOO * $ '' 0 0 OOOO * $ '' 0 0 OOOOOOOOO * $ '' 0 0 * $ '' - ** 2C OO * $ '' - ** 2 OOOOOOOOOO * $ '' - ** 2 O * $ '' - ** 2C0 OOOOO * $ '' - ** 2 OOOOOOOO * $ '' - ** 2C0 OOOOOOOO * $ '' - ** 2 OOOOOOO * $ '' - ** 222 OO% * $ '' - ** 22 OOOOO * $ '' - ** 22 OOOOOOO * $ '' - ** 2C0 OOOOOOO * $ '' - ** 2 OOOOOO * $ '' - ** 2 O 0 * # # 220 OOO 0 * # # 220 OOOO 0 * # # 220 OOO 0 * # # 220 OOO 0 * # # 220 OOO 0 * # # 2227 OOO 0 * # # 2C0 OOO 0 * # # 2227 OOOO 0 * # # 220 OOOOO 0 * # # 2 OOOOOOOO 0 * # # 2 OOOOOOOOOO 0 * # # 220 OOOOO 0 * # # 222 * OOO 0 * # # 2 OOO 0 * # # 2C0 OOO 0 * # # 2 OOOO 0 * # # 220 OOOOOOOOO 0 * # # 220 O 0 * # # 2227 OO 0 # # 220 OO * # # & 0 N # # # -L 220 O * N # # YL 220 1! LK # # 0 22 OOOO 1! LK # # -9 # 22 OOOO 1! LK # # # 9 22 OOOOO 1! LK # # OOOO 1! L 0 227 OOO 0 * # # @ - 2 OOOOO 0 $ # # # 220 OOO 0 * # # @ - 220 OO 0 * # # @ - 2 OOOO 0 * # # @ - 2 OOOOOOOOO 0 # # 2 OOOOOOOOO 0 * # # @ - 2227 0 * # # @ - 227 OOOOOOOOOO 0 * # # @ - 2227 OO 0 * # # @ - 2220 OO 0 * # # @ - 2227 0 * # # @ - 227 OOOOO 0 * # # @ - 220 OOOOOOO 0 * # # @ - 220

O O 0 * # # @ - 2227 * N # # Y L @ - 220 O O O O O O 0 # # 220 O O O O * # # 6 # # @ - 2220 O O. YL 0 @ - 27 OOOO 0 * # # @ - 227 OOOOOOOO * # # # @ - 227 OOOOOOOO 0 # # 220 OO * N # # YL @ - 22 OOOOOOOOO 0 N # # # # 220 O 0 # # 2227 O 1 ! LK # # $ # 22 OOO * N # # YL @ - FH 222 OOOOOOOO 0 # # 220 1! LK # # -9 # 222 * OOOOOOOO 0 # # 220 OOO 0 # # 220 OOO 0 * # # 220% OOOO 0 * # # 220% OO * # # $ # O # 0 220 OOOOO 0 * # # 220 OOOO * # # # 0 2 OOOOOOOO 0 # # 220 OOOOO 0 * # # 2227 OOO 0 * # # 2C0 O% OOOO 0 * # # 2 OOOOOO 0 * # # C # L # # 227 OOOOOOOOOOO * # # C # L # # 0 220 OOOOOOO 0 # # 2220 OOOO 0 * # # 2 OOOOOOO 0 * # # 2 OOOO 1! L * # L 2C OOOOOOO 0 * # # @ - 2220 OOOOOOOOO 0 * # # 27% 0 * # # @ - 220 OOO 0 * # # @ - 2220 OOOOOO 0 * # # @ - 2220 OOO 1! LK # # # ## 2220 OOOOOOOOOO 0 * # # @ - 2220 OOOO% 0 * # # @ - 2227 OOOOOOOOO 0 # # 2227 O% O% * N # # YL 227% OOOOO 1! LK # # $ # 2220 OO * N # # YL @ - 220 O * N # # YL @ - 220 O%% 1 ! LK # # # 9 2220 OOOOOO 0 * # # @ -H 2% OO% * # # C # L # # 220 O * YL 2C OOOOOOOOOO 0 * # # @ - 220 OOOOOOOOOO 0 $ # # # 2227 OOOOOOO 0 * # # 2220 OOOOO * # # # 220 OOOOOOOOOO * # # # & 0 220 OOOOOOOOO * # # # 2C OOOOOOOOO 0 * # # 220 OOOOOO 1! LK # # $ # 22 OOOOOO * # # # 220 OO 0 $ # # # 220 OO 0 * # # 222 * O 0 * # # 2227 OOOOOOO! 1 LK # # 22 OOOOOOO 0 * # # 2220 OOO 0 * # # 2C0 OOOOOOOO 1! LK # # -9 # 2220 OOOOOOO 0 * # # 2227

OOOOOOOOO 0 * # # 220 OOOOOO 0 * # # 2 OOOOOOOO 0 * # # 2 OO% OO 0 * # # 220 OOOOO 0 * # # 0 OOOOOOOOO 0 # # 0 OOOOOO 0 * # # 0 OOO 0 * # # OOOO 0 * # # 2C0 OOOOO 0 * # # 2 OOOO 0 * # # 222 * OOOOOOOOO 0 * # # 2227 OOO 0 * # # 220 OOOOOOOOOO 1! LK # # OOOOOOO 0 * # # 220 OOOOOO 0 * # # 2227 OOOOOO 0 * # # 2 OOOO 0 * # # 220 0 * # # 2C7 OOO 0 * # # 222 * OOOOOOO 0 * # # 2220 OOOOOOOOOO 0 # # 220 OOOOOO 0 * # # 2 OOOO * N # # # -L # # 2C7 OO 0 * # # 220 O 0 * # # 2 OOO 0 * # # 2C0 OO 0 * # # @ - 22 OOOOOOOO 0 * # # @ - 227 OOOOOO 0 * # # @ - 2 OOO 0 * # # @ - 2 OOOO 0 * # # @ - 2 OOOOOOOOO 0 # # 2 OOOO 0 * # # @ - 2 OOOOOOOO 0 * # # @ - 227% O%% 0 * # # @ - 2227 OO 0 * # # @ - 2227 OOOOO 1 ! LK # # $ # 22 O 0 # #FH 220 OOOOOOOOOO 0 * # # @ - 220 OOOOO 0 * # # @ - 222 * N # # YL @ - 220 OOOOOOOOOOO 0 * # # 220 OOOOO O O O O O 0 * # # 222 *% O O O * # # 2220 O O O O O O 0 * # # 2227 O O% O 0 * # # 220 O 0 * # # 2 O O O O O O O O O O 0 * # # 2227% O O O. KL 222 * OOOOOOOOO 0 * # # 227 OOO 0 * # # 2227 OOOOOOOOO 1! L * # L 22 OOOOOOO 0 # # 222 *% OOOO 0 * # # 220% OOOOO 0 * # # 2C0 OOOOOO * N # # YL 20 OOOOO 0 # # 2C7 OOOOO 0 * # # 2C0 OOO% OOO 0 * # # 2 OOO 0 * # # 220 OOOOOO 1! LK # # OOOOOOOOO 0 * # # 2C7 OOO% 0 * # # 222 * OOOOOOOOO 1! LK # # 2 OOOOOO 0 * # # 2

% 0 * # # 220 OOOOO 0 * # # 2 OOO 0 * # # 2227 OOOOO 0 * # # 220 OO 0 * # # 2 OOOOOOOO 0 * # # 2227 OOOOOO 1! LK # # OO 1! L * # L 22 OO 1! LK # # OOO 0 * # # 2 OOOOOOOOOO 0 # # 2227 OOOOOO 0 * # # 220 OOO 0 * # # 220 OOOOOOOOOO 0 * # # 2 OOOOOOOO * # # # 2 OOOO 0 * # # @ - 227 OO % OO 0 $ # # # 227 OOO 0 * # # 227 OOO 0 * # # 227 OOOOOOO 0 * # # 227 OOOOOO 0 * # # 2227 O% O% OO 0 * # # 2C0 OOOOOOOO 0 $ # # # 220 O % O 0 * # # 2C0 OOOOOOO * # # # 227 OOOO 0 * # # 227 OOOOOO 0 * # # 2C0 OOOOOO * # # 2227 OOO 0 * # # 2 OO 0 * # # 220% OOOOO% O * # # 2220 OO * YL 220 O% * N # # R '4 YL 22 OO 0 * # # 22 OOOOOOO 0 * # # 220 OOOOO 0 * # # 222 0 O 0 * # # 22 0 OOO% 1! LBO * # L 222 0 OO * N # # R '4 YL 22 0 OOOOOOOO * # # # 22 0 OOO% O 0 * # # 22 0 OOO 0 * # # 22 0% OOO 0 * # # 222 0 OOOOOOOOO * # # # 22 0 OOOOOOO% OO 1! LK # # -9 # 222 0 OO% * N # # R '4 YL 22 7 OOOOOOO 0 * # # 222 0% O% 1! L * # L 2C OOOO% OO 1! LK # # 22 OOOOOO 0 * # # 22 0 OOO 0 * # # 220 OOOOOOO * YL 2220% O% OO 0 * # # 220 OOOOOO 0 * # # 2C0 OO 0 * # # 2220 OOOO% 0 * # # 2227 OOOOO 0 # # 20 OOO 0 * # # 222 * OO 0 * # # 2227%% 0 * # # 222 * OOO 0 * # # 2227 OOOOO% OO 0 * # # 2C0 O 0 * # # 220 OO 0 * # # 2 OOOOOOOO 0 * # # 2 O. Y L $ # 2220

O% O% OO 0 * # # 2227 O% OO 0 * # # 20 OOOOOOO 0 * # # 220%% 0 * # # 2C0% OOO 0 * # # 2227 OOOOO% O 0 * # # 220 OO 0 * # # 220 OOOOOO 0 * # # 2 O% 0 * # # 2 OO 0 * # # 2% 0 * # # 2 O% 0 * # # 2 OOOO 0 * # # 22 7 OOOOOOOO * # $ # # '4 * # 222 7 OOOOOOOOO 0 * # # 22 7 OO% OO * # # '4 @ - 222 0 OOOOOOOOOO * # # # 22 7 OOOO * # # # 2 OOOOOOOO 0 * # # 222 7 OOOOOOO * # $ # #' 4 L ./ 22 0% OOO * # # '4 @ - 2 OOOOO% O * # #' 4 @ - 2 OOOOOOO * # # '4 @ - 2 OOOOOO 0 * # # 2C OOOOOO 0 # # 22 0 OOOOOO 0 # # 22 0 OOO * N # # R '4 YL 222 OOOOOOO 1! L * # * ## K # # 222 0 O 0 * # # 22 0 OOO * # #' 4 @ - 222 0 OO * # # '4 @ - 3 22 0 OOO 0 * # # 2C OO 0 * # # -L = L * ## 22 0 O * # # '4 @ - 22 0 O 7 & * 22 0 0 * # # 222 0 OO * N # # R '4 YL 22 0 OOO * # #' 4 @ - 222 0 OO * # # '4 @ - 22 7 OOOOOO * # # 0' 4 22 0 OOO * # # * # # '4 @ - 22 0 OOOO 0 * # # 22 0 * # # '4 @ - 222 0 * # # 0 -L = L * ## 22 0 * # # '4 @ - 222 0 * # #' 4 @ - 22 7 OOO 0 * # # & 22 0 OO * # # '4 @ - 22 0 OOOO * # #' 4 @ - O% OOOOOO 0 * # # 22 7 OOOO 0 * # # 22 7 OOOOO% O 0 * # # 22 7 OOOOOOOOOO 0 * # # 22 7 OO 0 * # # 22 7%% * # $ # # ' 4 0 # "# L 22 0 OO 0 * # # 22 0 OO 0 * # # 22 0 OOOOOOO 7 0 # OO * # # 0 N # # # -L 0 OOO 0 # # 0 OOOOOOOO * # # '4 0 * # # 0 OOOO 1! LK # # 9 # 9 OOOOOO * # # '4 OOOO 0 * # # 22 0 OOOO 0 * # # 0 O 0 * # # 0

O * # # '4 @ - 22 0 OOO 0 * # # 3' 4 0 OOO 7 * 3 # & * &## 2 OOO * # # '4 @ - 2 OOO 0 * # # 0 O 1! L * # L 2 0 * # # 22 0 OOO 7 * 3 # # '4 & 3 222 * * # #' 4 &# 20 * N # # ./ OOOOO 1! LK # # 0 22 * # $ # # '4 * #) 222 * O 0 * # # 22 7 OOOOOOO * # # # 20 OOOOO * # $ # # '4 C * #) 22 0 O * # # R & 2220 0 # # 2227 OOOOOOOO 0 * # # 2220 OOOOOOOOOO 0 * # # 3 '4 22 0 OOOOOOOO 0 # # 22 0 OOOOOOOO * # #' 4 3 * 2- * 222 0 O 0 * # # 22 OOO * # # # 2 OOOO * 3 # # '4 @ - 22 7 OOOOO * # # # 22 0 OOOOOOOOOO 1! L * # L 222 0 OOOOOO * # # '4 @ - 2 O * # #' 4 @ - 2 OOOOOO * N # # # -L &. YL 222 OOO * # # '4 @ - 22 0 OOOOO 7 * & 2 #' 4 L & 22 0 OOOOO * # $ # # '4 C 9 # 222 7 OO 1! LK # # 222 0 O 0 * # # 222 * * # $ # 220 OOOOO * # $ # 0 OOO 0 * # # 222 0 OOOOO 0 * # # 220 O 0 * # # 222 7 OOOOOOOOO 0 * # # 220 OOOO 0 * # # 2C0 OOOOOO 0 * # # 220 O 0 * # # 222 7 O 0 * # # 220 OOOO 0 * # # 2C0 OOOOOO 0 * # # 222 7 OOOOOO * # 0 OOOOOOOOOO 0 * # # 220 OOOOOOOOOO * # 0 OO 0 * # # 222 7 OOOOOOOOOOO 0 * # # 220 OO 0 * # # 222 7 OOOOOO * # 0 OOOOOOO * # 0 OOOOOOO * # 0 OOOOOO * # $ # 0 OOOOOOO * # 0 OOOOOOO 0 * # # 222 7 OOOO * # $ # 0 OOOOOOO * # 0 OOOOOOOO 0 # # 222 7 OOOOO * # 220 OOOOO * # 20 OOOOO 0 * # # 220 OOO 0 * # # 220 OOOOO 0 * # # 2 OOOOOOOOOO 1! LK # # 0 OOOOOOOOO 0 * # # 220

OOOOOOOO 0 * # # 2220 O 0 * # # 2220 OO 0 * # # 2C OOO 0 * # # 2227 0 * # # 2227 O 0 * # # 2227 * N # # YLR '4 222 OOOO * # #' 4 2220 OOO * N # # YLR '4 222 OOOO * # #' 4 227 OOOO * # # '4 220 OOOOO 0 # # 220 OOOO * # #' 4 2220 OOOOOO 1! LK # # $ # 2220 OOOOO 0 * # # 220 OO * YL 22 OOO 0 * # # 2 OOO 0 * # # 220 OOOO * # # # 27 OOOOOO * # # '4 220 OOOOO 0 * # # 220 0 * # # 227 OO 0 * # # 227 0 * # # 220 OO 1! LK # # -9 # 22 * # # # 220 OO 0 * # # 227 OOOOO 0 * # # 2 OOOO 1! LK # # - # 22 OO * N # # YLR '4 220 OO% 0 * # # 220 OOOOOO 1! LK # # $ # 22 OOOOOO 0 * # # 227 OOOOOOOOOO 0 # # 22 OOOOOOO 0 * # # 220 OOOOOOO 0 * # # 222 * OOOO 0 * # # 222 7 OOOOOOO 0 * # # 2 OOOOOOOOOOO 1! LK # # 0 OOO * # 220 OOOOOOO * N # # # -L # # 220 OOOO * # 220 OOOOOOOO * # $ # 227 OOOOOOO * # 222 0 OOOO 1! LK # # 0 OOOOO 1! LK # # 0 OOOO. YL 220 OOO 0 * # # 2C0 OOO 0 * # # 220 OOOOOO 0 * # # 220 OOO 0 * # # 222 0 OOOOO 0 * # # 222 0 OOOOOOOOO * N # # # -L # # 2C0 OO 0 * # # 222 7 OOOOOOO 1! LK # # 0 OOOOOOOOOO 0 # # 220 OOO 0 * # # 220 OOOOOOOOO 1! LK # # 0 OOOOOO * # $ # 222 * OOO 0 * # # 0 OOOOOO 0 * # # 222 0 OOOOOOO * # 220 OOO 0 * # # 222 7 OOOO 0 * # # 220 OO 0 * # # 222 7 OOOOOOO 0 * # # 222 7

OOOOO * # 0 OOOOOOOOO * # 20 OOOOOO * # $ # 220 OOOOO * # 20 O 0 * # # 222 7 OOOOOOOO 0 * # # 220 O 0 * # # 222 7 OO 0 * # # 222 * OOOOOOO * N # # R '4 YL 27 OOO 0 * # # 2C0 OOO%%% O * # # # 220 OOOO 1! LK # # 2220 OOOOOOOOO 0 $ # # # 220 OOO 0 * # # 220 OO * N # # YL 227 OOOOOOO 0 * # # 220 OOOOO * N # # YLR '4 227 OOOOOOOOO 0 # # 220 OOOO 0 * # # 220 OOO * # #' 4 2220 OO * # # '4 2220 OOOOO * # #' 4 2 OO * N # # YL 220 OO * # # '4 2 OOOOOO 1! LK # # 0 22 OOOOO 1! LK # # 2220 OOOOOO * # #' 4 2220 OO * # $ # # '4 227 OO * N # # YLR' 4 220 OO 1! LK # # $ # 2220 O%% OO% * 2 # 2 OOOOOO * # # '4 227 O * # $ # #' 4 2227 OOOO * # # '4 222 * OO% OOOOOO * # #' 4 2227 O * # $ # # '4 227 OOOO * # #' 4 227 OOOOOO * # # '4 20 OOO * # #' 4 2227 OO * # # '4 227 OOOOOOOO * # #' 4 227 OOOOOO * # # '4 227 O%% * # # '4 227 OOOOOO * # $ # #' 4 220 OO * # # '4 227 OOOOOOO * # #' 4 227 OOOOOO * # # '4 227 OOOO * #) 2227 O% * 9 YL 220 OOOOO * N # # YLR' 4 220 OOOOOO + 2 # # 222 OOO * N # # YL 220 * N # # YLR '4 220 OOO * N # # YL 227% OO * N # # YLR' 4 222 OOO * N # # YLR '4 220 OO * N # # YLR' 4 227 OO * N # # YL 4 R '4 222 O * N # # YL 4 R' 4 222 OOOOOOO * # 220 O% OOOOOO 1! LK # # 22 O% OO * # 220 OOOOO. Y L ## X L L ## O O 2C%. Y L O # 227. Y L 227 O O O O O O O. Y L 227

O O @ "O # 222 * O O O O O. Y L O # 222 7% O O @" O 2C%%. YL 222 * OOOOOO + 27 OOOOO + 227 OO + 227 OOOOOOO + 227 OOOOOO 1! L 0 0 OOOO 1! L 0 0 OOOOOOOO 1! L 0 0 OOO 1! L 0 0 OOOOOOOOOOO + 227 OOOOOOOO + 227 OOOOOOOO + 227 OOOOOO + 227 OOOOOOOO + 227 + 2C + 220 + 227 OOOOO + 227 + F7 # 3 H 227 OOOOOO + 220 OOOOOOO + 220 OOOOOOOOO + 27 OOOO + 27 OOOOOOOOOO + 2C OOOOOOOOO + 227 OOOOOOOO + F0 ## H 227 OOO + 227 OOOOOOOOO + F0 ## H 222 OOOOOOOOOO + 227 + 227 OOOOOO + 227 + 227 OOOOOOOO + 227 OOO + 227 O + 20 OOOO + 20 OOOO + 2C OOOOOO + 2C OOO + 2C OOOOOOOOOO + F0 ## H 227 OO + 227 OOOOOO + 227 OOOOOOO + 27 OOOOO + 220 OOOO + 2C OOOOO + 227 OOOO + 227 OOOOO + 227 OOO + 227 OOOO + 227 + 227% + 227 OOOOOOOOO + 227 OOOOO + 227 O + 227 OOOOO + 227 OOOOOO + 227 O + 227 OO + 227 OOO + 227 OOOOOO + 220 OOO + 227 OOO + 227

OOOOOOOO + 227 OOOOOOOO + 220 OOOOO + 220 OOOOOOO + 220 OOOOOOOOOO + 227 OOOOOO + 227 OOOOOOO + 227 OOOOO + 227 OOOOOO + 220 OOOOOOOOOO + 227 OOOOOOO + 227 OOOOOO + 227 OOOO + 227 OOOOOO + 220 OOOOOOO + 227 OOOOOOO + 227 OOOO 227 OO + 220 OOOOOOOOOO + 220 OOOO + 227 + 227 OOOOO + 227 OOOO + 227 OOOOO + 220 OOOOOOO + 227 OOOOOO + 220 OOOO + 227 OOO + 2C OOOOO + 227 OOO + 227 OOOOOOOOO + 227 OOOOOOOOO + 227 OOOOOOOOO + 227 OOOOOOO + 227 OOOOOO + 227 OOOOOOOO + 227 OOOOOO + 20 OOOOO + 227 OOOOO + 2C OOOOOOOOOOO + 227 OOOOOO + 227 OOOO + 227 OOOO + 20 OOOOOOOOO + 20 + 27 OOOOOO + 2C OOOOOO + 227 OOOO + 227 OOOOO + 227 OOOOOOOOO + + 227 OOOOOOOOOO + 227 OOOOOOOOO + 227 OOOOOOOO + 227 OOOO + 227 OOOOOOO + 2C OOOOOO O O O + 227 O O O O O O + 227 O O O O O O O O O O + 227 O O O + 2C O O O O O + 27 O O O O O O + 2C O O O O O O O O + 227 O O O O O O O O O O + 227 O O O O O O O + 227 + 20 O O O + 227

OOOOOOO + 227 OOOOOOO + 227 OOOOOOOO + 227 OOOOO + 227 OOOOOOOO + 20 OOOOOO + 20 OOOOO + 227 OOOOOO + 227 OOOOOOOOOOO + 222 O + 27 OOOOOOOO + 220 OOOOOOOO + 227 + 222 O + 227 + 220 + 227 + 227 + 220 + 222 OOOO + 222 OOOOOOOOO + 27 OOOOOOOO + 227 OOOOOO + 227 OOOOOOO + 227 OOOOOOOOO + 227 OOOO + 2C OOOOOOOOOO + 220 OOOOOOOOOO + 227 OOOOOOOOOOO + 227 OO + 227 OOOOOOO + 227 OOOOO + 227 OOOOOOOO + 27 OOOOOO + 227 OOO + 227 OOOOOOOO + 227 OOOOOOOOO + 27 OOOOO + 220 OOOOOOOOO + 2C OOOOOOO + 227 OOOOOO + 227 OOOOOOOO + 227 OOOOOOOO + 227 OOOO + 220 OOOOOOOOOOO + 220 OOOOOOOO + 227 OOOOOOOOOO + 227 OOOOOOOOOO + 227 OOOOOOOOOO + 227 OOOOOOOOOO + 227 OOO OO + 227 OOOOOOOO + 227 OOO O O O O O + 227 O O O O O O + 227 O O O O O O O O O + 227 O O O O O O O + 227 O O O O O O O O O O O + 227 O O O O O O O O O O + 227 O O O O O O O O O O O O + 227 + 227 O O O O O O O + + O

+ 27 + 227 OOOOOOOOOOO + 227 OOOOOO + 227 OOOOOOO + 227 OOOOOO + 227 OOOOOOOO + 227 OOOOOOOOO + 220 OOOOO + 220 OOOOOOOOOO + 220 OOOOOOOO + 227 OOOOO + 227 OOOOOO + 227 OOOO + 227 OOOOOOOOO + 27 OOOOOOO + 227 OOOO + 227 OOO + 227 OOOOOOOOOO + 227 OOOOOO + 227 OOOOOOOOO + 227 OOOOOOOOOO + 227 OOOOOOOOOO + 27 + 227 OOO + 227 OOOOOOOOO + 227 OOOOOOOOOO + 227 OOOOOOOOOOO + 227 OOOOOOOOO + 227 OOOOOOOOO + 227 OOOOOOOO + 227 OOOOOOOO + 2C + 227 OO + 2C OOOOO + 227 OOOOOOOOO + 227 OOOOOOOOO + 27 OO + 227 OOOOOOO + - # # O # SL 227

LOOOOOO + 227 OOO + 227 OOOOOOOOO + 227 OO + 227 OOOOOO + 222 OOOOOOO + 227 OOOOOOO + 227 OOOOOOOOOO + 20 OOOOOOOOOO + 227 OOOOOOO + 227 OOOOOOO + 227 O + 227 OOOOOOOO + 227 OOOOOOOOO + 227 OOO +OOOOOO + 227 OOO + OOO 227 OOOOOOOOOOO + 227 OOOOOOOOOO + 227 OOOOO + 227 OOOOOOOOOO + 227 OOOOOOOOO + 20 OOOOOO + 2C OOOOOO + 227

OO + 227 OOOO + 227 OOO + 27 OOOOOOOO + 227 OOO + 27 OOOOOOOOO + 227 OOOOOOO + 27 OOOOOOOOOOO + 227 OOOOOOOOO + 2C OOOOOOO + 227 OOOOOO + 227 + 227 OOOOOOOOO + 227 OOOOOOOOO + 227 OOOOOOO + 227 OOOOOO + 2C OO + 227 OOOOOOO + 227 + 20 OOOOOOO + 222 OOOOOOOO + 227 OOOOOOOOO + 227 OOO + 227 OOOOO + 227 OOO + 227 O + 220 OOO + 227 OOOOO + 227 OOOOOOOOO + 227 OOOOOOO + 227 OOOOOOOOOOO + 27 O + 227 OOOOOO + 220 OOOOOOO + 227 OOOOOOOO + 227 OOOOO + 227 OOOO + 20 OOOOOOOOO + 27 OOOOOOOO + 227 OOOOOOOOOO + 227 + 227 OOOOOOOOO + 0 OOOOO + 227 OOOOOOO + 227 + 27 + 2C + 227 OOOOOO + 227 OOOOOOOO + 227 + 2C OOOOO + 27 OOOO + 227 OOOOOO + 227 OOOO + 227 OOOOOO + 227 + 227 OOOOOOO + 227 OOOOOOOO + 2C + 227 OOOOOOOOOOO + 220 OOOOOO O O O O O + 227 O O O O O O O O + 227 + 227 O O O O O O O + 22 O O O O O O O O + 27 O O O O O O O O O O + 227

OOOOOOO + 227 + # 03A 6 227 OOOOOOO + # 03A 6 227 O + # 03A 6 227 O + # 03A 6 227 OO + F0 ## H SF ## H 6 227 OOOO + # 03A 6 227 O% O + # 03A 6 227 + # 03A 6 227 OOOOOO + # 03A 6 227 OOOOOO + # 03A 6 220 OOOOO + # 03A 6 227 OOOOOOOO + # 03A 6 227 OOOOOOOO + # 03A 6 227 OOOOOOOOOOO + # 03A 6 227 OOOOOOOOOOO + # 03A 6 227 OOOOOOO + # 03A 6 227 OOOOOOOO + # 03A 6 227 + # 03A 6 227 OOOOOOOOOO + # 03A 6 227 OOOOOOO + # 03A 6 227 + # 03A 6 227 OOO + # 03A 6 227 OOOOOOOOOO 0 Q 4 C # L # # 20 OOOOOOOOOOOOO 0 Q 4 C # L # # 27 OOOOOOOO 0 Q 4 C # L # # 27 OOOOOOO 0 Q &## OC # L # # 27 OOOOOOOOO 0 Q 4 C # L # # 27 OOOOOOOOOOOB #. # * # / 0! Q 0 O O O O O O O O O O 0 Q 4 C # L # # 20 O O O O O O O 0 Q 4 C # L # # 20 O O O O O O O O O B #. # * # / 0! Q O O O O O O O O O O 0 Q 4 C # L # # 20 O O O O O O O O O O 0 Q 4 C # L # # 20 O O O O O O O O O O O B #. # * # / 0! Q 0 O O O O O O O O O O. # 0 0 O O O O O O O O O O O. # 0 0 OOOOO @ "0 2C 6 OOOOOOOOO @" * 2C 6 O @ "3X L 20 6 OOOOO @" 0 20 6 OOOOOOO @ "* # * ## * 20 6 OOOOOOO% @" 0 K 20 6 OOOOOOO% OO . # 0 0 O O O O O O O O O 0 Q 27 6 O O O O O O%. # 0 0 O O O O O O O O O 0 Q 27 6% O O 0 Q 27 6 O O O O O O O O O] C # L # #. 0 20 6 O O. # 0 0 OO% OOO @ "0 * 2 * 6] C # L # #. 0 222 * 6 OOOOOOO @" 0 0 OOOOO @ "# - 0 20 6% OOOO% O @" # # * N # # 220 6 OOOOOO] C # L # #. 0 20 6 O% @ ". # 20 6 OOOOOO @" # - 0 222 * 6 OOO * # @ "2227 6 OOO @" 0 0 OO 0 # @ "2227 6 * @" 2 * 0 @ "20 6 OO @ "* # * ## * 2227 6 OOOOO 0 @" 2 * 6 OOOOOOOOO. # 0 0

O O O O O O O% O O. # 0 0 O O O O. # 0 0 O O O O O O. # 0 0 O O O 0 Q C # L # # 27 6 O O O O O O O O O O O. # 0 0 O O O O O O O O]. # 0 C # L # # 222 * 6 OOOOOOO 0 QC # L # # 27 6 OOOOOOOOOO 0 Q 20 OOOOOOOOO 0 Q 22 OOOOOOOOOO 0 "20 OOOOOOOOOO 0 Q ..3% 0 Q 20% OO] - 0 2 * OOOOOOOOOOO 0 Q 27 OOOOOOOOOO 0 Q 20 OO] 2C7 6 OOOOOOOOO. # 0 0 OOOOOOOOOOO 0 Q 222 * OOOOOOOOOO 0 Q 27 OOOOOOOOOOO 0 Q 222 * OOOOOOO 0 Q 27 OOOOOOOO Q 27 OOOOOOOOOO 0 Q 222 * 6 OOOOOOOOOO. # 0 0 OOOOOOO 0 Q 27 OOOOOOOO @ "#" 2227 OOO] 20 OOOOOOOOOOO. # 0 0 OOOOOOOOO 0 Q 222 * OOOOOOOOOOO 0 Q 20 OOOOOOOOO] 0 0 OOOOOOOOO. # 0 0 OOOOOOOO. # 0 0 OOOOOOOOO] 0 0 OOOOOOOOO 0 0 OO% OOO %] 0 0 OOOO @ "0 0 OOOOOOOOOO] 0 0 OOOOOOOO] 0 0 O] 0 0 OOOOOOOOOO] 0 0 OOOOOOOOOOO. # 0 0 O O O O O O O O. # 0 0 OOOOO 7 #L "2220 6 OOOOO 0" C # L # # 2C 6 OOO 0 "# 27 6 OOOOOOOOO% * @" 20 6 O * # "2220 6 OOOOOOOO * #" 20 6 O% 0 "L "AS $ L_ 2220 6 - N" 20 6 OOOOOOOOOO * # @ "27 6 OOOOOOOOO 0" @ "20 6 OOOOOOOO @" N # # # 20 6 OO% @ "20 6 O @" 2220 6 O% @ "N # # # 20 6 @ "20 6 OOOOOOOO 0 QC # L # # 220 6 OOOOOOOOO] 20 6 OOOOOOOOO. # 0 0. # 0 0 OOOOOOO 0 QC # L # # 27 6 OO. # 0 0 OOOOOOOOO 0 QC # L # # 27 6 OOOOOOOOOO] 2 * 6 OOOOOOOO 0 QC # L # # 27 6

O O O O O O O O O O. # 0 0 OOOOOOOOOO 0 QC # L # # 20 6 OOOOOO @ "- 222 * O% @" - 222 * OOOOO @ "- 2C O%%% @" - 222 * O @ "- 222 * O% O @" - 222 * OOOOO @ "- 222 * @" - 227 OO @ "- 222 * OO% @" - 222 * OOOOOOO @ "- 222 * O% O @" - 2C @ "- 2C%% OO @" - 222 * O @ "- 222 * OOOOO @" - 2C OO @ "- 222 * OOOOOOOOOOO. # 0 0 OOOOOOOOO. # 0 0 OOOOOO. # 0 0 OO @" - 222 * OO @ "- 222 * OOOOOOOOOO] N # # # U! 2C7 OOOOO. # 0 0 OOOOOOOOOO. # 0 0 O] 2C7 2C7 OOO @ "- 222 * OOOOOOOOOO. # 0 0 O O. # 0 0 O O O O O O O O. # 0 0 OOOOOOOOOO @ "- 222 * OOO] - 2C7 OOOOOOOOOO. # 0 0 OOOOO% OO @" - 2C OOO @ "- 2C OOOOO @" - 222 * OOOO @ "- 2C O. # 0 0 OOOOO] - 222 * OOOOO @ "- F $ N # # 2C OOOOOOOO. # 0 0 O O O O O O @ "- 222 * @" - 222 * O O% @ "# - * 2 * 6 O O O O O @". # 2227 6 O O O O O O O O O. # 0 0 O O O O O O O O O O 0 Q C # L # # 2 * 6 O O O O O O O O O. # 0 0 O O O O O O O O O O O 0 Q C # L # # 27 6 O O O O O O O O 0 0 C C # L # # 20 6 O O O O O O] * O F &# L & &# &#L H 2 * 6 O O O O O O O O. # 0 0 O O O O O O O O O O. # 0 0 O O O O O O O O 0 Q 2C 6 O O O O O O O O O 0 O C O L O # O 6 O O O O O O O O O O 0 Q C # L # # 27 6 O O O O O O O O O O O. # 0 0 O O O O O O O O O O O 0 Q C # L # # 0 6 O O O O O O 0 Q C # L # # 20 6 O O O O O O O O O O. # 0 0 O O O @ "- O # 22 * 6 O O O O O O O O O O O. # 0 0 O O O O O. # 0 0 O O O O O O O 0 O # Q 22 6 O O O O O O 0 Q 20 6

O O O O. # 0 0 O O O O O O O O O O O @ "L # ## 227 6 O O O @" 222 * 6 O O O O O O @ "- O # 222 * 6%% O @" 222 * 6 O O O O O O O. # 0 0 O O 0 Q 20 6 O O O O O O 0 O # Q 222 * 6 O O O O O O O O O O. # 0 0 O O O O O O O O 0 O # Q 20 O O O O O O O O O O O - O # O "222 * 6 O O O O O O O O O O O O O O O O O O O O O O O" "7? 20 O O O O O 0 Q 20 6 O O O O @ "- O # 2 * 6 O O O O O O O O O O 0 Q 20 6. # 0 0 O O O O O O O O 0 Q 27 6 O O O O O O O O O; U @". 3 @ &. = 2 @ 23. ? 6 2 * F H U 20 F H O O; U @ "-X)") N 2C0 OO @ "0 0 O] B6 -. = @ - @ TLL # # 20 OOOOO] B6 -. = @ - @ 2C7 OO @" 0 0 OOOOOOOOO @ "0 0 OOOOOOOOO @ @ "0 0 OOO @" 0 0 OOOOOOOOO @ "0 0 OOOOOOOOO 0 Q -9 # 227 6 OOOOOOOOO 0 QC # L # # 27 6 OOOO] ## X 0 2 * 6 OOOOOOOOOO. # 0 0 O O O O O O O O O. # 0 0 O O O% O. # 0 0 O O O O O O O O O O O. # 0 0 O O O O O O O O O O O. # 0 0 OOOOOOOOOOO] 22 6 OOOOOOO 0 Q 27 6 OOOOOOOOO @ "0 0 O 0 Q 20 6 OOOO. # 0 0 OOOOOOOOOOO 0 Q 20 6 OOOO @" 0 0 OOOOOOOOOOO 0 Q 20 6 OOOOOOO @ "0 0 OOO @" 0 0 O% OOOOOOO] - O # 20 OOO] 0 2220 6 OOOO @ "0 0 OOOOOOOOOO]. 20 6 OOOOOOOOOO]. 27 6 OOOOOOOOOO]. 20 6 OOOOOOOOOO]. 27 6 OOOOOOOOOOO]. 3 @ &. = 2 @ 23.? 6 20 OOOOOOOOOOO]. 3 @ &. = 2 @ 23.? 6 20 OOOOOO]. 3 @ &. = 2 @ 23. B6 20 OOOOOOOOO]. 3 @ &. = 2 @ 23. 6 = 0; 27Z OOOOOOOO] B6 -. = @ - @ 222 * OOOOOOOO]. 0 C # L # # 20 6 OOOOOOOO]. 0 C # L # # 2220 6 OOOOOOO]. 0 C # L # # 20 6 OOOOOOO]. 0 C # L # # 20 6 OOOOOOO]. 0 C # L # # 2 6 OO% O @ "0 0 OO. # 0 0% O @ "0 0% @" 0 0

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

O O O O O O O O]. 3 @ &. = 2 @ 23. B6 222 * O O O O] B6 -. = @ - @ 222 * O O O O O O]. 3 @ &. = 2 @ 23. 6 = 0; 222 * O O O O O O]. 3 @ &. = 2 @ 23. ? 6 222 * O O O O O O O O @ "0 0 O O O O O O O O O O O]. 3 @ &. = 2 @ 23.? 6 20 O O O O O]. 3 @ &. = 2 @ 23.? 6 20 @" 0 0]. 3 @ &. = 2 @ 23. 6 = 0; 22 O O N] G 22 L O # # F H 222 * # #F O <[B6- * H O O O O O O O O]. 3 @ &. = 2 @ 23. ? 6 20 O O O O O O O O O O]. 3 @ &. = 2 @ 23. 6 = 0; 20 OOOO @ "0 0 OOOOOOOOOO]. 3 @ &. = 2 @ 23.? 6 20 OOOOO. # 0 0 OOOOOOOOO]. 3 @ &. = 2 @ 23.? 6 27 OOO]. 3 @ &. = 2 @ 23.? 6 20 OOO%] B6 -. = @ - @ 222 * OOOOOOOOO]. 3 @ &. = 2 @ 23.? 6 222 * OOOOOOOOOOO]. 3 @ &. = 2 @ 23 B6 20 OOOOOOOOO]. 3 @ &. = 2 @ 23.? 6 2220 OOOOOO. # 0 0 OOOOOOOOOO. # 0 0 OOOOOOOOOO B6 2 321. =. * 203 @] 2220 OOOO. # 0 0 OOOOOOOOO @ "$ # #")% 0 OOOOOOOOOO ] * 2 * 6 OOO] B6 22% O% OO @ "0 0 OO @" B6 2C0 OOO B6] 27 OOOO] B6 27 OOOOOOOO. # 0 0 OOOOO @ "0 0 OOOOOOOOO] '4 20 6 OOOOOOOO @" 0 0 O @ "0 0 OOOOOOOOO. # 0 0 O O O. # 0 0 O O O O @ "" 0 0 O O O O O O O O O O O. # 0 0% @ "0 0 O O O O O O O O O. # 0 0 O O O O O O O O O. # 0 0 O O O O O O O O O. # 0 0 O O O O O O O O O O. # 0 0 OOOOOO @ "0. # C # L # # 220 6% O @" 0 0 OOOOOOOOOO @ "0 0 OOO @" 0 0 OOOOOOO @ "& / 6 * ## O - / - # 2220 6 OOO @ "0 0 OOOOOOOOOO @" 0 0 OO @ "0 0 OOOO @" 0 0 OOOOO @ "0 0 OOOOOOO @" 0 0 OOOOOOOO @ "0 0 OOOOOOOOOO @" 6 # * ## 20 6 OOOOOOOOO. # 0 0 OOOOOOOOOO] . 3 @ &. = 2 @ 23.? 6 20 OOOOOOOOO. # 0 0 OOOOOOOOO]. 3 @ &. = 2 @ 23.? 6 20 OOOOOOO]. 3 @ &. = 2 @ 23.? 6 20

O O O O O O O O O. # 0 0 O O O O O O O O. # 0 0 O O O O O O O O O. # 0 0 O] B6 -. = @ - @ 2C0 O @ "0 0 O O O O O O O O. # 0 0 O O O% O @" 0 0 O. # 0 0 O O O O O O O O O O. # 0 0 O. # 0 0 O O O O O O]. 3 @ &. = 2 @ 23. ? 6 20 O O O O O O O O O. # 0 0 O O O O O O O O O O. # 0 0 O O O O O O O O O. # 0 0 O O O O O O O. # 0 0 O O O O O O O O O O]. 3 @ &. = 2 @ 23. ? 6 20 O O O O O O O O O L O O O O O O O @ "B6 2220 O O O O O O O O O O] * 27 O O O O @" 222 * O O O O O O O O] * 20 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 20 OOOO B6] 27 / "#L)) B6 OO] B6 ## $ Q 222 * OOOOOOOOOOO] * 20 OOOO @" B6 "#" # (222 * OOOOOOOO] B6 222 * OOOOOOOOO] * 20 OOOOOOOOOOO] * 20 OOOOOOOOOOO] * 20 OOOOOOOOOOO] * 20 OOOOOOOOOOO] @ "0 0 OOOO @" 0 0 OOOOOOOOOO $ # @ "") 20 O% O @ "0 0 OOO. # 0 0 OOOOOO @" 0 0 OO @ "0 0 OOOOOOOOOO $ # @" 0 @ "B6- * - 2 * 222 OOOOOOOOO @ "0 0 OOOO @" 0 0%% O @ "0 0 OOOOOOOOO @" $ / LK 9 2220 OO% O @ "0 0 OOOOOOOO @" 0 0 @ "0 0 OOOOOOOOOO. # 0 0 O O O O O O O O. # 0 0 @ "0 0 O O O O O O O O. # 0 0 O O O O O O O O. # 0 0 O O O O O O O O O O] # FL 22H 20 O O O O O O O O O O O # 0 0 O O O O O O" "0 0 O O O O @" 0 0 O O O O O O O O O O O O O O O O # 0 0. # 0 0 O O O O O O O O. # 0 0 O O O O O O O O O O "" 0 0 O O O O O O O O O O O. # 0 0 O O O O 0 O "]

) &Quot; C2 " M? C2 * @.-D;. = 3 @ $ $ @ # # # O O O O O O O O O O O. # 0 0 O @ "0 0% OO. # 0 0 OOOOOOOOOO. # 0 0 OOOOOOOOOOO. # 0 0 OOOOOOOOOOO. # 0 0 OOOOOOOOOO. # 0 0 OOOOOOOO. # 0 0 @" B6 $ ") 222 * OOO @" 0 0 OOOOO. # 0 0 OOOOOOOOOO @ "0 0 OOOOOO. # 0 0 OOOOOOOOO 0 # 0 0 OOOOOOO 0 # 0 0 OOOO 0 # 0 0 OOOOO 0 # 0 0 OOOOOOOOO 0 # 0 0 OOOOOOO 0 # 0 0 OOOOOOO 0 # 0 0 OOOOO 0 # 0 0 OOO 0 # 0 0 OOOOO 0 # 0 0 OOOOOOOO 0 # 0 0 OOOOOOOO 0 # 0 0 OOOOOOO 0 # 0 0 OOOOOOO 0 # 0 0 OOOOOOOO 0 # 0 0 OOOOOOO 0 # 0 0 OOOOOO 0 # 0 0 OOOOOOOO 0 # 0 0 OOOOOOO 0 # 0 0 OOOOOOOO 0 # 0 0 OOOOOO 0 # 0 0 OOO 0 # 0 0 OOOOOOOOO 0 # 0 0 OOOOOO 0 # 0 0 OOOOOO 0 # 0 0 OOOOOOOOO 0 # 0 0 OOOOO 0 # 0 0 OOOOO 0 # 0 0 OOOOOOOO 0 # 0 0 OOO 0 # 0 0 OOOOO 0 # 0 0 OOOOO 0 # 0 0

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O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O% O O% O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O% O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O% O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O% O% O% O O O% O O% O O O O O O O O O O O O O O% O O O O O O O O O O O O O OOOOOOOOOOOOOOOOOOOOO OOOOOOOO% OOOOOOOOOO% O% OO% OO% OOOOOO% OOOOO% OOOOOOOOOOOOOOOOOOO% OOOOOO% O% OOOOOOOOOOO% OOO% OO% OOOOOOOOOOOOOO% OO% OOOO% OO% OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO % OOOOOOOOOOOOOOOOOO% OOOOOO% OOOOO% OOOOOOOOOOOOOOO% OOOOOOOOOOOOOOOOOOOOO OOOOOOO% O O O O O O O O O O O O O O O O O O O O O O O O O O O% O O O% O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O% O O O

OOOOOO ^ 02 ^ 02 ^ 02 ^ 02 ^ 02 ^ 02 ^ 02 ^ 02 ^ 02 ^ 02 ^ 02 ^ 02 ^ 02 ^ 02 ^ 02 ^ 02 ^ 02 ^ 02 ^ 02 ^ 02 * + OOOOOOOOOOOO + OOOOOOOOOOOOOOOO + OOOOOOOOOOOOO + OOOOOOOOOOOO + OOOOOOOOOOOO + OOOOOOOOOOOOOOO + OOOOOOOOOOOOOOO + OOOOOOOOOOOOOOOO + OOOOOOOOOOOOO + OOOOOOO% OOOOOOOO + OOOOOOOOOOO + OOOOOOOOO% OOOO + OOOOOOOOOOOOOO + OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO

OOOOO% OOOOOO +%%%% + OoOoOoOoOoOoOoO + oooooooooooooooo + OOOOOOOOOOOOOOOOOOO + OoOoOoOoOoOoOoO + OOOOOOO + OOOOOOOOO + ooooooooooooo + ooooooooOOo + ooooooooOOo + OoOoOoOoOoOoOoO + OOOOOOO + oooooooooooooooo + OOOOOOOO + oooooooooooooo + OOOOOOOOOOOOOOOOOOO + OOOOOOO + ooooooooOOo + ooooooooooooo + oooooooooooooo + OOOOOOOOOO + OOOOOOOOOOOOOOOOOOO + OOOOOOOOOOOO% OOOO + OOOOOOOOOOOOO + OOOOOOOOOO + OOOOOOOOOOO + OOOOOOOOOOOO + OOOOOOOOOOOOOOO + OOO% OOOOOOOOOOO + OOOOOOO% OOOOOOOOO + OOOOOOOOOOOOO + OOOOOOOOOO + OOOOOOOOOOO + OOOOOOOOO + OOO% Ooooooooooooo + OOOOOOOOOO + OOOOOO% OOOOOOOO + OOOOOOO + ooooooooooooo + OOOOOOOOOO + OoOoOoOoOoOoOoO + O% O% OOOOOOOOOO + ooooooooooooo + OoOoOoOoOoOoOoO + oooooooooooooo + OoOoOoOoOoOoOoO + oooooooooooooo + OOOO% ooooooooooooo + OOOOOOOOO + OOOOOOOOO + oooooooooooooo + OoOoOoOoOoOoOoO 0 ooooooooooooo 0 oooooooooooooo 0 OOO% OOOOOOOOOOOO 0 OOOOOOOOOOOOOO 0 OOO% OOOOOOOOOO 0 OOOOOOOOOOOOOO 0 OOOOO% OOOOOOOO 0 OOOOOOOOOOOOO 0 OOOOOOOOOOOOOOO 0 OOOOOOOOOOOOOOOOOOOOOOOOOOOOO OOOOOOOOOOOOOOOO OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO O% O O O O O O 0 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

Oooooooooooo 0 OOOOOOOOOOOOOOOOOOOO 0 ooooooooooooo 0 ooooooooooooo 0 oooooooooooooo 0 oooooooooooo% O% OO 0 oooooooooooooooo 0 OOOOOOOO% OOOOOOOOO 0 ooooooooOOo% OOOOOO 0 OoOoOoOoOoOoOoO 0 OOOOOOOOO% OOOOOOOO 0 OOO% OOOOOOOOOO% 0 ooooooooooooo 0 ooooooooOOo 0 ooooooooooooo 0 ooooooooOOo 0 oooooooooooooooo 0 ooooooooOOo 0 OOOO % OOOOOOOOOO 0 OOOOOOOOOO 0 OOOOOOOOOOOOOO 0 OOOOOOOOOOOOOOOO 0 OOOOOOOOOOOOOO 0 OOOOOOOOOOOOOOOO 0 OOOOOOOOOOOOOO OOO OOOOOOOOOOOOOOO OOOOOOOOOOOOOOOO OOO %OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO The% OOOOOOOO% OOOO 0 OOOOOOOOOOOOOOOOOO 0 oooooooooooooooo 0 OOOOOOOOOO 0 OoOoOoOoOoOoOoO 0 OoOoOoOoOoOoOoO 0 OoOoOoOoOoOoOoO 0 oooooooooooooo 0 oooooooooooooo 0 OOOO% OOOOOOO 0 oooooooooooo 0 oooooooooooooooo 0 OOOOO% OOOOOOOOO 0 oooooooooooooo 0 ooooooooooooo 0 OOOO% OOOOOOOOOO% OOO 0 OOOOOOOOOO 0 OOOOOOOOOOOOOOOOO 0 ooooooooooooo 0 OOOOOOOOOOOOOOO 0 OOOOOOOOOOOOOOO 0 OOOOOOOOOOOOOOO 0 OOOOOOOOOOOOOOOOO 0 OOOOOOOOOOOOOOO% + OOOOOOOOOOO + OOOOOOOOOOOOOOOO + OOOOOOOOOOO + OOOOOOOOOOO + OOO% OOOOOOOOOOOOO + OOOOOOOOOOOOOO + OOOOOOOOOOOOOO O O O O O + O O O O O O O O O O O O + O O O O O O O O O O O O O O + O O O O O O O O O O O + O O O O O O O O O O O O O + O O O O O O O O O O O O O O O O O + O O O O O O O O O O O O O O O + O O O O O O O O O O O O O O + O O O O O O O O O O O O O + O O O O O O O O O O O O O + O O O O O O O O O O O O O + O O O O O O O O O O O O O O O O + O O O O O O O O O O O + O O O O O O O O O O O O O O O O + O O O O O O O O O O O +

Ooooooooooooo + ooooooooooooo + OOOOOOO + OOOOOOOOO + OOOOOOOOOO + oooooooooooooooo + OoOoOoOoOoOoOoO + oooooooooooooo + OoOoOoOoOoOoOoO + oooooooooooooo + O + ooooooooooooo + ooooooooOOo + OoOoOoOoOoOoOoO + oooooooooooooo + OOOOOOOO + OOOOOOOO + ooooooooooooo + oooooooooooooo + OOOOOO% OOOOOOOOOO + ooooooooOOo% OOOOO + O% OOOOO% OOOOOOOOO + OOOOOOOOOOOOOOOO% + OOOOOOOOOOOOOOO + OOOOOOOOOOO + OOOOOOOOOOO + OOOOOOOOOOOO + OOOOOOOOOOOOO + OOOOOOOOO + OOOOOOOOO + OOOOOOOOOOOOOOOOO + OOOOOOOOOOO + OOOOOOOOOO + OOOOOOOOOOOOOOOOOOOOOOOOOOOO OOOOOOOO + oooooooooooooo + OOOOOOOOOO + oooooooooooo + OOOOOOOOOOOOOOOOOO + OOOOOOOOO + OOOOOOOO + oooooooooooooooo + ooooooooooooo + OOOOO% OOOOOOOO + ooooooooOOo + oooooooooooooooo + oooooooooooooo + oooooooooooo + oooooooooooooo + ooooooooooooo + ooooooooooooo + ooooooooooooo + ooooooooooooo + OOOOOOOOOO + oooooooooooooo + OOOOOOOOOO + ooooooooooooo + oooooooooooo + OOOOOOOOO + OOOOOOOOOOOOO + OOOOOOOO + OOOOOOOOOO + OOOOOOOOOOOOO + OOOOOOOOOOOOOO + OOOOOOOOOO + OOOOOOOOOOOOOOOOO + OOOOOOOO + OO +%% OOOO + OOO% OOOO% OOO + OOOOO% OOOOOOOOOOOO + OOOOOOOOOO + OOOOOOOOOO + O O O O O O O O + O O O O O O O O O O O O +

OOOOOOO% OOOO + OOOOOOOOOO + OOOOOOOOOO + OOOOOOOOOOOOOOO + OOOOOOOOOOOOOO + OOOOOOOOOOOO + OOOOOOOOOOO + OOOOOOOOOOOOO + OOOOOOOO + OOOOO% OOOOOOO + OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO OOOOOOOOOOOOOO + OOOOOOOOO + OO% OOOOOOOOOOOO + OOOOOOOOOO + OOOOO + OOOOOOOOOOOOOOO + OOOOOOOOOOOOO + OOOOOOOOOOOOO + OOOOOOOO + OOOOOOOOOO + OOO% O + OOOOOOOOOOOO + OOOOOOOOOOOOOOOOO + OOOOOOOOOOOOO + OOOOOOOOOOOOO OOOOOOO + OOOOOOOOOOOOO + OOOOOOOOOOOOOOO + OOOO% OOOOOOOOO + OOOOOOOOOOOO + OOOOOOOOOOOO + OOOOOOOOOOOOOOOOO + OO% OOOOOOOOO + OOOOOOOOOO OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO OOOOOOOOOOOOO + OOOOOOOOOOOOOO + OOOOOOOOO + OOOOOOOOOOOOOOOOO + OOOOOOOOOOOOOOO + OOOOOOOOOOOOOO + OOOOOOOOOOOOOO + OOOOOOOO + OOOOOOOOOOO + OOOOO + OOOOOOOOO% O + OOOOOOOOO + O% OO + OOOO% OO + OOOOOOOOOOO +OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO O O O% O O O O O O O O O O O + O O O O O O O O O O O O O O% O O +

Ooooooooooooo + OoOoOoOoOoOoOoO + oooooooooooooo + oooooooooooooo + oooooooooooooo + oooooooooooooo + OOOO% OOOOOOOOOO + oooooooooooooo + oooooooooooooo + O% OOOOOOO + OOOOOOO + OOOOOOOOOOOOOOOOO + OOOOOO% O% OO% OOOOOO + oooooooooooooo + OOOOOOOOOOOOOOOOO + OoOoOoOoOoOoOoO + OoOoOoOoOoOoOoO + oooooooooooo + ooooooooooooo + OOOOOOOOOO + OOOOOOOOOOOOOOOOOO + OOOOOOOOOOOOOOOO + OOOOOOOOOOOOOOOO + OOOOOOOOOOOO + OOOOOO% OOOOOOOOOO + OOOOOOOOOOOOO + OOOOOOOOO + OOOOOOOOOOOOOO + OOOOOOOOOOOOO + OOOOOOOOOOOOOOOOOO%% + %% + Oooooooooooo% OOOO + ooooooooOOo + oooooooooooo + OOOOOOO% OOOOOOOOO + oooooooooooo + OoOoOoOoOoOoOoO + OoOoOoOoOoOoOoO + ooooooooOOo% OOOOOO + oooooooooooo + OOOOOOOO% OOOOOOOOOO + ooooooooooooo + OOOOOOOOOOOOOOOOOO + OOOOOOOOOOOOOOOOO + ooooooooooooo + OOOOOOOOOO + ooooooooooooo + ooooooooOOo + oooooooooooooooo 0 oooooooooooo 0 The % OOOOOOOOOOOO 0 OOOO% OOOOOOOOOOOO% O 0 OOOOOOOOOOOO 0 OOOOOOOOOOOOOOO 0 OOOOOOOOOOOOOO 0 OOOOOOOOOO% OOOOOOO 0 OOOOOOOOOOOOOOOOO 0 OOOOOOOOOOOOOOOOO 0OOOOOOOOOOOOOOOO 0OOOOOOOOOOOOOOOOOOOO The 0 O O O O O O O O O 0 O O O O O O O O O O O O 0 O O O O O O O O O O O O O 0 O O O O O O O O O O O O 0 O O O O O O O O O O O O O O O 0 O O% O O O O O O O O O O O O 0 O O O O O O O O O O O O 0 O O O O O O O O O O O O O 0 O O O O O O O O O O O O 0 O O O O O O O O O O O O O O O O O O 0 O O O O O O O O O O O O O O O O 0 O O O O O O O O O O% O O O O O O O O 0 O O O O O O O O O O O O O O O 0

OOO% oooooooooooo 0 OOOOOOO% OOOOOO 0 oooooooooooooo% O 0 oooooooooooooooo 0 OOOOOOOOOOOOOOOOO 0 oooooooooooooooo 0 oooooooooooooo 0 OoOoOoOoOoOoOoO 0 ooooooooooooo 0 ooooooooooooo 0 OOOOOOOOOO% OOOO 0 OOOOO% OOOOO% OOOOO 0 OoOoOoOoOoOoOoO 0 OOOOOOOOOOOOOOOOO 0 oooooooooooooooo 0 OOOOOOOOOOOOOOOOO 0 OoOoOoOoOoOoOoO 0 ooooooooooooo 0 OoOoOoOoOoOoOoO 0 OOOOOOOOOOOOOOOOO% 0 OOOOOOOOOOOOOO 0 OOOOOOOOOOOOOO 0 OOOOOOOOOOOOO 0 OOOOOOO 0 OOOOOOOOOOOOOOO 0 OOOOOOOOOOOOOOOO 0 OOOOOOOOOOOOOO 0 OOOOOOOOO 0 OOOOOOOOOOOO 0 OOOOOOOOO 0 OOOOOOOOOOOOOOOO OOO OOOOOOOO 0 oooooooooooooooo 0 oooooooooooooooo 0 OOOOOOOOOOOOOOOOO 0 OOOOOOOOOOOOOOOOO 0 OoOoOoOoOoOoOoO 0 oooooooooooooo 0 oooooooooooooooo 0 OoOoOoOoOoOoOoO 0 OOOOO% ooooooooooooo 0 oooooooooooooo 0 OOOOOOOOOOOOOOOOO 0 oooooooooooo 0 oooooooooooooo 0 ooooooooooooo 0 oooooooooooooooo 0 OO% oooooooooooo 0 ooooooooOOo 0 oooooooooooooooo 0 OOOOOOOO% OOOOOOOO 0 oooooooooooooooo 0 ooooooooooooo 0 OOOOOOOOOOOOOOO 0 OOOOOOOOOOOOOOOO 0 OOOOOOOOOOOOOOO 0 OOOOOOOOOOOOOOOO 0 OOOOOOOOOOOOOO + OOOOOOOOOOO + OOOOOOOO + OOOOOOOOOOOOOOOOOO + OOOOOOOOOOOOOO + OOOOOOOOOOOOOOO + O%% % O% O% + O O O O O O O O O O O O O O + O O O O O O O O O O O O + O O O O O O O% O O O O O O O + O O% O O O O O O O O O O O O + O O O O O O O O O O O O O O O + O O O O O O O O O O O O + O O O O O O O O O O O + O O% O O O O O O O O O O O O O + O O O O O O O O O O O O O% O O O O + O O O O O O O O O O O O O + O O O O O O O O% O O O O O O O + O O O O O O O O O O O O O O O + O O O O O O O O O O O + O O O O O O O O O O O O O O O +

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OOOOOOOOOO * # # O% OOOOOOOOOOO% * # # OOOOOOOOOOOOOOOOO * # # OOOOOOOOOOOO% OOOOOO * # # OOOOOOOO * # # OOOOOOOOOOO * # # OOOOOOOOOOOOO * # # OOOOOOO * # # OOOOO% OOOOOOOOOOO * # # OOOOOOOOOOO *OO # # OOOOOOOOOOOO * # # OOOOOOOOOOOOOO * # # OOOOOOOO * # # OOOOOOOOOOOOOOOO * # # OOOOOOOOOO * # # OOOOOOOOOO * # # OOOOOOOOOOOOOO * # # OOOOOOOOOOOO * # # OOOOOOOOOOOOO * # # OOOOOOOOO% OOOOOOO * # OOOOOOO * % OOOOOOOOOOOOOOO * # # OOOOOOOOOOOOOO 7 OOOOO% OOOOOOOOOO 7 OOOOOOOOOOOOOOOOO 7 OOOOOOOOOOOO 7 OOOOOOOOOOOOOO 7 OOOOOOOOOOOOOO 7%% OO% OO @ "OOOOOOO +% OOO% %%% * # # OOOOOOOOOOOO * # # OOOO% OOOOOOOOO * # # OOOOOOOOOOOOOOO * # # OOOOOOOOOOOOOO * # # OOOOOOOO% OOOOOOOOOO * # # OOOOOOOOOOOOOO * # # OOOOOOOOOOOOOO @ "OOOOOOOOOOOOOOOOOO * # # OOOOO% OOOOOOOO @ "O% OOOOOOOOO% OO 7%%% O%% * # # O% OOOOOOOOOOOOOO * # # OOOOOOOOOOOOOO 7 OOOOO% OOOOOOO * # # OOOOOOOOOOO * # # OOOOOOOOOOOOOOOOOO * # # O% OOOOOOOOOOOO 7 OOOOOOOOOOOOOOOOO *OO #OOOOOOOOOOOO 7 OOOOOOOOOOOOOOO 7 OOOOOOOOOOOOOO 7 OOOOOOOOOOOOOO @ "OOOOOOOOOOOOOOOO * # # OOOOOOOOOOOOOO * # # OOOOOOOOOO% OO% OOOOO * # # OOOOOOOOOOOOOO 7 OOOOOOOOOOOO 7 OOOOOOOOOOOOO 7 OOOOOOOOOOOOO 7 OOOOOOOOOOOOO O O O * # # O O O O O O O O O O O O O O O O O O O O O O O O O O O O O * O # O O O O O O O O O O O O O O O + O O O O O O O O O O. Y L O O O O O O O O O O O O O. Y L%%%%%%%%%%%%%% + O% O O. Y L O O O O O O O O O O O O O. Y L O O O O O O O O O. Y L%%%%%%. Y L O O O O O O O O O. Y L%%%%%%%%%%%%. Y L O O O O O O O O. Y L O O O O O O O O O O O. Y L O O O O O O O O O O O O 0 O O O O O O O O O O O O O O O 0

OoOoOoOoOoOoOoO 0 ooooooooooooo 0 ooooooooooooo 0 OOO% OOOOOOOOOO 0 OoOoOoOoOoOoOoO 0 ooooooooOOo 0 oooooooooooooo 0 OOOOOOOOO 0 oooooooooooo% OOOOO 0 ooooooooOOo 0 oooooooooooo 0 oooooooooooooooo 0 oooooooooooo% OOO 0 ooooooooOOo 0 OOOOOOOOOOOOOOOOO 0 OOOO% oooooooooooo 0 OoOoOoOoOoOoOoO 0 oooooooooooooooo 0 OoOoOoOoOoOoOoO 0 ooooooooooooo 0 OOOOOO% OOOOOOOOOO 0 OOOOOOOOOOOOOOOOO 0 OOO% OOOOOOOO 0 OOOOOOOOOOOO 0 OOOOOOOOOOOOOOOOO 0 OOOO% OOOOOOOOO 0 OOOOOOOOOOOOOOO + OOOOOOOOOO + OOOOOOOOOOOOOO + OOOOOOOOOO% OOO + OOOOOOOOOOO + OOOOOOOOOOOOOOO + OOOOOOOOOOO OOOO + ooooooooooooo + oooooooooooooo + ooooooooOOo + oooooooooooooo + oooooooooooooo + oooooooooooooooo + oooooooooooooo + OOOOO% ooooooooOOo + oooooooooooooo + OoOoOoOoOoOoOoO + OOOOOOOO% OOOOOO + OOOOOO% OOOOO% OO + oooooooooooooo + OoOoOoOoOoOoOoO + ooooooooOOo + oooooooooooooooo + ooooooooooooo + OOO% OOOOOOOOOO + oooooooooooooo + OOOOOOOOOO + OOOOOOOOOOO + OOOOOOOOOOOO + OOOOOOOOOOO + OOOOOOOOOOOOOOO + OOOOOOOOOOOOO% OOO + OOOOOOOOOOOOOOO + OOOOOOOOOOOOO + OOOOOOOOOOOOOOO + OOOOOOOOOOOOOOO + OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO O O O O O O O O O O O O + O O O O O O O O O O O O O + O O O O O O O O O O + O O O O O O O O O O O + O O O O O O O O O O O O O O O O O O O + O O O O O O O O O O O O O O + O O O O O O O O O O O O O O O + O O O O O% O O O O O O O O O O + O O O O O O O O O O O O O O O O O + O O O O O O O O O O O O + O O% O O O O O O O% O + O O O O O O O O O O O O O O O O O O +

OoOoOoOoOoOoOoO + oooooooooooo% O + oooooooooooooo + oooooooooooo + oooooooooooooooo + oooooooooooo + OOO% oooooooooooo% O + OO% OoOoOoOoOoOoOoO + oooooooooooo + oooooooooooooo + OoOoOoOoOoOoOoO + ooooooooooooo + OOOOO% OOOOOOO + OOOOOOOOO + oooooooooooooooo + oooooooooooooo + oooooooooooooo + OOOOO% OOOOOOO + oooooooooooooooo + OOOOOOOOOOOOOOO + OO% OOOOOOOOOOOOO% O + OOOOOOOOOOOOOOO + OOOOOOOOOOO + OOOOOOOOOOOOO + OOOOOO% OOOOOOOOO + OOOOOOOOOOOOOOO + + OOOOOOOOO + OOOOOOOOOOOO OOOOOOOOOOOOOOO + OOOOOOOOOOOOOOOOOOOOOOOOOOO O + OOOOO% OOO% OOOOOOOOO + OOOOOOOOO + OOOOOOOOOOOOOO% OO + OOOOOOOOOOO + OOOOOOOOOOOOOO + OOOO% OOOOOOOOOOOO + OOOOOOOOOOOOOOO + OOOOOOOOOOOO% O + OO% OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO OOOOOOOOOOOOOO + OOOOOOOOOO + OOOOOOOOOOOOOOO + OOOOOOOOOOOOOOOO + OOOOOO% OOOOOOOOOOOO + OOOOO% OOOOOOOOO + OOOOO% OOOOOOOOOO + OOOOOOOOOOOOOOOO + OOOOOOOOOOOOOOOO + OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO O O O% O O O O O + O O O O O O O O O O O O O O O O O + O O O O O O O O O O O O O O O O + O O O% O O O O O O O O + O O O O O O O O O O O O O + O O O O O O O O O O O O O O O O O O + O O O O O O O O O O O O O O + O O O O O O O O O O O O O O O O + O O O O O O O O O O O O O + O O O O O O O% O% O O O O O O O + O O O O O O O O O O O O O O O O + O O O O O O O O O O O O O O O + O O O O O O O O O O O O O O O + O O O O O O O O O O O O O O O + O O O O O O O O O O O O O O +

O O O O O O O O O O O O O O O O O O O + O O O O O O O O O O O O O + O O O O O O O O O O O O O O O O + O O O O O O O O O O O O O O O + O O O O O O O O O% O% O O O O + O O O O O O O O O O O O O O + O O O O O O O O O O O O O O + O O O O O O O O O O O O O O O + O O O O O O O O O O O O + O O O% O O O O O O O O O + O O O O O O O O O O O% O O O O + O O O O O O O O O O O O O O O 7 O O O O O O O% O O O O O? "O O O O O O O O O O O?" O O O O O O O O O O O O O O O O O - K O O O O O O O O O O O O O O O O 7 O O O O O O O O O O O O O @ "O O O O O% O O O O O O O O 7 O O O O O O O O O O O O O O O O O O% 7 O O O O O O% O O O O O O O O O O. Y L O O O O O O O O O O O O O O O 7 O O O O O O O O - K O O O O O O O O O O O O O O O O O O 7 O O O O O O O O O O O O O O O% O O 7 O O O O O O O O O O O O O - K O O O O O O O O O O O O O O O O O - K O O O O O O O O O O O O O O O O - K O O O O O O O O O O O O O O O @"% O O O O% O O O @ "O O O O O O O O O O O O O O O O. Y L O O O O O O O O O O O O O O O O @" O O O O O O O O O O O O O O O O O O. YLOOOOOOOOOOOOOOOOOOO @ "OOOOOOOOOOOOOOOO. YLOOOOO% OO% OOOOOOOOOOOO 0 OOOOOOOOOOOOOOOO% O 0 OOOOOOOOOOOOO% OOOO 0 OOO% OO% OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO 0 OOOOO% O% OOOOOOOOOOO 0 OOOOOOOOOOOOOOOOOO 0 OO% O% OOOOOOOOOOOO 0 OOOOOOOOOOOOOOOOO 0 OOO% OOOOOOOOOOOOOOO 0 OOOOO% OOOOOOOOOOOOOOOO OOOOOOOOOOOOO 0 OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO OOOOOOO% OOO 0 O% OOOOOOOOOO% OOOO 0 OOOOOO% O% OOOOOOOOOOO 0 OOOOOOOOOOOOOOOOOO 0 OOOOOOOOOOOOOOOOOO 0 OOOOOOOOOOOOOOOOOOO 0 O% OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO OOOOOOOOOO 0 OOOOOOOOOOOO% OOOO 0 O% OOOOOOOOOOOOOOO% OO 0 OOOOOOOOOOO% OOOOO 0 OOOOOO% OOOOOOOOOOOOO% 0 OOO% OOO% OOOOOOOOO% OOOOO 0 OOOOOOO% OOOO 0 OOOO% OOOOOOO 0

OOOOOOOOOOOOOO% O 0 OO% OOOO 0 OOOOOOOOOOOOOOOOOO 0 OOOOOOO% 0 OOOOOOOOOO% OOOO 0 OOOOO% OOOOOOOOOOOOOO 0 OOOOOOOOOOOO 0 OOOOOOOOOOOOOOOOOO OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO % OOOOOOOOOOOO 0 OOOOOOOOOO 0 OO% 0 OOOOOOOO% 0 OOOOOOOOOOOOOOO 0 OOOOOOOOOOOOOOOOO 0 OOOOOOOOOOOOOOO 0 OOOOO% OOOOOOOOOOO 0 OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO OOOOOOOOOO 0 OOOOO 0 OOOOOOOOOO% OOOO 0 OOOOO% OOOOOOOOOOOO 0 OOOOOOOOOOOOOOOOO 0 OOO% OOOOOOOOOOOO OOOOOOOOOOOOOOOO 0 OOOOOO% OOOOOOOOOOOO OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO OOOOOOOOOOO% OOOO 0 OO% OOOOOOOOOOOOO 0 OOOOOOOOOOOOOOOOO 0 OO% OO% OOOOOOOOOOOOOOO 0 OOOOO% OOOOOOOO% O% OOOOOO 0 OOOOOOOOOOO% OOOOOOO 0 OOOOOOOOOOOOOOOO% 0 OOOOOO 0 OOOOOOOOOOOO 0 OOOOOOOOOO O 0 OOO% 0 OOOOOOOOOO 0 OOOOO 0 OOOOOO% OOO O O 0 O O O O O O O O O O O O O O O O O O 0 O O O O O O O O O O O O O O O O O 0 O O O O O% O O O O O O O O O O O O 0 O O O O O O O O O O O O O O O O O 0 O O O O O O O% O O O O O O O 0 O O O% O O O% O% O O O O O O O O O O 0 O O O O O O O O O O O O O O O O O O 0 O O O O O O% O% O O O O O O O O O O 0 O O O O O O% O O O O O O O O O O 0 O O O O O O O O% O O O O O O O O 0 O O O O O O O O O O O O O% O O 0 O O O O O O O O O O O O O O O O O 0 O O O O O O O O O O O O O O O O 0

Ooooooooooooo 0 OOOOOOOOOO 0 OOOOOOOOOOOOOOOOOO 0 ooooooooooooo 0 oooooooooooooo 0 OoOoOoOoOoOoOoO 0 OOOOOOOOOOOOOOOOO 0 The% oooooooooooooooo 0 oooooooooooooooo 0 oooooooooooooooo 0 OOOOOOOOOOOOOOOOOO 0 oooooooooooooooo 0 OOOOOO% OOOOOOO% 0 OoOoOoOoOoOoOoO 0 oooooooooooooo 0 oooooooooooooo 0 OoOoOoOoOoOoOoO 0 OOOOOOOOOOOOOOOOO 0 OOOOOOOOOOOOOOOOOO% O 0 oooooooooooooooo 0 OOOOO% oooooooooooooo 0 OOOOOOOOOOOOOOOOO 0 OOOOOOOOOOOOOOOO 0 O% OOOOOOOOOOOOOO 0 OOOOOO% OOOOOOOOOO 0 OOOOOOOOOOOOOOOOOO 0 OOOOOOOO% OOOOOOOO 0 OOOOOOOOOOOO% O% OOOOOO 0 OOOOO% O% OOOOOOOOOOOO 0 OOOOO% OOOOOOOOOOOO 0 OO% OOOOOOOOOOO% OOOOOO 0 OOOOO% O% OOOOOOOOOOO 0 OOOOOOOOOOOOOOOOOO 0 OOOOOOOOOOOOOOOOO 0 OOOOOO% OOOOOOOOOOOO OOOOOOOO OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO % OOOOOOOOOO 0 OOOOOO% OOOOOOO% OOOO 0 OOO% OOO% OOOOOOO% OOOOOO 0 OOOOOOOOOOOO 0 OOOOOOOOOOOOOO 0 OOOOOOOOOOOOOOOO OOOOOOOOOOOOOOOOO 0OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO OOOO 0 OOOOOOOOOOOOOOOO 0 OOOOOO% OOOOOOOOOOO 0 OOOOOOO% OOOOOOOO% OOO 0 OOOOOOOOOOO% OOOOO 0 O% OOOOOOOOOOOOOOO 0 O% OOOO% OOOOOOOOOOOO OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO % OOOOOOOOOOOO 0 OOOOOOOOOOOOOOOOOO 0 OOOOOOOOOOO% OOO 0 OOOOO% OOOOOOOO 0 OOOOO% O% OOOOOOOOO 0 OOOOOO% OOOOOOOOOO 0 OOOOO% OOOOOOO% OO 0 OOOOOOO% OOOOOOOO 0 OOOOOO% OOOOOOOO 0 OOOOOOOOOOOOOO 0 0

OOOOO% OOOOOOOOOO 0 oooooooooooooooo 0 OoOoOoOoOoOoOoO 0 oooooooooooooooo 0 ooooooooooooo 0 OOOOOOOOO 0 ooooooooOOo% OOOO 0 oooooooooooo 0 ooooooooooooo 0 oooooooooooo 0 oooooooooooooooo 0 OO% oooooooooooo 0 oooooooooooooooo 0 OOOOOOOOOOOOOOOOO 0 OOOOOOOOOOOOOOOOO 0 oooooooooooooo 0 oooooooooooooooo 0 OoOoOoOoOoOoOoO 0 oooooooooooooooo 0 OOOOOOOOOO% OOOOOOO 0 OOOOOOOOOOOOOOOOO 0 OOOOO% OO% OOOOOOOOOO 0 O% OOOO% OOOOOOOOO 0 O% OOOOOOOOOOOOOOO 0 O% OOOOO% OO% OOOOOOOOO 0 OOOOOOOOOOOO 0 OOOOOOOOOOOOOOO 0 OOOOOOOOOOOOOOOOOOO 0 OOOOOOOOOOOOOOOO 0 OOOO% OOOOOO% OOOOOOOOOO 0 oooooooooooooooo 0 OOOOO% ooooooooooooo 0 OOOOO% oooooooooooo 0 OOOOOOOOOOOOOOOOOO 0 The% OOOOOO% OOOOOOOOO 0 OO% oooooooooooooooo 0 OOOOOOOOOOOOOOOOO 0 OOOOOOOOOOOOOOOOO% OO 0 OOO% oooooooooooooooo 0 OOOOOO% ooooooooOOo 0 OOOOO% OOOOOOOOOO 0 OOOO% oooooooooooo 0 OOOO% OOOOOOOOO% O 0 O% OOOOOOOOOO% OOOOOO 0 OOOOOO% OOOOOOOOOOO 0 OO% O% OOO% OOOOOOOOOOOOO 0 OOOOOOOOOOOOOOOOO 0 OOOOOOOOOO% OOOOOOOO 0 O% OO% OOOOOOOOOOOOOOOO OOOOOOOOOOOOOOO OOOOOOOOOOOOOOOO 0 OOOOOOOOOOOOOOOOOOO 0 OOOOOO% OOOOO% OOOOOO 0 O% O O% OOOOO% OOOOOOOO 0 OOOOOOOOOOOOOOOOO 0 OOOOOOOOOOOOOO 0 OOOOOOOOOOOOOOO 0 O% O% OOOOOOOOOOOOOOOO 0 OOOOOOOOOOOOOOO 0 OO% OOOOOOOOOOOOOOO 0OOOO% OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO O% OOOOOOOOOOOO 0 OOOOOOOOOOOOOO 0 OOOOOOOOOOOOOOOO 0 OOOOOOOOOOOO 0 OOOOOOOOOOOOOO 0 OOOOOOOOOOO 0 OOOOOOO% OOOO% OOOOOO 0 OOOOOOOOO? "

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

0 0 0 A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- 0 0 0. ^% K%% 6 \ 0. 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ A = 0- 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^ % K%% * \ 0 0 0 17I, ^ ^ 0% K%% 6 \ 0 17I, 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ 6 A = 0- 0 0 0 ^ ^% K%% * \ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 17I, ^ ^% K%% * \ 17I, 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K%% 0 \ * 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ A = 0- 0 0 0 A = 0- ^ ^% K% \ * A = 0- 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- 0 0 0 6 0- ^% K%% 6 \ 0 6 0- 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 2A * 0 ^% K%% 6 \ 0 2A * 0 0 0 0 6 0- ^% K%% * \ 6 0- 0 0 0 ^ ^% K%% 0 \ 6 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- 0 0 0 6 0 - ^% K%% 6 \ 0 6 0- 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ A = 0- 0 0 0 ^ ^% K%% * \ 0 0 0 0 ^ ^% K% \ * 0 0 0 ^ 0% K%% 6 \ 0 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 ^ ^% K%% 0 \ 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 ^ ^% K%% 0 \ 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K%% * \ 0 0 0 A = 0- ^ ^% K%% * \ A = 0- 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 6 0- ^% K%% * \ 6 0- 0 0 0. ^% K%% 6 \ 0. 0 0 0 ^ ^% K%% * \ 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ A = 0- 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K%% * \ 0 0 0 0 ^ ^% K%% * \ 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ A = 0- 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ 6 A = 0- 0 0 0 ^ ^% K%% \ * 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ A = 0- 0 0 0 17I, ^ ^% K%% * \ 17I, 0 0 0 ^ 0% K%% 6 \ 0 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ 6 A = 0- 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ A = 0- 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ 6 A = 0- 0 0 0 ^ ^% K%% 0 \ 6 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 0 ^ 0% K%% * \ 0 0 0 A = 0- ^ ^% K%% * \ A = 0- 0 0 0 17I, ^ ^% K%% * \ 17I, 0 0 0 ^ ^% K% \ * 0 0 0 ^ ^% K%% 6 \ * 0 0 0 = 0 ^ ^% K%% 6 \ 0 = 0 0 0 0 0 ^ ^% K%% * \ 0 0 0 ^ 0% K%% * \ 0 0 0 ^% K%% 6 \ 0 0 0 0 = 0 ^ ^% K%% 6 \ 0 = 0 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 ^ ^% K%% 0 \ * 0 0 0 6 0- ^% K%% 6 \ 0 6 0- 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- 0 0 0 ^ ^% K%% * \ 0 0 0 0 ^ ^ % K%% 6 \ 0 0 0 0 6 0- ^% K%% 6 \ 0 6 0- 0 0 0 ^ 0% K%% * \ 0 0 0 ^ ^% K%% * \ 0 0 0 ^ 0% K%% * \ 0 0 0 ^ ^% K%% * \ 0 0 0 0 ^ ^% K%% * \ 0 0 0 0 ^ ^% K%% 0 \ 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ 6 A = 0- 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ 6 A = 0- 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ A = 0- 0 0 0 ^ 0% K%% * \ 0 0 0 * 3A 0 ^ ^% K%% * \ * 3A 0

0 0 0 2A * 0 ^ ^% K%% 6 \ 0 2A * 0 0 0 0 ^ ^% K%% * \ 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- 0 0 0 ^% K%% 6 \ 0 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ 6 A = 0- 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K%% 6 \ * 0 0 0 ^ ^% K%% 6 \ 0 0 0 ^ ^% K%% * \ 0 0 0 6 0- ^% K%% 6 \ 0 6 0- 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ A = 0- 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ A = 0- 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 ^ ^% K%% * \ 0 0 0 0 ^ ^% K%% * \ 0 0 0 0 ^ 0% K%% * \ 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K%% 0 \ * 0 0 0 ^ ^ % K%% 0 \ * 0 0 0 ^ 0% K%% * \ 0 0 0 ^ ^% K%% 6 \ 0 0 0 ^ ^% K%% * \ 0 0 0 0 ^ 0% K%% 0 \ * 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ A = 0- 0 0 0 6 0- ^% K%% * \ 6 0- 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K% \ * 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ A = 0- 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ A = 0- 0 0 0 0 2A * 0 ^ ^% K%% 0 \ 2A * 0 0 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 ^% K%% * \ 0 0 0 0 ^ ^% K%% * \ 0 0 0 0 6 0- ^% K%% 6 \ 0 6 0- 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K% \ * 0 0 0 ^ ^% K%% * \ 0 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- 0 0 0 * 3A 0 ^ ^% K%% 6 \ 0 * 3A 0 0 0 0 ^ ^% K%% * \ 0 0 0 ^ 0% K%% 6 \ * 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- 0 0 0 ^ % K%% 6 \ 0 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 ^ ^% K%% 0 \ * 0 0 0 ^ ^% K%% 0 \ 6 0 0 0 ^ ^% K %% 0 \ 6 0 0 0 ^ ^% K%% 0 \ 6 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ A = 0- 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 6 0- ^% K%% 6 \ 0 6 0- 0 0 0 6 0- ^% K%% 6 \ 0 6 0- 0 0 0 = 0 ^ ^% K%% * \ = 0 0 0 0 ^ ^% K%% * \ 0 0 0 A = 0- ^ ^ 0% K%% 6 \ 0 A = 0- 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 A = 0 - ^ ^ T ^ O% K%% * \ A = 0- 0 0 0 ^ 0% K%% 6 \ 0 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K% \ 0 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ 6 A = 0- 0 0 0 ^ ^% K%% * \ 0 0 0 0 17I , ^% K%% * \ 17I, 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 = 0- ^ ^% K% * \ = 0- 0 0 0 7 = 01 ^ ^% K% 0 \ 7 = 01 0 0 0 ^ ^% K% \ * 0 0 0 ^ 0% K%% * \

0 0 0 ^ 0% K%% * \ 0 0 0 ^ 0% K%% 6 \ 0 0 0 0 ^ ^% K%% 0 \ * 0 0 0 * 3A 0 ^ ^% K%% * \ * 3A 0 0 0 0 * 3A 0 ^ ^% K%% * \ * 3A 0 0 0 0 ^ ^% K% \ * 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ A = 0 - 0 0 0 ^ ^% K%% * \ 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ A = 0- 0 0 0 6 0- ^% K%% 6 \ 0 6 0 - 0 0 0 ^ 0% K%% * \ 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 ^ ^% K%% * \ 0 0 0 ^ 0% K%% * \ 0 0 0 17I , ^ ^ 0% K%% * \ 17I, 0 0 0 * 3A 0 ^ ^% K%% 6 \ 0 * 3A 0 0 0 0 * 3A 0 ^ ^% K%% 6 \ 0 * 3A 0 0 0 0 ^ ^% K%% 0 \ 0 0 0. ^% K%% 6 \ 0. 0 0 0. ^% K%% 6 \ 0. 0 0 0 ^% K%% 6 \ 0 0 0 0 ^ ^% K%% * \ 0 0 0 0 6 0- ^% K%% 6 \ 0 6 0- 0 0 0 ^% K%% 6 \ 0 0 0 0 ^ ^% K%% * \ 0 0 0 ^ 0% K%% * \ 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K%% * \ 0 0 0 6 0- ^% K %% 6 \ 0 6 0- 0 0 0 6 0- ^% K%% 6 \ 0 6 0- 0 0 0 ^ 0% K%% 6 \ 0 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ A = 0- 0 0 0 ^ ^% K%% * \ 6 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 17I, ^ ^% K%% 6 \ 0 17I, 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 ^ 0% K%% 6 \ 0 0 0 0 ^ ^% K%% 6 \ * 0 0 0 ^ ^% K%% 0 \ 6 0 0 0 ^ ^% K% \ * 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K%% * \ 0 0 0 ^% K%% * \ 0 0 0 0 ^ ^% K%% * \ 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ A = 0- 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 = 0 ^ ^% K%% * \ = 0 0 0 0 ^ ^ % K%% * \ 0 0 0 ^ ^% K%% 6 \ 0 0 0 0. ^ ^% K%% * \. 0 0 0 ^ ^% K%% * \ 0 0 0 0 ^ ^% K%% * \ 0 0 0 6 0- ^% K%% 6 \ 0 6 0- 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ A = 0- 0 0 0 = 0 ^ ^% K%% * \ = 0 0 0 0 6 0- ^% K%% 6 \ 0 6 0- 0 0 0 0 0% K%% 6 \ 0 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K% \ * 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 = 0 ^ ^% K%% * \ = 0 0 0 0 = 0 ^ ^% K%% * \ = 0 0 0 0 ^ ^% K% % 6 \ 0 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 6 0- ^% K%% 6 \ 0 6 0- 0 0 0 6 0 - ^% K%% 6 \ 0 6 0- 0 0 0 ^ ^% K% \ 0 0 0 0 0 0 0 0

0 0 0 17I, ^ ^% K%% * \ 17I, 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ A = 0- 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ A = 0- 0 0 0 ^ ^% K%% * \ 0 0 0. ^ ^% K%% * \ 6. 0 0 0 * 7 ^ ^% K%% 6 \ 0 * 7 0 0 0 ^% K%% 6 \ 0 0 0 0 * 3A 0 ^ ^% K%% 6 \ 0 * 3A 0 0 0 0 ^ ^ % K%% * \ 0 0 0. ^ ^% K%% * \ 6. 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 = 0 ^ ^% K%% * \ = 0 0 0 0 ^ ^% K% \ * 0 0 0 ^ ^% K %% * \ 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 ^ ^% K%% * \ 0 0 0 0 6 0- ^% K%% 6 \ 0 6 0- 0 0 0 ^ ^ % K%% * \ 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ A = 0- 0 0 0 ^ ^% K% \ * 0 0 0 6 0- ^% K%% 6 \ 0 6 0- 0 0 0 ^ ^% K%% * \ 0 0 0 0 ^ ^% K%% * \ 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ A = 0- 0 0 0 ^ ^% K%% * \ 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ A = 0- 0 0 0 2A * 0 ^ ^% K%% 6 \ 0 2A * 0 0 0 0 ^ ^% K%% * \ 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ A = 0- 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 ^ ^% K%% 6 \ * 0 0 0 17I, ^% K%% * \ 17I, 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 A = 0 - ^ ^ T ^ O% K%% * \ 0 A = 0- 0 0 0 = 0 ^ ^% K%% * \ = 0 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K %% 6 \ 0 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ A = 0- 0 0 0 ^ ^% K% \ * 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- 0 0 0 ^ ^% K%% * \ 0 0 0 17I, ^% K%% 6 \ 0 17I, 0 0 0 ^% K%% 6 \ 0 0 0 0 0 ^% K%% 6 \ 0 0 0 0 17I, ^ ^% K%% 6 \ 0 17I, 0 0 0 ^ ^% K % \ * 0 0 0 0 * ^ ^% K% * \ 0 * 0 0 0 = 0 ^ ^% K%% * \ = 0 0 0 0 ^ ^% K%% * \ 0 0 0 17I, ^ ^ % K%% * \ 17I, 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 ^ ^% K%% * \ 0 0 0 0 ^ ^% K% \ * 0 0 0 ^ ^% K%% * \ 0 0 0 17I, ^ ^% K%% * \ 17I, 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 ^% K%% 6 \ 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 ^ ^% K%% * \ 0 0 0 ^% K%% 6 \ 0 0 0 * 3A 0 ^ ^% K%% 6 \ 0 * 3A 0 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- 0 0 0 ^ ^% K%% 0 \ 0 0 0 ^ ^% K%% * \ 0 0 0 * 3A 0 ^ ^% K%% 6 \ 0 * 3A 0

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䯈 220 0 ^ 0 % K%% * \ 27 0 2A * 0 ^% K%% 6 \ 0 2A * 0 * N # # 27 0 ^ ^% K%% * \ 0 22 6 ^ ^% K%% 0 \ * 0 22 6 ^ ^% K%% * \ 0 20 6 ^ ^% K%% * \ 0 20 6 ^ ^% K%% * \ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 * K # # 0 22 0 ^ ^% K% \ * * K # # -9 # 22 0 = 0- ^ ^% K% * \ 0 = 0- * K # # # 22 0 ^ ^% K%% * \ 0 * K # # 0 0 ^ ^% K%% 6 \ 0 0 227 0 ^% K%% * \

* K # # $ # 22 0 ^ ^% K%% * \ 0 $ # O # 22 0 6 0- ^% K%% 6 \ 0 6 0-. # 6 O2 # 7/6 F62- H * 䯈 - # 27 0 ^ ^% K%% * \ 0 3 &# 3 # -L * 22 0 ^ ^% K% \ * 0 22 0 17I, ^ ^ 0 % K%% 6 \ 0 17I, * # L 2C 0 ^ ^% K%% 6 \ 0 0 22 0 ^ ^% K%% * \ 0 22 0. ^% K%% 6 \ 0. 0 # 2220 0 = 0 ^ ^% K%% 6 \ 0 = 0 0 22 0 ^ ^% K%% * \ 0 # 27 0 ^ ^% K%% 6 \ 0 0 N # # # -L 22 0 ^ ^% K%% 6 \ 0 * K # # N # # 2220 0 ^ ^% K%% * \ 0 * K # # $ # 2220 0 * 7 ^ ^% K% \ * * 7 * K # # # 2220 0 = 0 ^ ^% K%% * \ = 0 * K # # $ # 2220 0 * 7 ^ ^% K%% * \ * 7 * K # # $ # 22 0 ^ ^% K%% * \ 0 0 220 0 = 0 ^ ^% K%% * \ = 0 * K # # 22 0 ^ ^% K%% 6 \ 0 * K # # 僴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

* K # # - 僴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僴 22 0 ^ ^% K%% 6 \ 0 * K # # - 僴 22 0 ^ ^% K%% 0 \ * K # # $ # 22 0 * 7 ^ ^% K% \ 6 * 7 0 # 220 0 ^ ^% K%% 0 \ * * K # # 2220 0 ^ ^% K%% * \ $ # 220 0 17I, ^ ^% K%% * \ 17I, 0 220 0 ^ ^% K%% 0 \ 0 220 0 ^ ^% K% \ *

0 220 0 ^ ^% K%% * \ * K # # 0 22 0 17I, ^ ^ 0% K%% 6 \ 0 17I, * K # # 2220 0 ^ ^% K%% * \ 0 * K # # $ # 2220 0 A = 0- ^ ^ 0% K%% 6 \ 0 A = 0- * K # # 0 0 * 7 ^ ^% K% \ 6 * 7 0 0 0 ^ ^% K%% * \ 222 * 0 ^ ^% K% \ * 0 222 * 0 A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- 0 2227 0 A = 0- ^ ^% K%% 6 \ A = 0- 0 220 0 = 0 ^ ^% K%% * \ = 0 0 220 0 ^ ^% K%% * \ 0 2227 0 A = 0- ^ ^ T ^ O% K%% * \ A = 0- 0 2 0 ^ ^% K%% * \ 0 2C7 0 ^ ^% K%% * \ 0 2220 0 = 0 ^ ^% K%% * \ = 0 "O 222 * 0 ^ ^% K%% * \ 0 220 0 ^ ^% K%% 6 \ 0 0 2 0 = 0 ^ ^% K%% * \ = 0 0 22 * 0 A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- 227 0 = 0 ^ ^% K%% * \ = 0 "O # 2C 0 A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0-" O # 20 0 ^ ^% K%% 6 \ 0 0 227 0 A = 0- ^ ^ T ^ O% K%% * \ A = 0- * N # # 220 0 ^ ^% K%% * \ 0 * N # # 27 0. ^% K%% 6 \ 0. 20 0 ^ ^% K%% * \ 2C 0 ^ ^% K%% 6 \ 0 # 2C 0 17I, ^ ^% K%% * \ 17I, R * N # # 222 0 0 ^ ^% K%% 6 \ # 227 0 ^ ^% K%% * \ 0 # 222 0 ^ ^% K%% * \ # 220 0 = 0 ^ ^% K%% * \ = 0 0 227 0 ^ ^% K%% * \ 0 2C 0 ^ 0% K%% * \ * N # # 0 0 ^% K%% * \ 0 # 20 0 2A * 0 ^ ^% K% \ 6 2 A * 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ^ ^ % K%% 6 \ 0 0 0 0 ^% K%% * \ 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 17I, ^ ^% K%% * \ 17I, 0 0 0 A = 0 - ^ ^ T ^ O% K%% * \ A = 0- 0 0 0 ^ ^% K%% 6 \ * 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K%% * \ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 = 0 ^ ^% K%% 6 \ 0 = 0 0 0 0 ^ ^% K%% * \ 0 0 0 0 2A * 0 ^ ^% K%% 6 \ 2A * 0 0 0 0 17I, ^% K%% * \ 17I, 0 0 0 ^ ^% K%% * \ 0 0 0 ^ 0% K%% 6 \ 0 0 0 0 = 0 ^ ^% K%% 6 \ 0 = 0 0 0 0 17I, ^% K%% 6 \ 0 17I, 0 0 0 ^ 0% K%% * \ 0 0 0 = 0 ^ ^% K%% 6 \ 0 = 0 0 0 0 = 0 ^ ^% K%% * \ = 0 0 0 0 * 3A 0 ^ ^% K%% * \ * 3A 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ^ 0% K%% * \ 0 0 0 ^ ^% K%% * \

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

0 2227 0 ^ ^% K% \ * * N # # # -L 2227 0 ^ ^% K% \ * 0 2227 0 ^ ^% K%% 0 \ 6 0 2220 0 ^ ^% K%% * \ 0 0 3 '4 & 22 0 0. ^% K%% 6 \ 0. 0 22 0 0 ^ ^% K%% 6 \ 0 0 22 0 ^ ^% K%% * \ 0 # 2 0 ^ ^% K%% 6 \ 0 0 # 22 0 0 6 0- ^% K%% 6 \ 0 6 0- 222 7 0 7 = 01 ^ ^% K% 0 \ * 7 = 01 220 0 ^ ^% K%% 6 \ 0 0 222 7 0 A = 0- ^ ^ T ^ O% K% % * \ A = 0- # 220 0 ^ ^% K%% 6 \ 0 # 20 0 ^ ^% K%% 6 \ 0 "O # 222 * 0 ^ ^% K%% * \ 0 220 0 ^% K%% 6 \ 0 220 0 2A * 0 ^ ^% K%% 0 \ * 2A * 0 2 0 ^ ^% K%% * \ 2C0 0 ^% K%% 6 \ 0 220 0 A = 0- ^ ^ T ^ O% K%% * \ A = 0- # 220 0 ^ ^% K%% 6 \ 0 # 2C 0 A = 0- ^ ^ T ^ O% K%% * \ A = 0- # 227 0 ^ ^% K%% * \ 0 2220 0 ^ ^% K%% 6 \ 0 0 2220 0 ^ ^% K%% 6 \ 0 0 2C 0 * 3A 0 ^ ^% K%% * \ * 3A 0 0 222 * 0 17I, ^% K%% * \ 17I, 0 2227 0 A = 0- ^ ^% K%% * \ A = 0- 0 2227 0 2A * 0 ^% K%% \ 0 2A * 0 0 2227 0 ^ 0% K%% * \ 0 220 0 ^ ^% K%% * \ 0 220 0 ^ ^% K%% 6 \ 0 0 2 0 2A * 0 ^ ^% K%% * \ 2A * 0 0 220 0 ^ ^% K%% * \ 0 # 27 0 ^ ^% K%% * \ 0 220 0 0 * ^ ^% K% * \ 0 * 0 227 0 ^ ^% K%% * \ 0 227 0 ^ ^% K% \ * 0 220 0 2A * 0 ^ ^% K%% 6 \ 0 2A * 0 0 # 220 0 * 3A 0 ^ ^% K%% 6 \ 0 * 3A 0 0 227 0 A = 0- ^ ^ T ^ O% K%% * \ A = 0- 0 2 0 A = 0- ^ ^ T ^ O% K%% * \ A = 0- 0 220 0 = 0 ^ ^% K%% * \ = 0 0 227 0 17I, ^ ^% K%% * \ 17I, 0 22 0 ^ ^% K%% 6 \ 0 0 220 0 7 = 01 ^ ^% K% \ * 7 = 01 222 * 0 7 = 01 ^ ^% K% 0 \ 7 = 01 222 7 0 A = 0- ^ ^ T ^ O% K%% * \ A = 0- 2 0 ^ ^% K%% 6 \ 0 # 220 0 ^ ^% K%% * \ * N # # # L 220 0 ^ ^% K%% 6 \ 0 # 220 0. ^ ^% K%% 6 \. # $ # 227 0 A = 0- ^ ^ T ^ O% K%% * \ A = 0- # 222 0 0 A = 0- ^ ^ T ^ O% K%% * \ A = 0- # 227 0 ^ ^% K%% * \ # 227 0. ^ ^% K%% 0 \ 6. # 227 0 ^ ^% K%% * \ "O 22 * 0 ^ ^% K% \ * # 220 0. ^ ^% K%% * \. 2C0 0 A = 0- ^ ^ T ^ O% K% % * \ 6 A = 0- 220 0 = 0- ^ ^% K \ 6 = 0- 220 0 ^ ^% K%% * \ 222 0 0 ^% K%% 6 \ 0 222 0 0 6 0- ^ % K%% 6 \ 0 6 0- * N # # # L 2C0 0 6 0- ^% K%% * \ 0 6 0- 222 7 0 ^ ^% K%% 6 \ 0 "O 220 0 A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- 0 220 0 7 = 01 ^ ^% K% 0 \ 7 = 01 220 0 A = 0- ^ ^ T ^ O% K%% * \ A = 0- 222 7 0 ^% K%% 6 \ 0 # $ # 222 * 0 0A ^ ^% K%% * \ 0A 0 0 0 ^ 0% K%% 6 \ * 222 0 0 ^ ^% K%% * \ # 220 0 ^ ^% K%% * \ 222 7 0 = 0 ^ ^% K%% * \ = 0 220 0 ^ ^% K%% * \ 222 7 0 ^ 0% K%% * \ * & 220 0 ^ ^% K%% 6 \ * 222 7 0 0 * ^ ^% K% * \ 0 *

# 220 0 17I, ^% K%% 6 \ 0 17I, # 20 0 ^ ^% K%% * \ # $ # 220 0 ^ 0% K%% * \ # 20 0 ^ ^% K%% \ 0 "O # 2C 0 ^ ^% K%% 6 \ 0 222 7 0 ^ ^% K%% * \ 220 0 ^ ^% K%% * \ 222 7 0 0 * ^ ^% K% 6 \ 0 * # $ # 220 0 17I, ^% K%% * \ 17I, # 220 0 ^ ^% K%% * \ 222 * 0 ^ ^% K%% 6 \ 0 # 20 0 ^ ^% K%% 6 \ 0 2C0 0 A = 0- ^ ^ T ^ O% K%% * \ A = 0- # 220 0 ^ ^% K% \ * * 9 220 0 ^ ^% K%% * \ * N # # R 220 0 ^ ^% K% \ * * # # 222 0 A = 0- ^ ^% K% \ 0 A = 0- * N # # 220 0 ^ ^% K%% * \ * N # # R 220 0 ^ ^ % K%% 6 \ 0 * N # # 227 0 ^ ^% K%% 6 \ 0 * N # # R 222 0 ^ ^% K% \ 0 * N # # R 220 0 ^ ^% K%% * \ * N # # R 227 0 ^ ^% K%% * \ * N # # 4 R 222 0 ^ 0% K%% 6 \ 0 * N # # 4 R 222 0 ^ ^% K%% * \ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ^ 0% K%% * \ 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K%% * \ 0 0 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 6 0- ^% K%% 6 \ 0 6 0- 0 0 0 ^ ^% K%% * \ 0 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ A = 0- 0 0 0 ^ 0% K%% * \ 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K%% * \ 6 0 0 0 * 3A 0 ^ ^% K%% * \ * 3A 0 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ A = 0- 0 0 0 ^ 0% K%% 6 \ * 0 0 0 ^ ^% K%% * \ 0 0 0 * 3A 0 ^ ^% K%% * \ * 3A 0 0 0 0 = 0 ^ ^% K%% * \ = 0 0 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K% \ * 0 0 0 17I, ^ ^ 0% K%% 6 \ 0 17I, 0 0 0 ^ ^% K%% * \

0 0 0 17I, ^ ^% K%% * \ 17I, 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K%% * \ 0 0 0 ^ 0% K%% 6 \ * 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- 0 0 0 * 3A 0 ^ ^% K%% * \ * 3A 0 0 0 0 ^% K%% * \ 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^ % K%% * \ 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K%% * \ 0 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ 6 A = 0- 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K%% * \ 6 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 ^ ^% K%% 0 \ 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ A = 0- 0 0 0 * 3A 0 ^ ^% K%% * \ * 3A 0 0 0 0 ^ ^% K%% * \ 0 0 0 0 ^ ^% K%% 6 \ 0 0 0 0. ^% K%% 6 \ 0. 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- 0 0 0 ^ ^% K%% \ * 0 0 0 = 0 ^ ^% K%% 6 \ 0 = 0 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 ^ ^% K% \ * 0 0 0 6 0- ^% K%% * \ 6 0- 0 0 0 0 17I, ^ ^ 0% K%% 6 \ 0 17I, 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K%% * \ 0 0 0 ^ 0% K%% 0 \ * 0 0 0 A = 0- ^ ^% K % \ 0 A = 0- 0 0 0 = 0 ^ ^% K%% * \ = 0 0 0 0 17I, ^ ^% K%% * \ 17I, 0 0 0 ^ 0% K%% 6 \ 0 0 0 0. ^ ^% K%% * \. 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 = 0 ^ ^% K%% 6 \ 0 = 0 0 0 0 ^ ^% K%% * \ 0 0 0 * 7 ^ ^% K% \ * * 7 0 0 0 * 3A 0 ^ ^% K%% 6 \ 0 * 3A 0 0 0 0 ^ ^% K%% * \ 0 0 0 = 0 ^ ^% K%% * \ = 0 0 0 0 0 * 3A 0 ^ ^% K%% * \ * 3A 0 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 ^ 0% K%% \ 0 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 ^ ^% K%% * \ 6 0 0 0 ^% K%% 6 \ 0 0 0 ^ ^% K%% * \ 0 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 ^ ^% K%% * \ 0 0 0 B = 0- ^ ^% K% * \ B = 0- 0 0 0 ^ ^% K%% 0 \ 6 0 0 0 = 0 ^ ^% K%% * \ = 0 0 0 0 ^ ^% K%% * \ 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ A = 0- 0 0 0 ^ ^ % K%% 6 \ 0 0 0 0 ^ ^% K% \ * 0 0 0 17I, ^ ^% K%% * \ 17I, 0 0 0 ^ ^% K%% * \ 0 0 0 7 = 01 ^ ^% K% * \ 7 = 01 0 0 0 17I, ^ ^% K%% * \ 17I, 0 0 0 ^ ^% K%% * \ 0 0 0 0 = 0 ^ ^% K%% * \ = 0 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- 0 0 0 ^ ^% K%% * \ 0 0 0 ^ 0% K%% * \ 0 0 0 2A * 0 ^ ^% K%% 0 \ 6 2A * 0 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K% \ * 0 0 0 A = 0- ^ ^ T ^ O% K% % * \ 0 A = 0- 0 0 0 2A * 0 ^ ^% K%% 6 \ * 2A * 0

0 0 0 = 0 ^ ^% K%% * \ = 0 0 0 0 0. ^% K%% 6 \ 0. 0 0 0 = 0 ^ ^% K%% 6 \ 0 = 0 0 0 0 0 ^ ^% K%% * \ 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ 6 A = 0- 0 0 0 ^ ^% K%% * \ 0 0 0 17I, ^% K%% * \ 17I, 0 0 0 ^ ^% K%% 0 \ * 0 0 0 * 7 ^ ^% K% \ * * 7 0 0 0 ^ ^% K%% * \ 0 0 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ A = 0- 0 0 0 ^ ^% K% \ * 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K% \ * 0 0 0 ^ ^% K%% * \ 0 0 0 0 0 ^ ^% K%% * \ 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- 0 0 0 ^ ^% K%% 0 \ 6 0 0 0 ^% K%% 6 \ 0 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ 6 A = 0- 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ A = 0- 0 0 0 ^ ^% K%% * \ 0 0 0 = 0 ^ ^% K%% * \ = 0 0 0 0 = 0 ^ ^% K %% * \ = 0 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 ^ ^% K%% 0 \ * 0 0 0 ^ ^% K%% * \ 0 0 0 0 ^ ^% K%% * \ 0 0 0 = 0 ^ ^% K%% 6 \ 0 = 0 0 0 0 ^ ^% K%% 0 \ 0 0 0 * 3A 0 ^ ^% K%% * \ * 3A 0 # 220 6 ^ ^% K%% * \ * K # # 22 6 ^ ^% K%% * \ 0 2220 6 6 0- ^% K %% 6 \ 0 6 0- 0 2C 6 ^ ^% K%% * \ 0 220 6 ^ ^% K%% 6 \ 0 0 222 * 0 ^ ^% K%% * \ # 220 6 ^ ^% K%% * \ 0 227 6 ^ ^% K%% * \ 0 27 6 ^ ^% K%% * \ 0 2220 6 6 0- ^% K%% 6 \ 0 6 0- # N # # LOO 2C 6 ^ ^% K%% 6 \ 0 0 2C 6 ^ ^% K%% * \ 0 220 6 ^ ^% K%% * \ O # 227 6 ^ ^% K%% * \ 227 6 ^ ^% K%% * \ 227 6 ^ ^% K%% * \ 0 2227 0 ^ ^% K%% * \ O # 222 * 6 ^ ^% K%% * \ O # 222 7 6 ^ ^% K%% * \ O 2C 6 ^ ^% K%% 6 \ 0 0 20 6 ^ ^% K%% * \ 0 2 * 6 ^ ^ % K%% * \ 222 * 6 ^ ^% K%% * \ 0 0 0 17I, ^ ^% K%% 6 \ 0 17I, 0 0 0 0 0 0 0 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ A = 0- 0 0 0 ^ ^% K%% * \ 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ A = 0- 0 0 0 ^ ^ % K%% * \ 0 0 0 * 3A 0 ^ ^% K%% * \ * 3A 0 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 A = 0- ^ ^ T ^ O% K% % * \ A = 0- 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K%% * \ 0 0 0 0 17I, ^ ^ 0% K%% 6 \ 0 17I, 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ 6 A = 0- 0 0 0 ^ ^% K%% * \ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 ^% K%% 6 \ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 ^% K%% 6 \ 0 0 0 0 ^% K%% 6 \ 0 0 0 0 = 0 ^ ^% K%% * \ = 0 0 0 0 ^ ^% K%% * \ 0 0 0 = 0 ^ ^% K%% * \ = 0 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K%% * \ 0 0 0. ^ ^% K%% 6 \ 0. 0 0 0. ^ ^% K%% 0 \ 6. 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 = 0 ^ ^% K%% * \ = 0 0 0 0 ^ ^% K%% * \ 0 0 0 0 ^ ^% K%% * \ 0 0 0 = 0 ^ ^% K%% * \ = 0 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 = 0 ^ ^% K%% * \ = 0 0 0 0 ^ ^% K%% * \ 0 0 0 17I, ^% K%% * \ 0 17I, 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 = 0 ^ ^% K%% * \ = 0 0 0 0 * 3A 0 ^ ^% K%% 6 \ 0 * 3A 0 0 0 0 6 0- ^% K%% 6 \ 0 6 0- 0 0 0 ^ ^% K% % 6 \ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^ % K%% * \ 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K%% * \ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K%% * \ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

* K # # # L $ 0 0 ^ ^% K%% * \ 0

$ #% - * = # # /% 2 2 * = L4 0 # -.D "-.DLY *! # 23 @G! # 23 @G 6 L4 # 5% F $ H # F $ H = 0- ^ ^ 66 * * * 0 66 * 0 * 6 0 * 0 66 6000 ** 6 6 66 = 0- KGO = 0- ^ ^ 6 * 66 6 6000 60 * 60 60 6 * 06 6 * 6 * ** = 0- KGO * 7 ^ ^ 0 * 66 * 06 * 00 * 06 * 0 ** 0 * * 0606006606 * 6 66 06 * 7 KGO * 7 ^ ^ * 660 * * 6600 ** 0 * 6 ** * 0660 6 ** 0 ** * 7 KGO 2A * 0 ^ ^ 6000006 ** 60066 * 0 * 6 006 6 0 * * 006006 * 0600066 2A * 0 KGO 2A * 0 ^ * 0600 00000 * 6 6 * 00 ** 60 * 0 ** 0 * 00 6 * * 600 2A * 0 KGO 2A * 0 ^ ^ 06 * 0 * 0 ** 6 60 * ** 0 * 00 * 0 * 0 * 6060 ** * * * 2A * 0 KGO 2A * 0 ^ ^ ** 00 ** 0 6 6 ** 0 606 * 006066 * 6606 2A * 0 KGO 17I, ^ 0 *** ** 6 ** 0 * 0 0 6 66 ** 06 6 0 * 0 6 60 17I, KGO 17I, ^ ^ 6 * * 00 0 *** 000 *** 00 6 * 6 * *** * 6 17I, KGO 17I, ^ ^ 0 ** 0 60 * 0060 *** 0 ** * 0 ** 0 0 666 * 0 0 * ** 0 * 17I, KGO 17I, ^ ^ 6 * 0606 * 06 0 ** 066060 6 * 0 0 * 0 * 060 * 06 * 0 * 060 17I, KGO 0 * ^ ^ 60 * * 06 * 60 ** 06 ** 00 * 060 * 6 * 00 0 *** 6 * 00 0 * KGO 0 * ^ ^ 6 ** 0 * * * * * * 06 * 0 ** * 000 * 06 * * 000 ** 006 0 * KGO .61 = ^ ^ 6 * 06 * 0 6 * 0060 * 0 * 060 6 ** ** * 6 * 0 66 0 .61 = KGO 7 = 01 ^ ^ 6660 *** 0 * ** 0 * 6 0 0 * * 0 60060 ** * 0 * 06 00 7 = 01 KGO * 3A 0 ^ ^ * 00 * 6 * 6 ** 60 *** * * 06 * 6 6 ** 066006 ** * 3A 0 KGO * 3A 0 ^ ^ 0 *** 66 * * 60 ** 0 * * 06 * 06 * ** 6 ** 0 * * * 3A 0 KGO. ^ ^ 0 ** 0 * 06 6 * 0 * 00 0 ** * * 0 * 0000666 60 006 *. K G O. ^ * 0 6 6 * 06 * 00 * 0 * 6 ** 60 6 00 000 0 6 * 0 * 0 0 * 0 0 *. K G O. ^ 660006660 * 600 * 66 6 0666 ** * 6 6 ** 0. K G O. ^ ^ * * 66 ** 0 * **** 0 06 * 0 0 0 0 * 0 * 6666 * 000. KGO 161 = ^ 06 ** 66 * 660660 6 **** 0 * ** ** * * 161 = KGOB = 0- ^ ^ 6 * 60 60 * 6600 0 006 * 6 * 660 * 06 * 660 66 * 0 B = 0- KGOA = 0- ^ ^ 0 * 006006 0 6600 ** 0 60 0 * 06 6 0 * 0 ** 6 * 6 A = 0- KGOA = 0- ^ ^ 606000 ** 6 * * 660 0 * * 6 0 * 66 *** * 0 6 * 0 * A = 0- KGOA = 0- ^ ^ T ^ O * 6 * 0066 * 0 * * 6 ** 6 * 60000 60 * 600 0 000 * 6 A = 0- KGO 0A ^ ^ 6 ** 6 ** 066 * 60 6 666 * 60 * 666 0606 6 6 0A KGO ^ ^ 606 * 066 *** * 6 * 6 ** 0 ** 6006 ** 000006 KGO ^ ^ ^ 0 66 * 666066 060 * * 0 * * ** *** 6 * * KGO ^ *** 66 * ** *** 06 * * 6060 6 ** 60606 * KGO ^ ** * 6 6 * 6 * 060 ** 606 ** 0066 ** * 0 * KGO ^ * 0 * 60 006066 *** 0060 * 0 *** 06 ** 0006006000 ** 0 KGO ^ * * 66 * 6666060 0 * 0 ** ** 6 ** * KGO ^ ^ 000666606 ** * 0 ** 0 * 0 ** 6 * * 6 ** 6 * KGO ^ ^ 6 6066 * **** * 6 * 6 6 6 6 ** 6 ** 6 KGO ^ ^ 6660 * 6600 * 06 * 606 6 * 66060 * * ** * 6 KGO ^ ^ 00 * 0 * 6 * 0 ** * 0006 * 6 6 ** * 6 * * * ** KGO ^ ^ 6 66 * 006 66 * ** 60 * 0 666 * 66 * 0 600 * KGO ^ ^ 66 * * 60 * 6 0 ** 0 ** 0 0 666 ** ** 66 * 0 KGO ^ ^ *** 0 6 * 06600 * 6 0 * 0 * 66 ** 6 6 0 * ** 066 6 KGO ^ ^ 00 **** ** *** 060 60 * * 60 * 0 06 6 66 66 6 KGO ^ ^ 6 * 6 6 6606 0 660 060 ** * 066 * 66 * * 0 06 KGO ^ ^ * 600006 6 * 6 * 0 ** 0 6 **** ** * 06 * 0 * 0 KGO ^ ^ * 6 * 000 ** ** 0 * * 6 * 60 ** * 0 * 60 6 * * KGO ^ 0 06 **** 06 * 6 * * 0 ** 0 * 0 60 * 66066 6 6066 KGO ^ ^ 66 ** 0 * 0 * 006 * 06 * 0 606 * 06 * 6 * * 0 66 6 KGO ^ ^ * 6 * 0 6 6 * 6 60 * 6 * * 06 * 6 666 60 ** 0 KGO

^ ^ 600 * 00 *** 0 * 06 * 6 * 0 * 06 0 * **** 6 *** * 00 * KGO ^ ^ 66 ** 06 66 * 0000 * 0 * 6 *** 60 * * 00 * * 6 * KGO ^ 0 0 * 66 ** 066 * 0 6006 6660 * 06 ** 006 * 6 60 KGO ^ ^ **** * 0666 * 00 * 60 * 06 * 0 * 66 ** 6 * 66 KGO ^ ^ 66 **** 6 * 0 * * 6 * 0600 6 * 006006 *** 060 KGO ^ ^ * 666006660 * 060060 6 * 0 ** 06 * 06 * ** 0 * 0 * KGO ^ 606 * 06 ** * 66 * 0 * 6 **** 660 * 60 0 600 * KGO ^ ^ ** 666 * * 06 600 ** 0 * 6 ** 6 *** 006 * 00 KGO ^ ^ 66660 * 06 * 0 * 000 * 0 * 60 * 6 * * * 0 *** 0 * KGO ^ 06 *** 00 *** 6 ** 0 * ** 0 6660 * 60 * * 6 * KGO ^ ^ 660 ** 0 * * 0 * 06 ** 0 6 ** 6 * 06 * 060 ** 06 * KGO = 0 ^ ^ 0 * ** 6600 ** 6 6 * * 66 * 000 * ***** 06 * = 0 KGO 6 0- ^ 6 * 0660 ** 6 * * 6 ** 0 ** 6600 * 66 * * 6 0- KGO

5 * J # $ '4 KL L K F6 = * 6 = * K 1 "H $ #% - * = # # / = L4 2 -.D 2" -.D KL #! # 23 @! # 23 @ 6% F $ H # LKF $ HA = 0- ^ ^ 606000 ** 6 * * 660 0 * * 6 0 * 66 *** * 0 6 * 0 * A = 0- KGA = 0- ^ ^ T ^ O * 6 * 0066 * 0 * * 6 ** 6 * 60000 60 * 600 0 000 * 6 A = 0- KG ^ ** * 6 6 * 6 * 060 ** 606 ** 0066 ** * 0 * KG ^ ^ 000666606 ** * 0 ** 0 * 0 ** 6 * * 6 ** 6 * KG ^ ^ 6 6066 * **** * 6 * 6 6 6 6 ** 6 ** 6 KG ^ ^ 6660 * 6600 * 06 * 606 6 * 66060 * * ** * 6 KG ^ ^ 00 * 0 * 6 * 0 ** * 0006 * 6 6 ** * 6 * * * ** ** KG ^ ^ 6 66 * 006 66 * ** 60 * 0 666 * 66 * 0 600 * KG ^ ^ 66 * * 60 * 6 0 ** 0 ** 0 0 666 ** ** 66 * 0 KG ^ ^ *** 0 6 * 06600 * 6 0 * 0 * 66 ** 6 6 0 * ** 066 6 KG ^ ^ 00 **** ** *** 06060 * * 60 * 0 06 6 66 66 6 KG ^ ^ 6 * 6 6 6606 0 660 060 * * * 066 * 66 * * 0 06 KG ^ ^ * 600006 6 * 6 * 0 ** 0 6 **** ** * 06 * 0 * 0 KG ^ ^ * 6 * 000 ** ** 0 * * 6 * 60 ** * 0 * 60 6 * * KG ^ 0 06 **** 06 * 6 * * 0 ** 0 * 0 60 * 66066 6 6066 KG ^ ^ 66 ** 0 * 0 * 006 * 06 * 0 606 * 06 * 6 * * 0 66 6 KG ^ ^ * 6 * 0 6 6 * 6 60 * 6 * * 06 * 6 666 60 ** 0 KG ^ ^ 66 ** 06 66 * 0000 * 0 * 6 ** * 60 * * 00 * * 6 * KG

$ # $ # - G = L (LY $ # # /% * LY -.D -.D 6 L4 # 5 2 23 @G 2 "55 23 @G = 0- KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 60 * 0 ***** 0660 * 0 * 06 * 60 * 6 0000 * 60 * 66 ** 06O * 6 ** 6 ** * 0 6 0 66 = 0- KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 660 6 66 * * 6 ** 00 00 * * 60 * 6 0000 * 60 * 66 ** 06 O 60 6 * 06 6 * 6 * **. KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 606 ** 0 * 066 * *** 060 * * 60 * 6 0000 * 60 * 66 ** 06 60O * 0600066 000606606 * 0. KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 66 * 6 * 0 0 * 060 0 * ** * 60 * 6 0000 * 60 * 66 ** 06O * 0 * 0 * 00 0 6 ** 6 0 0 * 6 *. KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6660 ** 6 * 0600060 * 6 * 60 * 6 0000 * 60 * 66 ** 06O * * 000 60000 * 0 * 00 * 0 60. KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 600 06 6 060060 0 6 * 006 * * 60 * 6 0000 * 60 * 66 ** 06O 00 66 66 * 6 6. KGO * 0 * 0 * 066000 * 06 * 0 60 * * 0 60060 * 066 * 00 6 6 06 0 * 60 * 6 0000 * 60 * 66 ** 06O 6 * 00 000 66 * 66 6. KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 * 06 0060 0 * 06 * 0 * 666 006 * 60 * 6 0000 * 60 * 66 ** 06O 000 * * 06 6 6 * 60. KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 * 0 * 6066 0 * 0 ** 0 * 60 * 6 0000 * 60 * 66 ** 06O * 0 60 6 * 0 * * 0 * 06 **. KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 ** 0 00 *** 0 ** 0 * 06 * 060 * 60 * 6 0000 * 60 * 66 ** 06 6O * 0 * 0 0 * 0 0 * 0 ** 06 * 6 *. K G O * 0 * 0 * 066000 * 06 * 0 60 ** 0 66 * 00 * 0 * 6 0006 * 66000 * 60 * 6 0000 * 60 * 66 ** 06O 66 ** 0 * **** 0 0600. K G O * 0 * 0 * 066000 * 06 * 0 60 ** 0 6000 *** 0000 * 6 ** 00 * 60 * 6 0000 * 60 * 66 ** 06 O 066 * 0 * 006066 *** 060. K G O * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 * 00 66 * * * * * 60 * 6 0000 * 60 * 66 ** 06O * 0 * 66 * 0 00 ** 060 60 *

. KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 66 * 6 * 0 6 6 0 0 0 06 * 0 ** 6 * 60 * 6 0000 * 60 * 66 ** 06O * 6 ** 0 * 600 * 0 6600 06. K G O * 0 * 0 * 066000 * 06 * 0 60 ** 0 66 * 0 ** 6 000600 * 0 * 660 * 60 * 6 0000 * 60 * 66 ** 06O 6600660 60600 * 006 * 0 *. K G O * 0 * 0 * 066000 * 06 * 0 60 ** 0 6066 * 0 ** 6 6000 00 0 * 6 * 60 * 6 0000 * 60 * 66 ** 06O 600 * 6 * *** 6 * 6. K G O * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 6 66 * 6 ** 06 * 00066 * 60 * 6 0000 * 60 * 66 ** 06 6O * 6 6 6600600 * * 0 *. K G O * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 606 *** * 06 ** 6 0 * 60 * 6 0000 * 60 * 66 ** 06O 660 0 * *** 00 60006. K G O * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 0660 * 0000 6 * 0 * * 60 * 6 0000 * 60 * 66 ** 06O 6 * 0 * 6 * * 0 0 * 6 * 000 6. K G O * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 * 0 6 60 * 006000 * 60 06 * 0 * 60 * 6 0000 * 60 * 66 ** 06O 006 0 * 66 66 * 6 *. KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 00 * 000000 60 * 60 * 0006 * * 60 * 6 0000 * 60 * 66 ** 06O 0 * 006 * 00 * 00 ***** 0 * 0. K G O * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 666 * 0 000 * * * 60 * 6 0000 * 60 * 66 ** 06 O 066 * 0 6 * 0 * 00 * 0 * 66. K G O * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 0606606 ** 6 * 000 ** 0 * 60 * 6 0000 * 60 * 66 ** 06O * 60 * * 0 * 000066 * 0 *. KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 666 ** 0 * 06 0 * 006 0 * 60 * 6 0000 * 60 * 66 ** 06O * 60 * 0 ** 0 * 60 * * 60 * * 161 = KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 66 * 06 ** 06000 6 66 0 * 60 * 6 0000 * 60 * 66 ** 06 O 0 * 6 ** 0 * 60 * 0 * 0 66 161 = KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 66 * 0606 0 666 * 600 6 * 60 * 6 0000 * 60 * 66 ** 06O * 66 6 * 06060 66060 6 161 = KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 66 * 666 * 0 * 0 * 6 000 ** * 60 * 6 0000 * 60 * 66 ** 06O * *** * * ** 0 ** 060 A = 0- KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 666000 000 6 60 6 ** * * 60 * 6 0000 * 60 * 66 ** 06O * 06 6 0 * 0 ** 6 * 6 * A = 0- KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 * *** * * 0660 ** 0 * * 60 * 6 0000 * 60 * 66 ** 06O 6 0 * 66 ** * * 0 6 * 0 * A = 0- KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 0 ** * 0 6 660 * 0 0 * 6 * 60 * 6 0000 * 60 * 66 ** 06O 60 * 600 0 000 * 6 66 06 66 @ ?. KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 66 * 6 6 66 6 *** 06 * 60 * 6 0000 * 60 * 66 ** 06 O 60 * 6 *** 0 * 0660066 00 @? . KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 *** 0 *** 06606 * 0 * * 60 * 6 0000 * 60 * 66 ** 06O 66 * 0 60 * 0 6060 * 6 0A KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 ** 6 06 ** 00 60066 6 * 60 * 6 0000 * 60 * 66 ** 06 O 0666 * 60 * 666 0606 6 KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 66 ** 0 ** 6006 ** 000006 * 60 * 6 0000 * 60 * 66 ** 06 O 6066 * 6 * 06 666600 * KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 60 6 * 0 * * ** *** 6 * * 60 * 6 0000 * 60 * 66 ** 06 06O * 606 * 066 *** * 6 KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 66 ** 60606 * 600 6066 * 60 * 6 0000 * 60 * 66 ** 06 06O 60066 * 0666606 066 KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 *** 66 * * * *** 06 * 60 * 6 0000 * 60 * 66 ** 06 * O * ***** ** * 6 6 KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 600 600 60000 * 66 * 0 606 * 60 * 6 0000 * 60 * 66 ** 06O ** 06 ** 0006006000 ** 0 * KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 0 * 0 ** ** 6 ** * * 60 * 6 0000 * 60 * 66 ** 06 * 00O 60 * 06 0 ** 600666 600 KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 * 6 6 6 6 ** 6 ** 6 * 60 * 6 0000 * 60 * 66 ** 06 6 * O * 6 ** 6 * 6 * KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 6 ** * 6 * * * ** * 60 * 6 0000 * 60 * 66 ** 06 6 * 6O 6060 * * ** * 6 KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 66 * * 60 * 6 0 ** 0 ** 0 * * 60 * 6 0000 * 60 * 66 * * 06 6O 66 * 006 66 * ** 60 KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 6 60 60 66 60660 66 * 60 * 6 0000 * 60 * 66 ** 06 O * 0 * 666 ** 6 6 0 * KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 66 660066000 6 * 6 6 * 60 * 6 0000 * 60 * 66 ** 06 * O 00 **** ** ** * 0606 KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 66 **** 066 ** * 60 * 60 * 6 0000 * 60 * 66 ** 06O * 600006 6 * 6 * 0 ** 0 KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 66 ** 0 66 ** 0 * 0 * 006 * * 60 * 6 0000 * 60 * 66 ** 06 0 ** 06O *** 6 * 6 * * * KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 * ** 6 * 0 6 6 * 6 60 * * 60 * 6 0000 * 60 * 66 ** 06 * 00O * 0060 6 6 ** 00 * 6 KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 66 *** 60 * * 00 * * 6 * * 60 * 6 0000 * 60 * 66 ** 06O 66666 6 6600 * 00 ** KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 66 * 0 6006 * * 0 66006 * * 60 * 6 0000 * 60 * 66 ** 06O *** *** 060000 ** 0 ** KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 * 6 * 0 ** 06 * 06 * ** 0 * * 60 * 6 0000 * 60 * 66 ** 06 * 0600O 6 * 006006 *** 060 KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 606 * *** 0600 6 ** 0606 * 60 * 6 0000 * 60 * 66 ** 06 66O 6006660 * 060060 60 KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 66 * 00 660 60 60 6 * 6 * 60 * 6 0000 * 60 * 66 ** 06 * 66O 6 06606 * 6 * 66

KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 60 * 6 * * * 0 *** 0 * * 60 * 6 0000 * 60 * 66 ** 06 O * 0 * 660 ** 666 * * KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 606 ***** 06 * 06060 ** * 60 * 6 0000 * 60 * 66 ** 06 * 06O *** 00 *** 6 ** KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 ** ** 00 660 ** 0 * * 0 * * 60 * 6 0000 * 60 * 66 ** 06 0 * 6O ** ** 666 * 0 * = 1 KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 * 60 * 0 *** 0 * 0606600006 * 60 * 6 0000 * 60 * 66 ** 06 6O 60 ** 00660 0 6 * * 0 * 0 * = 1 KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 600 6 ** 06 60 60 * 60 * 00 * 60 * 6 0000 * 60 * 66 ** 06 6O 6 0666 * 6006 6 606 = 0 KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 60 * 0 66660 60 * * 0 * * * 60 * 6 0000 * 60 * 66 ** 06O 0 * ** 6600 ** 6 6 * * = 0 KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 *** * 60606 6 * 06 6 * 60 * 6 0000 * 60 * 66 ** 06O 66 60 6 *** 0 * * 6 * & 0 AKGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 60 * 0 * 00 *** 0 * 0 * 006066 * 60 * 6 0000 * 60 * 66 ** 06 O 0 * 0 6 ** * 6006 * 06 * 0 * 7 KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 66 ** 0 6600 ** 060 * 06000 * 60 * 6 0000 * 60 * 66 ** 06O ** * 0660 6 ** 0 ** 0 2A * 0 KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 ***** ** 0 * 00 * * * * 60 * 6 0000 * 60 * 66 ** 06 O 6000006 ** 60066 * 0 * 00 2A * 0 KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 0 ** 060 * 6 * 0 ** 0 * * 60 * 6 0000 * 60 * 66 ** 06 0O * 0 * 0 * 6 066 6 * 0 * 000 2A * 0 KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 * 00 600 006660000 60 * 000 * 60 * 6 0000 * 60 * 66 ** 06O * ** 0 06 * 0 * 0 ** 6 60 * 2A * 0 KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 66 * 0 6 ** 00 * * * 0 000 * * 60 * 6 0000 * 60 * 66 ** 06O ** 0006 ** * 6 * * 06 2A * 0 KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 60 60 * 0 6 * 0 0 * 0 * 6 * 60 * 6 0000 * 60 * 66 ** 06O 60 ** 00 ** 0 6 6 ** 06 17I, KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 6060 * 066 ** 06 6 0 * 0 * 60 * 6 0000 * 60 * 66 ** 06O * 06 * * 660 *** 0 * 0 ** 17I, KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 6 * 6 * *** * 6 * 60 * 6 0000 * 60 * 66 ** 06O * 6 * 00 * 0 60 *** 0 * 000 17I, KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 66006 *** 00 ** 0 60 * 0060 * 60 * 6 0000 * 60 * 66 ** 06O * 660 * 0 * * 0006 6 6600 2A = KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6000 ** 0060 606 * 0 6 6 * 60 * 6 0000 * 60 * 66 ** 06O * 0 06 060 * 6 * 0 * * 6 0 ** 2A = KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 * 0666006006 600 600000 * 60 * 6 0000 * 60 * 66 ** 06O * 06 * 0 0 *** 66 * 0 ** 2A = KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6666 666 * 60 0 0000 * * 60 * 6 0000 * 60 * 66 ** 06O ** 0 0 *** 0 * * 60 600 2A = KGO * 0 * 0 * 066000 * 06 * 0 60 * * 0 6 6 ** 0 6 * 06 * 0 6 * 60 * 6 0000 * 60 * 66 ** 06O 6 00 *** 0 * 06 * * 00 2A = KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 * 0660 * 006 6 * 00060060 * 60 * 6 0000 * 60 * 66 ** 06O * 6660 6 6 * 666 0 0 2A = KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6060 00 0 6006 * 6 0666000000 * 60 * 6 0000 * 60 * 66 ** 06O 0000 * * 0 ** 6660 6 6 * 2A = KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 6 * 06 006060 6 * 0 600066 * 60 * 6 0000 * 60 * 66 ** 06O 666 * 66 * 0 * 6 * * 0 2A = KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 ** 000 60000660 * 06 * 0 6 * 60 * 6 0000 * 60 * 66 ** 06O * * * 0 6 ** * 0 * 6 2A = KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 600 600 * 06 6 ** 060 ** * 60 * 6 0000 * 60 * 66 ** 06 O 06 * 0 * 00600 * 00666000 * 0 2A = KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 * 6 ** 6 * 0660 * 60 * 6 0000 * 60 * 66 ** 06 0 * O * 06 * 6 ** 6 * * * 2A = KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 60 * 06 060060060 6600600 * 60 * 6 0000 * 60 * 66 ** 06O 66 * * * 6 * * * 06 * 2A = KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 ** 600 6 06 * 00 * 0 ** 00 * 60 * 6 0000 * 60 * 66 ** 06 0O * 6 0 600 * 06 * 0 000 ** 060 2A = KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 660 60060 6 **** 0 * 0 * 60 * 6 0000 * 60 * 66 ** 06O * *** 660 * 00600006 6 0 * KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 *** 0066 * 0 * * 0 * 6 06 * 60 * 6 0000 * 60 * 66 ** 06 06O 60 * 06 * 0 6 6 ** 06 0 0 * KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 60 0 6 60 * 0 ** 00 * 6 * 60 * 6 0000 * 60 * 66 ** 06 O 600606606 * 666 00 * 0 * 6 0 * KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 * 00 * 0 60 * 6 ** * 0 ** 0 * 60 * 6 0000 * 60 * 66 ** 06 O 060 ** 00 ** 6 * 6 * * 0 * KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 6 0 *** 06 * ** * * 0 * * 60 * 6 0000 * 60 * 66 ** 06 O 006 ** 660 * 6 6 * 0 * KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 660600 * 6 * 6600 66 * 0 * 60 * 6 0000 * 60 * 66 ** 06O ** 0 60 * 66 * 00 * 66 0 * KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 666 * 00 * 0 ** 0 ** 06 * 00 * * 60 * 6 0000 * 60 * 66 ** 06O * * 6 * ** 606066 0 600 0 * KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 66 ** 0006 * 0 66006006 6 * 60 * 6 0000 * 60 * 66 ** 06 6O 6 0 666 * 06 * 0606 * 0 * KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 66 0 * 00 66 * 0 0066 * 0 060 * 60 * 6 0000 * 60 * 66 ** 06O 6 6 60 * 060 60606000 6 * 0 * KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6006 * 660000 * 000 0660 * 0 * 60 * 6 0000 * 60 * 66 ** 06O 60 ** * 0 ** 0 00 ** 0 * 0

0 * KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 66 * 0 6 ** 0 6 ** 000 060 * 60 * 6 0000 * 60 * 66 ** 06O 660 * 00 * 60666 * 0 0 * KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 60 66 0 66 00006066 * 0 * 60 * 6 0000 * 60 * 66 ** 06O 6 * 0 * 0 6 0 0 666 * 066 0 * KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 60060 60 60006 006 6 * 06 * 60 * 6 0000 * 60 * 66 ** 06O 66 6 0 * 06 * ** 0 * 0 0 * 0 0 * KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 606 ** 60 ** 06 *** 0 0000 * 60 * 6 0000 * 60 * 66 ** 06O 600660 * 6 ** ** 06 0 * KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6660 60 00 60 660600 * 060 0 * 0 * 60 * 6 0000 * 60 * 66 ** 06O 6 666 * 6 *** 0 * 0 * KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6060 606 * 06 600 * * 6600 * 60 * 6 0000 * 60 * 66 ** 06O ** 60 6 * 6 * * 0 * 0 * KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 60 6 66 6600 * 6066 6 * 60 * 6 0000 * 60 * 66 ** 06 60O *** 000 * 0 * 0 00 060060 600 0 * KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 * 60 6 * 600 ** 0 6 * 60 * 6 0000 * 60 * 66 ** 06O ** 6 0 0 * 60606 * 0 * 60 60 0 * KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 60 660 * 06 * 06600 6 6 * 60 * 6 0000 * 60 * 66 ** 06 O * 60 * 0 * 00060 * 66 * 0 * 0 * KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 ** 00 66 * 06000 * 000 * 6 * 60 * 6 0000 * 60 * 66 ** 06O * * 0606 00 * 6 * 0 * 0 00 66 0 * KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 6 66 * * * * * * 0 * 60 * 6 0000 * 60 * 66 ** 06O * 0000 6 006 ** 06 * 6 6 0 * KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 * 00 066 * 60 ** 0 * 0 60 * * 60 * 6 0000 * 60 * 66 ** 06O 60 006 ** 0 * * 00 0 06 600 * 6 0 * KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 600600606060 ** 00 * 000 0 06 * 0 * 60 * 6 0000 * 60 * 66 ** 06O 0000 60 * 6 * 6 60 60066 0 * KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 * 66 0 * 6 * 6 ** 06 * 60 * 6 0000 * 60 * 66 ** 06 * 6O * 0 6 660 * 06 ** 0 0 * KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 ** * 0 * 0 * 06000 * 06 * 0 * 60 * 6 0000 * 60 * 66 ** 06O 60 * 6 ** * 66 * 0 0 0 * KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 ** 600 * 0 0 6 * * 006 * 06 * 60 * 6 0000 * 60 * 66 ** 06 O 06 ** 6066 * 60 ** 0 * 0 * KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6600000 * 0 0 6606 0 * * 0 * 0 * 0 * 60 * 6 0000 * 60 * 66 * * 06O ** 0 ** 006 * 6 * 0 * 0606 0 * KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 * 6 * 0006 * * 0 00 ** 006 * 60 * 6 0000 * 60 * 66 ** 06O 60 0 * 0 ** 0 * 0 * * 06 * 0 * KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 660 6 06 * 0 0 * 0 * 6 * 0 * 0 * 60 * 6 0000 * 60 * 66 ** 06O 6 * 60 ** 06 ** 00 * * 0 * KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 66 6 * 6 60 * 0 * 6 * 6600 * 60 * 6 0000 * 60 * 66 ** 06 * 0O 60 * 60 * 0 * 066006 * 060 0 * KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 666 * 06 * 60060 ** 6 60 * 60 * 6 0000 * 60 * 66 ** 06O * 6 * ** 60060000 * 00 0 * KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 60 600 * 06 ** 066 * 0 * 0006 * 60 * 6 0000 * 60 * 66 ** 06 00O * 0 606 6666 * ** 6 0 * KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 666 6 * * 060 * 0 *** 00006 * 60 * 6 0000 * 60 * 66 ** 06O * 66 * 00 * 600 * 60 * * * 0 * KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 * 066060 **** 0 * * 0 6 * 60 * 6 0000 * 60 * 66 ** 06 O 0 6 ** 0 * 0 ** 0 ** 0 * 6 0 * KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 66 * ** 6 * 0606 600 ** 0 * 60 * 6 0000 * 60 * 66 ** 06 06O 6066 66 66066 6 0 * KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 * 0 6 ** 0 ** 006 * 06006 0 * 60 * 6 0000 * 60 * 66 ** 06 6O * 06 * 6 * 066 ** 0 * * 0 * KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 * * 000 * 06 * * 000 ** 006 * * 60 * 6 0000 * 60 * 66 * * 06 06O * 0 * 6600600 ** 660 0 * KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 606 * 0 * * 066 * 660 600 * * 60 * 6 0000 * 60 * 66 ** 06 6O 60 ** 006 * 006 * 6 * 06 00 0 * KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 6 ** 0 * 660006 0 * ** * 60 * 6 0000 * 60 * 66 ** 06O *** 0 6 * 0 ** 600 * 6 0 * KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6606 ** * 60 606 ** 0 * 60 * 6 0000 * 60 * 66 ** 06 66O * 0 60 * 066 * 0 * KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 666600 6000 * 0600 * 0606 * * 60 * 6 0000 * 60 * 66 ** 06 O * 00 0 * 0 * 0 * 0 * 600 * 0 * 0 0 * KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 60000 ** 006060006066 * 0600 * 60 * 6 0000 * 60 * 66 ** 06O 66 * 0 66 * 06000 00 0 * 0 0 * KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 60660 ** 0 060 60 * 060 60 6 * 60 * 6 0000 * 60 * 66 ** 06O 66 66066 00 6006 * 06 0 * KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 * 00 ** 6 6 60 66 06006 * 60 * 6 0000 * 60 * 66 ** 06O ** 0 6 ** 00 * 0006 * 0 * 0 .61 = KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 6 6 ** 06660 ** 0 ** * 60 * 6 0000 * 60 * 66 ** 06 660O 6006 * *** 00 ** 006 .61 = KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 * 660 *** 060066 6060 * 60 * 6 0000 * 60 * 66 ** 06O *** 0 * 0 * 06 * 0006 * 0600 .61 = KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 * 60 * 6 * 0 * 0 * 0 ** 06 6 * 60 * 6 0000 * 60 * 66 ** 06 6O * 0 * 6 ** * 0 ** ** 0 * .61 = KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 *** 66 6 * 0660000 6 * 60 * 6 0000 * 60 * 66 ** 06 6O * 06 * 0 6 * 0060 * 0 * 060 7 = 01 KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 6 ** 0 * 0 * 0 * 0 ** * 060 * 60 * 6 0000 * 60 * 66 ** 06O 00 * 6 * 000 * 60 6660 **

* 3A 0 KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 6 6 ** 0 * 0 * 0 * 60 ** * 60 * 6 0000 * 60 * 66 ** 06O * 6 0660 ** * 66 60 * 6 * 3A 0 KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 606606 * 666 ** 0 * 6 * 60 * 6 0000 * 60 * 66 ** 06 0 * 00O 0 * * 060 * 0 * 06 ** * * 3A 0 KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 * ** 660 * 0 * 6 * 66 * 60 * 6 0000 * 60 * 66 ** 06 6O * 066 0 ** 6 6 * 60 * 0 * 3A 0 KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 660 **** 6 ** 0 * * * 0 * * 60 * 6 0000 * 60 * 66 ** 06 6O * ** * 06 ** 066 ** 0 * 3A 0 KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 ** 606 66 * 6606 * 6 * 60 * 6 0000 * 60 * 66 ** 06 * 0 * O * 06 * 6 6 ** 066006 * 3A 0 KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6 66 * * 60 ** 0 * 6 * * 60 * 6 0000 * 60 * 66 ** 06O 666 * 666 60606 66 * 3A 0 KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 6666 * 666 06066066 6 * 60 * 6 0000 * 60 * 66 ** 06O * ** * 6 * 6 * 060 *** * 3A 0 KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 606 ** * 66 * 60 * 66 * 60 * 6 0000 * 60 * 66 ** 06 66O * * ** 60 ** 6 00 * 0 * 3A 0 KGO * 0 * 0 * 066000 * 06 * 0 60 ** 0 666660606 * 066 * 06 * * 60 * 6 0000 * 60 * 66 ** 06 * 0 * O * ** * 0 *** 60 ***

5 * J # $ '4 KL LK F6 = * 6 = * K 1 "H 55 [$ $ $ # L # 0 * 6 $ #% 2' (# # O # /. = L% 6 2 3 -.D 23 @ 2 = "-.D 23 @ * K BL 2! O1 * 3A 0 * 6 0660 ** * 66 60 * 6 6 6 ** 0 * 0 * 0 * 60 ** KO * 3A 0 0 * 000 * * 060 * 0 * 06 ** * 06606 * 666 ** 0 * 6 KO * 3A 0 6 * 066 0 ** 6 6 * 60 * 0 * ** 660 * 0 * 6 * 66 KO * 3A 0 6 * * * * 06 ** 066 ** 0 60 **** 6 ** 0 * * * 0 *** 6 KO * 3A 0 * 0 ** 06 * 6 6 ** 066006 ** 606 66 * 6606 * 6 KO * 3A 0 666 * 666 60606 66 66 * * 60 ** 0 * 6 * KO * 3A 0 * *** 6 * 6 * 060 *** 666 * 666 06066066 6 KO * 3A 0 66 ** ** 60 * * 6 00 * 0 06 ** * 66 * 60 * 66 KO * 3A 0 * 0 ** ** * 0 *** 60 *** 66660606 * 066 * 06 * KO. = 77 6 ** 0 ** * 6 * 6 * 0 666 0 6 66 * 0 * 0 6 ** KO 161 = 6 * 0 * 6 ***** 0 * 060 6 * 6 * 0 * 6 0 * 0 ** 6 * KO 161 = 0 ** 0 ** 0660 600 * 06600 ** * 00 * 6 *** 0 6 * KO 161 = 066 * 6 ** 0 * 66 * 0 60 0 ** 60660 * 00 * 6 60 60 KOB = 0- 6 66 * 0 60 6666060 * 60 66 * 000 * 0 * 0 * 0 * 0660 KOB = 0- 6 * 660 * 06 * 660 66 66 * 066060 *** 6 06606 KOA = 0- * 06 6 0 * 0 ** 6 * 6 * 66000 000 6 60 6 ** * KOA = 0- 6 0 * 66 *** * 0 6 * 0 * * *** * * 0660 ** 0 * KOA = 0- 60 * 600 0 000 * 6 66 06 66 0 ** * 0 6 660 * 0 0 * 6 K O &. 606 6 * 060 * ** 0 * 0066 6 * 60 66 * K O & ?? 6 6 ** ** 0 * 000 * 6 6 * 006 * 6 6 606 *** * KO 2A * 0 6000006 ** 60066 * 0 * 00 ***** ** 0 * 00 * * * KO 2A * 0 0 * 0 * 0 * 6 066 6 * 0 * 000 0 ** 060 * 6 * 0 ** 0 * KO 2A * 0 * ** 0 06 * 0 * 0 ** 6 60 * * 00 600 006660000 60 * 000 KO 2A * 0 ** 0006 ** * 6 * * 06 6 * 0 6 ** 00 * * * 0 000 * KO 2A * 0 60 ** 00 ** 0 6 6 ** 06 60 60 * 0 6 * 0 0 * 0 * 6 KO? .AB- 0 60060006 6 *** 0 00 * 060 * ** 0066 * 6660 60 KO 6066 * 6 * 06 666600 * 6 ** 0 ** 6006 ** 000006 KO 06 * 606 * 066 *** * 6 0 6 * 0 * * ** *** 6 * KO 0660066 * 0666606 066 6 ** 60606 * 600 6066 KO * * ***** ** * 6 6 *** 66 * ** *** 06 KO ** 06 ** 0006006000 ** 0 * 0060060000 * 66 * 0 606 KO * 0060 * 06 0 ** 600666 600 0 * 0 ** ** 6 ** * KO 6 * * 6 * * 6 * 6 * * 6 6 6 6 ** 6 ** 6 KO 6 * 66060 * * ** * 6 6 ** * 6 * * * ** KO 6 66 * 006 66 * ** 60 66 * * 60 * 6 0 ** 0 ** 0 * KO * 0 * 666 ** 6 6 0 * 6 60 60 66 60 660 66 KO * 00 **** ** *** 0606 6 660066000 6 * 6 6 KO * 600006 6 * 6 * 0 ** 0 6 **** 066 ** * 60 KO 0 ** 06 *** 6 * 6 * * * 6 ** 0 66 ** 0 * 0 * 006 * KO

* 00 * 0060 6 6 ** 00 * 6 * ** 6 * 0 6 6 * 6 60 * KO 66666 6 6600 * 00 ** 6 *** 60 * * 00 * * 6 * KO *** *** 060000 ** 0 ** 6 * 0 6006 * * 0 66006 * KO * 0600 6 * 006006 *** 060 * 6 * 0 ** 06 * 06 * ** 0 * KO 666006660 * 060060 60 06 * *** 0600 6 ** 0606 KO * 66 6 06606 * 6 * 66 6 * 00 660 60 60 6 * 6 KO * 0 * 660 ** 666 * * 60 * 6 * * * 0 *** 0 * KO * 06 *** 00 *** 6 ** 06 ***** 06 * 06060 ** KO 0 * 6 ** ** 666 * 0 * ** ** 00 660 ** 0 * * 0 * KO CB? ** 6 ** 6 * * * 066 *** 666 66 * 660 K O CB? * 66 ** ** 0 * ** ** 066 * 066 * 6 * 600606 0 K O CB? 660 * 6 * 60 6 * 060060 * 66 *** 6600606 ** K O CB? * 66 * 06 * 6 666 06 6 * 6 * 6 * * 66 600 * K O CB? * 6 6 * 0 * 6 *** 6 * ** 6 0 66 * * 00 * * 60 * K O CB? 6 * 6 ** 6 * 00 * 0 6060 ** 66 6 66 * * 00 * 00 K CB? ** 000 * 600 0 6 6 ** 0 * * 0 ** 6 0 * 0 ** 0 * 00 * 00 ** K CB? 66 66 * ** 060 * * 6 60 66 6 *** ** K O CB? * 00 * *** 0 ** 6 60 * 066060 * 660 * 0 * 6 * 0 K O CB? * 0 * 06 0 ** 0 * 00006 * 60 *** 000 6 6 * 0600060 ** K O. = 66 ** 6 * 6600066006 * 6 ** 6 **** * 0 ** K O

* K W BL L K% `to 0 # # O! L% - LO # '4 $ #% 2 $' 4 * K 2 -.D 23 @ 2 = "-.D 23 @ BL 2! O1 K ** 6 ** 0 0 * * * 0 6 * 06660600 * 000666600 ** OK 6 * 60 * 60006 06006 * 6 ** 6 6 * * 666 ** 66 OK 06 * 66 * 0 * ** 0606 * 06 660 * 0666 * ** 606 OK 06060606 ** 0 * 66 60 * 06 * 6 ** * 6 6066 * 060 OK 6 ** 006 ** 0 06 * 06 ** 0 6 * 0 * 6 ***** 006 OK 0 66006 * 0 66 *** 6 6 6 * 66 * ** 0 * * OK 6660 6 * *** 0 * * 00600 * 0600666 ** 6 0 * 0 * * OK 6 * 0006 6 ** * 0 * 60 * * 066606 * 6 * 066 6 OK 66006 * 06 * 060 * 0 * 0 * 06 * 0 * 66 ** ** 6 OK 00 * 006 ** 0 * 6 66 66 6 6 666060006 * 06 600 000 OK 60 6 * 0 6 6 * * 0 *** * 0 * * ** 6 0 OK ** * 0600 666 * 0606 * 0 6 *** 06 0 *** 6 ** 0 * OK 0 * 0 * 6 * 6 6 *** 0 ** 000 *** 006 * 0 * 0 * 0 600 * OK 066 6066 * 066 *** * 6 ** 06 ** 0 6 * * 006 OK * 0 * * * 066666 6 * 60 66 * ** 0 * ** 0 * OK 0 * *** 0006 ** 0660606 * 0 * 6006 6 **** * * OK 60 ** 6 * 6 6 66 * * 60 66 6 06 * 066 * 600 OK 066 0 * 6 * 06 60 06606 66 *** 00 * 0 0 * 0006606 * 0 OK * 6 * 606000666060 60 * ** 060 606660 ** 0 * 00 * 0 OK 6 *** 00 ** 06 * 0600 60 600 6 * * 6 * 0 * * 6 * 0 * OK * 0 ** * 6 0 6666 * * * 60 6 * 0 * 06660 * 06000 OK * 6600 ** 00 ** * 6 ** 000 ** 660 * 6 * OK ** 0 * 0 * 0606 *** 0606 6 ** 0066 * 0 * 0 * 0 66 OK 00 * 0 * * 0 0 * 0 **** 0 6 * *** * ** 0 *** 6 0 OK 6 ** 00600 * 066 ** 060 6 660600006 ** 006 * 06 * 0 OK * 666006 * 006 6 60 6 00 *** 0 66 *** 0 * 606 O

* K [* BL [6 K $ #% 2 &## / L 6 2 3 -.D 23 @ 2 = "-.D 23 @ * K BL 2! O1 * 3A 0 * 6 0660 ** * 66 60 * 6 6 6 ** 0 * 0 * 0 * 60 ** KO * 3A 0 0 * 000 * * 060 * 0 * 06 ** * 06606 * 666 ** 0 * 6 KO * 3A 0 6 * 066 0 ** 6 6 * 60 * 0 * ** 660 * 0 * 6 * 66 KO

* 3A 0 6 * ** * 06 ** 066 ** 0 60 **** 6 ** 0 * * * 0 *** 6 KO * 3A 0 * 0 ** 06 * 6 6 ** 066006 ** 606 66 * 6606 * 6 KO * 3A 0 666 * 666 60606 66 66 * * 60 ** 0 * 6 * KO * 3A 0 * *** 6 * 6 * 060 *** 666 * 666 06066066 6 KO * 3A 0 66 ** ** 60 ** 6 00 * 0 06 ** * 66 * 60 * 66 KO * 3A 0 * 0 ** ** * 0 *** 60 *** 66660606 * 066 * 06 * KO. = 77 6 ** 0 ** * 6 * 6 * 0 666 0 6 66 * 0 * 0 6 ** KO 161 = 6 * 0 * 6 ***** 0 * 060 6 * 6 * 0 * 6 0 * 0 ** 6 * KO 161 = 0 ** 0 ** 0660 600 * 06600 ** * 00 * 6 *** 0 6 * KO 161 = 066 * 6 ** 0 * 66 * 0 60 0 ** 60660 * 00 * 6 60 60 KOB = 0- 6 66 * 0 60 6666060 * 60 66 * 000 * 0 * 0 * 0 * 0660 KOB = 0- 6 * 660 * 06 * 660 66 66 * 066060 *** 6 06606 KOA = 0- * 06 6 0 * 0 ** 6 * 6 * 66000 000 6 60 6 ** * KOA = 0- 6 0 * 66 *** * 0 6 * 0 * * *** * * 0660 ** 0 * KOA = 0- 60 * 600 0 000 * 6 66 06 66 0 ** * 0 6 660 * 0 0 * 6 KO &. 606 6 * 060 * ** 0 * 0066 6 * 60 66 * K O & ?? 6 6 ** ** 0 * 000 * 6 6 * 006 * 6 6 606 *** * KO 2A * 0 6000006 ** 60066 * 0 * 00 ***** ** 0 * 00 * * * KO 2A * 0 0 * 0 * 0 * 6 066 6 * 0 * 000 0 ** 060 * 6 * 0 ** 0 * KO 2A * 0 * ** 0 06 * 0 * 0 ** 6 60 * * 00 600 006660000 60 * 000 KO 2A * 0 ** 0006 ** * 6 * * 06 6 * 0 6 ** 00 * * * 0 000 * KO 2A * 0 60 ** 00 ** 0 6 6 ** 06 60 60 * 0 6 * 0 0 * 0 * 6 KO? .AB- 0 60060006 6 *** 0 00 * 060 * ** 0066 * 6660 60 KO 6066 * 6 * 06 666600 * 6 ** 0 ** 6006 ** 000006 KO 06 * 606 * 066 *** * 6 0 6 * 0 * * ** *** 6 * KO 0660066 * 0666606 066 6 ** 60606 * 600 6066 KO * * ***** ** * 6 6 *** 66 * ** *** 06 KO ** 06 ** 0006006000 ** 0 * 0060060000 * 66 * 0 606 KO * 0060 * 06 0 ** 600666 600 0 * 0 ** ** 6 ** * KO 6 * * 6 * * 6 * 6 * * 6 6 6 6 ** 6 ** 6 KO 6 * 66060 * * ** * 6 6 ** * 6 * * * ** KO 6 66 * 006 66 * ** 60 66 * * 60 * 6 0 ** 0 ** 0 * KO * 0 * 666 ** 6 6 0 * 6 60 60 66 60 660 66 KO * 00 **** ** *** 0606 6 660066000 6 * 6 6 KO * 600006 6 * 6 * 0 ** 0 6 **** 066 ** * 60 KO 0 ** 06 *** 6 * 6 * * * 6 ** 0 66 ** 0 * 0 * 006 * KO * 00 * 0060 6 6 ** 00 * 6 * ** 6 * 0 6 6 * 6 60 * KO 66666 6 6600 * 00 ** 6 *** 60 * * 00 * * 6 * KO *** * ** 060000 ** 0 ** 6 * 0 6006 * * 0 66006 * KO * 0600 6 * 006006 *** 060 * 6 * 0 ** 06 * 06 * ** 0 * KO 666006660 * 060060 60 06 * ** * 0600 6 ** 0606 KO * 66 6 06606 * 6 * 66 6 * 00 660 60 60 6 * 6 KO * 0 * 660 ** 666 * * 60 * 6 * * * 0 *** 0 * KO * 06 * ** 00 *** 6 ** 06 ***** 06 * 06060 ** KO 0 * 6 ** ** 666 * 0 * ** ** 00 660 ** 0 * * 0 * KO CB? ** 6 ** 6 * * * 066 *** 666 66 * 660 K O CB? * 66 ** ** 0 * ** ** 066 * 066 * 6 * 600606 0 K O CB? 660 * 6 * 60 6 * 060060 * 66 *** 6600606 ** K O CB? * 66 * 06 * 6 666 06 6 * 6 * 6 * * 66 600 * K O CB? * 6 6 * 0 * 6 *** 6 * ** 6 0 66 * * 00 * * 60 * K O CB? 6 * 6 ** 6 * 00 * 0 6060 ** 66 6 66 * * 00 * 00 K CB? ** 000 * 600 0 6 6 ** 0 * * 0 ** 6 0 * 0 ** 0 * 00 * 00 ** K CB? 66 66 * ** 060 * * 6 60 66 6 *** ** K O CB? * 00 * *** 0 ** 6 60 * 066060 * 660 * 0 * 6 * 0 K O CB? * 0 * 06 0 ** 0 * 00006 * 60 *** 000 6 6 * 0600060 ** K O. = 66 ** 6 * 6600066006 * 6 ** 6 **** * 0 ** K O

* K [* BL [6 K $ #% 0 K '4 $ $ # OLL #' 4 = N = #R 7 - - 0 F7 - - 7 $ # = # 7 # 9L = #R 7 $ # 23 23 = # HF = # 0 HB # *

7 00 ^ 5 F7 H F00 ^ 00 ^ 00 ^ H * 9 / = # 0) # 9; ; * 8 7 00 ^ 5 F7 H F00 ^ 00 ^ 00 ^ H * 9 / = # 0) # 9; ; * 8 7 00 ^ 5 F7 H F00 ^ 00 ^ 00 ^ H * 9 / = # 0) # 9; ; * 8 7 -0 ^ 5 F7 H F-0 ^ -0 ^ 00 ^ H * 9 / = # 0; ; * 8 7 -0 ^ 5 F7 H F-0 ^ -0 ^ 00 ^ H * 9 / = # 0; ; * 8 7 -0 ^ 5 F7 H F-0 ^ -0 ^ 00 ^ H * 9 / = # 0; ; * 8 7 * @? F * @? * @? H . N # * 9 # * 1 # /. # 9 # # 7 * @? F * @? * @? H . N # * 9 # * * # 9 # # 7 * @? F * @? * @? * @? H . N # * 9 # * * # 9 # # 7 * @? F * @? * @? * @? H . N # * 9 # * * # 9 # # 7 * @? F * @? * @? * @? H . N # * 9 # * * # 9 # # 7 * @? F7 H F * @? * @? * @? * @? * @? * @? H . N # * 9 # -L * # 9 # #: "7 * @? F7 HF * @? * @? * @? * @? * @? * @? H? N # * 9 # -L * # 9 # #: "7 * @? F * @? * @? * @? * @? * @? * @? H . N # * 9 # * # 9 # #: "7 * @? F * @? * @? H. N # * 9 # & - OO 88 7 * @? F * @? * @? H. N # * 9 # & - OO 88 7 -2 2 3 #L 3 * # #L # 7 * @? F7 HF * @? * @? * @? * @? * @? * @? H. N # * 9 # -L * # 9 # #: "7 * @? F7 H F * @? * @? * @? * @? * @? * @? H . N # * 9 # -L * # 9 # #: "7 * @? F * @? * @? * @? * @? * @? * @? * @? * @? * @? H. N # * 9 # * # 9 # #: "7 * @? F * @? * @? * @? * @? * @? * @? H . N # * 9 # * # 9 # #: "7 * @? F * @? * @? * @? * @? * @? * @? * @? * @? * @? H. N # * 9 # * # 9 # #:" 7 00 ^ 5 F7 H F00 ^ 00 ^ 00 ^ H * 9 / = # 0) # 9; ; * 8 7 00 ^ 5 F7 H F00 ^ 00 ^ 00 ^ H * 9 / = # 0) # 9; ; * 8 7 00 ^ 5 F7 H F00 ^ 00 ^ 00 ^ H * 9 / = # 0) # 9; ; * 8 7 -0 ^ 5 F7 H F-0 ^ -0 ^ 00 ^ H * 9 / = # 0; ; * 8 7 -0 ^ 5 F7 H F-0 ^ -0 ^ 00 ^ H * 9 / = # 0; ; * 8 7 -0 ^ 5 F7 H F-0 ^ -0 ^ 00 ^ H * 9 / = # 0; ; * 8 7 A23 FA23 A23 A23 A23 H * 9 / = # * # F 4 H # 7 A23 FA23 A23 A23 A23 H * 9 / = # * # F 4 H # 7 A23 FA23 A23 A23 A23 H * 9 / = # * # F 4 H # 7 A23 FA23 A23 A23 A23 H * 9 / = # * # F 4 H # 7 A23 F7 7 H * 9 / = # 3 * # F 4 H # 7 A23 F7 7 H * 9 / = # -. * # F 4 H # 7 A23 F7 7 H * 9 / = # -. * # F 4 H # 7 7 = 0 F7 = 0 7 = 0 7 = 0 H * N $ * 9/1 # * # N $ # 7 7 = 0 F7 = 0 7 = 0 7 = 0 H * N $ * 9/1 # * # N $ # 7 7 = 0 F7 = 0 7 = 0 7 = 0 H * N $ * 9/1 # * # N $ # 7 7 = 0 F7 = 0 7 = 0 7 = 0 H * N $ * 9/1 # * # N $ #: "7 7 = 0 F7 = 0 7 = 0 7 = 0 H * N $ * 9/1 # * # N $ #:" 7 7 = 0 F7 = 0 7 = 0 7 = 0 H * N $ * 9/1 # * # N $ #: "7 7 = 0 F7 = 0 7 = 0 7 = 0 H * N $ * 9/1 # * # N $ # 7 7 = 0 F7 = 0 7 = 0 7 = 0 H * N $ * 9/1 # * # N $ # 7 7 = 0 F7 = 0 7 = 0 7 = 0 H * N $ * 9/1 # * # N $ #

5 '"R #' 4 # 8" & 23 88 "& 23 F 1! # H & 23 & 23 3 # '4 $ $ # 3 0" R # QK 0L # 2 7 - 7 $ # #N # * # - 1 3 0. - / F 3 D $ F LH # L * = F $ H5 3 * OB * K EO- # b; "XB # * ## L & b 1 3 * OB * K EO- # b;" XB # * ## L & b & 3 * OB * K EO- # b; "XB # * ## L & b 1 3 * OB * K EO- # b;" XB # * ## L & b & 3 * OB * K EO- # b; "XB # * ## L & b & 3 * OB * K EO- # b;" XB # * ## L & b & PKBS - K # & 7 # & 3 0 0 & PKBS - K # & 7 # & 3 * & PKBS - K # & 7 # & 3 * & PKBS - K # & 7 # & 3 * 1 PKBS - K # & 7 # & 3 * 1 PKBS - K # & 7 # & 3 * & PKBS - K # & 7 # & 3 * & PKBS - K # & 7 # & 3 * & PKBS - K # & 7 # & 3 * & PKBS - K # & 7 # & 3 * 1 PKBS - K # & 7 # & 3 * & PKBS - K # & 7 # & 3 * & PKBS - K # & 7 # & 3 * &; "X & X # 7 7 S 7 # & 3 * &;" X & X # 7 7 S 7 # & 3 * &

; "X & X # 7 7 S 7 # & 3 * 1 3 * OB * K EO- # b;" XB # * ## L & b 1 3 * OB * K EO- # b; "XB # * ## L & b & 3 * OB * K EO- # b;" XB # * ## L & b 1 3 * OB * K EO- # b; "XB # * ## L & b & 3 * OB * K EO- # b;" XB # * ## L & b & 3 * OB * K EO- # b; "XB # * ## L & b &, $ = K2, $ 0 * 1, $ = K2, $ 0 * 1, $ = K2, $ 0 * 1, $ = K2, $ 0 * 1, $ = K2 , $ 0 * &, $ = K2, $ 0 * &, $ = K2, $ 0 * &; "X & X # 7 7 S 7 # & 3 0 0 &X; "X & X # 7 7 S 7 # & 3 0 0 &;" X & X # 7 7 S 7 # & 3 * &; "X & 7. B 7 S & 1? 0 0 &;" X & 7. b 7 S & 1? 0 0 &; "X & 7. B 7 S & 1? 0 0 1;" X & 7. b 7 S & 1? * &; "X & 7. B 7 S & 1? * 1;" X & 7. b 7 S & 1? * 1

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

3.-C2 @ 03 = c @ <& .320 0 <& 0I <

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

3.-C2 @ 03 = c @%% <& .320 0 <& 0I <& 2 <51 # 1N N # 7 - - #

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

3.-C2 @ 03 = c @ & .320 0 & 0I & 2 51 #, 6- # 9 551 # N N # 7 - - #

T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T O T O O T T T T O T T T O O T T T T

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

7 A23 7 A23 7 7 = 0 7 7 = 0 7 7 = 0 7 7 = 0 7 7 = 0 7 7 = 0 7 7 = 0 7 7 = 0 7 7 = 0 & M320

3.-C2 @ 03 = c @ & .320 & 0I & 2 51 # bL # 9 551 # N N # 7 - - # 555 & '(- L # O "#

T T T T T T T T T T T T T T T T T T T T T T T T T T O O T T T O T T T O T T T T

O O O O T 1 #? 1 # & 1 # 1 # @ 1 # 55 555 555

%%%%% $ #% 7 7 - - # $ # 0 "3 *: QQ * L 7/1 6 1 # 3 '4 $ -; * - * B 6 7 b 6 = * K KL $ - $ # C # L "&L; * - * B 6 7 b 6 = * K KL 3 $ 6 C - #CL - 3; * - * B 6 7 b 6 = * K KL - L # 3 # 3 0 = "=; * - * B 6 7 b 6 = * K KL 1 L 2 = 3 0 =" # =; * - * B 6 7 b 6 = * K KL - # = 3 0 = "S; * - * B 6 7 b 6 = * K KL = & S $ -; * - * B 6 7 b 6 = * K KL $ - $ # C # L "&L; * - * B 6 7 b 6 = * K KL 3 $ 6 COC # L "- L; * - * B 6 7 b 6 = * K KL 3 $ 6

CC # L - 3; * - * B 6 7 b 6 = * K KL - L # 3 # O 3 0 = "=; * - * B 6 7 b 6 = * K KL 1 L 2 = 3 0 =" # =; * - * B 6 7 b 6 = * K KL - # = 3 0 = "S; * - * B 6 7 b 6 = * K KL = & S

4 #) "# 5 N d d 4 #" W) # $

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K - T K. `a / # 'K - O K. T `a / # 'L L O - T L. / # 'L O L - O `a - 7 - - # F O! O H `a * N 1 # &# 3 0 # 5` a `a / # / # K - T K. `a / # / # K - T K. - T K. `a / # / # K - T K. `a / # / # K - T K. `a / # / # L L - T L. `a` a / # / # K - T K. `a / # / # K - T K. `a / # / # K - T K. - T K. `a / # / # K - T K.

`a / # / # K - O K. T `a / # / # L L - T L. / # / # L O

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

7 * @? . N # * 9 # K & 1 K & = K & \ * = <7 * @? . N # * 9 # K & 1 K & = K & 6 \ 0 = <7 * @? . N # * 9 # K & 1 K & = K & 0 \ 6 1 <7 * @? . N # * 9 # K & 1 K & = K & 6 \ 0 1 <7 * @? . N # * 9 # K & 1 K & = K & 6 \ 0 = <7 * @? . N # * 9 # K & 1 K & = K & 0 \ 6 = <7 * @? . N # * 9 # K & 1 K & = K & * \ 1 <7 * @? . N # * 9 # K & 1 K & = K & \ * = <7 * @? . N # * 9 # K & 1 K & = K & \ * 1 <7 * @? . N # * 9 # K & 1 K & = K & 6 \ 0 1 <7 * @? . N # * 9 # K & 1 K & = K & 6 \ 0 = <7 * @? . N # * 9 # K & 1 K & = K & 6 \ 0 = <7 * @? . N # * 9 # K & 1 K & = K & \ * 1 <7 * @? . N # * 9 # K & 1 K & = K & 0 \ 6 = <7 * @? . N # * 9 # K & 1 K & = K & \ * 1 <7 * @? . N # * 9 # K & 1 K & = K & \ * 1 <7 * @? . N # * 9 # K & 1 K & = K & 6 \ 0 = <7 * @? . N # * 9 # K & 1 K & = K & 6 \ 0 1 <7 * @? . N # * 9 # K & 1 K & = K & 6 \ 0 = <7 * @? . N # * 9 # K & 1 K & = K & \ * = <7 * @? . N # * 9 # K & 1 K & = K & 6 \ * 1 <7 * @? . N # * 9 # K & 1 K & = K & 6 \ 0 1 <7 * @? . N # * 9 # K & 1 K & = K & 6 \ 0 = <7 * @? . N # * 9 # K & 1 K & = K & * \ = <7 * @? . N # * 9 # K & 1 K & = K & 6 \ 0 1 <7 * @? . N # * 9 # K & 1 K & = K & 6 \ 0 = <7 * @? . N # * 9 # K & 1 K & = K & 6 \ 0 = <7 * @? . N # * 9 # K & 1 K & = K & 6 \ 0 = <7 * @? . N # * 9 # K & 1 K & = K & \ * = <7 * @? . N # * 9 # K & 1 K & = K & \ * = <7 * @? . N # * 9 # K & 1 K & = K & 6 \ 0 1 <7 * @? . N # * 9 # K & 1 K & = K & * \ 0 = <7 * @? . N # * 9 # K & 1 K & = K & \ * = <7 * @? . N # * 9 # K & 1 K & = K & * \ 0 1 <7 * @? . N # * 9 # K & 1 K & = K & * \ 0 = <7 * @? . N # * 9 # K & 1 K & = K & * \ 1 <7 * @? . N # * 9 # K & 1 K & = K & * \ 1 <7 * @? . N # * 9 # K & 1 K & = K & \ * = <7 -2 2 #L 3 K & 1 K & = K & \ * = <7 -2 2 #L 3 K & 1 K & = K & * \ = <7 * @? . N # * 9 # K & 1 K & = K & 6 \ 0 = <7 * @? . N # * 9 # K & 1 K & = K & \ * = <7 * @? . N # * 9 # K & 1 K & = K & * \ = <7 * @? . N # * 9 # K & 1 K & = K & 6 \ 0 = <7 * @? . N # * 9 # K & 1 K & = K & \ * 1 <7 * @? . N # * 9 # K & 1 K & = K & \ * = <7 * @? . N # * 9 # K & 1 K & = K & 6 \ 0 = <7 * @? . N # * 9 # K & 1 K & = K & \ * = <7 * @? . N # * 9 # K & 1 K & = K & 0 \ 6 = <7 * @? . N # * 9 # K & 1 K & = K & 6 \ 0 1 <7 * @? . N # * 9 # K & 1 K & = K & 6 \ 0 = <7 * @? . N # * 9 # K & 1 K & = K & * \ = <7 * @? . N # * 9 # K & 1 K & = K & 6 \ 0 1 <7 * @? . N # * 9 # K & 1 K & = K & \ * 1 <7 * @? . N # * 9 # K & 1 K & = K & \ * = <7 * @? . N # * 9 # K & 1 K & = K & \ * = <7 * @? . N # * 9 # K & 1 K & = K & \ * 1 <7 * @? . N # * 9 # K & 1 K & = K & 6 \ 0 = <7 * @? . N # * 9 # K & 1 K & = K & \ * 1 <7 * @? . N # * 9 # K & 1 K & = K & \ * = <7 * @? . N # * 9 # K & 1 K & = K & 6 \ 0 = <7 * @? . N # * 9 # K & 1 K & = K & 6 \ * 1 <7 * @? . N # * 9 # K & 1 K & = K & 6 \ 0 1 <7 * @? . N # * 9 # K & 1 K & = K & 6 \ 0 = <7 * @? . N # * 9 # K & 1 K & = K & 6 \ 0 1 <7 * @? . N # * 9 # K & 1 K & = K & 6 \ 0 1 <

7 * @? . N # * 9 # K & 1 K & = K & \ * 1 <7 * @? . N # * 9 # K & 1 K & = K & 0 \ 6 1 <7 * @? . N # * 9 # K & 1 K & = K & \ * = <7 * @? . N # * 9 # K & 1 K & = K & \ * 1 <7 * @? . N # * 9 # K & 1 K & = K & 6 \ 0 1 <7 * @? . N # * 9 # K & 1 K & = K & \ * = <7 * @? . N # * 9 # K & 1 K & = K & 6 \ 0 1 <7 * @? . N # * 9 # K & 1 K & = K & 6 \ 0 = <7 * @? . N # * 9 # K & 1 K & = K & 6 \ 0 = <7 * @? . N # * 9 # K & 1 K & = K & \ * = <7 * @? . N # * 9 # K & 1 K & = K & 6 \ 0 1 <7 * @? . N # * 9 # K & 1 K & = K & \ * 1 <7 * @? . N # * 9 # K & 1 K & = K & 0 \ 6 = <7 * @? . N # * 9 # K & 1 K & = K & 0 \ 6 = <7 * @? . N # * 9 # K & 1 K & = K & \ * = <7 * @? . N # * 9 # K & 1 K & = K & \ * 1 <7 * @? . N # * 9 # K & 1 K & = K & \ * 1 <7 * @? . N # * 9 # K & 1 K & = K & 6 \ 0 = <7 * @? . N # * 9 # K & 1 K & = K & 6 \ 0 1 <7 * @? . N # * 9 # K & 1 K & = K & * \ 1 <7 * @? . N # * 9 # K & 1 K & = K & \ * 1 <7 00 ^ * 9 / = # K & 1 K & = K & 0 \ 6 = <7 00 ^ * 9 / = # K & 1 K & = K & \ 0 = <7 00 ^ * 9 / = # K & 1 K & = K & 6 \ * 1 <7 00 ^ * 9 / = # K & 1 K & = K & \ * 1 <7 00 ^ * 9 / = # K & 1 K & = K & 6 \ 0 = <7 00 ^ * 9 / = # K & 1 K & = K & 6 \ 0 = <7 00 ^ * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 00 ^ * 9 / = # K & 1 K & = K & \ * = <7 00 ^ * 9 / = # K & 1 K & = K & \ * = <7 00 ^ * 9 / = # K & 1 K & = K & 6 \ 0 = <7 00 ^ * 9 / = # K & 1 K & = K & * \ 6 = <7 00 ^ * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 00 ^ * 9 / = # K & 1 K & = K & \ * 1 <7 00 ^ * 9 / = # K & 1 K & = K & 0 \ 6 1 <7 00 ^ * 9 / = # K & 1 K & = K & \ * 1 <7 00 ^ * 9 / = # K & 1 K & = K & \ * 1 <7 00 ^ * 9 / = # K & 1 K & = K & 0 \ * 1 <7 00 ^ * 9 / = # K & 1 K & = K & \ * 1 <7 00 ^ * 9 / = # K & 1 K & = K & * \ 1 <7 -0 ^ * 9 / = # K & 1 K & = K & 6 \ 0 = <7 -0 ^ * 9 / = # K & 1 K & = K & 6 \ 0 = <7 -0 ^ * 9 / = # K & 1 K & = K & 0 \ 6 = <7 -0 ^ * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 -0 ^ * 9 / = # K & 1 K & = K & * \ 1 <7 -0 ^ * 9 / = # K & 1 K & = K & 0 \ 6 = <7 -0 ^ * 9 / = # K & 1 K & = K & \ * 1 <7 -0 ^ * 9 / = # K & 1 K & = K & * \ 1 <7 -0 ^ * 9 / = # K & 1 K & = K & 0 \ 6 1 <7 -0 ^ * 9 / = # K & 1 K & = K & 0 \ 6 = <7 -0 ^ * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 -0 ^ * 9 / = # K & 1 K & = K & 0 \ 6 1 <7 -0 ^ * 9 / = # K & 1 K & = K & 0 \ 6 1 <7 -0 ^ * 9 / = # K & 1 K & = K & 6 \ 0 = <7 -0 ^ * 9 / = # K & 1 K & = K & \ * = <7 -0 ^ * 9 / = # K & 1 K & = K & 6 \ 0 = <7 -0 ^ * 9 / = # K & 1 K & = K & 0 \ 6 = <7 -0 ^ * 9 / = # K & 1 K & = K & \ * 1 <7 -0 ^ * 9 / = # K & 1 K & = K & \ * = <7 -0 ^ * 9 / = # K & 1 K & = K & \ * 1 <7 -0 ^ * 9 / = # K & 1 K & = K & \ * 1 <7 -0 ^ * 9 / = # K & 1 K & = K & \ * 1 < 7 -0 ^ * 9 / = # K & 1 K & = K & \ * = <7 -0 ^ * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 -0 ^ * 9 / = # K & 1 K & = K & \ * 1 <7 -0 ^ * 9 / = # K & 1 K & = K & 0 \ 1 <

7 -0 ^ * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 -0 ^ * 9 / = # K & 1 K & = K & 6 \ 0 = <7 -0 ^ * 9 / = # K & 1 K & = K & \ 6 = <7 A23 * 9 / = # K & 1 K & = K & \ * 1 <7 A23 * 9 / = # K & 1 K & = K & 0 \ * 1 <7 A23 * 9 / = # K & 1 K & = K & 0 \ 6 1 <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 = <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 = <7 A23 * 9 / = # K & 1 K & = K & 0 \ 6 1 <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 A23 * 9 / = # K & 1 K & = K & \ * = <7 A23 * 9 / = # K & 1 K & = K & 0 \ 6 = <7 A23 * 9 / = # K & 1 K & = K & \ * = <7 A23 * 9 / = # K & 1 K & = K & \ * 1 <7 A23 * 9 / = # K & 1 K & = K & \ * 1 <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 = <7 A23 * 9 / = # K & 1 K & = K & 0 \ * 1 <7 A23 * 9 / = # K & 1 K & = K & \ * = <7 A23 * 9 / = # K & 1 K & = K & \ * = <7 A23 * 9 / = # K & 1 K & = K & \ * 1 <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 A23 * 9 / = # K & 1 K & = K & \ * 1 <7 A23 * 9 / = # K & 1 K & = K & \ * 1 <7 A23 * 9 / = # K & 1 K & = K & \ * 1 <7 A23 * 9 / = # K & 1 K & = K & \ * = <7 A23 * 9 / = # K & 1 K & = K & 0 \ 6 1 <7 A23 * 9 / = # K & 1 K & = K & \ * = <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 A23 * 9 / = # K & 1 K & = K & \ * 1 <7 A23 * 9 / = # K & 1 K & = K & 0 \ 6 1 <7 A23 * 9 / = # K & 1 K & = K & \ * = <7 A23 * 9 / = # K & 1 K & = K & 0 \ 6 = <7 A23 * 9 / = # K & 1 K & = K & \ * 1 <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 = <7 A23 * 9 / = # K & 1 K & = K & 0 \ = <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 = <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 A23 * 9 / = # K & 1 K & = K & \ 0 1 <7 A23 * 9 / = # K & 1 K & = K & \ * 1 <7 A23 * 9 / = # K & 1 K & = K & 0 \ 6 = <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 1 < 7 A23 * 9 / = # K & 1 K & = K & \ * 1 <7 A23 * 9 / = # K & 1 K & = K & 0 \ 6 = <7 A23 * 9 / = # K & 1 K & = K & 0 \ 6 = <7 A23 * 9 / = # K & 1 K & = K & \ * 1 <7 A23 * 9 / = # K & 1 K & = K & \ * 1 <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 A23 * 9 / = # K & 1 K & = K & \ * 1 <7 A23 * 9 / = # K & 1 K & = K & 6 \ * = <7 A23 * 9 / = # K & 1 K & = K & 6 \ * = <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 A23 * 9 / = # K & 1 K & = K & \ * 1 <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 A23 * 9 / = # K & 1 K & = K & \ * 1 <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 = <7 A23 * 9 / = # K & 1 K & = K & \ * 1 <7 A23 * 9 / = # K & 1 K & = K & \ * = <7 A23 * 9 / = # K & 1 K & = K & \ * = <7 A23 * 9 / = # K & 1 K & = K & \ * = <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 A23 * 9 / = # K & 1 K & = K & \ * 1 <

7 A23 * 9 / = # K & 1 K & = K & 0 \ 6 = <7 A23 * 9 / = # K & 1 K & = K & \ * 1 <7 A23 * 9 / = # K & 1 K & = K & \ * 1 <7 A23 * 9 / = # K & 1 K & = K & \ * 1 <7 A23 * 9 / = # K & 1 K & = K & 0 \ 6 = <7 A23 * 9 / = # K & 1 K & = K & 0 \ 6 1 <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 A23 * 9 / = # K & 1 K & = K & \ * = <7 A23 * 9 / = # K & 1 K & = K & \ * 1 <7 A23 * 9 / = # K & 1 K & = K & 0 \ * = <7 A23 * 9 / = # K & 1 K & = K & 0 \ 6 = <7 A23 * 9 / = # K & 1 K & = K & 0 \ 6 1 <7 A23 * 9 / = # K & 1 K & = K & 0 \ 6 1 <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 A23 * 9 / = # K & 1 K & = K & 0 \ 6 = <7 A23 * 9 / = # K & 1 K & = K & \ * = <7 A23 * 9 / = # K & 1 K & = K & 0 \ 6 1 <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 A23 * 9 / = # K & 1 K & = K & 0 \ 6 1 <7 A23 * 9 / = # K & 1 K & = K & \ * = <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 A23 * 9 / = # K & 1 K & = K & 0 \ * 1 <7 A23 * 9 / = # K & 1 K & = K & 0 \ 6 = <7 A23 * 9 / = # K & 1 K & = K & \ * = <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 = <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 A23 * 9 / = # K & 1 K & = K & \ * = <7 A23 * 9 / = # K & 1 K & = K & * \ 0 = <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 = <7 A23 * 9 / = # K & 1 K & = K & 0 \ 6 = <7 A23 * 9 / = # K & 1 K & = K & \ * = <7 A23 * 9 / = # K & 1 K & = K & \ * = <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 = <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 = <7 A23 * 9 / = # K & 1 K & = K & \ * = <7 A23 * 9 / = # K & 1 K & = K & 0 \ 6 = <7 A23 * 9 / = # K & 1 K & = K & * \ = <7 A23 * 9 / = # K & 1 K & = K & 0 \ * = <7 A23 * 9 / = # K & 1 K & = K & \ * 1 <7 A23 * 9 / = # K & 1 K & = K & * \ = <7 A23 * 9 / = # K & 1 K & = K & \ * = <7 A23 * 9 / = # K & 1 K & = K & \ * = <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 = <7 A23 * 9 / = # K & 1 K & = K & 0 \ 6 1 <7 A23 * 9 / = # K & 1 K & = K & 0 \ 6 = <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 = <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 = <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 A23 * 9 / = # K & 1 K & = K & \ * 1 <7 A23 * 9 / = # K & 1 K & = K & \ 6 = <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 A23 * 9 / = # K & 1 K & = K & \ * = <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 = <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 = <7 A23 * 9 / = # K & 1 K & = K & \ * 1 <7 A23 * 9 / = # K & 1 K & = K & 0 \ 6 = <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 = <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 = <7 A23 * 9 / = # K & 1 K & = K & 0 \ 6 = <7 A23 * 9 / = # K & 1 K & = K & \ * = <7 A23 * 9 / = # K & 1 K & = K & \ * 1 <

7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 = <7 A23 * 9 / = # K & 1 K & = K & \ * 1 <7 A23 * 9 / = # K & 1 K & = K & \ * = <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 = <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 = <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 = <7 A23 * 9 / = # K & 1 K & = K & \ * 1 <7 A23 * 9 / = # K & 1 K & = K & \ * = <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 = <7 A23 * 9 / = # K & 1 K & = K & \ * 1 <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 = <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 = <7 A23 * 9 / = # K & 1 K & = K & \ * 1 <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 = < 7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 = <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 A23 * 9 / = # K & 1 K & = K & 0 \ 6 = <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 A23 * 9 / = # K & 1 K & = K & * \ 0 1 <7 A23 * 9 / = # K & 1 K & = K & 0 \ 6 1 <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 A23 * 9 / = # K & 1 K & = K & \ * = <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 = <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 A23 * 9 / = # K & 1 K & = K & \ * 1 <7 A23 * 9 / = # K & 1 K & = K & * \ 0 1 <7 A23 * 9 / = # K & 1 K & = K & 0 \ * 1 <7 A23 * 9 / = # K & 1 K & = K & \ * = <7 A23 * 9 / = # K & 1 K & = K & \ * 1 <7 A23 * 9 / = # K & 1 K & = K & 0 \ = <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 = <7 A23 * 9 / = # K & 1 K & = K & \ * 1 <7 A23 * 9 / = # K & 1 K & = K & \ * 1 <7 A23 * 9 / = # K & 1 K & = K & \ * 1 <7 A23 * 9 / = # K & 1 K & = K & \ * = <7 A23 * 9 / = # K & 1 K & = K & 0 \ 6 = <7 A23 * 9 / = # K & 1 K & = K & 0 \ 6 1 <7 A23 * 9 / = # K & 1 K & = K & 0 \ 6 = <7 A23 * 9 / = # K & 1 K & = K & \ 6 = <7 A23 * 9 / = # K & 1 K & = K & \ 0 1 <7 A23 * 9 / = # K & 1 K & = K & 0 \ 1 <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 A23 * 9 / = # K & 1 K & = K & 0 \ * = < 7 A23 * 9 / = # K & 1 K & = K & \ * 1 <7 A23 * 9 / = # K & 1 K & = K & \ * 1 <7 A23 * 9 / = # K & 1 K & = K & \ * 1 <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 A23 * 9 / = # K & 1 K & = K & \ * = <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 = <7 A23 * 9 / = # K & 1 K & = K & 0 \ 6 = <7 A23 * 9 / = # K & 1 K & = K & 0 \ = <7 A23 * 9 / = # K & 1 K & = K & * \ = <7 A23 * 9 / = # K & 1 K & = K & 0 \ 6 1 <7 A23 * 9 / = # K & 1 K & = K & 0 \ 6 1 <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 = <7 A23 * 9 / = # K & 1 K & = K & \ * 1 <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 A23 * 9 / = # K & 1 K & = K & 0 \ 6 = <7 A23 * 9 / = # K & 1 K & = K & \ * 1 <7 A23 * 9 / = # K & 1 K & = K & \ * 1 <7 A23 * 9 / = # K & 1 K & = K & 0 \ 1 <7 A23 * 9 / = # K & 1 K & = K & \ * 1 <7 A23 * 9 / = # K & 1 K & = K & \ * 1 <

7 A23 * 9 / = # K & 1 K & = K & * \ 0 = <7 A23 * 9 / = # K & 1 K & = K & * \ = <7 A23 * 9 / = # K & 1 K & = K & \ * 1 <7 A23 * 9 / = # K & 1 K & = K & \ * 1 <7 A23 * 9 / = # K & 1 K & = K & \ * 1 <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 A23 * 9 / = # K & 1 K & = K & 0 \ 6 = <7 A23 * 9 / = # K & 1 K & = K & \ * = <7 A23 * 9 / = # K & 1 K & = K & 0 \ 6 = <7 A23 * 9 / = # K & 1 K & = K & \ * 1 <7 A23 * 9 / = # K & 1 K & = K & \ * = <7 A23 * 9 / = # K & 1 K & = K & * \ 1 <7 A23 * 9 / = # K & 1 K & = K & \ * = <7 A23 * 9 / = # K & 1 K & = K & \ * = <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 = <7 A23 * 9 / = # K & 1 K & = K & 0 \ 6 = <7 A23 * 9 / = # K & 1 K & = K & \ * = <7 A23 * 9 / = # K & 1 K & = K & 0 \ = <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 = <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 A23 * 9 / = # K & 1 K & = K & \ * 1 <7 A23 * 9 / = # K & 1 K & = K & \ * = <7 A23 * 9 / = # K & 1 K & = K & 0 \ 6 = <7 A23 * 9 / = # K & 1 K & = K & \ * 1 <7 A23 * 9 / = # K & 1 K & = K & 0 \ 1 <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 A23 * 9 / = # K & 1 K & = K & 0 \ 1 <7 A23 * 9 / = # K & 1 K & = K & 0 \ 6 = <7 A23 * 9 / = # K & 1 K & = K & 0 \ 6 = <7 A23 * 9 / = # K & 1 K & = K & \ * = <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 = <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 = <7 A23 * 9 / = # K & 1 K & = K & \ * = <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 = <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 = <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 A23 * 9 / = # K & 1 K & = K & 0 \ 6 1 <7 A23 * 9 / = # K & 1 K & = K & \ * 1 <7 A23 * 9 / = # K & 1 K & = K & \ * = <7 A23 * 9 / = # K & 1 K & = K & \ * = <7 A23 * 9 / = # K & 1 K & = K & \ * = <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 = <7 A23 * 9 / = # K & 1 K & = K & 0 \ 6 = <7 A23 * 9 / = # K & 1 K & = K & \ * = <7 A23 * 9 / = # K & 1 K & = K & 0 \ 6 = <7 A23 * 9 / = # K & 1 K & = K & * \ = <7 A23 * 9 / = # K & 1 K & = K & 0 \ 6 1 <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 A23 * 9 / = # K & 1 K & = K & * \ 0 = <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 = <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 = <7 A23 * 9 / = # K & 1 K & = K & \ * 1 <7 A23 * 9 / = # K & 1 K & = K & \ * 1 <7 A23 * 9 / = # K & 1 K & = K & \ * = <7 A23 * 9 / = # K & 1 K & = K & \ * = <7 A23 * 9 / = # K & 1 K & = K & \ * 1 <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 A23 * 9 / = # K & 1 K & = K & \ * = <7 A23 * 9 / = # K & 1 K & = K & \ * 1 <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 1 <

7 A23 * 9 / = # K & 1 K & = K & \ 6 = <7 A23 * 9 / = # K & 1 K & = K & \ * = <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 = <7 A23 * 9 / = # K & 1 K & = K & 0 \ 6 = <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 A23 * 9 / = # K & 1 K & = K & \ 6 1 <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 A23 * 9 / = # K & 1 K & = K & \ * = <7 A23 * 9 / = # K & 1 K & = K & 6 \ * 1 <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 = <7 A23 * 9 / = # K & 1 K & = K & 0 \ 6 1 <7 A23 * 9 / = # K & 1 K & = K & \ * = <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 A23 * 9 / = # K & 1 K & = K & \ * = <7 A23 * 9 / = # K & 1 K & = K & \ * = <7 A23 * 9 / = # K & 1 K & = K & 0 \ 6 = <7 A23 * 9 / = # K & 1 K & = K & 0 \ 6 = <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 = <7 A23 * 9 / = # K & 1 K & = K & 0 \ 6 = <7 A23 * 9 / = # K & 1 K & = K & 0 \ 6 1 <7 A23 * 9 / = # K & 1 K & = K & 0 \ * 1 <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 A23 * 9 / = # K & 1 K & = K & \ * = <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 A23 * 9 / = # K & 1 K & = K & 6 \ 0 1 <7 A23 * 9 / = # K & 1 K & = K & \ * = <7 A23 * 9 / = # K & 1 K & = K & * \ = <7 A23 * 9 / = # K & 1 K & = K & * \ 0 = <7 A23 * 9 / = # K & 1 K & = K & * \ = <7 A23 * 9 / = # K & 1 K & = K & 0 \ 6 = <7 A23 * 9 / = # K & 1 K & = K & 0 \ = <7 A23 * 9 / = # K & 1 K & = K & * \ 1 <7 A23 * 9 / = # K & 1 K & = K & 0 \ 6 = <7 A23 * 9 / = # K & 1 K & = K & * \ = <7 A23 * 9 / = # 3 K & 1 K & = K & * \ = <7 A23 * 9 / = # 7 #. K & 1 K & = K & \ * = <7 A23 * 9 / = # 7 #. K & 1 K & = K & * \ 6 = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & \ * = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & \ * = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 0 \ 1 < 7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & \ * 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & \ * = <7 7 = 0 * N $ * 9 / 1 # K & 1 K & = K & 6 \ 0 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 0 \ 6 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & \ * = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & \ * 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & * \ 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & * \ 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & \ * 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & * \ 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 0 \ 6 = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 0 \ 6 = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & \ * = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & * \ 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & * \ 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & \ * = <

7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 0 \ 6 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 0 \ 6 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & \ * = <7 7 = 0 * N $ * 9 / 1 # K & 1 K & = K & \ * 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 0 \ 6 = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & * \ = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 0 \ 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & * \ 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & \ * 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 0 \ 6 = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ * = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & * \ 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & * \ 0 = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & * \ 0 = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & \ * 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 1 <7 7 = 0 * N $ * 9 / 1 # K & 1 K & = K & \ * 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & \ * = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & * \ = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 0 \ 6 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 0 \ 6 = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & * \ 6 = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & \ * 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & \ * = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & \ * 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & * \ 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & \ 0 = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & * \ 0 = < 7 7 = 0 * N $ * 9/1 # K & 1 K & = K & * \ 6 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & \ * = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & * \ = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & \ * = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & * \ = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & * \ = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & * \ = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & \ * = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 0 \ 6 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & \ * 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & \ * = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & \ * 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & \ * = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 0 \ 6 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & * \ 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 0 \ 6 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 1 <

7 7 = 0 * N $ * 9/1 # K & 1 K & = K & \ * = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & * \ = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 1 <7 7 = 0 * N $ * 9 / 1 # K & 1 K & = K & * \ = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & \ * 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & \ * 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & \ 6 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & \ * 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 0 \ 6 = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 0 \ = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & \ * = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & * \ 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 1 < 7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & \ * = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 0 \ 6 = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & \ * = <7 7 = 0 * N $ * 9 / 1 # K & 1 K & = K & 6 \ 0 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 0 \ 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & * \ = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 0 \ = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & \ * 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & * \ = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & * \ 0 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 0 \ 6 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 1 <7 7 = 0 * N $ * 9 / 1 # K & 1 K & = K & 6 \ 0 = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 0 \ 6 = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & \ * 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & \ * 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & \ * = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & \ * = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & * \ = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & \ * = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & \ * = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & \ 0 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & \ * 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & * \ 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & \ * 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & \ * 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 0 \ 6 = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & \ * 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & * \ = < 7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 = <

7 7 = 0 * N $ * 9/1 # K & 1 K & = K & \ * 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 1 <7 7 = 0 * N $ * 9 / 1 # K & 1 K & = K & \ * = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & \ * = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & \ * = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & \ * = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & \ 6 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & \ * 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 0 \ 6 = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 0 \ 6 = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 0 \ 6 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & \ * 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 6 \ 0 = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 0 \ 6 1 <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 0 \ 6 = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & * \ = <7 7 = 0 * N $ * 9/1 # K & 1 K & = K & 0 \ 6 = <

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/ #; * - * & '4 6 B &' 4 & '4 2 L "LFH and 7 - - B KL F1" 6 "Q 7 $ # 23 0 2 # * 5 * K * 3 H 2 L KL M / * # 7 00 ^ * 9 / = # K 1 K = K 6 \ L? B! 7 00 ^ * 9 / = # K 1 K = K \ 0 L * 0 * 0.! 7 00 ^ * 9 / = # K 1 K = K 0 \ 6 L 7 00 ^ * 9 / = # K 1 K = K * \ 6 L 0? 1! 7 00 ^ * 9 / = # K 1 K = K 6 \ L 7 00 ^ * 9 / = # K 1 K = K \ 0 L 7 00 ^ * 9 / = # K 1 K = K 6 \ 0 L .71! 7 00 ^ * 9 / = # K 1 K = K * \ L 7 00 ^ * 9 / = # K 1 K = K 6 \ L 7 00 ^ * 9 / = # K 1 K = K * \ 0 L 0A! 7 00 ^ * 9 / = # K 1 K = K 6 \ L B. *,! 7 00 ^ * 9 / = # K 1 K = K * \ L 7 00 ^ * 9 / = # K 1 K = K * \ L? 2 0! 7 00 ^ * 9 / = # K 1 K = K 6 \ 0 L? == * *! 7 00 ^ * 9 / = # K 1 K = K 6 \ 0 L 7 00 ^ * 9 / = # K 1 K = K * \ 6 L 7 00 ^ * 9 / = # K 1 K = K 6 \ 0 L. @I! 7 00 ^ * 9 / = # K 1 K = K 0 \ 6 L 7 00 ^ * 9 / = # K 1 K = K * \ L 7 00 ^ * 9 / = # K I1 KI = KI \ 0 L 7 00 ^ * 9 / = # K I1 KI = KI 6 \ 0 L 6 = & *! 7 00 ^ * 9 / = # K I1 KI = KI 6 \ L 7 00 ^ * 9 / = # K 1 K = K 0 \ L 7 00 ^ * 9 / = # K 1 K = K 0 \ L 7 00 ^ * 9 / = # K 1 K = K 6 \ 0 L f 1! 7 00 ^ * 9 / = # K 1 K = K \ 0 L 7 00 ^ * 9 / = # K 1 K = K 0 \ L 7 00 ^ * 9 / = # K 1 K = K 0 \ L 7 00 ^ * 9 / = # K 1 K = K * \ L 7 00 ^ * 9 / = # K 1 K = K 6 \ L 7 00 ^ * 9 / = # K 1 K = K * \ L & 0! 7 00 ^ * 9 / = # K 1 K = K * \ 0 L 7 00 ^ * 9 / = # K 1 K = K 6 \ 0 L 6 & 0! 7 00 ^ * 9 / = # K 1 K = K * \ L 2! 7 00 ^ * 9 / = # K 1 K = K * \ 0 L * 3A0? ! 7 00 ^ * 9 / = # K 1 K = K 6 \ 0 L 7 00 ^ * 9 / = # K 1 K = K \ 0 L 7 00 ^ * 9 / = # K 1 K = K 6 \ 0 L IA =! 7 00 ^ * 9 / = # K 1 K = K * \ 0 L * - & 3! 7 00 ^ * 9 / = # K 1 K = K * \ 6 L fBI! 7 00 ^ * 9 / = # K 1 K = K 6 \ * L -B *! 7 00 ^ * 9 / = # K 1 K = K 6 \ 0 L 7 00 ^ * 9 / = # K 1 K = K * \ 6 L. 3 O0- ** 3 7! 7 00 ^ * 9 / = # K 1 K = K * \ 0 L & -1 N / 7 00 ^ * 9 / = # K 1 K = K * \ L 7 00 ^ * 9 / = # K 1 K = K 6 \ 0 L 7 00 ^ * 9 / = # K 1 K = K * \ L 6 = 20! 7 00 ^ * 9 / = # K 1 K = K \ 0 L 7 00 ^ * 9 / = # K 1 K = K 0 \ L = I! 7 00 ^ * 9 / = # K 1 K = K * \ 6 L 7 *! 7 00 ^ * 9 / = # K 1 K = K 0 \ 6 L 7 00 ^ * 9 / = # K 1 K = K * \ L 3 **! 7 00 ^ * 9 / = # K 1 K = K 6 \ 0 L 7 00 ^ * 9 / = # K 1 K = K * \ L 7 00 ^ * 9 / = # K 1 K = K 0 \ L - AND ! 7 00 ^ * 9 / = # K 1 K = K 6 \ * L 26-1! 7 00 ^ * 9 / = # K 1 K = K \ 0 L 3. 7! 7 00 ^ * 9 / = # K 1 K = K \ 6 L 7 00 ^ * 9 / = # K 1 K = K 6 \ * L 7 00 ^ * 9 / = # K 1 K = K * \ L f 1 7! 7 00 ^ * 9 / = # K 1 K = K * \ L B = 7! 7 00 ^ * 9 / = # K 1 K = K 6 \ 0 L 7 00 ^ * 9 / = # K 1 K = K 6 \ 0 L 7 00 ^ * 9 / = # K 1 K = K 6 \ 0 L 3 * 3 *! 7 00 ^ * 9 / = # K 1 K = K 0 \ L 7 00 ^ * 9 / = # K 1 K = K 0 \ L 7 00 ^ * 9 / = # K 1 K = K 6 \ * L 7 00 ^ * 9 / = # K 1 K = K * \ L B & 7 @ I! 7 00 ^ * 9 / = # K 1 K = K * \ L -! 7 00 ^ * 9 / = # K 1 K = K 6 \ L? @ *! 7 00 ^ * 9 / = # K 1 K = K 0 \ 6 L 7 00 ^ * 9 / = # K 1 K = K 6 \ L - 60? !

7 00 ^ * 9 / = # K 1 K = K 0 \ L 7 *! 7 00 ^ * 9 / = # K 1 K = K 6 \ L 7 00 ^ * 9 / = # K 1 K = K * \ L 7 00 ^ * 9 / = # K 1 K = K * \ 0 L @ 71 *! 7 00 ^ * 9 / = # K 1 K = K 6 \ 0 L 1E7! 7 00 ^ * 9 / = # K 1 K = K \ 0 L = *! 7 00 ^ * 9 / = # K 1 K = K * \ L 7 00 ^ * 9 / = # K 1 K = K * \ 0 L 7 00 ^ * 9 / = # K 1 K = K * \ 0 L 7 00 ^ * 9 / = # K 1 K = K * \ L 7 00 ^ * 9 / = # K I1 KI = KI * \ L 7 00 ^ * 9 / = # K I1 KI = KI 6 \ 0 L 7 -0 ^ * 9 / = # K 1 K = K * \ 0 L 0 01 N / 7 -0 ^ * 9 / = # K 1 K = K * \ L @ -7? ! 7 -0 ^ * 9 / = # K 1 K = K 6 \ 0 L @ = *! 7 -0 ^ * 9 / = # K 1 K = K 6 \ 0 L 0B * E? ! 7 -0 ^ * 9 / = # K 1 K = K 0 \ L & .1 3! 7 -0 ^ * 9 / = # K 1 K = K * \ L 0? ! 7 -0 ^ * 9 / = # K 1 K = K 6 \ L 7 -0 ^ * 9 / = # K 1 K = K 6 \ 0 L & 2 = 6 * & - 12? 2? ! 7 -0 ^ * 9 / = # K 1 K = K * \ L 7 -0 ^ * 9 / = # K 1 K = K 6 \ L. &! 7 -0 ^ * 9 / = # K 1 K = K * \ L - @ * A! 7 -0 ^ * 9 / = # K 1 K = K 6 \ 0 L 2 A! 7 -0 ^ * 9 / = # K 1 K = K 6 \ 0 L B. * 0! 7 -0 ^ * 9 / = # K 1 K = K * \ L 7 -0 ^ * 9 / = # K 1 K = K * \ L 7 -0 ^ * 9 / = # K 1 K = K * \ 6 L 3; -! 7 -0 ^ * 9 / = # K 1 K = K * \ L 7 * 7 *! 7 -0 ^ * 9 / = # K 1 K = K 6 \ 0 L f & 0! 7 -0 ^ * 9 / = # K 1 K = K 0 \ 6 L 7 -0 ^ * 9 / = # K 1 K = K 6 \ 0 L -? * 0! 7 -0 ^ * 9 / = # K 1 K = K * \ L 7 -0 ^ * 9 / = # K 1 K = K 6 \ 0 L *?. * 0! 7 -0 ^ * 9 / = # K 1 K = K 6 \ * L 7 -0 ^ * 9 / = # K 1 K = K * \ L! 7 -0 ^ * 9 / = # K 1 K = K 6 \ 0 L 7 -0 ^ * 9 / = # K 1 K = K 6 \ 0 L 3 0B! 7 -0 ^ * 9 / = # K 1 K = K 6 \ 0 L? 2 *! 7 -0 ^ * 9 / = # K I1 K I = KI * \ L 7 -0 ^ * 9 / = # K I1 K I = KI * \ L B30 *! 7 -0 ^ * 9 / = # K I1 K I = KI 6 \ 0 L B- -! 7 -0 ^ * 9 / = # K I1 K I = KI 6 \ 0 L 7 -0 ^ * 9 / = # K 1 K = K * \ L? = 21! 7 -0 ^ * 9 / = # K 1 K = K * \ L = 0760? ! 7 -0 ^ * 9 / = # K 1 K = K 6 \ L 2A * 7! 7 -0 ^ * 9 / = # K 1 K = K 6 \ L .- == 6! 7 -0 ^ * 9 / = # K 1 K = K 0 \ 6 L 2? 7! 7 -0 ^ * 9 / = # K 1 K = K * \ L & E = 2! 7 -0 ^ * 9 / = # K 1 K = K * \ L & 062! 7 -0 ^ * 9 / = # K 1 K = K * \ L 7 -0 ^ * 9 / = # K 1 K = K * \ L 7 -0 ^ * 9 / = # K 1 K = K * \ 0 L 7 -0 ^ * 9 / = # K 1 K = K * \ L 7 -0 ^ * 9 / = # K 1 K = K * \ L 7 -0 ^ * 9 / = # K 1 K = K * \ L 7 -0 ^ * 9 / = # K 1 K = K 6 \ L 7 -0 ^ * 9 / = # K 1 K = K * \ 0 L 7 -0 ^ * 9 / = # K 1 K = K 6 \ 0 L -E = 6 N / 7 -0 ^ * 9 / = # K 1 K = K * \ 6 L 3? 60! 7 -0 ^ * 9 / = # K I1 KI = KI 6 \ 0 L 7 -0 ^ * 9 / = # K E1 KE = KE * \ 0 L 7 -0 ^ * 9 / = # K 1 K = K * \ 0 L 7 *! 7 -0 ^ * 9 / = # K 1 K = K * \ L 7 -0 ^ * 9 / = # K 1 K = K 6 \ * L - 3E0! 7 -0 ^ * 9 / = # K 1 K = K 6 \ L & .2-! 7 -0 ^ * 9 / = # K 1 K = K 6 \ L 7 -0 ^ * 9 / = # K 1 K = K * \ L 7 -0 ^ * 9 / = # K 1 K = K * \ L 7 -0 ^ * 9 / = # K 1 K = K 6 \ 0 L & .1 *! 7 -0 ^ * 9 / = # K 1 K = K \ 6 L & = 3-! 7 -0 ^ * 9 / = # K 1 K = K 6 \ L 7 -0 ^ * 9 / = # K 1 K = K * \ L? .AB6! 7 -0 ^ * 9 / = # K 1 K = K * \ L 0A! 7 -0 ^ * 9 / = # K 1 K = K * \ 6 L 7 -0 ^ * 9 / = # K 1 K = K 6 \ * L 6 = 2A! 7 -0 ^ * 9 / = # K 1 K = K 6 \ 0 L = 2 * A ?. ! 7 -0 ^ * 9 / = # K 1 K = K 6 \ L 7 -0 ^ * 9 / = # K 1 K = K * \ L 3 1!

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

7 * @? . N # * 9 # K 1 K = K 6 \ * L 7 * @? . N # * 9 # K 1 K = K 6 \ * L 7 * 3 7! 7 * @? . N # * 9 # K 1 K = K 6 \ L 7 * @? . N # * 9 # K 1 K = K 6 \ L 3-! 7 * @? . N # * 9 # K 1 K = K 6 \ 0 L & *! 7 * @? . N # * 9 # K 1 K = K 6 \ * L 7 * @? . N # * 9 # K 1 K = K * \ 6 L & ?? ! 7 * @? . N # * 9 # K 1 K = K * \ 6 L &. &! 7 * @? . N # * 9 # K 1 K = K 6 \ 0 L 7 * @? . N # * 9 # K 1 K = K \ 6 L *.? 1! 7 * @? . N # * 9 # K 1 K = K 6 \ 0 L 001! 7 * @? . N # * 9 # K 1 K = K * \ L 0 = -! 7 * @? . N # * 9 # K 1 K = K 6 \ * L 7 * @? . N # * 9 # K 1 K = K * \ L 6 = 2A! 7 * @? . N # * 9 # K 1 K = K * \ L 7 * @? . N # * 9 # K 1 K = K 6 \ 0 L 7 * @? . N # * 9 # K 1 K = K * \ 0 L 7 * @? . N # * 9 # K 1 K = K 6 \ 0 L - 0! 7 * @? . N # * 9 # K 1 K = K \ * L 261 =! 7 * @? . N # * 9 # K 1 K = K 6 \ L = 71 @ I! 7 * @? . N # * 9 # K 1 K = K * \ 0 L 6-6? ! 7 * @? . N # * 9 # K 1 K = K 0 \ 6 L 7 * @? . N # * 9 # K 1 K = K * \ 0 L = 07 12! 7 * @? . N # * 9 # K 1 K = K * \ L ?? N / 7 * @? . N # * 9 # K I1 K I = KI 6 \ L 3; - N / 7 * @? . N # * 9 # K I1 K I = KI 6 \ 0 L 0-3! 7 * @? . N # * 9 # K E1 K E = KE 6 \ 0 L 7 * @? . N # * 9 # K 1 K = K 6 \ L * @ = 1! 7 * @? . N # * 9 # K 1 K = K 6 \ 0 L 7 * @? . N # * 9 # K 1 K = K 6 \ L 7 * @? . N # * 9 # K 1 K = K 6 \ L & 2 1! 7 * @? . N # * 9 # K 1 K = K 6 \ L 7 * @? . N # * 9 # K 1 K = K * \ 6 L ,, = N / 7 * @? . N # * 9 # K 1 K = K * \ L 7 * @? . N # * 9 # K 1 K = K * \ L 7 * @? . N # * 9 # K 1 K = K * \ 6 L 7 * @? . N # * 9 # K 1 K = K 6 \ L 7 * @? . N # * 9 # K 1 K = K 6 \ 0 L 3. 3! 7 * @? . N # * 9 # K 1 K = K \ 0 L 7 * @? . N # * 9 # K 1 K = K 6 \ 0 L & 0 0! 7 * @? . N # * 9 # K 1 K = K * \ L A200 - @ 60 N / 7 * @? . N # * 9 # K 1 K = K 6 \ 0 L B2C. ! 7 * @? . N # * 9 # K 1 K = K * \ L 7 * @? . N # * 9 # K 1 K = K 6 \ 0 L 7 * @? . N # * 9 # K 1 K = K 6 \ 0 L 2 A 0! 7 * @? . N # * 9 # K 1 K = K 6 \ L 0 A! 7 * @? . N # * 9 # K 1 K = K 6 \ 0 L? *. ! 7 * @? . N # * 9 # K 1 K = K 6 \ L 7 * @? . N # * 9 # K 1 K = K * \ 6 L 7 * @? . N # * 9 # K 1 K = K 6 \ 0 L *?. * 0! 7 * @? . N # * 9 # K 1 K = K * \ L 7 * @? . N # * 9 # K 1 K = K * \ L 7 * @? . N # * 9 # K 1 K = K * \ 0 L * @ f! 7 * @? . N # * 9 # K 1 K = K 6 \ 0 L 7 * @? . N # * 9 # K 1 K = K 0 \ L f 1! 7 * @? . N # * 9 # K 1 K = K 6 \ 0 L 7 * @? . N # * 9 # K 1 K = K * \ L 7 * @? . N # * 9 # K 1 K = K 6 \ 0 L 7 * @? . N # * 9 # K 1 K = K 6 \ L 0! 7 * @? . N # * 9 # K I1 K I = KI \ 6 L 7 -2 2 3 #L K 1 K = K 6 \ L 7 *! 7 -2 2 3 #L K 1 K = K * \ L - -7! 7 -2 2 3 #L K 1 K = K * \ 6 L 7 -2 2 3 #L K 1 K = K 6 \ 0 L 6 = 2A! 7 * @? . N # * 9 # K 1 K = K 0 \ 6 L f1EC. ! 7 * @? . N # * 9 # K 1 K = K 6 \ 0 L 7 * @? . N # * 9 # K 1 K = K * \ 0 L = @ 7 @! 7 * @? . N # * 9 # K 1 K = K 6 \ L - * @ *? @ *! 7 * @? . N # * 9 # K 1 K = K * \ L 7 * @? . N # * 9 # K 1 K = K \ 6 L 7 * @? . N # * 9 # K 1 K = K * \ L 7 * @? . N # * 9 # K 1 K = K 6 \ * L 7 * @? . N # * 9 # K I1 K I = KI 6 \ 0 L 7 * @? . N # * 9 # K 1 K = K 0 \ 6 L * 03 &! 7 * @? . N # * 9 # K 1 K = K 6 \ * L 7 * @? . N # * 9 # K 1 K = K * \ L 7 * @? . N # * 9 # K 1 K = K * \ L 7 * @? . N # * 9 # K 1 K = K \ 6 L = 6! 7 * @? . N # * 9 # K 1 K = K * \ 0 L

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f,! 7 00 ^ * 9 / = # K 1 K = K * \ L * - N / 7 00 ^ * 9 / = # K 1 K = K * \ L 7 00 ^ * 9 / = # K 1 K = K 0 \ L 7 00 ^ * 9 / = # K 1 K = K 6 \ 0 L 7 00 ^ * 9 / = # K 1 K = K * \ L 2D * PO- * B2 - * B2! 7 00 ^ * 9 / = # K 1 K = K * \ 0 L 7 00 ^ * 9 / = # K 1 K = K * \ L 7 00 ^ * 9 / = # K 1 K = K * \ L 7 00 ^ * 9 / = # K 1 K = K 6 \ 0 L 607 = 7! 7 00 ^ * 9 / = # K 1 K = K 0 \ L 7 00 ^ * 9 / = # K 1 K = K 6 \ 0 L = 0--1! 7 00 ^ * 9 / = # K 1 K = K \ 0 L = A6! 7 00 ^ * 9 / = # K 1 K = K * \ 0 L 7 00 ^ * 9 / = # K 1 K = K * \ L &. . ! 7 00 ^ * 9 / = # K 1 K = K \ 0 L; * *! 7 00 ^ * 9 / = # K 1 K = K * \ L 7 00 ^ * 9 / = # K 1 K = K \ 0 L 7 00 ^ * 9 / = # K 1 K = K * \ L 7 00 ^ * 9 / = # K 1 K = K * \ L 7 00 ^ * 9 / = # K 1 K = K * \ L 7 00 ^ * 9 / = # K 1 K = K * \ 0 L 7 00 ^ * 9 / = # K 1 K = K 6 \ 0 L 7 00 ^ * 9 / = # K 1 K = K 0 \ L 0 7! 7 00 ^ * 9 / = # K 1 K = K 6 \ L 7 00 ^ * 9 / = # K 1 K = K * \ 6 L 0 A- 0! 7 00 ^ * 9 / = # K 1 K = K * \ 6 LI @ N / 7 00 ^ * 9 / = # K 1 K = K \ 0 L 7 00 ^ * 9 / = # K 1 K = K 0 \ L 7. 3! 7 00 ^ * 9 / = # K 1 K = K * \ 6 L 7 00 ^ * 9 / = # K 1 K = K 6 \ 0 L = 0 = -! 7 00 ^ * 9 / = # K 1 K = K * \ L 0E! 7 00 ^ * 9 / = # K 1 K = K 6 \ * L = 6! 7 00 ^ * 9 / = # K 1 K = K * \ L 7 00 ^ * 9 / = # K 1 K = K * \ L 6 = 2A! 7 00 ^ * 9 / = # K 1 K = K \ * L 7 00 ^ * 9 / = # K 1 K = K 6 \ L * @ 0! 7 00 ^ * 9 / = # K 1 K = K * \ L 0A! 7 00 ^ * 9 / = # K 1 K = K 6 \ 0 L 7 00 ^ * 9 / = # K 1 K = K \ 0 L.? & @!

7 00 ^ * 9 / = # K 1 K = K 0 \ L 3 0P * 7! 7 00 ^ * 9 / = # K 1 K = K 6 \ 0 L .E0! 7 00 ^ * 9 / = # K 1 K = K 6 \ * L ,,! 7 00 ^ * 9 / = # K 1 K = K 6 \ * L 7 00 ^ * 9 / = # K 1 K = K 6 \ L 7 00 ^ * 9 / = # K 1 K = K 6 \ LC? 3? =! 7 00 ^ * 9 / = # K 1 K = K 6 \ L * @ = 1! 7 00 ^ * 9 / = # K 1 K = K 6 \ L 7 00 ^ * 9 / = # K 1 K = K 0 \ 6 L 7 00 ^ * 9 / = # K 1 K = K \ 0 L 6 =! 7 00 ^ * 9 / = # K 1 K = K * \ L A200! 7 00 ^ * 9 / = # K 1 K = K 6 \ 0 L * @ = 1! 7 00 ^ * 9 / = # K 1 K = K * \ 0 L 7 01! 7 00 ^ * 9 / = # K 1 K = K 0 \ 6 L & -1! 7 00 ^ * 9 / = # K 1 K = K * \ L * &! 7 00 ^ * 9 / = # K 1 K = K 6 \ * L * @ = 1! 7 00 ^ * 9 / = # K 1 K = K 0 \ 6 L -B I3 0! 7 00 ^ * 9 / = # K 1 K = K 0 \ L ** 3 *! 7 00 ^ * 9 / = # K 1 K = K 6 \ 0 L 0C! 7 00 ^ * 9 / = # K 1 K = K 6 \ L? 2 *! 7 00 ^ * 9 / = # K 1 K = K * \ L 7 00 ^ * 9 / = # K 1 K = K 6 \ 0 L & = - N / 7 00 ^ * 9 / = # K 1 K = K 6 \ * L 7 00 ^ * 9 / = # K 1 K = K 0 \ 6 L 7 00 ^ * 9 / = # K 1 K = K 0 \ L 7 00 ^ * 9 / = # K 1 K = K 6 \ * L 21! 7 00 ^ * 9 / = # K 1 K = K \ * L * 3A 0! 7 00 ^ * 9 / = # K 1 K = K 6 \ L 7 00 ^ * 9 / = # K 1 K = K * \ L 7 00 ^ * 9 / = # K 1 K = K 6 \ L 3 * ?THE ! 7 00 ^ * 9 / = # K 1 K = K \ 0 L 7 00 ^ * 9 / = # K 1 K = K \ 0 L 6 *! 7 00 ^ * 9 / = # K 1 K = K 6 \ 0 L 7 00 ^ * 9 / = # K 1 K = K 6 \ L 7 00 ^ * 9 / = # K 1 K = K 6 \ 0 L 7 00 ^ * 9 / = # K 1 K = K \ 6 L .A! 7 00 ^ * 9 / = # K 1 K = K \ 0 L 7 00 ^ * 9 / = # K 1 K = K 6 \ L 7 00 ^ * 9 / = # K 1 K = K * \ L 7 00 ^ * 9 / = # K 1 K = K 6 \ 0 L 0-7! 7 00 ^ * 9 / = # K 1 K = K 6 \ L 6 &! 7 00 ^ * 9 / = # K 1 K = K 6 \ 0 L 0 7! 7 00 ^ * 9 / = # K 1 K = K * \ L 7 00 ^ * 9 / = # K 1 K = K * \ L * B3 N / 7 00 ^ * 9 / = # K 1 K = K 0 \ 6 L 7 00 ^ * 9 / = # K 1 K = K * \ 0 L A3! 7 00 ^ * 9 / = # K 1 K = K 6 \ 0 L 0A 2! 7 00 ^ * 9 / = # K 1 K = K 6 \ * L - & 3! 7 00 ^ * 9 / = # K 1 K = K 6 \ * L -! 7 00 ^ * 9 / = # K 1 K = K \ * L 7 00 ^ * 9 / = # K 1 K = K 6 \ * L & E @ 7! 7 00 ^ * 9 / = # K 1 K = K * \ 6 L f 1! 7 00 ^ * 9 / = # K 1 K = K 6 \ * L *! 7 00 ^ * 9 / = # K 1 K = K * \ L 7 00 ^ * 9 / = # K 1 K = K 6 \ * L 3 @ - @ 2 -! 7 00 ^ * 9 / = # K 1 K = K * \ 6 L 7 00 ^ * 9 / = # K 1 K = K 6 \ L -E! 7 00 ^ * 9 / = # K I1 K I = KI 0 \ 6 L -B = @@ &! 7 00 ^ * 9 / = # K I1 K I = KI * \ 0 L? @ *! 7 00 ^ * 9 / = # K I1 K I = KI * \ L 7 00 ^ * 9 / = # K I1 K I = KI * \ 0 L * I! 7 00 ^ * 9 / = # K I1 KI = KI 6 \ L 7 00 ^ * 9 / = # K I1 KI = KI * \ L 7 00 ^ * 9 / = # K 1 K = K \ * L 7 00 ^ * 9 / = # K 1 K = K 6 \ * L 7 *! 7 00 ^ * 9 / = # K 1 K = K 6 \ 0 L - @ =! 7 00 ^ * 9 / = # K 1 K = K \ 0 L 7 00 ^ * 9 / = # K 1 K = K 6 \ L .I @ * N / 7 00 ^ * 9 / = # K 1 K = K 0 \ L 7 00 ^ * 9 / = # K 1 K = K 0 \ L 7 00 ^ * 9 / = # K 1 K = K 6 \ 0 L f 1! 7 00 ^ * 9 / = # K 1 K = K 0 \ * L. I @ * 7! 7 00 ^ * 9 / = # K 1 K = K 0 \ L * 0! 7 00 ^ * 9 / = # K 1 K = K \ 0 L 3! 7 00 ^ * 9 / = # K 1 K = K \ * L 2 67! 7 00 ^ * 9 / = # K 1 K = K \ 0 L 7 00 ^ * 9 / = # K 1 K = K 0 \ L 7 00 ^ * 9 / = # K 1 K = K 0 \ * LC, *? ! 7 00 ^ * 9 / = # K 1 K = K 0 \ L

7 00 ^ * 9 / = # K 1 K = K 6 \ 0 L f 1 3! 7 00 ^ * 9 / = # K 1 K = K 0 \ L 7 00 ^ * 9 / = # K 1 K = K 0 \ L 7 00 ^ * 9 / = # K 1 K = K 0 \ LB? 37 ! 7 00 ^ * 9 / = # K 1 K = K 6 \ 0 L 6 = 0 & 3 *! 7 00 ^ * 9 / = # K 1 K = K \ 0 L IIE? ! 7 00 ^ * 9 / = # K 1 K = K \ 0 L 7 00 ^ * 9 / = # K 1 K = K 6 \ L 7 00 ^ * 9 / = # K 1 K = K 6 \ 0 L 7 00 ^ * 9 / = # K 1 K = K 6 \ 0 L 7 00 ^ * 9 / = # K 1 K = K 0 \ L 7 00 ^ * 9 / = # K 1 K = K 6 \ 0 L 7 00 ^ * 9 / = # K 1 K = K * \ 0 L 7 00 ^ * 9 / = # K 1 K = K \ 0 L 7 00 ^ * 9 / = # K 1 K = K * \ 6 L 7 00 ^ * 9 / = # K 1 K = K \ 6 L 7 00 ^ * 9 / = # K 1 K = K \ 0 L = 6! 7 00 ^ * 9 / = # K 1 K = K 0 \ L 7 00 ^ * 9 / = # K 1 K = K 0 \ L 7 00 ^ * 9 / = # K 1 K = K 0 \ L 3 0B N / 7 00 ^ * 9 / = # K 1 K = K \ 6 L 6 = 7! 7 00 ^ * 9 / = # K 1 K = K 6 \ * L? 0! 7 00 ^ * 9 / = # K 1 K = K 6 \ L 7 00 ^ * 9 / = # K 1 K = K 6 \ 0 L 7 00 ^ * 9 / = # K 1 K = K * \ L - * 2-7! 7 00 ^ * 9 / = # K 1 K = K * \ 0 L 7 00 ^ * 9 / = # K 1 K = K 6 \ 0 L & 0 A0! 7 00 ^ * 9 / = # K 1 K = K \ 0 L 7 00 ^ * 9 / = # K 1 K = K * \ L 7 00 ^ * 9 / = # K 1 K = K 0 \ L 7 00 ^ * 9 / = # K 1 K = K 6 \ 0 L 0C! 7 00 ^ * 9 / = # K 1 K = K 0 \ L 3A1f A = *! 7 00 ^ * 9 / = # K 1 K = K 0 \ L * 3! 7 00 ^ * 9 / = # K 1 K = K 0 \ L 7 00 ^ * 9 / = # K 1 K = K 0 \ * L 7 00 ^ * 9 / = # K 1 K = K 6 \ 0 L * 3 6! 7 00 ^ * 9 / = # K 1 K = K * \ 0 L 7 *! 7 00 ^ * 9 / = # K 1 K = K 6 \ 0 L 7 00 ^ * 9 / = # K 1 K = K 6 \ 0 L 7 00 ^ * 9 / = # K 1 K = K \ 0 L 3? 60! 7 00 ^ * 9 / = # K 1 K = K 6 \ * L = 6-! 7 00 ^ * 9 / = # K 1 K = K \ 0 L 7 00 ^ * 9 / = # K 1 K = K 6 \ 0 L 7 00 ^ * 9 / = # K 1 K = K 0 \ L 2D *B ! 7 00 ^ * 9 / = # K 1 K = K 0 \ L 261 =! 7 00 ^ * 9 / = # K 1 K = K 0 \ L = * 0! 7 00 ^ * 9 / = # K 1 K = K * \ L 7 00 ^ * 9 / = # K 1 K = K 0 \ L 7 00 ^ * 9 / = # K 1 K = K \ 0 L 3 0B ! 7 00 ^ * 9 / = # K 1 K = K 6 \ 0 L? 2 *! 7 00 ^ * 9 / = # K 1 K = K * \ L 7 00 ^ * 9 / = # K 1 K = K 0 \ L 7 00 ^ * 9 / = # K 1 K = K * \ L? @ *! 7 00 ^ * 9 / = # K 1 K = K \ 0 L 0A! 7 00 ^ * 9 / = # K 1 K = K * \ L I. 3 0P7! 7 00 ^ * 9 / = # K I1 K I = KI 6 \ 0 L & 23! 7 00 ^ * 9 / = # K I1 KI = KI 0 \ L 7 00 ^ * 9 / = # K I1 KI = KI * \ L 7 00 ^ * 9 / = # K I1 KI = KI \ 0 L * 3I ! 7 00 ^ * 9 / = # K I1 K I = KI 0 \ L 7 00 ^ * 9 / = # K 1 K = K 0 \ L = @ =! 7 00 ^ * 9 / = # K 1 K = K \ 0 L 7 *! 7 00 ^ * 9 / = # K 1 K = K 6 \ 0 L 7 00 ^ * 9 / = # K 1 K = K 0 \ L -3B * N / 7 00 ^ * 9 / = # K 1 K = K * \ L 7I 0I! 7 00 ^ * 9 / = # K 1 K = K * \ L 0 A = 3! 7 00 ^ * 9 / = # K 1 K = K 0 \ L 7 00 ^ * 9 / = # K 1 K = K 0 \ L 3? ! 7 00 ^ * 9 / = # K 1 K = K \ 0 L & 0 = A! 7 00 ^ * 9 / = # K 1 K = K \ 0 L 7 00 ^ * 9 / = # K 1 K = K 0 \ L = I! 7 00 ^ * 9 / = # K 1 K = K \ * L - = .3! 7 00 ^ * 9 / = # K 1 K = K \ 0 L 7 00 ^ * 9 / = # K 1 K = K * \ 6 L 0? & - N / 7 00 ^ * 9 / = # K 1 K = K 0 \ L & 0? ! 7 00 ^ * 9 / = # K 1 K = K \ 0 L 011! 7 00 ^ * 9 / = # K 1 K = K \ 0 L 3! 7 00 ^ * 9 / = # K 1 K = K \ 0 L f 1 7! 7 00 ^ * 9 / = # K 1 K = K \ 0 L 0 A = 3!

7 00 ^ * 9 / = # K 1 K = K * \ L 7 00 ^ * 9 / = # K 1 K = K * \ L 60? ! 7 00 ^ * 9 / = # K 1 K = K * \ L & @ 7! 7 00 ^ * 9 / = # K 1 K = K \ 0 L? Ef? ! 7 00 ^ * 9 / = # K 1 K = K \ 0 L 7 00 ^ * 9 / = # K 1 K = K * \ L = @ 7 @! 7 00 ^ * 9 / = # K 1 K = K \ 0 L 7 00 ^ * 9 / = # K 1 K = K \ 0 L 7 00 ^ * 9 / = # K 1 K = K \ 0 L 7 00 ^ * 9 / = # K 1 K = K \ 0 L 06 =! 7 00 ^ * 9 / = # K 1 K = K \ 0 L 7 00 ^ * 9 / = # K 1 K = K 0 \ L 7 00 ^ * 9 / = # K 1 K = K \ 0 L? 2 *! 7 00 ^ * 9 / = # K 1 K = K * \ L 0A! 7 00 ^ * 9 / = # K 1 K = K 0 \ L ,, *! 7 00 ^ * 9 / = # K 1 K = K 6 \ 0 L 3f3! 7 00 ^ * 9 / = # K 1 K = K \ * L 7 00 ^ * 9 / = # K 1 K = K \ 0 L 7 00 ^ * 9 / = # K 1 K = K \ * L 7 00 ^ * 9 / = # K 1 K = K 0 \ L 7 00 ^ * 9 / = # K 1 K = K \ 0 L 7 00 ^ * 9 / = # K 1 K = K 0 \ L = *! 7 00 ^ * 9 / = # K 1 K = K * \ L 7 00 ^ * 9 / = # K 1 K = K \ 0 L B = -B *! 7 00 ^ * 9 / = # K 1 K = K 0 \ 6 L 7 00 ^ * 9 / = # K 1 K = K 0 \ L 7 00 ^ * 9 / = # K 1 K = K * \ L 7 00 ^ * 9 / = # K 1 K = K 6 \ 0 LA? B? ! 7 00 ^ * 9 / = # K 1 K = K 0 \ L 7 00 ^ * 9 / = # K 1 K = K 0 \ L .E0 0A! 7 00 ^ * 9 / = # K 1 K = K 0 \ L 7 00 ^ * 9 / = # K 1 K = K 0 \ L 7 00 ^ * 9 / = # K 1 K = K 0 \ L * 0 ! 7 00 ^ * 9 / = # K 1 K = K * \ L 7 00 ^ * 9 / = # K 1 K = K \ * L -! 7 00 ^ * 9 / = # K 1 K = K \ * L -! 7 00 ^ * 9 / = # K 1 K = K 6 \ 0 L 7 00 ^ * 9 / = # K 1 K = K 6 \ 0 L 1? P! 7 00 ^ * 9 / = # K 1 K = K * \ L 7 00 ^ * 9 / = # K 1 K = K * \ 0 L? @ *! 7 00 ^ * 9 / = # K 1 K = K * \ L 7 00 ^ * 9 / = # K 1 K = K 6 \ 0 L .7? ! 7 00 ^ * 9 / = # K 1 K = K \ 0 L .7? ! 7 00 ^ * 9 / = # K 1 K = K * \ 0 L 7 00 ^ * 9 / = # K 1 K = K 0 \ L B.??-! 7 00 ^ * 9 / = # K 1 K = K 0 \ 6 L - @ = 7- A200! 7 00 ^ * 9 / = # K 1 K = K 6 \ L 7 00 ^ * 9 / = # K 1 K = K 0 \ L 7 00 ^ * 9 / = # K 1 K = K 6 \ L = 3 & 0A N / 7 00 ^ * 9 / = # K 1 K = K 0 \ L 7 00 ^ * 9 / = # K 1 K = K 6 \ 0 L 7 00 ^ * 9 / = # K 1 K = K 6 \ 0 L 7 00 ^ * 9 / = # K 1 K = K 6 \ * L * B! 7 00 ^ * 9 / = # K 1 K = K * \ L A- =! 7 00 ^ * 9 / = # K 1 K = K \ 0 L &. & *! 7 00 ^ * 9 / = # K 1 K = K \ 0 L 7 00 ^ * 9 / = # K 1 K = K 6 \ 0 L 7 00 ^ * 9 / = # K 1 K = K 6 \ 0 L 161! 7 00 ^ * 9 / = # K 1 K = K \ 0 L 7 00 ^ * 9 / = # K 1 K = K 0 \ L -? * 1! 7 00 ^ * 9 / = # K 1 K = K 0 \ L 7 00 ^ * 9 / = # K 1 K = K * \ L 7 00 ^ * 9 / = # K 1 K = K 0 \ L -B2 -0! 7 00 ^ * 9 / = # K 1 K = K * \ L B.0 =! 7 00 ^ * 9 / = # K 1 K = K * \ L? @ *! 7 00 ^ * 9 / = # K 1 K = K \ 0 L? 3? = 03! 7 00 ^ * 9 / = # K 1 K = K * \ 0 L & 27! 7 00 ^ * 9 / = # K 1 K = K * \ L 7 00 ^ * 9 / = # K 1 K = K 0 \ L 7 00 ^ * 9 / = # K 1 K = K * \ L 3 * *! 7 00 ^ * 9 / = # K 1 K = K 6 \ 0 L 7 00 ^ * 9 / = # K 1 K = K 0 \ L 7 00 ^ * 9 / = # K 1 K = K 6 \ 0 L 7 00 ^ * 9 / = # K 1 K = K \ 0 L = 0? E! 7 00 ^ * 9 / = # K 1 K = K 6 \ L -6A! 7 00 ^ * 9 / = # K 1 K = K 0 \ L? @ *! 7 00 ^ * 9 / = # K 1 K = K 6 \ L 030 = 7! 7 00 ^ * 9 / = # K I1 K I = KI * \ 6 L 7 00 ^ * 9 / = # K I1 K I = KI 0 \ L 3 & 3!

7 00 ^ * 9 / = # K I1 K I = KI \ 0 L 7I! 7 00 ^ * 9 / = # K I1 KI = KI 6 \ L 7 00 ^ * 9 / = # K I1 KI = KI 0 \ L 7 00 ^ * 9 / = # K I1 KI = KI 6 \ * L 7 00 ^ * 9 / = # K E1 KE = KE * \ L 7 -0 ^ * 9 / = # K 1 K = K * \ L & E- & N / 7 -0 ^ * 9 / = # K 1 K = K 6 \ * L 7 -0 ^ * 9 / = # K 1 K = K * \ 6 L .- == 6! 7 -0 ^ * 9 / = # K 1 K = K * \ L = I! 7 -0 ^ * 9 / = # K 1 K = K 6 \ 0 L 7 -0 ^ * 9 / = # K 1 K = K * \ L 7 -0 ^ * 9 / = # K 1 K = K * \ L 3 *? A! 7 -0 ^ * 9 / = # K 1 K = K 6 \ 0 L 7 -0 ^ * 9 / = # K 1 K = K * \ 0 L 7 -0 ^ * 9 / = # K 1 K = K 6 \ * L 7 -0 ^ * 9 / = # K 1 K = K * \ 0 L 7 -0 ^ * 9 / = # K 1 K = K 6 \ 0 L 7 -0 ^ * 9 / = # K 1 K = K 0 \ L 7 -0 ^ * 9 / = # K 1 K = K 6 \ L & E. @ C! 7 -0 ^ * 9 / = # K 1 K = K 6 \ 0 L 7 -0 ^ * 9 / = # K 1 K = K * \ L 7 -0 ^ * 9 / = # K 1 K = K * \ L 7 -0 ^ * 9 / = # K 1 K = K \ 6 L 261 =! 7 -0 ^ * 9 / = # K 1 K = K 6 \ 0 L 7 -0 ^ * 9 / = # K 1 K = K * \ L 7 -0 ^ * 9 / = # K 1 K = K * \ L f 1! 7 -0 ^ * 9 / = # K 1 K = K 6 \ * L 7 -0 ^ * 9 / = # K 1 K = K * \ L 7 -0 ^ * 9 / = # K 1 K = K * \ L 7 -0 ^ * 9 / = # K I1 KI = KI * \ L 7 -0 ^ * 9 / = # K 1 K = K * \ L 7 -0 ^ * 9 / = # K 1 K = K 6 \ 0 L = E =! 7 -0 ^ * 9 / = # K 1 K = K * \ 0 L 7 -0 ^ * 9 / = # K 1 K = K 6 \ 0 L 0A! 7 -0 ^ * 9 / = # K 1 K = K 6 \ L 7 -0 ^ * 9 / = # K 1 K = K 6 \ L 1 @ I! 7 -0 ^ * 9 / = # K 1 K = K * \ L. B7! 7 -0 ^ * 9 / = # K 1 K = K 6 \ L 7 -0 ^ * 9 / = # K 1 K = K 6 \ 0 L 0 * 0! 7 -0 ^ * 9 / = # K 1 K = K * \ L 7 -0 ^ * 9 / = # K 1 K = K * \ L 7 -0 ^ * 9 / = # K 1 K = K 6 \ 0 L -2? ! 7 -0 ^ * 9 / = # K 1 K = K 6 \ * L? 2 *! 7 -0 ^ * 9 / = # K 1 K = K 6 \ 0 L A? B? ! 7 -0 ^ * 9 / = # K 1 K = K 6 \ 0 L? 0 & 0! 7 -0 ^ * 9 / = # K 1 K = K 6 \ 0 L .. 3! 7 -0 ^ * 9 / = # K 1 K = K * \ 6 L 33I! 7 -0 ^ * 9 / = # K 1 K = K 6 \ 0 L 7 -0 ^ * 9 / = # K 1 K = K 6 \ * L f7 7 N / 7 -0 ^ * 9 / = # K 1 K = K * \ L 7 -0 ^ * 9 / = # K 1 K = K 6 \ L *! 7 -0 ^ * 9 / = # K 1 K = K 6 \ 0 L 7 -0 ^ * 9 / = # K 1 K = K 6 \ 0 L. ?? ! 7 -0 ^ * 9 / = # K 1 K = K * \ 6 L 7 -0 ^ * 9 / = # K 1 K = K 6 \ 0 L @ * &? ! 7 -0 ^ * 9 / = # K 1 K = K * \ 6 L 6 = 2 7! 7 -0 ^ * 9 / = # K 1 K = K * \ 6 L 6 = 2 7! 7 -0 ^ * 9 / = # K 1 K = K 6 \ 0 L 7 -0 ^ * 9 / = # K 1 K = K * \ L 0 * 03 N / 7 -0 ^ * 9 / = # K 1 K = K * \ L 7 -0 ^ * 9 / = # K 1 K = K * \ 0 L -; *? 0! 7 -0 ^ * 9 / = # K 1 K = K * \ L = I! 7 -0 ^ * 9 / = # K 1 K = K 6 \ 0 L B- -! 7 -0 ^ * 9 / = # K 1 K = K * \ 0 L 7 -0 ^ * 9 / = # K 1 K = K \ * L 7 -0 ^ * 9 / = # K 1 K = K * \ L * 1! 7 -0 ^ * 9 / = # K 1 K = K * \ L 7 -0 ^ * 9 / = # K 1 K = K 6 \ L 3- * 0 &! 7 -0 ^ * 9 / = # K I1 K I = KI * \ L 7 -0 ^ * 9 / = # K 1 K = K 6 \ 0 L A0f! 7 -0 ^ * 9 / = # K 1 K = K 0 \ L 3. 7! 7 -0 ^ * 9 / = # K 1 K = K * \ L 7 * & 2 = 0 0A! 7 -0 ^ * 9 / = # K 1 K = K \ 0 L & E7 B? ! 7 -0 ^ * 9 / = # K 1 K = K 6 \ 0 L -? * 0! 7 -0 ^ * 9 / = # K 1 K = K 6 \ 0 L 7 -0 ^ * 9 / = # K 1 K = K 6 \ 0 L * 0 * E7 N / 7 -0 ^ * 9 / = # K 1 K = K * \ 6 L 0 7! 7 -0 ^ * 9 / = # K 1 K = K * \ 6 L 07 * 7! 7 -0 ^ * 9 / = # K 1 K = K 6 \ 0 L

7 -0 ^ * 9 / = # K 1 K = K 6 \ 0 L & 0! 7 -0 ^ * 9 / = # K 1 K = K 6 \ * L 7 -0 ^ * 9 / = # K 1 K = K * \ 6 L 3E * 2! 7 -0 ^ * 9 / = # K 1 K = K \ 0 L 7 -0 ^ * 9 / = # K 1 K = K 6 \ * L * -! 7 -0 ^ * 9 / = # K 1 K = K * \ 6 L 7 -0 ^ * 9 / = # K 1 K = K 0 \ 6 L 0 * 00! 7 -0 ^ * 9 / = # K 1 K = K * \ L * 0! 7 -0 ^ * 9 / = # K 1 K = K * \ L & 2 1! 7 -0 ^ * 9 / = # K 1 K = K 6 \ * L 7 -0 ^ * 9 / = # K 1 K = K * \ L 7 -0 ^ * 9 / = # K 1 K = K \ * L 1A72f! 7 -0 ^ * 9 / = # K 1 K = K 0 \ L 7 -0 ^ * 9 / = # K 1 K = K 6 \ 0 L 361 = 0? I! 7 -0 ^ * 9 / = # K 1 K = K 6 \ 0 L 011! 7 -0 ^ * 9 / = # K 1 K = K 6 \ * L! 7 -0 ^ * 9 / = # K 1 K = K * \ L 7 -0 ^ * 9 / = # K 1 K = K * \ 0 L & 0 &? ! 7 -0 ^ * 9 / = # K 1 K = K * \ L 7 -0 ^ * 9 / = # K 1 K = K 6 \ 0 L 312 N / 7 -0 ^ * 9 / = # K 1 K = K * \ L 7 -0 ^ * 9 / = # K 1 K = K 6 \ 0 L 7 -0 ^ * 9 / = # K 1 K = K * \ L 7 -0 ^ * 9 / = # K 1 K = K 6 \ L @; 1 ! 7 -0 ^ * 9 / = # K 1 K = K * \ L -! 7 -0 ^ * 9 / = # K 1 K = K * \ 0 L 7 -0 ^ * 9 / = # K 1 K = K * \ L 01! 7 -0 ^ * 9 / = # K 1 K = K * \ 0 L 7 -0 ^ * 9 / = # K 1 K = K 6 \ * L, B- *? N / 7 -0 ^ * 9 / = # K 1 K = K * \ L fBI! 7 -0 ^ * 9 / = # K 1 K = K 6 \ 0 L -. * 2-7! 7 -0 ^ * 9 / = # K 1 K = K 6 \ L 7 *! 7 -0 ^ * 9 / = # K 1 K = K 6 \ * L 2A7A0! 7 -0 ^ * 9 / = # K 1 K = K * \ L = 0? 6 -! 7 -0 ^ * 9 / = # K 1 K = K 0 \ * L *! 7 -0 ^ * 9 / = # K 1 K = K 6 \ 0 L 7 -0 ^ * 9 / = # K 1 K = K 6 \ L 7 -0 ^ * 9 / = # K 1 K = K * \ L & =? ! 7 -0 ^ * 9 / = # K 1 K = K 6 \ 0 L * I * =! 7 -0 ^ * 9 / = # K 1 K = K 6 \ 0 L 1.f! 7 -0 ^ * 9 / = # K 1 K = K 6 \ 0 L 7 -0 ^ * 9 / = # K 1 K = K 6 \ 0 L 7 -0 ^ * 9 / = # K 1 K = K 6 \ 0 L 7 -0 ^ * 9 / = # K 1 K = K 0 \ L 7 -0 ^ * 9 / = # K 1 K = K * \ 6 L 7 -0 ^ * 9 / = # K 1 K = K * \ 6 L 7 -0 ^ * 9 / = # K 1 K = K 6 \ L * @? 0! 7 -0 ^ * 9 / = # K 1 K = K 6 \ 0 L.? 2! 7 -0 ^ * 9 / = # K 1 K = K 6 \ 0 L -? * 0! 7 -0 ^ * 9 / = # K 1 K = K * \ L.? & - 0! 7 -0 ^ * 9 / = # K 1 K = K * \ L 7 -0 ^ * 9 / = # K 1 K = K * \ L & 2 = B @I! 7 -0 ^ * 9 / = # K 1 K = K * \ 0 L 7 -0 ^ * 9 / = # K 1 K = K * \ L = A! 7 -0 ^ * 9 / = # K 1 K = K 6 \ L; -! 7 -0 ^ * 9 / = # K 1 K = K 6 \ 0 L 7 -0 ^ * 9 / = # K 1 K = K 6 \ 0 L 7 -0 ^ * 9 / = # K 1 K = K * \ L 7 -0 ^ * 9 / = # K 1 K = K 6 \ 0 L 7 -0 ^ * 9 / = # K 1 K = K * \ L 7 -0 ^ * 9 / = # K 1 K = K \ 0 L 7 -0 ^ * 9 / = # K 1 K = K 6 \ 0 L = 1! 7 -0 ^ * 9 / = # K 1 K = K * \ L * 0 * 0 0! 7 -0 ^ * 9 / = # K 1 K = K 6 \ * L 7 -0 ^ * 9 / = # K 1 K = K 6 \ 0 L @ -7! 7 -0 ^ * 9 / = # K I1 KI = KI * \ 0 L 7 -0 ^ * 9 / = # K I1 KI = KI * \ L 7 -0 ^ * 9 / = # K E1 KE = KE * \ 0 L 7 A23 * 9 / = # K 1 K = K * \ 6 L 10 & 7! 7 A23 * 9 / = # K 1 K = K 0 \ 6 L 7 A23 * 9 / = # K 1 K = K * \ 0 L A3 & 0! 7 A23 * 9 / = # K 1 K = K \ * L 3! 7 A23 * 9 / = # K 1 K = K \ 6 L 7 A23 * 9 / = # K 1 K = K 0 \ L 0 A = 3! 7 A23 * 9 / = # K 1 K = K 0 \ 6 L 7 A23 * 9 / = # K 1 K = K \ 0 L = 1? 7! 7 A23 * 9 / = # K 1 K = K 6 \ 0 L? @ *! 7 A23 * 9 / = # K 1 K = K 0 \ L.? & @! 7 A23 * 9 / = # K 1 K = K \ 0 L 0; -!

7 A23 * 9 / = # K 1 K = K 6 \ 0 L 7 A23 * 9 / = # K 1 K = K 6 \ 0 L? & @! 7 A23 * 9 / = # K 1 K = K \ * L 6 = 20! 7 A23 * 9 / = # K 1 K = K 6 \ * L 7 A23 * 9 / = # K 1 K = K * \ 0 L 7 A23 * 9 / = # K 1 K = K \ * L -? * 0! 7 A23 * 9 / = # K 1 K = K 0 \ 6 L 7 A23 * 9 / = # K 1 K = K * \ 6 L 7 A23 * 9 / = # K I1 K I = KI 6 \ 0 L 2? = 0? ! 7 A23 * 9 / = # K I1 K I = KI 0 \ 6 L & 0 @ 7! 7 A23 * 9 / = # K 1 K = K * \ 0 L; 7. 3! 7 A23 * 9 / = # K 1 K = K * \ L -0-B! 7 A23 * 9 / = # K 1 K = K \ * L 7 A23 * 9 / = # K 1 K = K 0 \ 6 L 7 A23 * 9 / = # K 1 K = K 6 \ L 7 A23 * 9 / = # K 1 K = K \ 6 L 7 A23 * 9 / = # K 1 K = K 0 \ 6 L 261 =! 7 A23 * 9 / = # K I1 K I = KI 6 \ 0 L 7 A23 * 9 / = # K I1 K I = KI * \ 6 L 2? = 0? ! 7 A23 * 9 / = # K 1 K = K 0 \ * L 3 @ * A! 7 A23 * 9 / = # K 1 K = K 6 \ 0 L 7 * 0 =! 7 A23 * 9 / = # K 1 K = K * \ L = .C! 7 A23 * 9 / = # K 1 K = K \ 6 L 7 A23 * 9 / = # K 1 K = K 0 \ 6 L 7 A23 * 9 / = # K 1 K = K * \ L 7 A23 * 9 / = # K 1 K = K * \ L = 2 &! 7 A23 * 9 / = # K 1 K = K * \ L * 3B! 7 A23 * 9 / = # K 1 K = K \ 0 L 7 A23 * 9 / = # K 1 K = K \ 6 L 7 A23 * 9 / = # K 1 K = K 0 \ L 026! 7 A23 * 9 / = # K 1 K = K \ 0 L 7 A23 * 9 / = # K 1 K = K \ 6 L 6 *! 7 A23 * 9 / = # K 1 K = K \ 0 L f1! 7 A23 * 9 / = # K I1 K I = KI 6 \ L 7 A23 * 9 / = # K I1 K I = KI * \ 0 L 6 = 20! 7 A23 * 9 / = # K 1 K = K \ * L 1! 7 A23 * 9 / = # K 1 K = K 0 \ 6 L 7 A23 * 9 / = # K 1 K = K 6 \ 0 L 3! 7 A23 * 9 / = # K 1 K = K \ 0 L B. *,! 7 A23 * 9 / = # K 1 K = K 0 \ * L 7 A23 * 9 / = # K 1 K = K 0 \ 6 L 7 A23 * 9 / = # K 1 K = K \ 0 L 0 A! 7 A23 * 9 / = # K 1 K = K \ * L & *! 7 A23 * 9 / = # K 1 K = K \ 0 L 7 A23 * 9 / = # K 1 K = K 0 \ 6 L 7 A23 * 9 / = # K 1 K = K 6 \ L *! 7 A23 * 9 / = # K 1 K = K 0 \ * L 7 A23 * 9 / = # K 1 K = K * \ L *; I! 7 A23 * 9 / = # K 1 K = K 6 \ 0 L = A! 7 A23 * 9 / = # K 1 K = K \ * L 0 3! 7 A23 * 9 / = # K 1 K = K 0 \ L .1; 3! 7 A23 * 9 / = # K I1 K I = KI 0 \ * L 3 & 3! 7 A23 * 9 / = # K I1 K I = KI 0 \ 6 L 7 A23 * 9 / = # K I1 K I = KI * \ L 7 A23 * 9 / = # K I1 K I = KI 6 \ 0 L 2? = 0? ! 7 A23 * 9 / = # 3 K 1 K = K 6 \ * L *! 7 A23 * 9 / = # 3 K 1 K = K 6 \ * L & E = 2! 7 A23 * 9 / = # 3 K 1 K = K * \ 0 L 01! 7 A23 * 9 / = # 3 K 1 K = K * \ 0 L * B- N / 7 A23 * 9 / = # 3 K 1 K = K * \ 6 L? 0 =! 7 A23 * 9 / = # 7 #. K 1 K = K 6 \ * L 7 A23 * 9 / = # 7 #. K 1 K = K 6 \ * L 7 7 = 0 * N $ * 9/1 # K 1 K = K * \ 0 L A200 N / 7 7 = 0 * N $ * 9/1 # K 1 K = K * \ 6 L 7 7 = 0 * N $ * 9/1 # K 1 K = K \ 0 L & = 3- @ 1 **! 7 7 = 0 * N $ * 9/1 # K 1 K = K * \ 6 L 7 7 = 0 * N $ * 9/1 # K 1 K = K * \ L 7 7 = 0 * N $ * 9 / 1 # K 1 K = K * \ 6 L = 1! 7 7 = 0 * N $ * 9/1 # K 1 K = K * \ 6 L -B2-0! 7 7 = 0 * N $ * 9/1 # K 1 K = K 0 \ 6 L & 0 A! 7 7 = 0 * N $ * 9/1 # K 1 K = K * \ 0 L 7 7 = 0 * N $ * 9/1 # K 1 K = K 6 \ 0 L -? * 1! 7 7 = 0 * N $ * 9/1 # K 1 K = K * \ L 7 7 = 0 * N $ * 9/1 # K 1 K = K * \ L? = 1! 7 7 = 0 * N $ * 9/1 # K 1 K = K \ 6 L 6 6! 7 7 = 0 * N $ * 9/1 # K 1 K = K 0 \ 6 L * 3B! 7 7 = 0 * N $ * 9/1 # K 1 K = K 6 \ 0 L A * 2! 7 7 = 0 * N $ * 9/1 # K 1 K = K 0 \ 6 L 6 @ *! 7 7 = 0 * N $ * 9/1 # K 1 K = K 6 \ * L &. &!

7 7 = 0 * N $ * 9/1 # K 1 K = K 0 \ 6 L 01! 7 7 = 0 * N $ * 9/1 # K 1 K = K \ 6 L 60 0! 7 7 = 0 * N $ * 9/1 # K 1 K = K \ 6 L 7 7 = 0 * N $ * 9/1 # K 1 K = K \ 6 L 7 7 = 0 * N $ * 9 / 1 # K 1 K = K 6 \ L *! 7 7 = 0 * N $ * 9/1 # K 1 K = K \ * L? .AB & N / 7 7 = 0 * N $ * 9/1 # K 1 K = K 6 \ * L 0 I! 7 7 = 0 * N $ * 9/1 # K 1 K = K * \ L 7 7 = 0 * N $ * 9/1 # K 1 K = K 6 \ 0 L 7 7 = 0 * N $ * 9 / 1 # K 1 K = K \ 0 L? @ *! 7 7 = 0 * N $ * 9/1 # K 1 K = K * \ L * 03 & N / 7 7 = 0 * N $ * 9/1 # K 1 K = K * \ L .6? N / 7 7 = 0 * N $ * 9/1 # K 1 K = K 0 \ * L 7 7 = 0 * N $ * 9/1 # K 1 K = K \ * L

Q Q? , Q 1 '4? * S 1 '4 Q Q &' 4 Q & '4? Q - @ '4 &' 4? , & '4? 3 # * S # - L * $ '4 F? H, 3 # F? H * S * S & '4? = # 1 <<- L = <<- L 1 <<4 #R = <<- L 1 <<- L = <<- L = <<<- L 1 <<4 #R 1 <<- L = <<- L 1 <<- L 1 <<- L 1 <<- L = <<4 #R = <<- L = <<- L = <<- L = <<- L 1 <<- L = <<<- L = <<4 #R 1 <<- L = <<4 #R = <<4 #R = <<- L 1 <<- L 1 <<- L 1 <<- L 1 < <4 #R 1 <<4 #R 1 <<- L = <<- - L = <<- L 1 <<4 #R = <<- L = <<- L 1 <<- L = <<4 #R = <<4 #R 1 <<- L = <<- L = <<4 #R = <<4 #R = <<- L 1 <<4 #R = <<- L 1 <<- - L = <<4 #R = <<- L = <<- L = <<- L 1 <<- L = <<- L 1 <<- L = <<- L 1 <<4 #R 1 < <- L = <<- L 1 <<- L

1 <<- L 1 <<- L = <<- L = <<4 #R = <<- L = <<4 #R = <<- - L 1 <<- L 1 <<4 #R 1 < <- L 1 <<4 #R = <<- L 1 <<4 #R = <<4 #R 1 <<- L 1 <<- L = <<- L = <<<- L 1 <<4 #R 1 <<- L = <<4 #R = <<- L 1 <<- L 1 <<- L = <<- L = <<4 #R 1 <<- L = <<4 #R = <<4 #R 1 <<4 #R 1 <<4 #R 1 <<- L = <<- - L 1 <<- L 1 <<- L 1 <<4 #R = <<- L = <<- L 1 <<4 #R 1 <<4 #R = <<- L 1 <<- L = <<- L = <<- L = <<- L 1 <<- L = <<4 #R 1 <<- L 1 <<- L = <<4 #R = <<- L = <<- - L 1 <<- L 1 <<- L = <<4 #R = <<- L = <<- L 1 <<- L 1 <<- L 1 <<4 #R = <<- L 1 <<4 #R 1 <<- L 1 <<- L 1 <<- L = <<4 #R 1 <<- L 1 <<4 #R 1 <<- L 1 <<- L = <<4 #R = <<4 #R = <<4 #R = <<- L = <<- L = <<4 #R 1 <<4 #R 1 <<- L 1 <<- L

1 <<- L 1 <<4 #R 1 <<- L = <<4 #R 1 <<- L 1 <<- L 1 <<- L 1 <<- L = <<- L 1 << - L = <<- L 1 <<4 #R 1 <<- L 1 <<- L = <<- - L 1 <<- L 1 <<- L 1 <<- L = <<- L = < <4 #R = <<- L 1 <<- L = <<- L = <<- L 1 <<4 #R 1 <<4 #R 1 <<4 #R = <<- L 1 << - L 1 <<- L = <<- L 1 <<- L = <<4 #R = <<- - L 1 <<4 #R = <<- L = <<- L 1 <<- L 1 <<4 #R 1 <<4 #R 1 <<- L = <<- L = <<4 #R 1 <<- L = <<<- L 1 <<- L = <<- L = << - L = <<4 #R 1 <<- L = <<4 #R = <<4 #R = <<- L 1 <<4 #R 1 <<4 #R = <<- L = << - L = <<- L = <<- L 1 <<- L = <<4 #R 1 <<- L = <<- L = <<<- L = <<- L 1 <<- L = < <- L = <<- L 1 <<- L = <<- L = <<- L 1 <<- - L 1 <<- L = <<- L 1 <<- L = <<4 #R 1 <<- L 1 <<- L 1 <<- L

1 <<- L 1 <<- L = <<- L = <<- L 1 <<- L 1 <<- L = <<- L 1 <<- L 1 <<- L = <<- L = <<- L = <<- L 1 <<- L 1 <<- L = <<- L 1 <<- L 1 <<- L = <<- L 1 <<- L = <<- L = <<- L = <<- L = <<- L = <<- - L 1 <<4 #R 1 <<4 #R 1 <<- L 1 <<- L = <<- L = << - L = <<- L = <<- L 1 <<- L = <<- L = <<- L = <<- L 1 <<- L 1 <<- L = <<- L = << - L 1 <<- L = <<- L = <<- L 1 <<- L 1 <<- L = <<- L 1 <<- - L = <<- L = <<- L 1 << - L = <<- L 1 <<- L = <<- L 1 <<- L = <<- L 1 <<- L = <<<- L = <<- L 1 <<- L = << - L 1 <<- L = <<- L = <<- L 1 <<- L 1 <<- L = <<- L = <<- - L = <<4 #R = <<- L 1 < <- L = <<- L 1 <<- L = <<- L 1 <<4 #R 1 <<- L = <<- L 1 <<4 #R = <<- L = <<4 # R

= <<- L 1 <<- L 1 <<- L = <<- - L = <<- L 1 <<- L = <<- L = <<- L 1 <<- L 1 <<- L = <<- - L = <<- L 1 <<4 #R = <<4 #R 1 <<4 #R = <<- L 1 <<4 #R 1 <<4 #R 1 <<4 # R = <<- L = <<<4 #R = <<- L = <<- L 1 <<- L 1 <<4 #R = <<4 #R = <<- L = <<4 #R 1 <<4 #R = <<- L = <<- L 1 <<- L 1 <<4 #R = <<- L = <<<- L 1 <<- L = <<- L 1 << 4 #R = <<- L 1 <<- L = <<- L = <<- L 1 <<4 #R = <<- L = <<4 #R 1 <<- L = <<4 # R 1 <<4 #R 1 <<- L 1 <<- L = <<4 #R = <<- L 1 <<- L 1 <<- L 1 <<4 #R 1 <<4 #R = <<- L 1 <<- L = <<- L 1 <<- L 1 <<- L = <<4 #R = <<4 4R = <<4 #R = <<- L = < <4 #R 1 <<4 #R 1 <<- L 1 <<4 #R = <<4 #R 1 <<- L 1 <<4 #R 1 <<- L = <<- L = < <4 #R 1 <<- L 1 <<- L = <<- L = <<- L

= <<4 #R = <<- L = <<- L = <<4 #R 1 <<- L 1 <<4 #R 1 <<- L 1 <<- L = <<4 #R 1 <<- L 1 <<4 #R = <<- L 1 <<- L 1 <<- L = <<- L 1 <<4 #R 1 <<4 #R 1 <<4 #R 1 < <4 #R 1 <<- L 1 <<- L 1 <<4 #R 1 <<- L 1 <<4 #R = <<4 4R = <<- L = <<- L 1 << - L 1 <<- L = <<- L = <<- L = <<- L 1 <<- L 1 <<4 #R = <<- L 1 <<- L = <<4 #R = <<- L 1 <<4 #R 1 <<- L 1 <<- L 1 <<- L 1 <<4 #R = <<4 4R = <<- L = <<- L 1 << - L = <<- L 1 <<4 #R 1 <<- L 1 <<- L 1 <<- L = <<- L 1 <<- L = <<- L 1 <<- L 1 < <4 #R = <<- L = <<- L = <<- L 1 <<- L 1 <<- L = <<- L 1 <<- L = <<<4 #R 1 <<- L 1 <<- L 1 <<- L = <<- L 1 <<4 #R = <<4 #R = <<- L 1 <<4 #R = <<4 #R 1 <<- L 1 <<4 #R 1 <<- L 1 <<- L = <<- L

1 <<4 #R 1 <<4 #R 1 <<4 #R 1 <<4 #R = <<4 #R = <<- L = <<- L = <<4 #R 1 <<- L = <<- L 1 <<4 #R 1 <<- L 1 <<- L = <<4 #R 1 <<4 #R 1 <<- L = <<4 #R = <<4 # R 1 <<- L 1 <<4 #R 1 <<- L = <<4 #R = <<- L = <<- L = <<<4 #R = <<- L 1 <<4 #R = <<- L 1 <<- L = <<- L = <<- L = <<- L = <<4 #R = <<- L 1 <<- L 1 <<4 #R = << 4 #R 1 <<- L 1 <<- L = <<- L = <<- L 1 <<- L 1 <<- L = <<<- L = <<4 #R = <<- L = <<- L = <<- L = <<- L = <<- L = <<4 #R = <<4 #R = <<- L 1 <<- L 1 <<4 #R = << 4 #R = <<- L 1 <<- L = <<- L 1 <<4 #R 1 <<- L 1 <<4 #R = <<- L 1 <<- L 1 <<- L = <<- L = <<- L 1 <<- L = <<- L 1 <<- L 1 <<4 #R = <<- L 1 <<- L 1 <<- L = <<- - L = <<4 #R 1 <<- L 1 <<- L 1 <<- L

= <<- L 1 <<- L 1 <<4 #R 1 <<- L 1 <<- L = <<- L 1 <<4 #R = <<4 #R 1 <<- L = < <- L 1 <<4 #R = <<4 #R 1 <<- L 1 <<- L 1 <<4 #R 1 <<4 #R = <<- L 1 <<- L 1 << - L = <<4 #R = <<- L = <<4 #R = <<- L 1 <<- - L 1 <<4 #R = <<- L 1 <<4 #R 1 <<4 #R = <<- L = <<4 #R = <<- L 1 <<- L 1 <<- L = <<- L = <<<- L 1 <<- L 1 <<- L 1 < <4 #R 1 <<- L 1 <<- L = <<- L = <<4 #R 1 <<- L = <<- L = <<4 #R 1 <<- L 1 <<- - L 1 <<4 #R = <<- L = <<- L = <<4 #R 1 <<- L = <<- L 1 <<4 #R = <<- L 1 <<- L = <<4 #R 1 <<- L 1 <<- L 1 <<- L = <<- L 1 <<- L 1 <<- L = <<- L = <<4 #R 1 <<4 #R 1 <<- L 1 <<- L 1 <<- L = <<- L = <<4 #R 1 <<4 #R 1 <<- L 1 <<- L 1 <<- L 1 <<4 #R 1 <<4 #R 1 <<- L = <<4 #R

= <<- L 1 <<- L = <<- L 1 <<4 #R = <<- L = <<- L = <<<4 #R = <<- L 1 <<- L = << 4 #R 1 <<4 #R = <<- L = <<- - L 1 <<- L 1 <<- L = <<- L 1 <<- L 1 <<- L 1 <<- L 1 <<- L = <<4 #R 1 <<- L = <<- L 1 <<- L 1 <<- L = <<- L = <<4 #R 1 <<- L = <<4 #R = <<4 #R = <<- L 1 <<- L = <<- L 1 <<- L 1 <<4 #R = <<- L = <<- L 1 <<- L 1 <<- L 1 <<- L 1 <<4 #R 1 <<4 #R 1 <<- L 1 <<- L = <<<4 #R = <<- L = <<4 #R 1 < <- L 1 <<- L 1 <<- L 1 <<- L = <<4 #R 1 <<- L 1 <<- L = <<- L 1 <<4 #R = <<- L = <<- - L = <<- L = <<4 #R = <<4 #R 1 <<4 #R 1 <<4 #R 1 <<- L = <<4 #R = <<4 # R 1 <<4 #R 1 <<- L = <<- L = <<- L = <<- L 1 <<4 #R 1 <<4 #R = <<- L 1 <<- L = <<- L = <<- L = <<- L 1 <<- L

1 <<4 #R 1 <<4 #R 1 <<- L = <<4 #R = <<4 #R 1 <<- L 1 <<4 #R = <<- L = <<- L 1 <<4 #R = <<4 #R = <<- L = <<- L = <<4 #R 1 <<4 #R = <<4 #R 1 <<- L 1 <<- L = <<- L = <<4 #R 1 <<4 #R 1 <<- L 1 <<- L = <<- L 1 <<- L 1 <<- L = <<- L = << - L 1 <<- L 1 <<- L = <<- L 1 <<- L = <<4 #R 1 <<- L = <<<- L 1 <<4 #R 1 <<- L 1 <<4 #R 1 <<4 #R 1 <<4 #R 1 <<- L 1 <<- L 1 <<4 #R = <<- L 1 <<- L = <<- L 1 < <4 #R 1 <<- L = <<- L = <<- L 1 <<4 #R = <<- L = <<- L = <<- L = <<- L = <<- L = <<4 #R = <<4 #R 1 <<4 #R 1 <<- L = <<- - L = <<4 #R 1 <<- L 1 <<4 #R 1 <<- L = <<4 #R 1 <<- L 1 <<4 #R = <<- L = <<- L 1 <<- L = <<<4 #R 1 <<4 #R = <<- L = <<- L 1 <<- L 1 <<- L = <<- L = <<- L

1 <<4 #R = <<- L = <<4 #R = <<- L = <<- L 1 <<- L = <<<- L 1 <<- L 1 <<4 #R 1 < <4 #R 1 <<- L = <<- L = <<4 #R 1 <<4 #R 1 <<- L = <<<4 #R 1 <<4 #R 1 <<- L 1 < <- L = <<- L 1 <<4 #R = <<- L = <<4 #R = <<- L 1 <<4 #R 1 <<4 #R 1 <<4 #R = < <4 #R 1 <<- L 1 <<- L 1 <<- L = <<4 #R 1 <<- L = <<4 4R = <<- L 1 <<- L = <<4 #R 1 <<4 #R = <<- L = <<4 #R 1 <<- L = <<4 #R 1 <<- L 1 <<- L 1 <<4 #R = <<4 #R 1 <<- L 1 <<- L 1 <<4 #R = <<- L 1 <<- - L = <<4 #R 1 <<4 #R = <<- L = <<- L 1 <<4 #R 1 <<- L 1 <<- L 1 <<- L 1 <<- L = <<- L 1 <<- L 1 <<- L 1 <<- L 1 <<- - L 1 <<- L = <<- L = <<- L 1 <<- L 1 <<- L = <<- L = <<- L = <<- L 1 <<4 #R 1 << 4 #R = <<4 #R = <<4 #R 1 <<4 #R 1 <<4 #R

= <<4 #R = <<4 #R = <<4 #R 1 <<4 #R = <<4 #R 1 <<4 #R 1 <<4 #R 1 <<4 #R = < <4 #R 1 <<4 #R = <<4 #R = <<4 #R 1 <<4 #R = <<4 #R = <<4 #R = <<4 #R 1 <<4 #R 1 <<4 #R 1 <<4 #R 1 <<4 #R 1 <<4 #R 1 <<4 #R = <<4 #R = <<4 #R = <<4 #R = <<4 #R = <<4 #R $ #% -) '4 O 3 0

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0 * 0 0 * 0? - "L" + 0 220 &

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0 * 0 0 * 0? - "L" + 0 227 &

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0 * 0 0 * 0? - L "L" + 0 220 &

0 * 0 0 * 0? - "L" + 0 227 & - 0 * 0 0 * 0? - "L" + 0 227 &

0 * 0 0 * 0? - L "L" + 0 220 &

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0 * 0 0 * 0? - "L" + 0 220 &

0 * 0 0 * 0? - "L" + 0 227 &

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0 * 0 0 * 0? - L "L" + 0 220 &

0 * 0 0 * 0? - "" + 0 227 &

0 * 0 0 * 0? - "L" + 0 227 &

0 * 0 0 * 0? - L "L" + 0 227 &

0 * 0 0 * 0? - "L" + 0 220 &

0 * 0 0 * 0? - "L" + 0 227 &

0 * 0 0 * 0? - "L" + 0 227 &

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0 * 0 0 * 0? - 8 0 "+ 0 0 0 2 32 2 32? -" L "#% * +% #% N # # 4 227 6 2 32 2 32? - L" L "#% * +% #% N # # 4 220 6 2 32 2 32? - "L" #% * +% #% N # # 4 2220 6 2 32 2 32? - "L" #% * +% #% N # # 4 20 6 2 32 2 32? - L "L" #% * +% #% N # # 4 27 6 2 32 2 32? - 8 0 "#% 2C 0 2 32 2 32? -" L "# % * +% #% N # # 4 220 6 2 32 2 32? - 8 0 L "#% * +% #% N # # 4 2227 6 2 32 2 32? -" "#% * +% #% N # # 4 220 6 2 32 2 32? - "L" #% * +% #% N # # 4 227 0 2 32 2 32? - L "L" #% * +% #% N # # 4 27 6 2 32 2 32? - "L" #% * +% #% N # # 4 27 6 2 32 2 32? - L "L" #% * +% #% N # # 4 2220 6 2 32 2 32 ? - "L" #% * +% #% N # # 4 220 6 2 32 2 32? - L "L" #% * +% #% N # # 4 27 6 2 32 2 32? - "L" #% * +% #% N # # 4 2220 6 2 32 2 32? - L "L" #% * +% #% N # # 4 27 6 2 32 2 32? - "L" #% * +% #% N # # 4 2227 6 2 32 2 32? - 8 0 L "#% 2220 6 2 32 2 32? - 8 0 L" #% * +% #% N # # 4 2C 6 2 32 2 32? - 8 0 "#% * +% #% N # # 4 2C 6 2 32 2 32? -" "#% * +% #% N # # 4 2220 6 2 3 2 2 32? - "L" #% * +% #% N # # 4 227 6 2 32 2 32? - L "L" #% * +% #% N # # 4 220 6 2 32 2 32? - "L" #% * +% #% N # # 4 27 0 2 32 2 32? - L "L" #% * +% #% N # # 4 27 6 2 32 2 32? - "L" #% * +% #% N # # 4 27 6 2 32 2 32? - "L" #% * +% #% N # # 4 2220 6 2 32 2 32? - L "L" #% * +% #% N # # 4 220 6 2 32 2 32? - "" #% * +% #% N # # 4 2220 6 2 32 2 32? - 8 0 L "#% 20 6 2 32 2 32? -" L " #% * +% #% N # # 4 2220 6 2 32 2 32? - 8 0 L "#% * +% #% N # # 4 2C 6 2 32 2 32? - L" L "#% * + % #% N # # 4 227 6 2 32 2 32? - "L" #% * +% #% N # # 4 20 6 2 32 2 32? - "L" #% * +% #% N # # 4 227 6 2 32 2 32? - "L" #% * +% #% N # # 4 2220 6 2 32 2 32? - "L" #% * +% #% N # # 4 2220 6 2 32 2 32? - L "L" #% * +% #% N # # 4 20 6 2 32 2 32? - L "L" #% * +% #% N # # 4 2220 6 2 32 2 32? - " L "#% * +% #% N # # 4 27 6 2 32 2 32? - L" L "#% * +% #% N # # 4 27 6 2 32 2 32? -" L "#% * +% #% N # # 4 220 6 2 32 2 32? - "" #% * +% #% N # # 4 2227 6

2 32 2 32? - L "L" #% * +% #% N # # 4 27 6 2 32 2 32? - L "L" #% * +% #% N # # 4 2227 0 2 32 2 32 ? - "L" #% * +% #% N # # 4 20 6 2 32 2 32? - L "L" #% * +% #% N # # 4 20 6 2 32 2 32? - "L" #% * +% #% N # # 4 27 6 2 32 2 32? - "L" #% * +% #% N # # 4 220 6 2 32 2 32? - "L" #% * +% # % N # # 4 220 0 2 32 2 32? - "L" #% * +% #% N # # 4 20 6 2 32 2 32? - L "L" #% * +% #% N # # 4 20 6 2 32 2 32? - L "L" #% * +% #% N # # 4 27 6 2 32 2 32? - "L" #% 0 2220 0 2 32 2 32? - L "L" # % 0 27 0 2 32 2 32? - 8 0 L "#% * N # # 2220 0 2 32 2 32? - L" L "#% * 9 N # # 220 0 2 32 2 32? -" L " #% 0 27 0 2 32 2 32? - "L" #% * N # # 27 0 2 32 2 32? - "L" #% * +% #% N # # 4 22 6 2 32 2 32? - "L" #% * +% #% N # # 4 22 6 2 32 2 32? - L "L" #% * +% #% N # # 4 20 6 2 32 2 32? - "L" #% * +% #% N # # 4 20 6 2 32 2 32? - "" 1! L% * B # # 22 6 2 32 2 32? - "L" 1! L% * B # # 22 6 2 32 2 32? - "L" 1! L% * B # # 22 6 2 32 2 32? - "L" 1! L% * B # # 2 6 2 32 2 32? - "L" 1! L% 0 227 6 2 32 2 32? - "" 1! L% * B # # 22 6 2 32 2 32? - "L". YL 0 22 6 2 32 2 32? - 8 0 L ". YL # 6 O2 # 27 6 2 32 2 32? -" ". YL 0 22 6 2 32 2 32? -" ". YL 0 22 6 2 32 2 32? - 8 0 "1! L% # L 2C 6 2 32 2 32? -" ". Y L 0 22 6 2 32 2 32? - "L". Y L 0 22 6 2 32 2 32? - "L". Y L 0 2220 6 2 32 2 32? - "". Y L 0 22 6 2 32 2 32? - "L". Y L 0 27 6 2 32 2 32? - "". YL 0 22 6 2 32 2 32? - "" 1! L% * B # # 2220 6 2 32 2 32? - "L" 1! L% * B # # 2220 6 2 32 2 32? - "L" 1! L% * B # # 2220 6 2 32 2 32? - "L" 1! L% * B # # 2220 6 2 32 2 32? - "" 1! L% * B # # 22 6 2 32 2 32? - "L". YL 0 220 6 2 32 2 32? - "" 1! L% * B # # 22 6 2 32 2 32? - "" 1! L% * B # # 2220 6 2 32 2 32? - "L" 1 ! L% * B # # 22 6 2 32 2 32? - 8 0 "1! L% * B # # 0 6 2 32 2 32? - 8 0" 1! L% # L 2C7 0 2 32 2 32? - "L" * # # 0 22 6 2 32 2 32? - L "L" * # # 0 227 6 2 32 2 32? - L "L" * # # 0 2 6 2 32 2 32? - L " "* # # 0 2 6 2 32 2 32? - L" L "* # # 0 2 6 2 32 2 32? -" L "* # # 0 2 6 2 32 2 32? -" "* # # 0 2 6 2 32 2 32? - "" * # # 0 227 6 2 32 2 32? - "" * # # 0 2227 6 2 32 2 32? - "L" * # # 0 2227 6 2 32 2 32? - "" 1! L% * B # # 22 6 2 32 2 32? - "" * # # 0 220 6 2 32 2 32? - "" * # # 0 220 6 2 32 2 32? - "" * # # 0 222 6 2 32 2 32? - L "" * # # 0 2 6 2 32 2 32? - "" 1! L% # L 22 6 2 32 2 32? - "L" 1! L% * B # # 2 6 2 32 2 32? - "L" 1! L% * B # # 2 6

2 32 2 32? - "L" 1! L% * B # # 2 6 2 32 2 32? - "" 1! L% # L 22 6 2 32 2 32? - "" 1! L% * B # # 22 6 2 32 2 32? - 8 0 "1! L% * B # # 0 6 2 32 2 32? - 8 0" 1! L% * B # # 0 0 2 32 2 32? - L "L "* # # 0 2 6 2 32 2 32? -" L "* # # 0 2 6 2 32 2 32? - 8 0" * # # 0 227 6 2 32 2 32? - L "L" * # # 0 227 6 2 32 2 32? - "L" * # # 0 227 6 2 32 2 32? - "L" * # # 0 227 6 2 32 2 32? - "L" * # # 0 227 6 2 32 2 32? - "L" * # # 0 2227 6 2 32 2 32? - "" * # # 0 220 6 2 32 2 32? - 8 0 L "* # # 0 2C0 6 2 32 2 32? - L "" * # # 0 220 6 2 32 2 32? - 8 0 L "* # # 0 2C0 6 2 32 2 32? -" L "* # # 0 227 6 2 32 2 32? - L" L "* # # 0 227 6 2 32 2 32? - 8 0 L "* # # 0 2C0 6 2 32 2 32? -" L "* # # 0 2227 6 2 32 2 32? -" L "* # # 0 2 6 2 32 2 32? - L "L" * # # 0 220 6 2 32 2 32? - "L" * # # 0 2220 6 2 32 2 32? - "L". Y L * N # # 22 6 2 32 2 32? - "" * # # 0 22 6 2 32 2 32? - "L". Y L * N # # 22 6 2 32 2 32? - "". YL * N # # 227 6 2 32 2 32? - "L" * # # 0 22 6 2 32 2 32? - "L" * # # 0 220 6 2 32 2 32? - L "L" * # # 0 222 0 6 2 32 2 32? - "" * # # 0 22 0 6 2 32 2 32? - "" 1! L% BO # L 222 0 6 2 32 2 32? - "" * # # 0 22 0 6 2 32 2 32? - "L" * # # 0 22 0 6 2 32 2 32? - "" * # # 0 22 0 6 2 32 2 32? - "" * # # 0 222 0 6 2 32 2 32? - "L" * # # 0 22 0 6 2 32 2 32? - "L" 1! L% * B # # 222 0 6 2 32 2 32? - "L" * # # 0 222 0 6 2 32 2 32? - 8 0 "1! L% # L 2C 6 2 32 2 32? -" "1! L% * B # # 22 6 2 32 2 32? - L" L "* # # 0 22 0 6 2 32 2 32? - 8 0 L "1! L% * B # # 0 6 2 32 2 32? - 8 0" #% 220 6 2 32 2 32? - "L" #% * +% # % N # # 4 20 6 2 32 2 32? - L "L" #% * +% #% N # # 4 227 6 2 32 2 32? - "L" #% * +% #% N # # 4 2220 6 2 32 2 32? - L "L" #% * +% #% N # # 4 2220 6 2 32 2 32? - 8 0 "#% 220 6 2 32 2 32? - 8 0 L" 1! L% # L 0 0 2 32 2 32? - "L" * # # 0 2 6 2 32 2 32? - L "L" * # # 0 2 6 2 32 2 32? - "" * # # 0 2 6 2 32 2 32? - 8 0 L "* # # 0 2 6 2 32 2 32? - L" L " * # # 0 2 6 2 32 2 32? - "" * # # 0 22 7 6 2 32 2 32? - "L" * # # 0 22 7 6 2 32 2 32? - L "L" * # # 0 22 7 6 2 32 2 32? - L "L" * # # 0 2 6 2 32 2 32? - "L" * # # 0 222 7 6 2 32 2 32? - 8 0 L "* # # 0 2 6 2 32 2 32? - 8 0 "* # # 0 2C 6 2 32 2 32? -" "* # # 0 22 0 6 2 32 2 32? -" L "* # # 0 22 0 6 2 32 2 32? - "L" * # # 0 22 0 6

2 32 2 32? - "L" * # # 0 22 0 6 2 32 2 32? - "L" 1! L% * B # # 222 0 6 2 32 2 32? - "L" * # # 0 22 0 6 2 32 2 32? - 8 0 "* # # 0 2C 6 2 32 2 32? -" "* # # 0 22 0 6 2 32 2 32? -" L "* # # 0 222 0 6 2 32 2 32? - "L" * # # 0 22 0 6 2 32 2 32? - "" * # # 0 22 0 6 2 32 2 32? - L "L" * # # 0 22 0 6 2 32 2 32 ? - "L" * # # 0 22 0 6 2 32 2 32? - "L" * # # 0 22 0 6 2 32 2 32? - "L" * # # 0 22 7 6 2 32 2 32? - "L" * # # 0 22 7 6 2 32 2 32? - "L" * # # 0 22 7 6 2 32 2 32? - "L" * # # 0 22 7 6 2 32 2 32? - "L "* # # 0 22 0 6 2 32 2 32? -" "* # # 0 22 0 6 2 32 2 32? -" L "* # # 0 2227 6 2 32 2 32? -" "* # # 0 2227 6 2 32 2 32? - "L" * # # 0 220 6 2 32 2 32? - L "L" * # # 0 220 6 2 32 2 32? - "L" 1! L% * B # # 2220 6 2 32 2 32? - "" 1! L% * B # # 222 0 2 32 2 32? - "L" 1! L% * B # # 22 2 32 2 32? - "" 1! L% * B # # 222 0 2 32 2 32? - 8 0 "#% 0 0 0 2 32 2 32? - 8 0 L" #% 0 0 0 2 32 2 32? - "L" 1! L% # L 222 0 6 2 32 2 32? - "" 1! L% * B # # 222 0 6 2 32 2 32? - "L" #% * +% #% N # # 4 27 6 2 32 2 32? - L "L" #% * +% #% N # # 4 20 6 2 32 2 32? - 8 0 L "#% 20 6 2 32 2 32? - L" L "#% * +% #% N # # 4 220 6 2 32 2 32? - L" L "1! L% * B # # 222 0 6 2 32 2 32? - "L" #% * +% #% N # # 4 222 0 6 2 32 2 32? - L "L" #% * +% #% N # # 4 222 0 6 2 32 2 32? - L "L" #% * +% #% N # # 4 27 6 2 32 2 32? - "" #% * +% #% N # # 4 222 0 6 2 32 2 32? - L "L" #% * +% #% N # # 4 20 6 2 32 2 32? - "L" #% * +% #% N # # 4 220 6 2 32 2 32? - "L" # % * +% #% N # # 4 222 0 6 2 32 2 32? - 8 0 "1! L% # L 0 0 2 32 2 32? -" "1! L% * B # # 2220 6 2 32 2 32? - "L" 1! L% * B # # 22 6 2 32 2 32? - "L" 1! L% * B # # 22 6 2 32 2 32? - "L" 1! L% * B # # 22 6 2 32 2 32? - "L" * # # 0 220 6 2 32 2 32? - "" 1! L% * B # # 2220 6 2 32 2 32? - L "L" * # # 0 220 6 2 32 2 32? - L "L" * # # 0 220 6 2 32 2 32? - "L" * # # 0 220 6 2 32 2 32? - "L" * # # 0 220 6 2 32 2 32? - "" 1! L% * B # # 22 6 2 32 2 32? - "L" 1! L% * B # # 2220 6 2 32 2 32? - "L" 1! L% * B # # 2220 6 2 32 2 32? - 8 0 L "1! L% * B # # 0 0 2 32 2 32? - 8 0 L" 1! L% 0 0 6 2 32 2 32? - "" @ "* # # 222 * 6 2 32 2 32? -" L "* # # 0 222 * 6 2 32 2 32? -" L "* # # 0 2227 6 2 32 2 32? - L "L" * # # 0 220 6 2 32 2 32? - "L" * # # 0 220 6 2 32 2 32? - "" * # # 0 2227 6 2 32 2 32? - L "L" * # # 0 2 6 2 32 2 32? - 8 0 "* # # 0 2C7 6 2 32 2 32? - L" L "* # # 0 2220 6

2 32 2 32? - "" @ "* # # 222 * 6 2 32 2 32? -" "* # # 0 220 6 2 32 2 32? - L" L "* # # 0 2 6 2 32 2 32 ? - "L" * # # 0 22 * 6 2 32 2 32? - "L" + 0 227 6 2 32 2 32? - 8 0 "@" * # # 2C 6 2 32 2 32? - "L" @ "* # # 20 6 2 32 2 32? -" L "+ 0 227 6 2 32 2 32? -" L "#% * +% #% N # # 4 220 6 2 32 2 32? - L" L "#% * +% #% N # # 4 27 6 2 32 2 32? -" L "#% * +% #% N # # 4 20 6 2 32 2 32? - 8 0 L" #% * +% #% N # # 4 2C 6 2 32 2 32? - 8 0 L "+ 0 2C 6 2 32 2 32? -" L "#% * +% #% N # # 4 222 0 6 2 32 2 32? - "L" + 0 227 6 2 32 2 32? - "L" + 0 222 6 2 32 2 32? - "L" & * 220 6 2 32 2 32? - "L" + 0 227 6 2 32 2 32? - 8 0 L "+ 0 2C 0 2 32 2 32? -" L "#% * +% #% N # # 4 20 6 2 32 2 32? - L" L "& 20 6 2 32 2 32? - 8 0 L "+ 0 N # # 0 6 2 32 2 32? -" L "* # # 0 220 6 2 32 2 32? - 0" "#% 0 2220 6 2 32 2 32? - 0 L "L" #% * N # # 227 6 2 32 2 32? - 0 "" #% 0 27 6 2 32 2 32? - 0 "L" #% * N # # 2220 6 2 32 2 32? - 0 L "L" #% 0 2220 0 2 32 2 32? - 0 "L" #% 0 20 6 2 32 2 32? - 0 "" #% * N # # L 220 6 2 32 2 32? - 0 L "L" #% 0 27 6 2 32 2 32? - 0 L "L" #% * N # # L 27 6 2 32 2 32? - 0 L "L" #% * N # # L 220 6 2 32 2 32? - 0 "L" #% 227 6 2 32 2 32? - 0 L "L "#% 0 27 6 2 32 2 32? - 0 L" L "#% * N # # 27 6 2 32 2 32? - 0" L "#% 0 2220 6 2 32 2 32? - 0" L " #% 0 220 6 2 32 2 32? - 0 L "L" #% * N # # 27 6 2 32 2 32? - 0 L "L" #% * N # # 2220 6 2 32 2 32? - 0 "L" #% * N # # 2227 6 2 32 2 32? - 0 "L" #% 0 2220 6 2 32 2 32? - 0 L "L" #% * N # # 27 0 2 32 2 32? - 0 "L" #% * N # # 220 6 2 32 2 32? - 0 "L" #% * N # # 27 6 2 32 2 32? - 0 L "L" #% * N # # 2220 6 2 32 2 32? - 0 L "L" #% 0 27 6 2 32 2 32? - "L" * # # 0 22 0 2 32 2 32? - "L" * # # 0 22 0 2 32 2 32 ? - "L" * # # 0 22 7 2 32 2 32? - "L" * # # 0 22 7 2 32 2 32? - "" * # # 0 # 20 2 32 2 32? - "" * # # 0 '4 2220 6 2 32 2 32? - "" * # # 0 2227 6 2 32 2 32? - "L" * # # * N # # # -L 2227 6 2 32 2 32? - L "L "* # # 0 2227 6 2 32 2 32? - L" L " * # # 0 2220 6 2 32 2 32? - L "" * # # 0 3 '4 & 22 0 6 2 32 2 32? - L "" * # # 0 22 0 6 2 32 2 32? - "" * # # 0 22 6 2 32 2 32? - "L" * # # 0 # 2 6 2 32 2 32? - L "L" * # # 0 # 22 0 6 2 32 2 32? - "" * # # 0 222 7 6 2 32 2 32? - L "L" * # # 0 220 6 2 32 2 32? - L "L" * # # 0 222 7 6 2 32 2 32? - L "L" & * # 220 6

2 32 2 32? - L "L" & * # 20 6 2 32 2 32? - "L" @ "*" O # 222 * 6 2 32 2 32? - L "L" * # # 0 220 6 2 32 2 32? - "L" * # # 0 220 6 2 32 2 32? - L "L" * # # 0 2 6 2 32 2 32? - 8 0 "* # # 0 2C0 6 2 32 2 32? - L "L" * # # 0 220 6 2 32 2 32? - "L" + 0 # 220 6 2 32 2 32? - 8 0 L "+ 0 # 2C 6 2 32 2 32? -" "+ 0 # 227 6 2 32 2 32? - "" * # # 0 2220 6 2 32 2 32? - "L" * # # 0 2220 6 2 32 2 32? - 8 0 "* # # 0 2C 6 2 32 2 32? - L "" * # # 0 222 * 6 2 32 2 32? - "L" * # # 0 2227 6 2 32 2 32? - "L" * # # 0 2227 6 2 32 2 32? - " "* # # 0 2227 6 2 32 2 32? -" L "* # # 0 220 6 2 32 2 32? -" L "* # # 0 220 6 2 32 2 32? - L" L "* # # 0 2 6 2 32 2 32? - "L" * # # 0 220 6 2 32 2 32? - "" * # # 0 # 27 6 2 32 2 32? - "L" * # # 0 220 6 2 32 2 32? - "L" * # # 0 227 6 2 32 2 32? - "L" * # # 0 227 6 2 32 2 32? - "L" * # # 0 220 6 2 32 2 32? - " "* # # 0 # 220 6 2 32 2 32? -" L "* # # 0 227 6 2 32 2 32? -" L "* # # 0 2 6 2 32 2 32? -" L "* # # 0 220 6 2 32 2 32? - "L" * # # 0 227 6 2 32 2 32? - "L" * # # 0 22 6 2 32 2 32? - "L" * # # 0 220 6 2 32 2 32? - "" * # # 0 222 * 6 2 32 2 32? - L "L" * # # 0 222 7 6 2 32 2 32? - L "L" * # # 0 2 6 2 32 2 32? - L "L" & * # 220 6 2 32 2 32? - "L" * # # * N # # # L 220 6 2 32 2 32? - L "L" & * # 220 6 2 32 2 32? - "L" & * # $ # 227 6 2 32 2 32? - L "L" & * # 222 0 6 2 32 2 32? - L "" & * # 227 6 2 32 2 32? - L "L" & * # 227 6 2 32 2 32? - L "L" & * # 227 6 2 32 2 32? - "" @ "*" O 22 * 6 2 32 2 32? - "L" + 0 # 220 6 2 32 2 32? - 8 0 "* # # 0 2C0 6 2 32 2 32? - L" L "* # # 0 220 6 2 32 2 32? -" L "* # # 0 220 6 2 32 2 32? - L" L " * # # 0 222 0 6 2 32 2 32? - L "L" * # # 0 222 0 6 2 32 2 32? - 8 0 L "* # # * N # # # L 2C0 6 2 32 2 32? - 8 0 L "* # # 0 222 7 6 2 32 2 32? -" L "@" * "O 220 6 2 32 2 32? -" L "* # # 0 220 6 2 32 2 32? - L "L" * # # 0 220 6 2 32 2 32? - "" @ "0 222 7 6 2 32 2 32? -" "& * # $ # 222 * 6 2 32 2 32? - 8 0" * # # 0 0 6 2 32 2 32? - "" * # # 0 222 0 6 2 32 2 32? - L "L" & * # 220 6 2 32 2 32? - L "L" * # # 0 222 7 6 2 32 2 32? - "L" * # # 0 220 6 2 32 2 32? - "L" * # # 0 222 7 6 2 32 2 32? - L "L" & * 220 6 2 32 2 32? - L "L" * # # 0 222 7 6

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0 * 0 *? - 8 0 L "+ 0 27 0 * 0 *? - 8 0 L" + 0 220 0 * 0 *? - 8 0 "+ 0 220 0 * 0 *? - 8 0" + 0 220 0 * 0 *? - 8 0 L "+ 0 220 0 * 0 *? - 8 0 L" + 0 227 0 * 0 *? - 8 0 L "+ 0 227 0 * 0 *? - 8 0 L" + 0 227 0 * 0 *? - 8 0 L "+ 0 220 0 * 0 *? - 8 0 L" + 0 27 6 0 * 0 *? - 8 0 "+ 0 27 6 0 * 0 *? - 8 0 "+ 0 227 6 0 * 0 *? - 8 0" + 0 227 6 0 * 0 *? - 8 0 "+ 0 27 6 0 * 0 *? - 8 0" + 0 27 6 0 * 0 *? - 8 0 L "+ 0 227 6 0 * 0 *? - 8 0 L" + 0 227 6 0 * 0 *? - 8 0 L "+ 0 227 6 0 * 0 *? - 8 0 L" + 0 27 6 0 * 0 *? - 8 0 L "+ 0 220 6 0 * 0 *? - 8 0" + 0 2C 6 0 * 0 *? - 8 0 L "+ 0 227 6 0 * 0 *? - 8 0 L "+ 0 0 0 0 * 0 *? - 8 0 L" + 0 0 0 0 * 0 *? - 8 0 L "+ 0 0 0 0 * 0 *? - 8 0 L" + 0 0 0 0 * 0 *? - 8 0 L "+ 0 0 0 0 * 0 *? - 8 0" + 0 0 0 0 * 0 *? - 8 0 L "+ 0 0 0 0 * 0 *? - 8 0 L" + 0 0 0 0 * 0 *? - 8 0 L "+ 0 0 0 0 * 0 *? - 8 0 L" + 0 0 0 0 * 0 *? - 8 0 L "+ 0 0 0 0 * 0 *? - 8 0 "+ 0 0 0 0 * 0 *? - 8 0 L" + 0 0 0 0 * 0 *? - 8 0 L "+ 0 0 0 0 * 0 *? - 8 0 L" + 0 0 0 0 0* 0 *? - 8 0 L "+ 0 0 0 0 * 0 *? - 8 0 L" + 0 0 0 0 * 0 *? - 8 0 "+ 0 0 0 0 * 0 *? - 8 0 L" + 0 0 0 0 * 0 *? - 8 0 "+ 0 0 0 0 * 0 *? - 8 0 L" + 0 0 0 0 * 0 *? - 8 0 L "+ 0 0 0 0 * 0 *? - 8 0 L "+ 0 0 0 0 * 0 *? - 8 0 L" + 0 0 0 0 * 0 *? - 8 0 "+ 0 0 0 0 * 0 *? - 8 0 L" + 0 0 0 0 * 0 *? - 8 0 "+ 0 0 0 0 * 0 *? - 8 0 L" + 0 0 0 0 * 0 *? - 8 0 L "+ 0 0 0 0 * 0 *? - 8 0" + 0 0 0 0 * 0 *? - 8 0 L "+ 0 0 0 0 * 0 *? - 8 0 L" + 0 0 0 0 * 0 *? - 8 0 L "+ 0 0 0 0 * 0 * ? - 8 0 "+ 0 0 0 0 * 0 *? - 8 0" + 0 0 0 0 * 0 *? - 8 0 L "+ 0 0 0 0 * 0 *? - 8 0" + 0 0 0 0 0 * 0 *? - 8 0 L "+ 0 0 0 0 * 0 *? - 8 0 L" + 0 0 0 0 * 0 *? - 8 0 L "+ 0 0 0 0 * 0 *? - 8 0 L "+ 0 0 0 0 * 0 *? - 8 0 L" + 0 0 0 0 * 0 *? - 8 0 L "+ 0 0 0 0 * 0 *? - 8 0 L" + 0 0 0

0 * 0 *? - 8 0 L "+ 0 0 0 0 * 0 *? - 8 0 L" + 0 0 0 0 * 0 *? - 8 0 L "+ 0 0 0 0 * 0 *? - 8 0 L "+ 0 0 0 0 * 0 *? - 8 0 L" + 0 0 0 0 * 0 *? - 8 0 L "+ 0 0 0 0 * 0 *? - 8 0" + 0 0 0 0 * 0 *? - 8 0 L "+ 0 0 0 0 * 0 *? - 8 0 L" + 0 0 0 0 * 0 *? - 8 0 L "+ 0 0 0 0 * 0 *? - - 8 0 L" + 0 0 0 0 * 0 *? - 8 0 L "+ 0 0 0 0 * 0 *? - 8 0 L" + 0 0 0 0 * 0 *? - 8 0 "+ 0 0 0 0 * 0 *? - 8 0 L "+ 0 0 0 0 * 0 *? - 8 0 L" + 0 0 0 0 * 0 *? - 8 0 L "+ 0 0 0 0 * 0 *? - 8 0 L" + 0 0 0 0 * 0 *? - 8 0 L "+ 0 0 0 0 * 0 *? - 8 0 L" + 0 0 0 0 * 0 *? - 8 0 L "+ 0 0 0 0 * 0 *? - 8 0 "+ 0 0 0 0 * 0 *? - 8 0" + 0 0 0 0 * 0 *? - 8 0 L "+ 0 0 0 0 * 0 *? -" L "+ 0 227 & - - 0 * 0 *? - L "L" + 0 227 & - - 0 * 0 *? - "L" + 0 227 &

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0 * 0 *? - L "L" + 0 227 & - 0 * 0 *? - L "L" + 0 220 - - 0 * 0 *? - "L" + 0 227 & - - 2 32 2 32? - L "L" & * # 220 6 2 32 2 32? - 8 0 L "1! L% * B # # 22 6 2 32 2 32? -" "1! L% 0 2220 6 2 32 2 32? - 8 0 L ". YL 0 2C 6 2 32 2 32? - L "L" & 0 220 6 2 32 2 32? - "" @ "0 222 * 0 2 32 2 32? - L" L "& * # 220 6 2 32 2 32? - L "L" & 0 227 6 2 32 2 32? - L "L". YL 0 27 6 2 32 2 32? - L "L" & 0 2220 6 2 32 2 32? - 8 0 ". Y L 0 ## X L L ## O O 2C 6 2 32 2 32? - L "L" & 0 2C 6 2 32 2 32? - L "L" & 0 220 6 2 32 2 32? - "L". Y L 0 O # 227 6 2 32 2 32? - L "L". Y L 0 227 6 2 32 2 32? - L "L". Y L 0 227 6 2 32 2 32? - "L" @ "0 2227 0 2 32 2 32? -" "@" * O # 222 * 6 2 32 2 32? - L "L". YL L 0 O # 222 7 6 2 32 2 32? - 8 0 L "@" * O 2C 6

2 32 2 32? - L "L". YL 0 20 6 2 32 2 32? - L "L" @ "0 2 * 6 2 32 2 32? - L" L ". YL 0 222 * 6 2 32 2 32? -" "1! L% * B # # # L $ 0 0 0 * 0 *? - 8 0 L "+ 0 227 0 * 0 *? - 8 0 L" + 0 227 0 * 0 *? - 8 0 "+ 0 227 0 * 0 *? - 8 0 "+ 0 227 0 * 0 *? - 8 0" + 0 227 0 * 0 *? - 8 0 L "+ 0 227 0 * 0 *? - 8 0" + 0 222 0 * 0 *? - 8 0 L "+ 0 227 0 * 0 *? - 8 0 L" + 0 227 0 * 0 *? - 8 0 L "+ 0 20 0 * 0 *? - 8 0 L" + 0 227 0 * 0 * ? - 8 0 "+ 0 227 0 * 0 *? - 8 0 L" + 0 227 0 * 0 *? - - 8 0 L "+ 0 227 0 * 0 *? - 8 0 L" + 0 227 0 * 0 *? - 8 0 L "+ 0 227 0 * 0 *? - 8 0 L" + 0 220 0 * 0 *? - 8 0 L "+ 0 220 0 * 0 *? - 8 0 L" + 0 220 0 * 0 *? - 8 0 L "+ 0 27 0 * 0 *? - 8 0 L" + 0 227 0 * 0 *? - 8 0 L "+ 0 227 0 * 0 *? - 8 0 L" + 0 227 0 * 0 *? - 8 0 L "+ 0 227 0 * 0 *? - 8 0 L" + 0 227 0 * 0 *? - 8 0 "+ 0 227 0 * 0 *? - 8 0" + 0 227 0 * 0 *? - 8 0 L "+ 0 20 0 * 0 *? - 8 0 L" + 0 227 0 * 0 *? - 8 0 L "+ 0 227 0 * 0 *? - 8 0" + 0 227 0 * 0 *? - 8 0 "+ 0 227 0 * 0 *? - 8 0 L" + 0 27 0 * 0 *? - 8 0 L "+ 0 227 0 * 0 *? - 8 0 L" + 0 27 0 * 0 *? - 8 0 L "+ 0 27 0 * 0 *? - 8 0 L" + 0 27 0 * 0 * ? - 8 0 "+ 0 227 0 * 0 *? - 8 0" + 0 27 0 * 0 *? - 8 0 L "+ 0 227 0 * 0 *? - 8 0" + 0 227 0 * 0 *? - 8 0 "+ 0 227 0 * 0 *? - 8 0 L" + 0 2C 0 0 * 0 *? - 8 0 L "+ 0 227 0 * 0 *? - 8 0 L" + 0 227 0 * 0 *? - 8 0 L "+ 0 227

1 '4 0 # # 1' 4 & & '4 6. 2 - / . A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- & K * ^ ^% K%% * \ 0 & K * ^ ^% K%% 0 \ 6 1 * ^ ^% K %% * \ 0 & K * ^ ^% K% \ * & K * A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- & K * 17I, ^ ^% K%% * \ 17I, 1 * ^ ^% K%% 6 \ 0 1 * A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- 1 * ^ ^% K%% * \ & K * A = 0- ^ ^ T ^ O% K%% * \ A = 0- & K * A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- & K * A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- & K *

^ ^% K%% 6 \ 1 * A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- & K * 6 0- ^% K%% * \ 6 0- & KL ^ ^ % K%% 6 \ 0 1 * ^ ^% K%% 0 \ * & K * A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- & K * 17I, ^ ^ 0% K%% * \ 17I, & K * A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- & K * ^ 0% K%% * \ 1 L * 3A 0 ^ ^% K %% * \ * 3A 0 1 * ^ ^% K%% 6 \ 0 0 0 0 ^ ^% K%% 0 \ * 1 * 17I, ^ ^ 0% K%% 6 \ 0 17I, 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ A = 0- & KL ^ ^% K%% 0 \ * & KL ^ ^% K%% 0 \ * & K * A = 0- ^ ^ T ^ O% K%% * \ A = 0- 0 0 0 * 3A 0 ^ ^% K%% * \ * 3A 0 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 ^ ^% K%% 6 \ 0 & K * ^ ^% K%% * \ 0 0 ^ ^% K%% 0 \ * 0 0 ^ ^% K%% 6 \ 0 0 0 0 ^ ^% K%% 0 \ * 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K%% * \ 0 0 ^ ^% K%% 6 \ 0 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 ^ 0% K %% 6 \ 0 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K% \ 0 & K * ^ ^% K%% * \ & K * ^ ^% K%% 0 \ * 0 0 0 ^ ^% K%% 0 \ * 1 * ^ ^% K%% 0 \ * & K * ^ ^% K%% 6 \ 0 & K * ^ ^ % K%% * \ 0 0 0 ^ ^% K% \ * 1 B + ^ 0% K%% * \ 1 * ^ ^% K%% 6 \ 0 & K * ^ ^% K%% 6 \ 0 & K * A = 0- ^ ^ T ^ O% K%% * \ A = 0- & K * ^ ^% K%% * \ & K * A = 0- ^ ^ T ^ O% K%% * \ A = 0- 1 * ^ ^% K% \ 0 & K * 17I, ^ ^ 0% K%% 6 \ 0 17I, 0 0 0 ^ ^% K%% * \ 0 1 * ^ ^% K%% 0 \ * 1 * ^ 0% K%% 6 \ 0 & K *. ^ ^% K%% 0 \ 6. 1 * = 0 ^ ^% K%% 6 \ 0 = 0 1 * ^ ^% K%% 6 \ 0 & K * ^ ^% K%% 6 \ 0 0 0 0 6 0- ^% K%% 6 \ 0 6 0- & K * ^ 0% K%% 6 \% & K * ^ ^% K%% 6 \ 0 1 * ^ ^% K%% * \ 6 & K * A = 0- ^ ^ 0 % K%% * \ A = 0- & K * A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- & K * A = 0- ^ ^ T ^ O% K%% * \ A = 0-% 1 * A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- & K *. ^% K%% 6 \ 0. 1 * ^ ^% K%% * \ & K * ^ ^% K%% 0 \ * 1 * A = 0- ^ ^ T ^ O% K%% * \ A = 0- 1 *

A = 0- ^ ^% K% \ * A = 0- & K * A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- 1 * 6 0- ^% K%% 6 \ 0 6 0- & K * ^ ^% K%% 6 \ 0 1 * 2A * 0 ^% K%% 6 \ 0 2A * 0 1 * 6 0- ^% K%% * \ 6 0- 1 * ^ ^% K%% 0 \ 6 1 * ^ ^% K%% 6 \ 0 1 * A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- 1 * 6 0- ^% K% % 6 \ 0 6 0- 1 * A = 0- ^ ^ T ^ O% K%% * \ A = 0- 1 * ^ ^% K%% * \ 0 & KL ^ ^% K% \ *% & K * ^ 0% K%% 6 \ 0 1 * ^ ^% K%% 6 \ 0% & K * ^ ^% K%% 0 \ 1 * ^ ^% K%% 6 \ 0 & K * ^ ^ % K%% 0 \ & K * A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- 1 * ^ ^% K%% * \ 1 * ^ ^% K%% * \ 1 * A = 0- ^ ^% K%% * \ A = 0-% & K * ^ ^% K%% * \ & K * ^ ^% K%% 6 \ 0 & K * 6 0- ^% K %% * \ 6 0-% & K 0 0. ^% K%% 6 \ 0. 1 0 ^ ^% K%% * \% 1 * A = 0- ^ ^ T ^ O% K%% * \ A = 0- & K * ^ ^% K%% * \ & K * ^ ^% K %% * \ 0 1 * ^ ^% K%% * \% 1 * A = 0- ^ ^ T ^ O% K%% * \ A = 0- 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ 6 A = 0- & K * ^ ^% K%% \ * 1 * A = 0- ^ ^ T ^ O% K%% * \ A = 0- 1 * 17I, ^ ^% K %% * \ 17I, 1 * ^ 0% K%% 6 \ 0 & K * A = 0- ^ ^ T ^ O% K%% * \ 6 A = 0- & K * A = 0- ^ ^ T ^ O% K%% * \ A = 0- & K * A = 0- ^ ^ T ^ O% K%% * \ 6 A = 0- 1 * ^ ^% K%% 0 \ 6 1 * ^ ^ % K%% 6 \ 0 & K * ^ 0% K%% * \ 1 * A = 0- ^ ^% K%% * \ A = 0- 0 0 0 17I, ^ ^% K%% * \ 17I , & K * ^ ^% K% \ * & K * ^ ^% K%% 6 \ * 1 * = 0 ^ ^% K%% 6 \ 0 = 0 1 * ^ ^% K%% * \ 1 * ^ 0% K%% * \ & K @ ^% K%% 6 \ 0 & K @ = 0 ^ ^% K%% 6 \ 0 = 0 & K * ^ ^% K%% 6 \ 0 & K * ^ ^% K%% 0 \ * & K * 6 0- ^% K%% 6 \ 0 6 0- & K * ^ ^% K%% * \ & K * ^ ^% K%% 6 \ 0 & K * A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- & K * ^ ^% K%% * \ 0 & K * ^ ^% K%% 6 \ 0 & K * 6 0- ^% K%% 6 \ 0 6 0- 1 * ^ 0% K%% * \ 1 * ^ ^% K%% * \ 1 * ^ 0% K%% * \ & K * ^ ^% K %% * \ 0 & K * ^ ^% K%% * \ 0 & K *

^ ^% K%% 0 \ 1 * A = 0- ^ ^ T ^ O% K%% * \ 6 A = 0- & K * A = 0- ^ ^ T ^ O% K%% * \ 6 A = 0- & K * A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- & K * A = 0- ^ ^ T ^ O% K%% * \ A = 0- 0 0 0 ^ 0% K%% * \ 0 0 0 * 3A 0 ^ ^% K%% * \ * 3A 0 0 0 0 2A * 0 ^ ^% K%% 6 \ 0 2A * 0 & K * ^ ^% K%% * \ 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- & K * ^% K%% 6 \ 0 1 * A = 0- ^ ^ T ^ O % K%% * \ 0 A = 0- & K * ^ ^% K%% 6 \ 0 & K * A = 0- ^ ^ T ^ O% K%% * \ 6 A = 0- & K * ^ ^% K%% * \ & K * ^ ^% K%% 6 \ * 1 * ^ ^% K%% 6 \ 1 * ^ ^% K%% * \ 1 * 6 0- ^% K%% 6 \ 0 6 0- 1 * ^ ^% K%% 6 \ 0 1 * A = 0- ^ ^ T ^ O% K%% * \ A = 0- & K * ^ ^% K%% 6 \ 0 & K * ^ ^% K%% 6 \ 0 & K * ^ ^% K%% 6 \ 0 & K * A = 0- ^ ^ T ^ O% K%% * \ A = 0- & K * ^ ^ % K%% 6 \ 0 1 * ^ ^% K%% * \ 0 1 * ^ ^% K%% * \ 0 1 * ^ 0% K%% 6 \ 0 & K * ^ ^% K%% 6 \ 0 & K * ^ ^% K%% * \ & K * ^ ^% K% \ 0 1 * A = 0- ^ ^ T ^ O% K%% * \ 6 A = 0- & K * ^ ^ % K%% * \ 0 1 * 17I, ^% K%% * \ 17I, 1 * ^ ^% K%% 6 \ 0 1 * = 0- ^ ^% K% * \ = 0- & K * 7 = 01 ^ ^% K% 0 \ 7 = 01 & K @ ^ ^% K% \ * 1 * ^ 0% K%% * \ & KL ^ 0% K%% * \ & K * ^ 0% K% % 6 \ 0 & K * ^ ^% K%% 0 \ * 1 * * 3A 0 ^ ^% K%% * \ * 3A 0 1 * * 3A 0 ^ ^% K%% * \ * 3A 0 1 L ^ ^% K% \ * & K * A = 0- ^ ^ T ^ O% K%% * \ A = 0- 1 * ^ ^% K%% * \ 1 * A = 0- ^ ^ T ^ O% K%% * \ A = 0- & K * 6 0- ^% K%% 6 \ 0 6 0- 1 * ^ 0% K%% * \ & K * ^ ^% K%% 6 \ 0 & K * ^ ^% K%% * \ 1 * ^ 0% K%% * \ & K * 17I, ^ ^ 0% K%% * \ 17I, & K * * 3A 0 ^ ^% K%% 6 \ 0 * 3A 0 & K * * 3A 0 ^ ^% K%% 6 \ 0 * 3A 0 & K * ^ ^% K%% 0 \ 1 *. ^% K%% 6 \ 0. 1 * . ^% K%% 6 \ 0. 1 * ^% K%% 6 \ 0 1 * ^ ^% K%% * \ 0 1 * 6 0- ^% K%% 6 \ 0 6 0- & K * ^% K%% 6 \ 0 & K * ^ ^% K%% * \ 1 * ^ 0% K%% * \ 1 *

^ ^% K%% 6 \ 0 & K * ^ ^% K%% 6 \ 0 & K * ^ ^% K%% * \ & K * ^ ^% K%% * \ & K * ^ ^% K %% * \ 1 * ^ ^% K%% * \ 1 * 6 0- ^% K%% 6 \ 0 6 0- 0 0 0 6 0- ^% K%% 6 \ 0 6 0- 0 0 0 ^ 0% K%% 6 \ 0 & K * A = 0- ^ ^ T ^ O% K%% * \ A = 0- & K * ^ ^% K%% * \ 6 1 * ^ ^% K% % 6 \ 0 & K * ^ ^% K%% * \ & K * ^ ^% K%% 6 \ 0 & K * 17I, ^ ^% K%% 6 \ 0 17I, & K * ^ ^% K %% * \ 1 * ^ ^% K%% * \ 1 * ^ ^% K%% 6 \ 0 & K * ^ 0% K%% 6 \ 0 & K * ^ ^% K%% 6 \ * 0 0 0 ^ ^% K%% 0 \ 6 0 0 0 ^ ^% K% \ * 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K%% * \ 0 0 0 ^% K% % * \ 0 0 0 0 ^ ^% K%% * \ 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ A = 0- 1 * ^ ^% K%% 6 \ 0 0 0 0 = 0 ^ ^% K%% * \ = 0 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K%% 6 \ 0 0 0 0. ^ ^% K%% * \. 0 0 0 ^ ^% K%% * \ 0 0 0 0 ^ ^% K%% * \ 0 0 0 6 0- ^% K%% 6 \ 0 6 0- 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ A = 0- 0 0 0 = 0 ^ ^% K%% * \ = 0 0 0 0 6 0- ^% K%% 6 \ 0 6 0- 0 0 0 0 0% K%% 6 \ 0 & K * ^ ^% K%% 6 \ 0 1 * ^ ^% K%% * \ 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K% \ * 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 = 0 ^ ^% K%% * \ = 0 & K * = 0 ^ ^% K%% * \ = 0 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 ^ ^% K%% 6 \ 0 & K * ^ ^% K%% 6 \ 0 & K * 6 0- ^% K%% 6 \ 0 6 0- 1 * 6 0- ^ % K%% 6 \ 0 6 0- & K * ^ ^% K% \ 0 1 * A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- & K * A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- & K * A = 0- ^ ^ T ^ O% K%% * \ A = 0- 1 * A = 0- ^ ^ T ^ O% K %% * \ A = 0- & K * ^ ^% K%% * \ 1 *. ^ ^% K%% * \ 6. 0 0 0 * 7 ^ ^% K%% 6 \ 0 * 7 0 0 0 ^% K%% 6 \ 0 0 0 0 * 3A 0 ^ ^% K%% 6 \ 0 * 3A 0 0 0 0 ^ ^ % K%% * \ 0 0 0. ^ ^% K%% * \ 6. 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K%% * \ 0 0 0

^ ^% K%% * \ 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 = 0 ^ ^% K%% * \ = 0 0 0 0 0 ^ ^% K% \ * 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 ^ ^% K%% * \ 0 0 0 0 6 0- ^% K%% 6 \ 0 6 0- 0 0 0 ^ ^% K%% * \ 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ A = 0- 0 0 0 ^ ^% K% \ * 0 0 0 6 0- ^% K%% 6 \ 0 6 0- 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K%% * \ 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ A = 0- 0 0 0 ^ ^% K%% * \ & K * A = 0- ^ ^ T ^ O% K%% * \ A = 0- & K * 2A * 0 ^ ^% K%% 6 \ 0 2A * 0 1 0 0 ^ ^% K%% * \ & K * A = 0- ^ ^ T ^ O% K%% * \ A = 0- & K * ^ ^% K%% * \ & K * ^ ^% K%% 6 \ 0 & K 0 ^ ^% K%% 6 \ * 1 * 17I, ^% K%% * \ 17I, 1 * ^ ^% K%% 6 \ 0 1 * A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- & K * = 0 ^ ^% K%% * \ = 0 & K * ^ ^% K %% * \ & K * ^ ^% K%% 6 \ 0 1 * A = 0- ^ ^ T ^ O% K%% * \ A = 0- & K * ^ ^% K% \ * & K * A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- 1 * ^ ^% K%% * \ & K * 17I, ^% K%% 6 \ 0 17I, 1 * ^% K %% 6 \ 0 & K * ^% K%% 6 \ 0 & K * 17I, ^ ^% K%% 6 \ 0 17I, 1 * ^ ^% K% \ * & K * 0 * ^ ^% K % * \ 0 * 1 * = 0 ^ ^% K%% * \ = 0 1 * ^ ^% K%% * \ & K * 1 7I, ^ ^% K%% * \ 17I, & K * ^ ^% K%% 6 \ 0 & K * ^ ^% K%% 6 \ 0 & K * ^ ^% K%% * \ 0 & K * ^ ^% K% \ * 1 * ^ ^% K%% * \ 1 * 17I, ^ ^% K%% * \ 17I, & K @ ^ ^% K%% 6 \ 0 1 * ^% K% % 6 \ 1 * ^ ^% K%% 6 \ 0 & K * ^ ^% K%% * \ & K * ^% K%% 6 \ 1 * * 3A 0 ^ ^% K%% 6 \ 0 * 3A 0 1 * A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- & K * ^ ^% K%% 0 \ & K * ^ ^% K%% * \ & K * * 3A 0 ^ ^% K%% 6 \ 0 * 3A 0 1 * A = 0- ^ ^% K% \ 0 A = 0- & K * = 0 ^ ^% K%% * \ = 0 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ A = 0- & K * ^ ^% K%% 0 \ 6 0 0 0 ^ ^% K%% 6 \ & K * ^ ^% K%% * \ 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ A = 0- & K *

^ ^% K%% * \ 0 0 0 * 3A 0 ^ ^% K%% * \ * 3A 0 1 * 0 * ^ ^% K% * \ 0 * 1 *. ^% K%% 0 \ 6. & K * 17I, ^ ^ 0% K%% * \ 17I, & K * = 0 ^ ^% K%% * \ = 0 & K * ^ ^% K%% 6 \ 0 & K * ^ ^% K %% 6 \ 0 1 * 17I, ^ ^% K%% 6 \ * 17I, & K * ^ ^% K%% * \ 0 & K * ^ ^% K%% 6 \ 0 & K *. ^% K%% 6 \ 0. & K * 2A * 0 ^ ^% K%% 6 \ 0 2A * 0 & K * ^ ^% K%% 0 \ 6 & K * ^ ^% K%% * \ 0 & K * 2A * 0 ^ ^ % K%% 0 \ 2A * 0 1 * ^ ^% K%% * \ 0 1 * ^ ^% K%% 0 \ * & K * A = 0- ^ ^ T ^ O% K%% * \ 6 A = 0- 1 * ^ ^% K%% * \ 0 & K * ^ ^% K%% * \ 0 & K * 2A * 0 ^ ^% K%% 0 \ 2A * 0 & K * 2A * 0 ^ ^% K%% * \ 2A * 0 1 * ^ ^% K%% * \ 0 & K * ^ ^% K%% * \ 1 K ^ ^% K%% 6 \ * 1 * 2A * 0 ^ ^% K%% 0 \ 2A * 0 1 * ^ ^% K%% 6 \ 0 1 * ^ ^% K%% 6 \ 0 & K * ^ ^% K%% * \ 0 & K * A = 0 - ^ ^ T ^ O% K%% * \ A = 0- 1 * ^ ^% K%% 0 \ 6 1 * ^ ^% K%% 6 \ * & K * ^ ^% K% \ * & K * ^ ^% K%% * \ & K * ^ ^% K%% * \ 1 * 17I, ^% K%% * \ 17I, 1 * ^ ^% K%% 6 \ 0 & K * 17I, ^ ^% K%% 6 \ 0 17I, 1 * ^ ^% K%% * \ & K * ^ ^% K%% 6 \ 0 & K * ^ ^% K%% * \ 0 1 * ^ ^% K %% * \ 1 * ^% K%% * \ 0 1 * ^ ^% K%% 6 \ 0 1 * ^ ^% K%% * \ & K 0) ^% K%% 6 \ 1 0) ^ ^% K%% 0 \ 1 * 17I, ^ ^% K%% 6 \ 0 17I, & K * 2A * 0 ^% K%% 0 \ 6 2A * 0 1 * 17I, ^ ^% K%% * \ 17I, & K 0) 6 0- ^% K%% * \ 6 0- & K 0) ^% K%% * \ 0 & K 0) = 0 ^ ^% K%% * \ = 0 & K * ^ ^% K%% * \ & K * * 3A 0 ^ ^% K%% 6 \ 0 * 3A 0 & K * ^ 0% K%% * \ 1 * A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- & K * ^ 0 % K%% * \ & K * 2A * 0 ^% K%% 6 \ 0 2A * 0 1 * ^ ^% K%% * \ & K * ^ ^% K%% 0 \ * & K * ^ ^ % K%% * \ & K * ^ ^% K%% * \ 1 * ^ ^% K%% * \ & K * ^ ^% K% \ * & K *

= 0- ^ ^% K% * \ 0 = 0- & K * ^ ^% K%% * \ 0 & K 0) ^ ^% K%% 6 \ 0 & K 0) ^% K%% * \ & K 0) ^ ^% K%% * \ 1 0) 6 0- ^% K%% 6 \ 0 6 0- 1 0) ^ ^% K%% * \ 1 0) ^ ^% K% \ * 10) 17I, ^ ^ 0% K%% 6 \ 0 17I, 10) ^ ^% K%% 6 \ 0 & K 0) ^ ^% K%% * \ & K 0). ^% K%% 6 \ 0. & K 0) = 0 ^ ^% K%% 6 \ 0 = 0 & K 0) ^ ^% K%% * \ 1 0) ^ ^% K%% 6 \ 0 & K 0) ^ ^% K% % 6 \ 0 & K 0) ^ ^% K%% * \ 0 1 0) * 7 ^ ^% K% \ * * 7 & K 0) = 0 ^ ^% K%% * \ = 0 & K 0 ) * 7 ^ ^% K%% * \ * 7 & K 0) ^ ^% K%% * \ 0 & K 0) = 0 ^ ^% K%% * \ = 0 & K 0) ^ ^% K %% 6 \ 0 1 0) A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- & K * ^ ^% K%% * \ 0 1 * 17I, ^% K%% 6 \ 0 17I, & K * = 0 ^ ^% K%% * \ = 0 & K * 6 0- ^% K%% 6 \ 0 6 0- 1 * ^ ^% K%% 6 \ 0 1 * 7 = 01 ^ ^% K% 6 \ 0 7 = 01 1 * * 3A 0 ^ ^% K%% * \ * 3A 0 & K * ^ ^% K%% * \ & K * ^ ^% K%% 6 \ 0 & K * 17I, ^ ^% K%% * \ 17I, & K * 0 * ^ ^% K% * \ 0 * & K * ^ 0% K%% * \ & K * ^ ^% K% % * \ & K * ^ ^% K%% * \ & K 0) ^ 0% K%% * \ & K 0) 2A * 0 ^ ^% K%% 0 \ * 2A * 0 & K 0) 17I , ^ ^% K%% * \ 17I, 1 0) ^% K%% * \ 1 * * 3A 0 ^ ^% K%% 6 \ 0 * 3A 0 1 * * 7 ^ ^% K% \ 6 * 7 & K * A = 0- ^ ^ T ^ O% K%% * \ A = 0- & K * ^ ^% K%% 0 \ 6 & K * 2A * 0 ^ ^% K%% 6 \ 0 2A * 0 1 * ^ ^% K%% 6 \ 0 & KK * 7 ^ ^% K%% 6 \ * 7 & K * ^% K%% * \ 0 & K * ^ ^% K%% \ * 1 * ^ 0% K%% * \ & K * ^ ^% K%% * \ & K * 17I, ^ ^% K%% * \ 17I, 1 * ^ 0% K%% * \ 1 * ^ ^% K%% 6 \ 0 & K * ^ ^ % K%% * \ 1 * ^ ^% K%% * \ & K * 17I, ^ ^% K%% * \ 17I, 1 * ^ ^% K%% * \ 1 * ^ ^% K%% 0 \ * & K * ^ ^% K%% * \ 1 * ^ ^% K%% 6 \ 0 1 * ^ ^% K% \ * 1 * A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- 1 * ^ ^% K%% 6 \ 0 1 *

^ ^% K%% 6 \ 0 & K * ^ ^% K%% 6 \ * & K * ^ ^% K%% 6 \ 0 & K 0) ^ ^% K% \ * & K 0) ^ ^ % K%% * \ 0 & K 0) ^ ^% K%% * \ 0 & K 0) ^% K%% * \ & K 0) ^ ^% K%% 0 \ & K 0) ^ ^% K%% * \ 0 1 0) ^ ^% K%% * \ 0 & K 0) ^ ^% K%% 6 \ 0 1 0) ^ ^% K% \ 0 & K 0) A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- 1 0) ^ 0% K%% * \ 1 0) 17I, ^ ^% K%% * \ 17I, 1 0) ^ ^% K%% 6 \ 0 1 0) ^ ^% K%% 6 \ 0 1 0). ^ ^% K%% 0 \ 6. & K 0) ^ ^% K% \ * & K 0) ^ ^% K% \ * 1 * ^ ^% K%% 0 \ 6 1 * ^ ^% K%% 6 \ 0 1 * ^ ^% K %% 6 \ 0 1 * ^ ^% K%% * \ 6 & K * 17I, ^% K%% 6 \ 0 17I, 1 * ^ ^% K%% 6 \ 0 1 * ^ ^% K%% 6 \ 0 1 * ^ ^% K%% 6 \ 0 & K * ^ ^% K%% * \ 0 1 * ^ ^% K%% \ * & K 0) A = 0- ^ ^ T ^ O% K%% * \ A = 0- & K * A = 0- ^ ^ T ^ O% K%% * \ A = 0- & K * ^ ^% K%% * \ 1 * ^ ^% K%% 6 \ 0 1 *. ^% K%% * \ 0. & K * ^ 0% K%% * \ & K * 17I, ^ ^% K%% 6 \ 0 17I, & K * ^ 0% K%% * \ & K * ^ ^% K%% * \ 1 * ^ ^% K%% * \ 1 * ^ ^% K%% 6 \ 0 & K * A = 0- ^ ^ T ^ O% K%% * \ A = 0- 1 0) A = 0- ^ ^ T ^ O% K%% * \ A = 0- 1 0) ^ 0% K%% * \ & K 0) = 0 ^ ^% K%% * \ = 0 1 0) 17I, ^ ^% K %% * \ 0 17I, & K 0) ^ ^% K% \ * 1 0) ^ ^% K%% * \ & K 0) ^ ^% K%% 0 \ & K 0) ^ ^% K% % * \ & K 0) ^ ^% K%% * \ 1 0) ^ 0% K%% * \ & K 0) ^ ^% K%% 6 \ 0 1 0) 6 0- ^% K%% 6 \ 0 6 0- & K 0) ^ ^% K%% * \ 0 1 0) ^ ^% K%% 6 \ 0 1 0) ^ ^% K%% 6 \ 0 1 0) ^ 0% K %% * \ & K 0) = 0 ^ ^% K%% * \ = 0 & K 0) ^ ^% K%% 0 \ 6 1 0) = 0- ^ ^% K% * \ = 0- 1 0) = 0 ^ ^% K%% 6 \ 0 = 0 & K 0) ^ ^% K%% * \ & K 0) ^ ^% K%% * \ 0 & K 0). ^ ^% K%% 0 \ 6. & K 0) ^ 0% K%% * \ & K 0)

^ ^% K%% 0 \ 6 & K 0) * 7 ^ ^% K%% 0 \ 6 * 7 & K 0) ^ ^% K%% * \ 0 & K 0) ^ ^% K%% * \ 0 & K 0) * 3A 0 ^ ^% K%% * \ * 3A 0 0 0 0 ^ ^% K%% \ * 0 0 0 ^ ^% K%% * \ 0 1 0) ^ ^% K %% 0 \ & K * ^ ^% K%% 6 \ 0 1 * 2A * 0 ^ ^% K%% 0 \ * 2A * 0 & K * 7 = 01 ^ ^% K% \ * 7 = 01 1 * ^ ^% K%% 6 \ & K * ^ ^% K%% * \ & K * A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- & K * ^ ^% K %% 6 \ 0 & K * A = 0- ^ ^% K% \ 6 A = 0- 1 * ^ ^% K%% 6 \ 0 & K * ^ ^% K%% * \ 1 * ^ ^% K%% * \ & K * ^ ^% K%% * \ 0 & K * * 3A 0 ^ ^% K%% 6 \ 0 * 3A 0 & K * ^ ^% K%% 0 \ & K 0) ^ ^% K%% 6 \ 0 & K 0) ^ ^% K%% 0 \ & K 0) * 7 ^ ^% K% \ 6 * 7 & K 0) ^ ^% K%% 0 \ * & K 0) ^ ^% K%% * \ 1 0) 17I, ^ ^% K%% * \ 17I, & K 0) ^ ^% K%% 0 \ & K 0) ^ ^% K% \ * & K 0) ^ ^% K%% * \ & K 0) 17I, ^ ^ 0% K%% 6 \ 0 17I, & K 0) ^ ^% K%% * \ 0 & K 0) A = 0- ^ ^ 0% K%% 6 \ 0 A = 0- & K 0) * 7 ^ ^% K% \ 6 * 7 & K * ^ ^% K%% * \ 1 * ^ ^% K% \ * 1 0) A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- & K * A = 0- ^ ^% K%% 6 \ A = 0- 1 * = 0 ^ ^% K% % * \ = 0 & K * ^ ^% K%% * \ & K * A = 0- ^ ^ T ^ O% K%% * \ A = 0- 1 * ^ ^% K%% * \ 1 * ^ ^% K%% * \ & K * = 0 ^ ^% K%% * \ = 0 & K * ^ ^% K%% * \ 1 * ^ ^% K%% 6 \ 0 1 * = 0 ^ ^% K%% * \ = 0 & K * A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- 1 0) = 0 ^ ^% K%% * \ = 0 & K * A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0 - 1 * ^ ^% K%% 6 \ 0 1 * A = 0- ^ ^ T ^ O% K%% * \ A = 0- 1 * ^ ^% K%% * \ 0 & K *. ^% K%% 6 \ 0. & K * ^ ^% K%% * \ 1 * ^ ^% K%% 6 \ 0 1 * 17I, ^ ^% K%% * \ 17I, 1 * ^ ^% K%% 6 \ & K * ^ ^% K%% * \ 0 1 * ^ ^% K%% * \ 1 * = 0 ^ ^% K%% * \ = 0 1 * ^ ^% K%% * \ & K * ^ 0% K% % * \ & K * ^% K%% * \ 0 & K * 2A * 0 ^ ^% K% \ 6 2A * 0 1 *

^ ^% K%% 0 \ 6 1 0) 0 * ^ ^% K% * \ 0 * 1 * A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- 1 * ^ ^% K%% 6 \ 0 & K * ^% K%% * \ 1 *. ^ ^% K%% * \. & K * 17I, ^ ^% K%% * \ 17I, & K * ^ 0% K%% * \ 0 & K * ^ ^% K%% * \ 0 1 * 6 0- ^% K%% 6 \ 0 6 0- & K * 17I, ^ ^% K%% 6 \ 0 17I, & K * A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- 1 * ^% K% % * \ 0 1 * ^ ^% K% \ * 1 * ^ ^% K%% \ * & K * A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- & K * ^ ^% K% \ * 1 * ^ ^% K%% * \ & K * 2A * 0 ^ ^% K%% 0 \ 2A * 0 & K * ^ ^% K% \ * & K * A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- 1 * ^ ^% K%% * \ & K * 2A * 0 ^ ^% K%% 6 \ 0 2A * 0 & KK = 0 ^ ^ % K%% 6 \ 0 = 0 1 * ^ ^% K%% 6 \ 0 & K * ^ ^% K%% 6 \ 0 & K * ^ 0% K%% * \ 1 0) 2A * 0 ^ ^% K%% 6 \ 0 2A * 0 & K 0) ^ ^% K%% * \ & K 0) ^ ^% K%% * \ 1 0) ^ 0% K%% * \ & K 0) ^ ^% K%% * \ 0 & K 0) ^ ^% K% \ * & K 0) ^ ^% K% \ * & K 0) ^ ^% K%% 0 \ 6 & K 0) ^ ^ % K%% * \ 0 & K 0). ^% K%% 6 \ 0. & K 0) ^ ^% K%% 6 \ 0 1 0) ^ ^% K%% * \ 1 0) ^ ^% K%% 6 \ 0 1 0) 6 0- ^% K%% 6 \ 0 6 0- 1 0) 7 = 01 ^ ^% K% 0 \ * 7 = 01 & K * ^ ^% K%% 6 \ 0 1 * A = 0- ^ ^ T ^ O% K%% * \ A = 0- & K * ^ ^% K%% 6 \ 0 1 * ^ ^% K%% 6 \ 0 1 * ^ ^% K%% * \ 0 1 * ^% K%% 6 \ 0 & K * 2A * 0 ^ ^% K%% 0 \ * 2A * 0 & K * ^ ^% K%% * \ & K * ^% K%% 6 \ 0 1 * A = 0- ^ ^ T ^ O% K %% * \ A = 0- & K * ^ ^% K%% 6 \ 0 & K * A = 0- ^ ^ T ^ O% K%% * \ A = 0- & K * ^ ^% K% % * \ & K 0) ^ ^% K%% 6 \ 0 & K 0) ^ ^% K%% 6 \ 0 1 0) * 3A 0 ^ ^% K%% * \ * 3A 0 1 0) 17I , ^% K%% * \ 17I, 1 0) A = 0- ^ ^% K%% * \ A = 0- & K 0) 2A * 0 ^% K%% \ 0 2A * 0 & K 0) ^ 0% K%% * \ & K 0) ^ ^% K%% * \ 1 0) ^ ^% K%% 6 \ 0 1 0) 2A * 0 ^ ^% K%% * \ 2A * 0 1 0) ^ ^% K%% * \ & K 0)

^ ^% K%% * \ & K 0) 0 * ^ ^% K% * \ 0 * & K 0) ^ ^% K%% * \ & K 0) ^ ^% K% \ * & K 0) 2A * 0 ^ ^% K%% 6 \ 0 2A * 0 1 0) * 3A 0 ^ ^% K%% 6 \ 0 * 3A 0 1 0) A = 0- ^ ^ T ^ O% K%% * \ A = 0- & K 0) A = 0- ^ ^ T ^ O% K%% * \ A = 0- 1 0) = 0 ^ ^% K%% * \ = 0 1 0) 17I, ^ ^ % K%% * \ 17I, 1 0) ^ ^% K%% 6 \ 0 1 0) 7 = 01 ^ ^% K% \ * 7 = 01 & K 0) 7 = 01 ^ ^% K% 0 \ 7 = 01 1 * A = 0- ^ ^ T ^ O% K%% * \ A = 0- & K * ^ ^% K%% 6 \ 0 & K * ^ ^% K%% * \ 1 * ^ ^% K%% 6 \ 0 1 *. ^ ^% K%% 6 \. & K * A = 0- ^ ^ T ^ O% K%% * \ A = 0- 1 * A = 0- ^ ^ T ^ O% K%% * \ A = 0- 1 * ^ ^% K% %*\ 1 * . ^ ^% K%% 0 \ 6. 1 * ^ ^% K%% * \ 1 * ^ ^% K% \ * 1 *. ^ ^% K%% * \. 1 * A = 0- ^ ^ T ^ O% K%% * \ 6 A = 0- & K * = 0- ^ ^% K \ 6 = 0- 1 * ^ ^% K%% * \ 1 * ^ % K%% 6 \ 0 & K * 6 0- ^% K%% 6 \ 0 6 0- & K * 6 0- ^% K%% * \ 0 6 0- & K * ^ ^% K%% 6 \ 0 & K * A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- 1 * 7 = 01 ^ ^% K% 0 \ 7 = 01 1 * A = 0- ^ ^ T ^ O% K%% * \ A = 0- 1 * ^% K%% 6 \ 0 1 * 0A ^ ^% K%% * \ 0A 1 * ^ 0% K%% 6 \ * & K * ^ ^ % K%% * \ 1 * ^ ^% K%% * \ 1 * = 0 ^ ^% K%% * \ = 0 1 * ^ ^% K%% * \ & K * ^ 0% K%% * \ 1 * ^ ^% K%% 6 \ * 1 * 0 * ^ ^% K% * \ 0 * & K * 17I, ^% K%% 6 \ 0 17I, 1 * ^ ^% K%% * \ 1 * ^ 0% K%% * \ 1 * ^ ^% K%% \ 0 1 * ^ ^% K%% 6 \ 0 1 * ^ ^% K%% * \ & K * ^ ^% K%% * \ 1 * 0 * ^ ^% K% 6 \ 0 * 1 * 17I, ^% K%% * \ 17I, 1 * ^ ^% K%% * \ 1 * ^ ^% K%% 6 \ 0 1 * ^ ^% K%% 6 \ 0 1 * A = 0- ^ ^ T ^ O% K%% * \ A = 0- 1 * ^ ^% K% \ * & K 0) ^ ^% K%% * \ & K 0) ^ ^% K% \ * & K 0) A = 0- ^ ^% K% \ 0 A = 0- 1 0) ^ ^% K%% * \ & K 0) ^ ^% K%% 6 \ 0 & K 0) ^ ^% K%% 6 \ 0 1 0) ^ ^% K% \ 0 & K 0)

^ ^% K%% * \ & K 0) ^ ^% K%% * \ & K 0) ^ 0% K%% 6 \ 0 1 0) ^ ^% K%% * \ & K 0) ^ 0 % K%% * \ & K * ^ ^% K%% * \ & K * ^ ^% K%% * \ 0 & K * ^ ^% K%% 6 \ 0 1 * ^ ^% K%% 6 \ 0 1 * ^ ^% K%% 6 \ 0 & K * 6 0- ^% K%% 6 \ 0 6 0- 1 * ^ ^% K%% * \ 0 1 0) ^ ^% K%% 6 \ 0 1 * A = 0- ^ ^ T ^ O% K%% * \ A = 0- & K * ^ 0% K%% * \ & K * A = 0- ^ ^ T ^ O% K% % * \ 0 A = 0- & K @ ^ ^% K%% * \ & K * ^ ^% K%% * \ 6 1 * * 3A 0 ^ ^% K%% * \ * 3A 0 1 * A = 0- ^ ^ T ^ O% K%% * \ A = 0- & K * ^ 0% K%% 6 \ * 1 * ^ ^% K%% * \ & K * * 3A 0 ^ ^% K %% * \ * 3A 0 1 * = 0 ^ ^% K%% * \ = 0 1 * ^ ^% K%% * \ 1 @ ^ ^% K%% 6 \ 0 & K * ^ ^% K% % * \ & K * ^ ^% K% \ * & K @ 17I, ^ ^ 0% K%% 6 \ 0 17I, & K @ ^ ^% K%% * \ 0 0 0 17I, ^ ^% K %% * \ 17I, 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K%% * \ & K * ^ 0% K%% 6 \ * 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- & K * * 3A 0 ^ ^% K%% * \ * 3A 0 1 * ^% K%% * \ 1 * ^ ^% K%% * \ 1 * ^ ^% K%% * \ 1 * ^ ^% K%% * \ 1 * A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- 1 * ^ ^% K%% * \ & K * ^ ^ % K%% * \ 0 & K * A = 0- ^ ^ T ^ O% K%% * \ 6 A = 0- 1 * ^ ^% K%% * \ & K * ^ ^% K%% * \ & K * ^ ^% K%% * \ 6 & K * ^ ^% K%% 6 \ 0 & K * ^ ^% K%% 0 \ & K * A = 0- ^ ^ T ^ O% K %% * \ A = 0- 1 * * 3A 0 ^ ^% K%% * \ * 3A 0 1 * ^ ^% K%% * \ 0 1 * ^ ^% K%% 6 \ 0 1 *. ^% K%% 6 \ 0. 1 * A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- 1 * ^ ^% K%% \ * 1 * = 0 ^ ^% K%% 6 \ 0 = 0 & K * ^ ^% K%% 6 \ 0 & K * ^ ^% K% \ * 1 * 6 0- ^% K%% * \ 6 0- 1 * 17I, ^ ^ 0% K%% 6 \ 0 17I, 1 * ^ ^% K%% * \ & K * ^ ^% K%% * \ & K * ^ 0% K%% 0 \ * & K *

A = 0- ^ ^% K% \ 0 A = 0- 1 * = 0 ^ ^% K%% * \ = 0 0 0 0 17I, ^ ^% K%% * \ 17I, 0 0 0 ^ 0% K%% 6 \ 0 0 0 0. ^ ^% K%% * \. 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 = 0 ^ ^% K%% 6 \ 0 = 0 0 0 0 ^ ^% K%% * \ 0 0 0 * 7 ^ ^% K% \ * * 7 0 0 0 * 3A 0 ^ ^% K%% 6 \ 0 * 3A 0 0 0 0 ^ ^% K%% * \ 0 0 0 = 0 ^ ^% K%% * \ = 0 0 0 0 0 * 3A 0 ^ ^% K%% * \ * 3A 0 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 ^ 0% K%% \ 0 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 ^ ^% K%% * \ 6 0 0 0 ^% K%% 6 \ 0 0 0 ^ ^% K%% * \ 0 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 ^ ^% K%% * \ 0 0 0 B = 0- ^ ^% K% * \ B = 0- 0 0 0 ^ ^% K%% 0 \ 6 0 0 0 = 0 ^ ^% K%% * \ = 0 0 0 0 ^ ^% K%% * \ 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ A = 0- 0 0 0 ^ ^ % K%% 6 \ 0 0 0 0 ^ ^% K% \ * 0 0 0 17I, ^ ^% K%% * \ 17I, 0 0 0 ^ ^% K%% * \ 0 0 0 7 = 01 ^ ^% K% * \ 7 = 01 0 0 0 17I, ^ ^% K%% * \ 17I, 0 0 0 ^ ^% K%% * \ 0 0 0 0 = 0 ^ ^% K%% * \ = 0 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- 0 0 0 ^ ^% K%% * \ 0 0 0 ^ 0% K%% * \ 0 0 0 2A * 0 ^ ^% K%% 0 \ 6 2A * 0 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K% \ * 0 0 0 A = 0- ^ ^ T ^ O% K% % * \ 0 A = 0- 0 0 0 2A * 0 ^ ^% K%% 6 \ * 2A * 0 0 0 0 = 0 ^ ^% K%% * \ = 0 0 0 0. ^% K%% 6 \ 0. 0 0 0 = 0 ^ ^% K%% 6 \ 0 = 0 0 0 0 0 ^ ^% K%% * \ 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ 6 A = 0- 0 0 0 ^ ^% K%% * \ 0 0 0 17I, ^% K%% * \ 17I, 0 0 0 ^ ^% K%% 0 \ * 0 0 0 * 7 ^ ^% K% \ * * 7 0 0 0 ^ ^% K%% * \ 0 0 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ A = 0- 0 0 0 ^ ^% K% \ * 0 0 0 ^ ^% K%% 6 \ 0 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K%% * \ 0 0 0 ^ ^% K% \ * 0 0 0 ^ ^% K%% * \ 0 0 0 0 0 ^ ^% K%% * \ 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- 0 0 0 ^ ^% K%% 0 \ 6 0 0 0 ^% K%% 6 \ 0 0 0 0 A = 0- ^ ^ T ^ O% K%% * \ 6 A = 0- 0 0 0

A = 0- ^ ^ T ^ O% K%% * \ A = 0- 0 0 0 ^ ^% K%% * \ 0 0 0 = 0 ^ ^% K%% * \ = 0 0 0 0 = 0 ^ ^% K%% * \ = 0 & K * A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- 1 * A = 0- ^ ^ T ^ O% K%% * \ 0 A = 0- & K * ^ ^% K%% 6 \ 0 & K * ^ ^% K%% * \ 1 * ^ ^% K%% 6 \ 0 & K * ^ ^% K%% 0 \ * 1 * ^ ^% K%% * \ 0 1 * ^ ^% K%% * \ & K * = 0 ^ ^% K%% 6 \ 0 = 0 & K 0 0 ^ ^% K%% 0 \ & K * * 3A 0 ^ ^% K%% * \ * 3A 0 1 * ^ ^% K%% * \ 1 * ^ ^% K%% * \ 1 * 6 0- ^% K%% 6 \ 0 6 0- & K * ^ ^% K%% * \ & K * ^ ^% K%% 6 \ 0 1 * ^ ^% K%% * \ 1 * ^ ^% K%% * \ 1 * ^ ^ % K%% * \ 1 * ^ ^% K%% * \ & K * 6 0- ^% K%% 6 \ 0 6 0- 1 * ^ ^% K%% 6 \ 0 & K * ^ ^% K%% * \ 1 * ^ ^% K%% * \ 1 * ^ ^% K%% * \ & K * ^ ^% K%% * \ & K * ^ ^% K%% * \ & K * ^ ^% K%% * \ 1 * ^ ^% K%% * \ 1 * ^ ^% K%% * \ & K * ^ ^% K%% 6 \ 0 1 * ^ ^% K%% * \ & K * ^ ^% K%% * \ 1 * ^ ^% K%% * \ & K * ^ ^% K%% * \ 0 & K * ^ 0% K%% * \ & K * ^ ^% K%% * \ & K * ^ ^% K%% 0 \ * & K * ^ ^% K%% 0 \ * & K * ^ 0% K%% * \ & K * ^ ^% K%% 6 \ & K * ^ ^% K%% * \ 0 1 * ^ 0% K%% 0 \ * & K * A = 0- ^ ^ T ^ O% K%% * \ A = 0- 1 * 6 0- ^% K%% * \ 6 0- 1 * ^ ^% K%% 6 \ 0 1 * ^ ^ % K%% * \ 1 * ^ ^% K% \ * 1 * A = 0- ^ ^ T ^ O% K%% * \ A = 0- 1 * ^ ^% K%% 6 \ 0 1 * A = 0- ^ ^ T ^ O% K%% * \ A = 0- & K * 2A * 0 ^ ^% K%% 0 \ 2A * 0 1 * ^ ^% K%% * \ 1 * ^ ^% K%% * \ 1 * ^ ^% K%% 6 \ 0 & K * ^% K%% * \ 0 & K * ^ ^% K%% * \ 0 & K * 6 0- ^% K%% 6 \ 0 6 0- 1 * ^ ^% K%% * \ & K * ^ ^% K% \ * & K * ^ ^% K%% * \ 0 & K * A = 0- ^ ^ T ^ O % K%% * \ 0 A = 0- & K *

* 3A 0 ^ ^% K%% 6 \ 0 * 3A 0 & K * ^ ^% K%% * \ 1 * ^ 0% K%% 6 \ * 1 * A = 0- ^ ^ T ^ O% K %% * \ 0 A = 0- 1 L ^% K%% 6 \ 0 & K * ^ ^% K%% 6 \ 0 & K * ^ ^% K%% 0 \ * 1 L ^ ^% K% % 0 \ 6 & K * ^ ^% K%% 0 \ 6 & K * ^ ^% K%% 0 \ 6 & K * A = 0- ^ ^ T ^ O% K%% * \ A = 0- 1 * ^ ^% K%% 6 \ 0 & K * 6 0- ^% K%% 6 \ 0 6 0- & K * 6 0- ^% K%% 6 \ 0 6 0- & K * = 0 ^ ^% K%% * \ = 0 & K * ^ ^% K%% * \ & K * A = 0- ^ ^ 0% K%% 6 \ 0 A = 0- 1 * ^ ^% K%% 6 \ 0 & K * A = 0- ^ ^ T ^ O% K%% * \ A = 0- 1 *

Claims (52)

1. Method for identifying the presence of pancreatic cancer in an individual, characterized by the fact that it comprises: detecting in a first biological sample obtained from the individual the presence of one or more genetic biomarkers in one or more of the following genes: KRAS, TP53, CDKN2A or SMAD4; detect a level of one or more of the following protein biomarkers in a second biological sample obtained from the individual: carbohydrate antigen 19-9 (CA19-9), carcinoembryonic antigen (CEA), hepatocyte growth factor (HGF), or osteopontin (OPN); comparing the detected levels of one or more protein biomarkers with one or more reference levels of protein biomarkers; and identifying the presence of pancreatic cancer in the individual when the presence of one or more genetic biomarkers is detected, the detected levels of one or more protein biomarkers are higher than the reference levels of one or more protein biomarkers , or both. 2. Method according to claim 1, characterized by the fact that the first biological sample, the second biological sample, or both comprise plasma. 3. Method, according to claim 1, characterized by the fact that the first and second biological samples are the same. 4. Method, according to claim 1, characterized by the fact that the presence of one or more genetic biomarkers in each of: KRAS, TP53, CDKN2A and SMAD4 is detected. 5. Method, according to claim 1, characterized by the fact that the level of each carbohydrate antigen 19-9 (CA19-9), carcinoembryonic antigen (CEA), hepatocyte growth factor (HGF) and osteopontin (OPN) are detected. 6. Method, according to claim 1, characterized by the fact that the presence of one or more genetic biomarkers in one or more of KRAS, TP53, CDKN2A, or SMAD4 is detected using a PCR-based sequencing assay multiplex comprising: a. assigning a unique identifier (UID) to each of a plurality of template molecules present in the sample; B. amplify each tagged molecule solely to create UID families; and c. sequencing the amplification products redundantly. 7. Method, according to claim 1, characterized by the fact that the detection of the presence of one or more genetic biomarkers, the detection of the level of one or more protein biomarkers, or both, is performed when the individual does not it is known to harbor a cancer cell. 8. Method according to claim 1, characterized by the fact that the individual is administered one or more of the following therapeutic interventions: surgery, adjuvant chemotherapy, neoadjuvant chemotherapy, radiation therapy, immunotherapy, target therapy or an inhibitor immune checkpoint. 9. Method for identifying the presence of cancer in an individual, characterized by the fact that it comprises: detecting in a first biological sample obtained from the individual the presence of one or more genetic biomarkers in one or more of the following genes: NRAS , CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A or GNAS; detecting a level of one or more of the following protein biomarkers in a second biological sample obtained from the individual: CA19-9, CEA, HGF, OPN, CA125, prolactin, TIMP-1 or MPO; comparing the detected levels of one or more protein biomarkers with one or more reference levels of protein biomarkers; and identifying the presence of cancer in the individual, when the presence of one or more genetic biomarkers is detected, the detected levels of one or more protein biomarkers are higher than the reference levels of one or more protein biomarkers, or both. 10. Method according to claim 9, characterized by the fact that the first biological sample, the second biological sample, or both comprise plasma. 11. Method, according to claim 9, characterized by the fact that the first and second biological samples are the same. 12. Method, according to claim 9, characterized by the fact that the presence of one or more genetic biomarkers in each of: NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS , KRAS, AKT1, TP53, PPP2R1A and GNAS is detected. 13. Method according to claim 9, characterized by the fact that the level of each of CA19-9, CEA, HGF, OPN, CA125, prolactin, TIMP-1 and MPO is detected. 14. Method, according to claim 9, characterized by the fact that the presence of one or more genetic biomarkers in one or more of NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS , KRAS, AKT1, TP53, PPP2R1A or GNAS is detected using a multiplex PCR-based sequencing assay comprising: a. assigning a unique identifier (UID) to each of a plurality of template molecules present in the sample; B. enlarge each tagged molecule solely to create UID families; and c. sequencing the amplification products redundantly. 15. Method according to claim 9, characterized by the fact that the cancer is liver cancer, ovarian cancer, esophageal cancer, stomach cancer, pancreatic cancer, colorectal cancer, lung cancer, breast cancer or cancer prostate cancer. 16. Method, according to claim 9, characterized by the fact that the detection of the presence of one or more genetic biomarkers, the detection of the level of one or more protein biomarkers, or both, is performed when the individual does not it is known to harbor a cancer cell. 17. Method according to claim 9, characterized in that the individual is administered one or more of the following therapeutic interventions: surgery, adjuvant chemotherapy, neoadjuvant chemotherapy, radiation therapy, immunotherapy, target therapy or an inhibitor immune checkpoint. 18. Method for identifying the presence of cancer in an individual, characterized by the fact that it comprises: detecting in a first biological sample obtained from the individual the presence of one or more genetic biomarkers in one or more of the following genes: NRAS , CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A or GNAS; detect a level of one or more of the following protein biomarkers in a second biological sample obtained from the individual: CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1, follistatin, G-CSF, or CA15 -3; comparing the detected levels of one or more protein biomarkers with one or more reference levels of protein biomarkers; and identifying the presence of cancer in the individual, when the presence of one or more genetic biomarkers is detected, the detected levels of one or more protein biomarkers are higher than the reference levels of one or more protein biomarkers, or both. 19. Method according to claim 18, characterized by the fact that the first biological sample, the second biological sample, or both comprise plasma. 20. Method, according to claim 18, characterized by the fact that the first and second biological samples are the same. 21. Method, according to claim 18, characterized by the fact that the presence of one or more genetic biomarkers in each of: NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS , KRAS, AKT1, TP53, PPP2R1A and GNAS is detected. 22. Method according to claim 18, characterized by the fact that the level of each of CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1, follistatin, G-CSF, and CA15 -3 is detected. 23. Method, according to claim 18, characterized by the fact that the presence of one or more genetic biomarkers in one or more of NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS , KRAS, AKT1, TP53, PPP2R1A or GNAS is detected using a multiplex PCR-based sequencing assay, comprising: a. assigning a unique identifier (UID) to each of a plurality of template molecules present in the sample; B. amplify each tagged molecule solely to create UID families; and c. sequencing the amplification products redundantly. 24. Method according to claim 18, characterized by the fact that cancer is liver cancer, ovarian cancer, esophageal cancer, stomach cancer, pancreatic cancer, colorectal cancer, lung cancer, breast cancer, or prostate cancer. 25. Method, according to claim 18, characterized by the fact that the detection of the presence of one or more genetic biomarkers, the detection of the level of one or more protein biomarkers, or both, is performed when the individual does not it is known to harbor a cancer cell. 26. Method according to claim 18, characterized in that the individual is administered one or more of the following therapeutic interventions: surgery, adjuvant chemotherapy, neoadjuvant chemotherapy, radiation therapy, immunotherapy, target therapy or an inhibitor immune checkpoint. 27. Method for identifying the presence of cancer in an individual, characterized by the fact that it comprises: detecting in a first biological sample obtained from the individual the presence of one or more genetic biomarkers in one or more of the following genes: NRAS, CTNNB1 , PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS, KRAS, AKT1, TP53, PPP2R1A, or GNAS; detecting a level of one or more of the following protein biomarkers in a second biological sample obtained from the individual: CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1, or CA15-3; comparing the detected levels of one or more protein biomarkers with one or more reference levels of protein biomarkers; and identifying the presence of cancer in the individual, when the presence of one or more genetic biomarkers is detected, the detected levels of one or more protein biomarkers are higher than the reference levels of one or more protein biomarkers, or both. 28. Method according to claim 27, characterized by the fact that the first biological sample, the second biological sample, or both comprise plasma. 29. Method according to claim 27, characterized by the fact that the first and second biological samples are the same. 30. Method according to claim 27, characterized by the fact that the presence of one or more genetic biomarkers in each of: NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS , KRAS, AKT1, TP53, PPP2R1A and GNAS is detected. 31. Method, according to claim 27, characterized by the fact that the level of each of CA19-9, CEA, HGF, OPN, CA125, AFP, prolactin, TIMP-1, and CA15-3 is detected. 32. Method according to claim 27, characterized by the fact that the presence of one or more genetic biomarkers in one or more of NRAS, CTNNB1, PIK3CA, FBXW7, APC, EGFR, BRAF, CDKN2A, PTEN, FGFR2, HRAS , KRAS, AKT1, TP53, PPP2R1A, or GNAS is detected using a multiplex PCR-based sequencing assay, which comprises: a. assigning a unique identifier (UID) to each of a plurality of template molecules present in the sample; B. amplify each tagged molecule solely to create UID families; and c. sequencing the amplification products redundantly. 33. Method according to claim 27, characterized by the fact that the cancer is liver cancer, ovarian cancer, esophageal cancer, stomach cancer, pancreatic cancer, colorectal cancer, lung cancer, breast cancer or prostate cancer. 34. Method, according to claim 27, characterized by the fact that the detection of the presence of one or more genetic biomarkers, the detection of the level of one or more protein biomarkers, or both, is performed when the individual does not it is known to harbor a cancer cell. 35. Method according to claim 27, characterized in that the individual is administered one or more of the following therapeutic interventions: surgery, adjuvant chemotherapy, neoadjuvant chemotherapy, radiation therapy, immunotherapy, target therapy or an inhibitor from the immune checkpoint. 36. Method to identify the presence of bladder cancer or urothelial carcinoma of the upper tract in an individual, characterized by the fact that it comprises: detecting in a first biological sample obtained from the individual the presence of one or more genetic biomarkers in one or more of the following genes: TP53, PIK3CA, FGFR3, KRAS, ERBB2, CDKN2A, MLL, HRAS, MET or VHL; detect the presence of at least one mutation in a TERT promoter in a second biological sample obtained from the individual; and to detect the presence of aneuploidy in a third biological sample obtained from the individual; and to identify the presence of bladder cancer or an urothelial carcinoma of the upper tract in the individual, when the presence of one or more genetic biomarkers is detected, the presence of at least one mutation in the TERT promoter, the presence of aneuploidy is detected , or combinations thereof. 37. Method, according to claim 36, characterized by the fact that: the first biological sample and the second biological sample are the same; the first biological sample and the third biological sample are the same; the second biological sample and the third biological sample are the same; or the first biological sample, the second biological sample and the third biological sample are the same. 38. Method according to claim 37, characterized by the fact that the first biological sample, the second biological sample or the third biological sample is a urine sample. 39. Method according to claim 36, characterized by the fact that the presence of aneuploidy is detected in one or more of the chromosome 5q, 8q or 9p arms. 40. Method, according to claim 36, characterized by the fact that the presence of one or more genetic biomarkers in each of: TP53, PIK3CA, FGFR3, KRAS, ERBB2, CDKN2A, MLL, HRAS, MET, and VHL is detected. 41. Method according to claim 36, characterized by the fact that the presence of one or more genetic biomarkers in one or more of TP53, PIK3CA, FGFR3, KRAS, ERBB2, CDKN2A, MLL, HRAS, MET, or VHL is detected using a multiplex PCR-based sequencing assay comprising: a. assigning a unique identifier (UID) to each of a plurality of template molecules present in the sample; B. amplify each tagged molecule solely to create UID families; and c. sequencing the amplification products redundantly. 42. Method according to claim 36, characterized by the fact that detecting the presence of one or more genetic biomarkers, detecting the presence of at least one mutation in the TERT promoter, or detecting the presence of aneuploidy is performed when the individual is not known to harbor a cancer cell. 43. Method according to claim 36, characterized in that the individual is administered one or more of the following therapeutic interventions: surgery, adjuvant chemotherapy, neoadjuvant chemotherapy, radiation therapy, immunotherapy, target therapy, or a immune checkpoint inhibitor. 44. Method for identifying the presence of ovarian or endometrial cancer in an individual, characterized by the fact that it comprises: detecting in a first biological sample obtained from the individual the presence of one or more genetic biomarkers in one or more of the following genes: NRAS, PTEN, FGFR2, KRAS, POLE, AKT1, TP53, RNF43, PPP2R1A, MAPK1, CTNNB1, PIK3CA, FBXW7, PIK3R1, APC, EGFR, BRAF or CDKN2A; detect the presence of aneuploidy in a second biological sample obtained from the individual; and to identify the presence of ovarian or endometrial cancer in the individual when the presence of one or more genetic biomarkers is detected, the presence of aneuploidy is detected or both. 45. Method according to claim 44, characterized by the fact that the first biological sample and the second biological sample are the same. 46. Method according to claim 45, characterized by the fact that the first biological sample or the second biological sample is a cervical sample or a sample of the endometrium. 47. Method according to claim 44, characterized by the fact that the presence of aneuploidy is detected in one or more of the chromosome arms 4p, 7q, 8q or 9q. 48. Method according to claim 44, characterized by the fact that the presence of one or more genetic biomarkers in each of: NRAS, PTEN, FGFR2, KRAS, POLE, AKT1, TP53, RNF43, PPP2R1A, MAPK1, CTNNB1 , PIK3CA, FBXW7, PIK3R1, APC, EGFR, BRAF and CDKN2A is detected. 49. Method according to claim 44, characterized by the fact that the presence of one or more genetic biomarkers in one or more of: NRAS, PTEN, FGFR2, KRAS, POLE, AKT1, TP53, RNF43, PPP2R1A, MAPK1, CTNNB1, PIK3CA, FBXW7, PIK3R1, APC, EGFR, BRAF or CDKN2A is detected using a multiplex PCR-based sequencing assay comprising: a. assigning a unique identifier (UID) to each of a plurality of template molecules present in the sample; B. amplify each tagged molecule solely to create UID families; and c. sequencing the amplification products redundantly. 50. Method according to claim 44, characterized by the fact that it also comprises detecting in a sample of circulating tumor DNA (ctDNA) obtained from the individual the presence of at least one genetic biomarker in one or more of the following genes : AKT1, APC, BRAF, CDKN2A, CTNNB1, EGFR, FBXW7, FGFR2, GNAS, HRAS, KRAS, NRAS, PIK3CA, PPP2R1A, PTEN or TP53. 51. Method, according to claim 44, characterized by the fact that the detection of the presence of one or more genetic biomarkers or the detection of the presence of aneuploidy is performed when the individual is not known to harbor a cancer cell. 52. Method according to claim 44, characterized in that the individual is administered one or more of the following therapeutic interventions: surgery, adjuvant chemotherapy, neoadjuvant chemotherapy, radiation therapy, immunotherapy, target therapy or an inhibitor from the immune checkpoint.